A method for building and processing digital street metadata model

Abstract

The present invention provides a digital street metadata model construction and processing method, which belongs to the field of model management technology, and specifically comprises: when it is determined based on construction data targets and distribution data in different streets that the complexity of personalized construction of the component construction target device meets the requirements, the component construction target device is used as the optimization target device, and when it is determined based on the construction data targets corresponding to the optimization target devices that the construction data targets have deviations, the appearance deviation of the optimization target device in the digital construction process is optimized, and the appearance deviation is used to determine the optimizable data targets in the construction data targets, and according to the data deviations of different optimizable data targets in different streets, the optimization method of the optimizable data targets is determined, and the construction processing of the street components of the optimization target devices is carried out by using the optimization method, thereby improving the efficiency of the construction processing of the digital streets.

Description

Technical Field

[0001] The present invention belongs to the technical field of model management, and in particular relates to a method for constructing and processing a digital street metadata model. Background Art

[0002] Metadata is data that describes data, and is mainly used to describe the attributes, characteristics, and contextual information of data resources. Metadata can help users locate, understand, and manage data, and ensure the accuracy, consistency, and accessibility of data.

[0003] In order to achieve metadata management, the invention patent application CN202310477798.8 "Smart Street Generation Method, Smart Street Generation Device, Electronic Device and Computer Storage Medium" determines the placement method of different numbers of target street components in the street digital base based on the preset placement rules, and uses the placement method corresponding to the target street components to generate multiple optional smart street sand table models, which improves the generation efficiency of the smart street digital sand table. However, at the same time, there are the following technical problems:

[0004] In the process of building the metadata model of digital streets, different street components often involve multiple data dimensions. This means that if the data deviations of different data dimensions of street components in the construction process of different digital streets are ignored during the construction of the metadata model of street components, the data dimension imported by the digital street may be too high, making the construction more difficult.

[0005] In view of the above technical problems, the present invention provides a method for building and processing a digital street metadata model. Summary of the invention

[0006] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0007] According to one aspect of the present invention, a method for constructing a digital street metadata model is provided.

[0008] A method for constructing a digital street metadata model, specifically comprising:

[0009] S1 uses the distribution data of different types of equipment in different streets to determine the equipment that needs to build street components and uses it as the target equipment for component construction;

[0010] S2 determines the construction data targets of the metadata models of the street components of different component construction target devices, and when it is determined based on the construction data targets and the distribution data in different streets that the complexity of the personalized construction of the component construction target device meets the requirements, the component construction target device is used as the optimization target device;

[0011] S3: when it is determined that the construction data target has a deviation based on the construction data target corresponding to the optimization target device, the appearance deviation of the optimization target device during the digital construction process is determined, and the optimizable data target in the construction data target is determined by using the appearance deviation;

[0012] S4 determines the optimization method of the optimizable data target according to the data deviation of different optimizable data targets in different streets, and constructs and processes the street components of the optimization target device using the optimization method.

[0013] The beneficial effects of the present invention are:

[0014] The distribution data of different types of equipment in different streets is used to determine the equipment that needs to build street components, thereby realizing the determination of the equipment that needs to build street components based on the distribution quantity and distribution clustering of equipment in different streets, avoiding the occurrence of technical problems caused by the difficulty of building street components for all equipment, thereby improving the efficiency and specificity of the construction processing of street components.

[0015] According to the data deviation of different optimizable data targets in different streets, the optimization method of the optimizable data targets is determined, which not only ensures the construction of metadata models for optimizable data targets with data deviations in different streets, thereby improving the efficiency of equipment construction and processing in digital models, but also avoids the construction of metadata models for optimizable data targets with no data deviations in different streets. On the basis of ensuring the efficiency of construction and processing, it also reduces the difficulty of construction and processing of optimizable data targets of the metadata model.

[0016] A further technical solution is that the equipment includes different types of walls, windows, pipelines, lines, roads and auxiliary road facilities, and door bodies.

[0017] A further technical solution is that the distribution data of the equipment includes the streets where the equipment exists and the distribution of equipment settings in the streets where the equipment exists.

[0018] A further technical solution is that the streets are streets of a target city that is digitally constructed and processed.

[0019] A further technical solution is that the method for determining the optimization method of the optimizable data target is:

[0020] According to the data deviation of the optimizable data targets in different streets, the optimizable data targets in different streets are divided into different data range intervals;

[0021] Determine the interval quantity ratio of different data range intervals by the quantity ratio of optimizable data targets in different data range intervals;

[0022] Based on the interval quantity ratio of different data range intervals, the data range interval whose interval quantity ratio is greater than the preset interval quantity ratio is determined, and it is used as the screening range interval, and the optimization method of the optimizable data target is determined according to the number of the screening range intervals.

[0023] A further technical solution is that the optimization method of the optimizable data target includes setting the optimizable data target in the metadata model and not setting the optimizable data target.

[0024] A further technical solution is to determine the optimization method of the optimizable data target according to the number of screening range intervals, specifically including:

[0025] When the number of the screening range intervals is less than the preset number of screening intervals, it is determined that no optimizable data target is set in the metadata model;

[0026] When the number of the screening range intervals is not less than the preset number of screening intervals, it is determined to set an optimizable data target in the metadata model.

[0027] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0028] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings;

[0030] Figure 1 It is a flow chart of a method for building a digital street metadata model;

[0031] Figure 2 is a flow chart of a method for determining the equipment required to construct a street assembly;

[0032] Figure 3 is a flow chart of a method for determining an optimizable data target in constructing a data target;

[0033] Figure 4 It is a flow chart of a method for determining an optimization method that can optimize a data target. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0035] With the rapid development of big data and data analysis technology, in order to improve the reliability of urban management, more and more cities have built platforms such as city brains and urban smart operation and maintenance platforms. The above platforms are often built based on existing communication networks and communication facilities. In view of this, in the process of building such platforms, how to realize the generation and processing of digital models of streets, so as to intuitively realize the intuitive management of different streets has become a technical problem that needs to be solved urgently.

[0036] At the same time, in the process of building the metadata model of digital streets, if the data deviation of street components in different data dimensions during the construction of different digital streets is ignored when building the metadata model, it may cause the data dimension imported by the digital street to be too high, making the construction more difficult and thus unable to meet the goal of large-scale digital street construction.

[0037] Component construction target devices: The target devices are determined by the percentage of streets where the devices exist. Devices with a percentage of more than 60% are used as component construction target devices.

[0038] The complexity of personalized construction of component construction target devices: It is determined according to the deviation of construction data targets in different streets. Specifically, the construction data target with the largest number of streets consistent with the construction data target is used as the basic data target, and the proportion of streets that deviate from the basic data target is used to determine it. Specifically, when the proportion of the number of streets is 0.8, the complexity of personalized construction is 0.8, and the complexity less than 0.6 is used as the optimization target device.

[0039] Determination of optimizable data targets in the build data targets: When there are deviations in the build data targets, the build data targets that have no deviations in the appearance of the optimization target device are taken as optimizable data targets. This is to reduce the dimension of the build data targets when constructing the metadata model.

[0040] Optimization method of optimizable data targets: According to the data deviation of the optimizable data targets in different streets, the optimizable data targets in different streets are divided into different data range intervals, and the interval quantity ratios of different data range intervals are determined by the number ratios of optimizable data targets in different data range intervals. Based on the interval quantity ratios of different data range intervals, data range intervals whose interval quantity ratios are greater than the preset interval quantity ratios are determined, and used as screening range intervals. According to the number of screening range intervals, the optimization method of the optimizable data targets is determined.

[0041] The optimization methods of the optimizable data target include setting the optimizable data target in the metadata model and not setting the optimizable data target. Example

[0042] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a method for building a digital street metadata model is provided, which specifically includes:

[0043] S1 uses the distribution data of different types of equipment in different streets to determine the equipment that needs to build street components and uses it as the target equipment for component construction;

[0044] Furthermore, the equipment includes different types of walls, windows, pipelines, lines, roads and auxiliary road facilities, and doors.

[0045] Optionally, the distribution data of the device includes the streets where the device exists and the distribution of device settings in the streets where the device exists.

[0046] Furthermore, the streets are streets of a target city that is digitally constructed and processed.

[0047] It should be noted that if Figure 2 As shown, the method for determining the equipment required to build the street component is:

[0048] Based on the distribution data of the device, determine the street where the device exists and use it as the device matching street;

[0049] Whether the device is a device that needs to construct a street component is determined according to the proportion of the number of streets matched by the device in the streets.

[0050] Specifically, when the proportion of the number of streets matched by the device among the streets is greater than the proportion of the preset number of streets, it is determined that the device is a device that belongs to building a street component.

[0051] Optionally, the method for determining the equipment required to construct the street component is:

[0052] Based on the distribution data of the device, determine the street where the device exists and use it as the device matching street;

[0053] Determine a device matching street where the distribution number of devices is greater than a preset distribution number based on the distribution data of devices in the device matching street, and use it as a screening matching street;

[0054] Whether the device is a device that needs to build a street component is determined according to the proportion of the number of the screened matching streets in the streets.

[0055] In another embodiment, the method for determining the equipment required to construct the street component is:

[0056] Optionally, before entering step S11, it is necessary to determine the total number of distribution of the devices based on the distribution data of the devices in the device matching street. When the total number of distribution of the devices is less than the preset number of devices, it is directly determined that the devices do not belong to the devices that need to build street components. When the total number of distribution of the devices is not less than the preset number of devices, proceed to step S11.

[0057] S11 determines the street where the device exists based on the distribution data of the device, and uses it as the device matching street, and determines the street quantity impact factor of the device according to the number of the device matching streets and the proportion of the number of the device matching streets in the street;

[0058] Optionally, the above step S11 includes steps S111-S113, which are specifically:

[0059] S111 determines the street where the device exists based on the distribution data of the device, and uses it as the device matching street. When the number of the device matching streets is less than the preset street number threshold, the process proceeds to step S112. When the number of the device matching streets is not less than the preset street number threshold, the process proceeds to step S113.

[0060] S112 obtains the total number of distribution of the devices. When the total number of distribution of the devices is less than the preset device number threshold, it is directly determined that the devices do not belong to the devices that need to build street components. When the total number of distribution of the devices is not less than the preset device number threshold, proceed to step S113;

[0061] S113 determines the street quantity influence factor of the device according to the number of streets matched by the device and the proportion of the number in the streets. When the street quantity influence factor of the device is greater than the preset influence factor threshold, it is determined that the device belongs to a device that needs to build a street component. When the street quantity influence factor of the device is not greater than the preset influence factor threshold, proceed to step S12.

[0062] S12: determining the number of devices distributed in different device matching streets based on the distribution data of the devices in the device matching streets, and determining the number influencing factor of the devices in combination with the total number of distribution of the devices;

[0063] Optionally, the above step S12 includes steps S121-S123, specifically:

[0064] Determine the number of devices distributed in different device matching streets based on the distribution data of the devices in the device matching streets, and determine the number influencing factor of the devices in combination with the total number of distribution of the devices

[0065] S121 determines the distribution quantity of devices in different device matching streets based on the distribution data of devices in the device matching streets. When there is a device matching street with a distribution quantity greater than a preset distribution quantity, the process proceeds to step S122. When there is no device matching street with a distribution quantity greater than the preset distribution quantity, the process proceeds to step S123.

[0066] S122: the device matching streets whose distribution number of devices is greater than the preset distribution number are used as screening matching streets. When the number of screening matching streets is greater than the preset matching street number threshold, it is determined that the device belongs to a device that needs to construct a street component. When the number of screening matching streets is not greater than the preset matching street number threshold, the process proceeds to step S123.

[0067] S123 determines the quantity influence factor of the device based on the distribution quantity of the device in different device matching streets and the total distribution quantity of the device. When the quantity influence factor of the device is less than the preset quantity factor threshold, it is determined that the device does not belong to the device that needs to build a street component. When the quantity influence factor of the device is not less than the preset quantity factor threshold, proceed to step S13.

[0068] S13 determines the comprehensive impact impression of the device according to the street quantity impact factor and the weight of the quantity impact factor, and uses the comprehensive impact factor to determine whether the device is a device that needs to construct a street component.

[0069] S2 determines the construction data targets of the metadata models of the street components of different component construction target devices, and when it is determined based on the construction data targets and the distribution data in different streets that the complexity of the personalized construction of the component construction target device meets the requirements, the component construction target device is used as the optimization target device;

[0070] Furthermore, the metadata model of the street component constructs data targeting device data of a target device according to the component corresponding to the street component.

[0071] It should be noted that determining whether the complexity of the personalized construction of the component target device meets the requirements specifically includes:

[0072] Building distribution data of target devices in different streets based on the components determines the street with the largest number of distributions and uses it as the target street;

[0073] Determine the total number of build data targets in the target street according to the distribution number of component build target devices in the target street and the build data targets of different component build target devices;

[0074] Whether the complexity of the personalized construction of the component construction target device meets the requirement is determined by the total number of the construction data targets in the target street.

[0075] Further, when the total number of construction data targets in the target street is greater than a preset target number, it is determined that the complexity of the personalized construction of the component construction target device does not meet the requirement.

[0076] In another embodiment, determining whether the complexity of the personalized construction of the component construction target device meets the requirement specifically includes:

[0077] Determine the distribution quantity in different streets based on the distribution data of the component build target devices in different streets, and determine the total quantity of the build data targets in the different streets in combination with the build data targets of the component build target devices;

[0078] The total number of the construction data targets is used to determine the screening target streets among the streets, and whether the complexity of the personalized construction of the component construction target device meets the requirements is determined according to the number of the screening target streets.

[0079] Specifically, the screening target streets are streets whose total number of constructed data targets is not within a preset data target number range.

[0080] Optionally, determining whether the complexity of the personalized construction of the component-built target device meets the requirement specifically includes:

[0081] Acquire the number of construction data targets of the component construction target device, and when the number of construction data targets of the component construction target device is less than the preset data target number, determine that the complexity of the personalized construction of the component construction target device meets the requirements;

[0082] When the number of build data targets of the component build target device is not less than the preset data target number:

[0083] Based on the distribution data of the component build target devices in different streets, a street with the largest number of distributions is determined, and the street is used as a target street. According to the distribution number of the component build target devices in the target street and the build data targets of different component build target devices, the total number of build data targets in the target street is determined. When the total number of build data targets in the target street is greater than a preset target number threshold, it is determined that the complexity of the personalized construction of the component build target device does not meet the requirements.

[0084] When the total number of constructed data targets in the target street is not greater than the preset target number threshold:

[0085] Determine the distribution quantity in different streets based on the distribution data of the component build target device in different streets, and determine the total quantity of the build data targets in the different streets in combination with the build data targets of the component build target device, and determine that there is no screening target street in the street by using the total quantity of the build data targets:

[0086] Then determining that the complexity of the personalized construction of the target device constructed by the component meets the requirements;

[0087] When determining whether a screening target street exists in the streets by using the total number of the constructed data targets:

[0088] The number of the screening target streets is obtained, and when the number of the screening target streets is greater than a preset screening target street number threshold: it is determined that the complexity of the personalized construction of the component construction target device does not meet the requirements;

[0089] When the number of target streets to be screened is greater than the preset threshold for the number of target streets to be screened:

[0090] The complexity coefficient of the personalized construction of the component construction target device is determined by the number of the screening target streets and the total number of construction data targets in different screening target streets, and whether the complexity of the personalized construction of the component construction target device meets the requirements is determined based on the responsibility coefficient.

[0091] S3: when it is determined that the construction data target has a deviation based on the construction data target corresponding to the optimization target device, the appearance deviation of the optimization target device during the digital construction process is determined, and the optimizable data target in the construction data target is determined by using the appearance deviation;

[0092] Specifically, Figure 3 As shown, the method for determining the optimizable data target in the constructed data target is:

[0093] Based on the data range of the constructed data target, the data range of the constructed data target is divided into a plurality of data range intervals at equal intervals;

[0094] Based on the devices corresponding to the different data range intervals, determining the appearance deviation amount of the devices corresponding to the different data range intervals;

[0095] The data range interval with appearance deviation is determined by the appearance deviation amount of the device corresponding to different data range intervals, and is used as the appearance deviation data range. Whether the constructed data target is an optimizable data target is determined by the proportion of the number of appearance deviation data ranges.

[0096] Further, the appearance deviation of the device is determined by the deviation of the image features of the device in the digital street.

[0097] It can be understood that when the proportion of the number of the appearance deviation data range is greater than the preset proportion, it is determined that the constructed data target does not belong to the optimizable data target.

[0098] S4 determines the optimization method of the optimizable data target according to the data deviation of different optimizable data targets in different streets, and constructs and processes the street components of the optimization target device using the optimization method.

[0099] Specifically, Figure 4 As shown, the method for determining the optimization method of the optimizable data target is:

[0100] According to the data deviation of the optimizable data targets in different streets, the optimizable data targets in different streets are divided into different data range intervals;

[0101] Determine the interval quantity ratio of different data range intervals by the quantity ratio of optimizable data targets in different data range intervals;

[0102] Based on the interval quantity ratio of different data range intervals, the data range interval whose interval quantity ratio is greater than the preset interval quantity ratio is determined, and it is used as the screening range interval, and the optimization method of the optimizable data target is determined according to the number of the screening range intervals.

[0103] Furthermore, the optimization method of the optimizable data target includes setting the optimizable data target in the metadata model and not setting the optimizable data target.

[0104] Optionally, the optimization method of the optimizable data target is determined according to the number of screening range intervals, specifically including:

[0105] When the number of the screening range intervals is less than the preset number of screening intervals, it is determined that no optimizable data target is set in the metadata model;

[0106] When the number of the screening range intervals is not less than the preset number of screening intervals, it is determined to set an optimizable data target in the metadata model.

[0107] In one possible embodiment, the method for determining the optimization mode of the optimizable data target is:

[0108] According to the data deviation of the optimizable data target in different streets, the optimizable data targets in different streets are divided into different data range intervals, and when the number of data range intervals of the optimizable data target is less than the number of preset range intervals, it is determined that the optimizable data target is not set in the metadata model;

[0109] When the number of data range intervals that can optimize the data target is not less than the preset number of range intervals:

[0110] Determine the interval quantity ratio of different data range intervals by the quantity ratio of optimizable data targets in different data range intervals, and when there is no data range interval whose interval quantity ratio is greater than a preset quantity ratio threshold, determine to set the optimizable data target in the metadata model;

[0111] When there is a data range interval whose interval quantity ratio is greater than the preset quantity ratio threshold:

[0112] Based on the interval quantity ratios of different data range intervals, determine the data range intervals whose interval quantity ratios are greater than the preset interval quantity ratios, and use them as the screening range intervals. When the number of the screening range intervals is not less than the preset screening range interval number, determine to set the optimizable data target in the metadata model;

[0113] When the number of filter range intervals is less than the preset number of filter intervals:

[0114] Determine the weight coefficients of different data range intervals according to the proportion of the number of intervals in different data range intervals and the number of optimizable data targets; when the weight coefficient is greater than the preset weight coefficient and the number of data range intervals is not less than the preset number of screening intervals, determine to set the optimizable data target in the metadata model;

[0115] When the number of data range intervals with a weight coefficient greater than the preset weight coefficient is less than the preset number of screening intervals:

[0116] The distribution dispersion coefficient of the optimizable data target is determined according to the weight coefficients of different data range intervals, and the optimization method of the optimizable data target is determined using the distribution dispersion coefficient.

[0117] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0118] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do 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.

[0119] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A method for constructing a digital street metadata model, characterized in that: Specifically include: Using the distribution data of different types of equipment in different streets, the equipment required to construct the street component is determined and used as the target equipment for component construction; Determine the construction data targets of the metadata models of the street components of different component construction target devices, and when it is determined based on the construction data targets and the distribution data in different streets that the complexity of the personalized construction of the component construction target devices meets the requirements, use the component construction target devices as optimization target devices; Based on the construction data target corresponding to the optimization target device, when it is determined that the construction data target has a deviation, the appearance deviation of the optimization target device during the digital construction process is determined, and the optimizable data target in the construction data target is determined by using the appearance deviation; Determine the optimization method of the optimizable data target according to the data deviation of different optimizable data targets in different streets, and construct and process the street component of the optimization target device using the optimization method; The method for determining the optimization method of the optimizable data target is: According to the data deviation of the optimizable data targets in different streets, the optimizable data targets in different streets are divided into different data range intervals; Determine the interval quantity ratio of different data range intervals by the quantity ratio of optimizable data targets in different data range intervals; Determine data range intervals whose interval quantity ratio is greater than a preset interval quantity ratio based on the interval quantity ratio of different data range intervals, and use them as screening range intervals, and determine the optimization method of the optimizable data target according to the quantity of the screening range intervals; The optimization method of the optimizable data target is determined according to the number of screening range intervals, specifically including: When the number of the screening range intervals is less than the preset number of screening intervals, it is determined that no optimizable data target is set in the metadata model; When the number of the screening range intervals is not less than the preset number of screening intervals, it is determined to set an optimizable data target in the metadata model.

2. The method for constructing a digital street metadata model according to claim 1, wherein: The equipment includes different types of walls, windows, pipelines, lines, roads, doors and auxiliary road facilities.

3. The method for constructing a digital street metadata model according to claim 1, wherein: The distribution data of the devices include streets where the devices exist and distribution conditions of device settings in the streets where the devices exist.

4. The method for constructing a digital street metadata model according to claim 1, wherein: The streets are streets of a target city to be digitally constructed.

5. The method for constructing a digital street metadata model according to claim 4, wherein: The method for determining the equipment required to build the street component is: Based on the distribution data of the device, determine the street where the device exists and use it as the device matching street; Whether the device is a device that needs to construct a street component is determined according to the proportion of the number of streets matched by the device in the streets.

6. The method for constructing a digital street metadata model according to claim 5, characterized in that: When the proportion of the number of streets matched by the device among the streets is greater than the proportion of the preset number of streets, it is determined that the device is a device that belongs to building a street component.

7. The method for constructing a digital street metadata model according to claim 1, wherein: The construction data target of the metadata model of the street component constructs the device data of the target device according to the component corresponding to the street component.

8. The method for constructing a digital street metadata model according to claim 1, wherein: The optimization method of the optimizable data target includes setting the optimizable data target in the metadata model and not setting the optimizable data target.

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