Digital road model dynamic update method and system for smart parking

Abstract

The present invention discloses a method and system for dynamically updating a digital road model for smart parking, and relates to data processing related fields. The method includes: obtaining basic scene information of a target road scene and constructing a digital simulation map; determining a digital road model through digital twin modeling, performing equivalent simplification, coordinating with a digital simulation map, and determining a dynamic update module; performing real-time monitoring and screening based on front-end constraints based on distributed sensing equipment to determine data fragments; transmitting back and performing data structure conversion, performing equivalent update analysis based on a dynamic update module and updating a digital road model based on back-end constraints to determine an updated road model. The method solves the technical problem that the existing digital road model update lacks a dynamic update mechanism, resulting in low model accuracy and poor real-time performance, and achieves the technical effect of improving the accuracy and real-time performance of a digital road model through real-time monitoring and dynamic updating of road scenes.

Description

Technical Field

[0001] The present application relates to data processing related fields, and in particular to a method and system for dynamically updating a digital road model for smart parking. Background Art

[0002] With the acceleration of urbanization and the rapid growth of the number of private cars, the problem of parking difficulties has become increasingly prominent and has become an important factor restricting urban development. As an effective means to solve the problem of parking difficulties, smart parking has realized the integration of real-time update, query, reservation and navigation services of parking space resources by integrating advanced technologies such as wireless communication, mobile terminals and geographic information systems (GIS), greatly improving parking efficiency and user experience. The effective operation of the smart parking system depends on accurate and real-time road model data. At present, the update of digital road models mainly depends on regular updates. However, regular updates cannot reflect the actual changes of the road in a timely manner, resulting in low accuracy and poor real-time performance of the model.

[0003] In the current related technologies, the update of digital road models for smart parking lacks a dynamic update mechanism, which leads to technical problems such as low model accuracy and poor real-time performance. Summary of the invention

[0004] The present application provides a method and system for dynamic updating of digital road models for smart parking, obtains basic scene information of the target road scene, constructs a digital simulation map, determines the digital road model through digital twin modeling based on the basic scene information, and performs equivalent simplification, uses distributed sensing equipment to monitor the road scene in real time, and screens data fragments based on front-end constraints, transmits data fragments and converts data structures, performs equivalent update analysis based on a dynamic update module and updates the digital road model based on back-end constraints, and finally determines the updated road model, etc., thereby achieving the technical effect of improving the accuracy and real-time performance of the digital road model through real-time monitoring and dynamic updating of the road scene.

[0005] The present application provides a method for dynamically updating a digital road model for smart parking, including: obtaining basic scene information of a target road scene and constructing a digital simulation map; based on the basic scene information, determining a digital road model through digital twin modeling, equivalently simplifying the digital road model, and coordinating with the digital simulation map to determine a dynamic update module; based on distributed sensing equipment, real-time monitoring of road scenes and screening based on front-end constraints are performed to determine data fragments, wherein the front-end constraints perform data stream screening based on a trigger factor matrix based on smart parking management; transmitting back the data fragments and performing data structure conversion, performing equivalent update analysis based on the dynamic update module and updating the digital road model based on back-end constraints to determine an updated road model.

[0006] In a possible implementation, the dynamic update module is determined to perform the following processing: taking the smart parking decision-making requirements as equivalent conditions, simplifying and equilibrating the characteristic elements of the digital road model to determine the simplified road model; mapping the digital road model and the simplified road model to establish equivalent conditions for characteristic elements, wherein the equivalent conditions are element conversion conditions before and after simplification and equivalence; coordinating the simplified road model with the digital simulation map to build a dynamic decision block; coordinating the digital road model with the dynamic decision block to determine the dynamic update module.

[0007] In a possible implementation, by setting front-end constraints, determining data fragments, and performing the following processing: interactive road parking records, mining the correlation between trigger factors and road data, and constructing the trigger factor matrix, wherein the trigger factor is a factor used to measure whether it is related to smart parking management; based on the trigger factor matrix, setting the front-end constraints, wherein each matrix item is a trigger factor critical value; real-time monitoring and determination of distributed sensor data of the target road, data stream screening based on the front-end constraints, and determining the data fragments, wherein the data fragments are road data related to smart parking.

[0008] In a possible implementation, based on the equivalent update analysis performed on the dynamic update module and the update of the digital road model based on the back-end constraint conditions, the updated road model is determined and the following processing is performed: the data format of the dynamic update module is interacted, the data fragments are converted, and the converted data fragments are determined; based on the dynamic decision block, the converted data fragments are updated and synchronized to the digital road model based on the equivalent conditions to determine the updated road model.

[0009] In a possible implementation, the conversion data fragments are updated and synchronized to the digital road model based on the equivalent conditions, and the following processing is performed: the conversion data fragments are transmitted to the dynamic decision block, the data position is located based on the digital simulation map, and the update position is determined; based on the update position, a simplified update is performed in the road simplification model to determine the updated simplified model; based on the back-end constraint conditions, a synchronous update judgment is made on the digital road model to generate a synchronization instruction; according to the synchronization instruction, the digital road model is synchronously updated with the equivalent conditions.

[0010] In a possible implementation, before transmitting the converted data fragments to the dynamic decision block, the following processing is performed: identifying the converted data fragments, determining static road data and dynamic road data; making judgments on the static road data based on the back-end constraints and updating the digital road model; and making simplified analysis and judgments on the dynamic road data based on the dynamic decision block and updating the digital road model.

[0011] In a possible implementation, the digital road model is subjected to a synchronous update judgment, a synchronization instruction is generated, and the following processing is performed: back-end constraints are set, wherein the back-end constraints are used to perform model update constraints, with a preset information difference as the first constraint and an influence based on smart parking management as the second constraint; the simplified road model and the updated simplified model are identified, and the first information difference and the second influence are measured; if the first information difference is greater than or equal to the first constraint, or the second influence is not empty, the synchronization instruction is generated.

[0012] The present application also provides a digital road model dynamic update system for smart parking, including: a digital simulation map construction module, the digital simulation map construction module is used to obtain the basic scene information of the target road scene and construct a digital simulation map; a digital road model determination module, the digital road model determination module is used to determine the digital road model through digital twin modeling based on the basic scene information, perform equivalent simplification on the digital road model, and coordinate with the digital simulation map to determine the dynamic update module; a data fragment determination module, the data fragment determination module is used to perform real-time monitoring of road scenes and screening based on front-end constraints based on distributed sensing equipment to determine data fragments, wherein the front-end constraints perform data stream screening based on a trigger factor matrix based on smart parking management; a digital road model update module, the digital road model update module is used to transmit the data fragments and perform data structure conversion, perform equivalent update analysis based on the dynamic update module and update the digital road model based on back-end constraints, and determine the updated road model.

[0013] The method and system for dynamic updating of digital road models for smart parking proposed in this application first obtain the basic scene information of the target road scene and construct a digital simulation map. Then, based on the basic scene information, the digital road model is determined through digital twin modeling, the digital road model is equivalently simplified, and the digital simulation map is coordinated to determine the dynamic update module. Then, based on distributed sensing equipment, real-time monitoring of the road scene and screening based on front-end constraints are performed to determine data fragments, wherein the front-end constraints use a trigger factor matrix based on smart parking management to screen the data stream. Finally, the data fragments are transmitted back and the data structure is converted. Based on the dynamic update module, equivalent update analysis and digital road model update based on back-end constraints are performed to determine the updated road model, thereby achieving the technical effect of improving the accuracy and real-time performance of the digital road model through real-time monitoring and dynamic updating of the road scene. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A flowchart of a method for dynamically updating a digital road model for smart parking provided in an embodiment of the present application.

[0016] Figure 2 A schematic diagram of the structure of a digital road model dynamic update system for smart parking provided in an embodiment of the present application.

[0017] Explanation of reference numerals: digital simulation map construction module 10 , digital road model determination module 20 , data fragment determination module 30 , digital road model update module 40 . DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0021] The present application embodiment provides a method for dynamically updating a digital road model for smart parking, such as Figure 1 As shown, the method includes:

[0022] Step S100, obtain the scene basic information of the target road scene and construct a digital simulation map. Specifically, use geographic information system (GIS), remote sensing technology, drone aerial photography and other means to collect the geographical location, road structure, traffic signs, parking facilities and other basic information of the target road scene. Input this basic information into the geographic information system software, and after processing and analysis, generate a digital simulation map containing information such as road network, topography, and building distribution.

[0023] Step S200, based on the basic information of the scene, determine the digital road model through digital twin modeling, perform equivalent simplification on the digital road model, and determine the dynamic update module in coordination with the digital simulation map. Specifically, according to the basic information of the scene, a detailed digital road model is constructed using digital twin technology. The digital twin technology is to achieve real-time monitoring and simulation analysis of entities by establishing a virtual model corresponding to an entity in the real world (such as a road). In order to reduce the complexity of the model and improve the update efficiency, the digital road model is equivalently simplified, that is, under the premise of ensuring the function of the model, the complexity of the model is reduced by simplifying the model structure, reducing the model elements, etc. Finally, in combination with the digital simulation map, determine the parts of the digital road model that need to be dynamically updated, including real-time changing information such as traffic flow and parking status, and form a dynamic update module.

[0024] In a possible implementation, the dynamic update module is determined, and step S200 further includes step S210, which simplifies and equilibrates the characteristic elements of the digital road model based on the equivalent condition of the smart parking decision-making requirements to determine the simplified road model. Specifically, each element in the digital road model, such as lane lines, traffic signs, parking spaces, obstacles, etc., is analyzed to identify complex elements that have a greater impact on smart parking decisions. Then, these complex elements are simplified according to the needs of smart parking decisions. For example, lane lines are simplified into straight lines or curves, and parking spaces are simplified into rectangles or circles, etc. Finally, the simplified elements are replaced by equivalents to ensure that the simplified model is functionally consistent with the original model.

[0025] Step S220, mapping the digital road model with the simplified road model, establishing equivalent conditions of characteristic elements, wherein the equivalent conditions are element conversion conditions before and after simplification and equivalence. Specifically, each element in the digital road model is matched with the simplified element, and a mapping relationship between them is established. Then, based on the mapping relationship, the conversion conditions of the elements before and after simplification, i.e., equivalent conditions, are determined, including conversion rules in terms of shape, size, position, etc.

[0026] Step S230, coordinate the simplified road model with the digital simulation map to build a dynamic decision block. Specifically, the simplified road model and the digital simulation map are superimposed and fused to ensure the consistency and coordination of information between them. According to the needs of smart parking decision-making, dynamic decision blocks are divided on the fused map. The dynamic decision blocks refer to areas divided according to the needs of smart parking decision-making and used for real-time decision-making, which can be parking space areas, areas with large traffic flow, etc.

[0027] Step S240, coordinate the digital road model and the dynamic decision block to determine the dynamic update module. Specifically, the simplified and equivalent digital road model and the dynamic decision block are superimposed and fused to ensure the information consistency and coordination between them. According to the needs of smart parking decisions and the requirements of real-time updates, a dynamic update module is determined in the fused model, that is, a module for real-time updating of specific information in the digital road model, including a parking space status update module, a traffic flow monitoring module, etc. This implementation method simplifies the complex elements in the digital road model and builds a dynamic decision block, so that key information for smart parking decisions can be obtained more quickly, thereby improving decision-making efficiency. At the same time, the simplified digital road model reduces the computational complexity while maintaining functional consistency.

[0028] Step S300, based on distributed sensing equipment, real-time monitoring of road scenes and screening based on front-end constraints are performed to determine data fragments, wherein the front-end constraints are used to screen data streams based on a trigger factor matrix of smart parking management. Specifically, distributed sensing equipment, i.e., sensors (such as cameras, radars, sensors, etc.) installed on both sides of the road are used to collect road data in real time, and then data is screened according to front-end constraints (conditions for screening data, trigger factor matrices related to smart parking management), and data fragments related to smart parking are extracted, i.e., the data fragments refer to road data fragments related to smart parking obtained after screening.

[0029] In a possible implementation, by setting front-end constraints and determining data fragments, step S300 further includes step S310, interacting with road parking records, mining the correlation between trigger elements and road data, and constructing the trigger element matrix, wherein the trigger element is an element used to measure whether it is related to smart parking management. Specifically, parking record data is collected from various road parking management systems, including parking time, parking location, parking duration, parking fees, etc. These data are processed using data mining technology to extract key information related to smart parking management. The correlation analysis technology is used to analyze the degree of correlation between these key information and smart parking management, and determine the trigger elements, that is, the key factors or variables that can trigger smart parking management decisions. According to the results of the correlation analysis, a trigger element matrix is ​​constructed. The trigger element matrix is ​​a matrix used to represent the degree of correlation between the trigger elements and the variables related to smart parking management. Each row in the matrix represents a trigger element, and each column represents a variable or indicator related to smart parking management. The value in the matrix represents the degree of correlation or weight between the trigger element and the variable.

[0030] Step S320, based on the trigger factor matrix, set the front-end constraint condition, wherein each matrix item is a trigger factor critical value. Specifically, analyze the trigger factor matrix to determine the degree of association between each trigger factor and the smart parking management related variables. According to the degree of association or the size of the weight, set a critical value for each trigger factor. According to the set critical value, formulate the front-end constraint condition, that is, when the value of a trigger factor exceeds or falls below this critical value, it is considered to meet the front-end constraint condition. These conditions are used to filter road data related to smart parking.

[0031] Step S330, real-time monitoring determines the distributed sensor data of the target road, and based on the front-end constraints, the data stream is screened to determine the data fragments, which are road data related to smart parking. Specifically, the road scene is monitored in real time using sensing devices (such as cameras, radars, sensors, etc.) distributed on the target road. According to the front-end constraints, the data stream monitored in real time is screened. Only when the value of one or some trigger elements in the data stream meets the front-end constraints, it is regarded as road data related to smart parking, that is, data fragments (fragments or subsets of road data related to smart parking). Finally, the screened data fragments are sorted and stored for data structure conversion and digital road model update. This implementation method uses a trigger element matrix constructed by data mining and correlation analysis technology to help quickly identify key information related to smart parking management, reduce the amount of data to be processed, reduce data processing costs and improve decision-making efficiency.

[0032] Step S400, transmit the data fragments back and perform data structure conversion, perform equivalent update analysis based on the dynamic update module and update the digital road model based on the back-end constraints, and determine the updated road model. Specifically, the collected data fragments are transmitted back to the data processing center and converted into a data format that can be recognized by the digital road model. Perform equivalent update analysis based on the dynamic update module, that is, analyze the impact of data fragments on the digital road model, determine which data needs to be updated to the digital road model, and update the digital road model according to the back-end constraints (conditions used to constrain the update of the digital road model to ensure that the updated model still meets the needs of smart parking management, such as model update constraints, impact assessment, etc.). The embodiment of the present application obtains basic scene information of the target road scene, constructs a digital simulation map, determines the digital road model through digital twin modeling based on the basic scene information, and performs equivalent simplification, uses distributed sensing equipment to monitor the road scene in real time, and screens data fragments based on front-end constraints, transmits data fragments and converts data structures, performs equivalent update analysis based on a dynamic update module and updates the digital road model based on back-end constraints, and finally determines the updated road model, etc., thereby achieving the technical effect of improving the accuracy and real-time performance of the digital road model through real-time monitoring and dynamic updating of the road scene.

[0033] In a possible implementation, based on the dynamic update module, equivalent update analysis is performed and the digital road model is updated based on the back-end constraint conditions to determine the updated road model. Step S400 further includes step S410, exchanging the data format of the dynamic update module, converting the data fragments, and determining the converted data fragments. Specifically, the original data format of the data fragments is identified, including the file type, encoding method, data structure, etc. According to the requirements of the dynamic update module, rules for data format conversion are formulated, which specify how to convert from the original data format to the data format required by the dynamic update module. Then, a data format conversion tool is used or a conversion script is written to convert the data fragments into converted data fragments according to the formulated rules. Among them, the conversion process includes operations such as data cleaning and data reconstruction to ensure the accuracy and integrity of the data. Finally, the converted data fragments are verified to ensure that they meet the requirements of the dynamic update module and can be correctly updated in subsequent steps.

[0034] Step S420, based on the dynamic decision block, the conversion data fragments are updated and processed, based on the equivalent conditions, they are synchronized to the digital road model, and the updated road model is determined. Specifically, the conversion data fragments are associated with the dynamic decision block, including operations such as matching of spatial coordinates and corresponding data attributes. According to the update rules of the dynamic decision block, the conversion data fragments are updated, including operations such as inserting, deleting, and modifying data to ensure the consistency and accuracy of the data. Then, the updated data is synchronized to the digital road model using equivalent conditions, that is, the element conversion conditions before and after the simplification of the equivalence are applied to ensure that the digital road model still maintains its original characteristics and attributes after the update. Finally, the updated road model is verified to ensure that it meets the requirements of smart parking management and can play the expected role in practical applications. The updated road model reflects the latest status and information of the road scene. This implementation method ensures that data fragments from different sources and in different formats can be correctly processed and updated by the dynamic update module through data format conversion, avoiding problems caused by incompatible data formats. Through the application of dynamic decision blocks and equivalent conditions, the digital road model can be quickly updated, improving the efficiency and accuracy of smart parking management.

[0035] In a possible implementation, the conversion data fragments are updated and synchronized to the digital road model based on the equivalent conditions. Step S420 further includes step S421, transmitting the conversion data fragments to the dynamic decision block, locating the data position based on the digital simulation map, and determining the update position. Specifically, the conversion data fragments are transmitted from the front-end system to the back-end system using a reliable data transmission protocol (such as HTTP, FTP, etc.). During the transmission process, data encryption, compression and other processing are performed to ensure data security and transmission efficiency. Then, on the digital simulation map, according to the spatial coordinate information (such as longitude and latitude, coordinate points, etc.) in the conversion data fragments, through map matching, coordinate conversion and other geographic information technology, the specific location of the data on the map is determined.

[0036] Step S422, based on the update position, simplify and update the simplified road model, and determine the updated simplified model. Specifically, match the converted data fragments with the data in the simplified road model to find the data items that need to be updated. Then, according to the new data in the converted data fragments, update the corresponding data in the simplified road model. During the update process, a data consistency check is required to ensure that the data in the updated simplified road model is consistent with the actual situation and there is no conflict between different data items.

[0037] Step S423, based on the back-end constraint condition, the digital road model is subjected to synchronous update determination and a synchronization instruction is generated. Specifically, according to the back-end constraint condition, the converted data fragment is checked to determine whether it meets the update condition. According to the check result, a synchronous update determination is performed. If the converted data fragment meets the update condition, a synchronization instruction is generated; otherwise, no update operation is performed.

[0038] Step S424, according to the synchronization instruction, the digital road model is synchronously updated with the equivalent conditions. Specifically, according to the synchronization instruction, the data in the updated simplified road model is synchronized to the digital road model. During the synchronization process, equivalent conditions are used to convert and map data to ensure that the data in the digital road model is consistent with the data in the simplified road model. Then, the updated digital road model is verified to ensure the accuracy and completeness of its data. Finally, the updated digital road model is published to the system for use in smart parking management. This implementation method ensures that the data in the digital road model is consistent with the data in the simplified road model by using equivalent conditions to convert and map data, thereby maintaining the consistency of system data, more effectively realizing the dynamic update of the digital road model, and improving the efficiency and accuracy of smart parking management.

[0039] In one possible implementation, the digital road model is subjected to synchronous update determination, and a synchronization instruction is generated. Step S423 further includes step S4231, setting a backend constraint condition, wherein the backend constraint condition is used to perform model update constraints, with a preset information difference as the first constraint condition, and an influence based on smart parking management as the second constraint condition. Specifically, a preset information difference is set as the first constraint condition, and this preset information difference is a threshold used to measure the information difference between the simplified road model and the updated simplified model. When this difference exceeds the preset threshold, it is considered that a synchronous update of the digital road model is required. Secondly, the influence based on smart parking management is set as the second constraint condition. This constraint condition focuses on the impact of the update on smart parking management, such as the availability of parking spaces, changes in traffic flow, etc. If the update has a significant impact on these aspects, it is considered that a synchronous update is required.

[0040] Step S4232, identify the road simplification model and the updated simplification model, and measure the first information difference and the second influence. Specifically, by comparing the data structure, element information, etc. of the road simplification model and the updated simplification model, the differences between them are identified. Based on the identified differences, the first information difference is calculated. This difference can be obtained by comparing the information values ​​of the same elements in the two models, such as road length, width, number of parking spaces, etc. Then, the impact of the update on smart parking management is evaluated, which can be obtained by analyzing the changes in parking spaces and traffic flow caused by the update. If the update leads to negative impacts such as a reduction in parking spaces or increased traffic congestion, the second influence is considered to be non-empty.

[0041] Step S4233, if the first information difference is greater than or equal to the first constraint, or the second influence is not empty, generate the synchronization instruction. Specifically, check whether the first information difference is greater than or equal to the first constraint (i.e., the preset information difference). If so, it is considered that a synchronous update is required. Secondly, check whether the second influence is not empty. If it is not empty, that is, the update will have a significant impact on smart parking management, then it is considered that a synchronous update is required. If any of the above conditions is met, a synchronization instruction is generated, which is used to trigger the synchronous update process of the digital road model. This implementation method ensures that the digital road model is synchronously updated only when certain conditions are met by setting back-end constraints, thereby avoiding unnecessary update operations and improving the accuracy of the update.

[0042] In a possible implementation, before transmitting the converted data fragments to the dynamic decision block, step S420 further includes step S425, identifying the converted data fragments and determining static road data and dynamic road data. Specifically, the converted data fragments are preprocessed, including data cleaning, format unification, etc., to ensure the accuracy and consistency of the data. Next, the characteristic information of the data, such as timestamp, data type, data value, etc., is extracted, and the converted data fragments are divided into static road data and dynamic road data according to the characteristic information. Among them, the static road data includes the physical characteristics of the road, such as road length, width, number of lanes, etc., and these characteristics will not change in a short time. Dynamic road data includes traffic flow, vehicle speed, parking space occupancy, etc., and these characteristics will change over time.

[0043] Step S426, the static road data is judged based on the back-end constraints and the digital road model is updated. Specifically, the static road data is judged based on the back-end constraints. If the change of the static road data exceeds a preset threshold or may have a significant impact on smart parking management, it is considered that an update is required. If the result of the determination requires an update, the digital road model is updated according to the change of the static road data, including correcting the relevant parameters or features in the model to ensure the accuracy and reliability of the model.

[0044] Step S427, based on the dynamic decision block, the dynamic road data is simplified and analyzed and determined, and the digital road model is updated. Specifically, the dynamic road data is input into the dynamic decision block, and the simplified analysis function of the block is used to process the data. If the analysis result of the dynamic decision block indicates that an update is required, the digital road model is updated according to the changes in the dynamic road data, including real-time correction of dynamic features such as traffic flow and vehicle speed in the model to ensure that the model can accurately reflect the current road conditions. This implementation method determines the static road data according to the back-end constraints, ensuring that updates are only performed when certain conditions are met, avoiding unnecessary update operations. At the same time, the dynamic decision block is used to perform a simplified analysis and determination on the dynamic road data, and it is more accurately determined whether an update is required and how to update, thereby ensuring the accuracy of the update.

[0045] In the above, refer to Figure 1 The digital road model dynamic update method for smart parking according to an embodiment of the present invention is described in detail. Figure 2 A digital road model dynamic updating system for smart parking according to an embodiment of the present invention is described.

[0046] The digital road model dynamic update system for smart parking according to the embodiment of the present invention is used to solve the technical problem that the existing digital road model update for smart parking lacks a dynamic update mechanism, resulting in low accuracy and poor real-time performance of the model, and achieves the technical effect of improving the accuracy and real-time performance of the digital road model through real-time monitoring and dynamic updating of road scenes. The digital road model dynamic update system for smart parking includes: a digital simulation map construction module 10, a digital road model determination module 20, a data fragment determination module 30, and a digital road model update module 40.

[0047] The digital simulation map construction module 10 is used to obtain the basic scene information of the target road scene and construct a digital simulation map; the digital road model determination module 20 is used to determine the digital road model through digital twin modeling based on the basic scene information, perform equivalent simplification on the digital road model, and coordinate with the digital simulation map to determine the dynamic update module; the data fragment determination module 30 is used to perform real-time monitoring of road scenes and screening based on front-end constraints based on distributed sensing equipment to determine data fragments, wherein the front-end constraints perform data stream screening based on a trigger factor matrix based on smart parking management; the digital road model update module 40 is used to transmit the data fragments back and perform data structure conversion, perform equivalent update analysis based on the dynamic update module and update the digital road model based on the back-end constraints, and determine the updated road model.

[0048] The specific configuration of the digital road model determination module 20 will be described in detail below. As described above, the dynamic update module is determined, and the digital road model determination module 20 may further include: a road simplification model determination unit is used to simplify and equalize the characteristic elements of the digital road model with the intelligent parking decision-making requirements as the equivalent conditions, and determine the road simplification model; a model mapping unit is used to map the digital road model with the road simplification model, and establish the equivalent conditions of the characteristic elements, and the equivalent conditions are the element conversion conditions before and after the simplification and equivalence; a dynamic decision block construction unit is used to coordinate the road simplification model with the digital simulation map to build a dynamic decision block; a dynamic update module determination unit is used to coordinate the digital road model with the dynamic decision block to determine the dynamic update module.

[0049] The specific configuration of the data fragmentation determination module 30 will be described in detail below. According to the above, by setting the front-end constraint conditions, the data fragments are determined, and the data fragmentation determination module 30 may further include: a trigger factor matrix construction unit is used to interact with road parking records, mine the correlation between trigger factors and road data, and construct the trigger factor matrix, wherein the trigger factor is a factor used to measure whether it is related to smart parking management; a front-end constraint condition setting unit is used to set the front-end constraint conditions based on the trigger factor matrix, wherein each matrix item is a trigger factor critical value; a data stream screening unit is used to monitor and determine the distributed sensor data of the target road in real time, and perform data stream screening based on the front-end constraint conditions to determine the data fragments, wherein the data fragments are road data related to smart parking.

[0050] The specific configuration of the digital road model update module 40 will be described in detail below. As described above, based on the dynamic update module performing equivalent update analysis and updating the digital road model based on the back-end constraint conditions, the updated road model is determined, and the digital road model update module 40 may further include: a data fragment conversion unit for exchanging the data format of the dynamic update module, converting the data fragments, and determining the converted data fragments; an update processing unit for updating the converted data fragments based on the dynamic decision block, synchronizing to the digital road model based on the equivalent conditions, and determining the updated road model.

[0051] Among them, the conversion data fragments are updated and synchronized to the digital road model based on the equivalent conditions. The update processing unit may further include: a data position positioning subunit is used to transmit the conversion data fragments to the dynamic decision block, perform data position positioning based on the digital simulation map, and determine the update position; a simplified update subunit is used to perform simplified update in the road simplification model based on the update position, and determine the updated simplified model; a synchronous update judgment subunit is used to perform synchronous update judgment on the digital road model based on the back-end constraint conditions and generate a synchronization instruction; a synchronous update subunit is used to synchronously update the digital road model with the equivalent conditions according to the synchronization instruction.

[0052] Among them, before transmitting the converted data fragments to the dynamic decision block, the update processing unit may further include: a converted data fragment identification subunit is used to identify the converted data fragments and determine the static road data and the dynamic road data; the determination and update subunit is used to determine the static road data based on the back-end constraints and update the digital road model, and to perform simplified analysis and determination on the dynamic road data based on the dynamic decision block and update the digital road model.

[0053] Among them, the digital road model is subjected to synchronization update judgment and a synchronization instruction is generated. The synchronization update judgment subunit may further include: a back-end constraint condition setting micro-unit is used to set the back-end constraint condition, wherein the back-end constraint condition is used to perform model update constraints, with a preset information difference as the first constraint condition, and an influence based on smart parking management as the second constraint condition; a simplified model identification micro-unit is used to identify the simplified road model and the updated simplified model, and measure the first information difference and the second influence degree; a synchronization instruction generation micro-unit is used to generate the synchronization instruction if the first information difference is greater than or equal to the first constraint condition, or the second influence degree is not empty.

[0054] The digital road model dynamic update system for smart parking provided by the embodiment of the present invention can execute the digital road model dynamic update method for smart parking provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0055] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0056] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for dynamically updating a digital road model for smart parking, characterized in that: The method comprises: Obtain basic scene information of the target road scene and build a digital simulation map; Based on the basic scene information, a digital road model is determined through digital twin modeling, the digital road model is equivalently simplified, and a dynamic update module is determined in coordination with the digital simulation map; Based on distributed sensing equipment, real-time monitoring of road scenes and screening based on front-end constraints are carried out to determine data fragments, wherein the front-end constraints screen data streams based on a trigger factor matrix based on smart parking management; Transmitting back the data fragments and converting the data structure, performing equivalent update analysis based on the dynamic update module and updating the digital road model based on backend constraints, and determining an updated road model; The determining of the dynamic update module comprises: Taking the smart parking decision-making requirement as an equivalent condition, simplifying and equilibrating the characteristic elements of the digital road model to determine a simplified road model; Mapping the digital road model and the simplified road model to establish equivalent conditions of characteristic elements, wherein the equivalent conditions are element conversion conditions before and after simplification and equivalence; Collaborating the simplified road model with the digital simulation map to build a dynamic decision block; Determine the dynamic update module in coordination with the digital road model and the dynamic decision block; Determine data fragmentation by setting front-end constraints, including: Interact with road parking records, mine the correlation between trigger elements and road data, and construct the trigger element matrix, wherein the trigger element is a factor used to measure whether it is related to smart parking management; Based on the trigger factor matrix, the front-end constraint condition is set, wherein each matrix item is a trigger factor critical value; The distributed sensor data of the target road is determined by real-time monitoring, and the data stream is screened based on the front-end constraint conditions to determine the data fragments, where the data fragments are road data related to smart parking.

2. The method for dynamically updating a digital road model for smart parking according to claim 1, characterized in that: Performing equivalent update analysis based on the dynamic update module and updating the digital road model based on backend constraints to determine an updated road model includes: Interacting the data format of the dynamic update module, converting the data fragments, and determining the converted data fragments; Based on the dynamic decision block, the conversion data fragments are updated and synchronized to the digital road model based on the equivalent conditions to determine an updated road model.

3. The method for dynamically updating a digital road model for smart parking according to claim 2, characterized in that: The conversion data fragments are updated and synchronized to the digital road model based on the equivalent condition, including: The converted data fragments are transmitted to the dynamic decision block, and the data position is located based on the digital simulation map to determine the update position; Based on the update position, perform a simplified update in the simplified road model to determine an updated simplified model; Based on the back-end constraint condition, performing synchronization update determination on the digital road model and generating a synchronization instruction; According to the synchronization instruction, the digital road model is synchronously updated under the equivalent condition.

4. The method for dynamically updating a digital road model for smart parking according to claim 3, characterized in that: Before transmitting the converted data fragments to the dynamic decision block, the method includes: identifying the converted data fragments to determine static road data and dynamic road data; For the static road data, determining based on the back-end constraint condition and updating the digital road model; The dynamic road data is subjected to simplified analysis and determination based on the dynamic decision block and the digital road model is updated.

5. The method for dynamically updating a digital road model for smart parking according to claim 3, characterized in that: Performing a synchronous update determination on the digital road model and generating a synchronization instruction includes: Setting backend constraints, wherein the backend constraints are used to perform model update constraints, with a preset information difference as the first constraint and an influence based on smart parking management as the second constraint; Identifying the road simplification model and the updated simplification model, and measuring a first information difference and a second influence; If the first information difference is greater than or equal to the first constraint condition, or the second influence degree is not empty, the synchronization instruction is generated.

6. A digital road model dynamic update system for smart parking, characterized in that: The system is used to implement the method for dynamically updating a digital road model for smart parking according to any one of claims 1 to 5, and the system includes: A digital simulation map construction module, wherein the digital simulation map construction module is used to obtain scene basic information of a target road scene and construct a digital simulation map; A digital road model determination module, the digital road model determination module is used to determine the digital road model through digital twin modeling based on the scene basic information, perform equivalent simplification on the digital road model, and determine the dynamic update module in coordination with the digital simulation map; A data fragmentation determination module, the data fragmentation determination module is used to perform real-time monitoring of road scenes based on distributed sensing equipment and screening based on front-end constraints to determine data fragments, wherein the front-end constraints screen data streams based on a trigger factor matrix based on smart parking management; A digital road model update module is used to transmit the data fragments back and perform data structure conversion, perform equivalent update analysis based on the dynamic update module and update the digital road model based on backend constraints, and determine an updated road model.

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