CN114090700B

CN114090700B - Method, system and equipment for generating feature data

Info

Publication number: CN114090700B
Application number: CN202111384277.5A
Authority: CN
Inventors: 林臻哲; 刘萍昌; 李镓岐; 游健; 史旭; 周华龙; 李鑫; 海刚; 蔡忠兴; 王远生; 林栋熙; 林嘉俊
Original assignee: Guangzhou Huasen Building And Engineering Design Consultants Co ltd
Current assignee: Guangzhou Huasen Building And Engineering Design Consultants Co ltd
Priority date: 2021-11-22
Filing date: 2021-11-22
Publication date: 2022-05-17
Anticipated expiration: 2041-11-22
Also published as: CN114090700A

Abstract

The invention provides a method, a system and equipment for generating characteristic data, which comprises the steps of classifying the entity geometric data according to a physical object of a target delivery object to generate entity object data of the target delivery object; performing abstract processing according to the geometric data of the target delivery object to generate abstract geometric data; abstract classifying the abstract geometric data according to a physical object of the target delivery object to generate first abstract object data; mapping the first abstract object data and the entity object data to obtain a mapping relation and a constraint rule of the first abstract object data and the entity object data; classifying the abstract geometric data according to a functional partition of a target delivery object and endowing the abstract geometric data with a functional label to obtain an abstract functional partition, matching the first abstract object data according to the abstract functional partition and endowing the label of the abstract functional partition on the first abstract object data to obtain second abstract object data with a label.

Description

Method, system and equipment for generating feature data

Technical Field

The invention relates to the technical field of building software, in particular to a method, a system and equipment for generating feature data.

Background

In the traditional data interaction mode, data transmission is realized by matching object codes among different software formats, the coding rule of an object is determined by object characteristics, all service logic characteristics generated by inconsistency of 'environment', 'stage' and 'requirement' of the object are converged in object attributes in the project practice process, so that one object corresponds to a group of characteristic data, the object is subjected to 'exhaustion' and stored in a database according to requirements, and service personnel are allowed to select in the future in a preset form, so that the problem of difficult unified input standard is solved, and the top-down digital circulation of the object data is completed.

The problem with this approach is that, as the business content becomes complex and the feature data becomes more and more, the number of "exhaustion" objects is no longer controlled, and the difficulty of selecting from a huge number of data objects by business personnel increases.

Disclosure of Invention

The invention provides a method, a system and equipment for generating characteristic data, which are used for solving the technical problems that in the prior art, when the service content is complex and the characteristic data is more and more, the number of 'exhaustion' objects is not controlled any more, and the difficulty of selecting from a huge number of data objects by service personnel is increased gradually.

The invention provides a method for generating feature data, which comprises the following steps:

acquiring entity geometric data of a target delivery object;

classifying the entity geometric data according to the physical object of the target delivery object to generate entity object data of the target delivery object;

performing abstract processing according to the geometric data of the target delivery object to generate abstract geometric data;

performing abstract classification on the abstract geometric data according to a physical object of the target delivery object to generate first abstract object data;

mapping the first abstract object data and the entity object data to obtain a mapping relation between the first abstract object data and the entity object data and constraint rules of the first abstract object data and the entity object data;

classifying the abstract geometric data according to the functional partition of the target delivery object, giving a functional label to the abstract geometric data, and obtaining an abstract functional partition with a label, wherein the label comprises functional characteristics and a boundary range:

matching the first abstract object data according to the abstract function partition, endowing a label of the abstract function partition on the first abstract object data, and acquiring second abstract object data with a label;

wherein the second abstract object data, the mapping relationships, and the constraint rules are used to generate target deliverable data.

In some embodiments, matching the first abstract object data according to the abstract function partition and assigning a tag of the abstract function partition to the first abstract object data, and obtaining second abstract object data with the tag:

performing Boolean operation logic on the abstract function partition and the first abstract object data to acquire a dynamic linkage relation between the abstract function partition and the first abstract data;

and endowing the label of the abstract function partition to the corresponding first abstract data according to the dynamic linkage relation, and acquiring the second abstract data with the label.

In some embodiments, the constraint rule includes a geometric constraint and a boundary condition for the first abstract object data to satisfy a change in a state of an entity object in the entity object data.

In some embodiments, the entity object data comprises an entity object tag and the first abstract object data comprises a first abstract tag, wherein the entity object tag and the first abstract tag are both tags that are classified as a physical object of the target deliverable;

and generating a mapping relation according to the entity label and the first abstract label.

In some embodiments, performing boolean logic on the abstract functional partition and the first abstract object data, and obtaining the dynamic linkage relationship between the abstract functional partition and the first abstract data specifically includes:

performing Boolean operation logic on the abstract function partition and the first abstract object data, and when the first abstract object data has difference data outside the abstract function partition, acquiring the difference abstract data according to the mapping relation and the constraint rule by using the difference data;

and analyzing the difference abstract data and the entity object data, if the difference abstract data and the entity object data are different, modifying the first abstract object data according to the entity object data, and if the difference abstract data and the entity object data are the same, feeding back modification information.

In some embodiments, the abstract function partition comprises a first abstract function partition and a second abstract function partition;

the step of assigning the label of the abstract function partition to the corresponding first abstract data according to the dynamic linkage relationship, and the step of obtaining the second abstract data with the label further comprises:

assigning the first label of the first abstract function partition to the corresponding first abstract data according to the dynamic linkage relation, and generating third abstract data with the first label;

matching according to the first tag of the third abstract data with the second tag of the second abstract functional partition,

if the first abstract data and the second abstract data are matched, assigning a value to a first label of the third abstract data according to a first preset rule to generate fourth abstract data, and determining that the fourth abstract data is second abstract data with a label;

and if the first abstract data is not matched with the second abstract data, acquiring information interaction with a service end, acquiring a second preset rule, assigning a value to the first label of the third abstract data, generating fifth abstract data, and determining that the fifth abstract data is the second abstract data with the label.

In some embodiments, the second preset rule is a preset rule with the largest number of times when the first tag and the second tag are selected to match, among a plurality of preset rules acquired by the service end.

In some embodiments, the preset rule with the largest number of times of adoption is determined as a second preset rule corresponding to the matching of the corresponding first tag and the corresponding second tag in the second abstract partition.

The embodiment of the invention also provides a system for generating the feature data, which comprises the following modules:

the first acquisition module is used for acquiring entity geometric data of the target delivery object;

the first generation module is used for classifying the entity geometric data according to the physical object of the target delivery object to generate entity object data of the target delivery object;

the second generation module is used for carrying out abstract processing according to the geometric data of the target deliverable to generate abstract geometric data;

the third generation module is used for carrying out abstract classification on the abstract geometric data according to the physical object of the target delivery object to generate first abstract object data;

the second obtaining module is used for mapping the first abstract object data and the entity object data to obtain a mapping relation between the first abstract object data and the entity object data and a constraint rule of the first abstract object data and the entity object data;

a third obtaining module, configured to classify the abstract geometric data according to a functional partition of the target delivery object, assign a functional tag to the abstract geometric data, and obtain an abstract functional partition with a tag, where the tag includes a functional feature and a boundary range:

a fourth obtaining module, configured to match the first abstract object data according to the abstract functional partition, and iterate the tag on the first abstract object data to obtain second abstract object data with a tag;

wherein the second abstract object data, the mapping relationships, and the constraint rules are used to generate the target deliverable data.

The embodiment of the invention provides a feature data generation device, which comprises a processor and a memory;

the memory is used for storing program codes and transmitting the program codes to the processor;

the processor is configured to execute the above-mentioned method for generating feature data according to the instructions in the program code.

According to the technical scheme, the embodiment of the invention has the following advantages:

the embodiment of the invention provides a method, a system and equipment for generating feature data, wherein the method comprises the following steps: acquiring entity geometric data of a target delivery object; classifying the entity geometric data according to the physical object of the target delivery object to generate entity object data of the target delivery object; performing abstract processing according to the geometric data of the target deliverable to generate abstract geometric data; performing abstract classification on the abstract geometric data according to a physical object of the target delivery object to generate first abstract object data; mapping the first abstract object data and the entity object data to obtain a mapping relation between the first abstract object data and the entity object data and constraint rules of the first abstract object data and the entity object data; classifying the abstract geometric data according to the functional partition of the target delivery object, giving a functional label to the abstract geometric data, and obtaining an abstract functional partition with a label, wherein the label comprises functional characteristics and a boundary range: matching the first abstract object data according to the abstract function partition, endowing a label of the abstract function partition on the first abstract object data, and acquiring second abstract object data with a label; wherein the second abstract object data, the mapping relationships, and the constraint rules are used to generate target deliverable data.

According to the generation method provided by the embodiment of the invention, object-oriented multi-service logic dimension classification in the building engineering industry is realized through the physical object commonality characteristic; the method comprises the steps of performing abstraction processing on physical objects of a target deliverable without directly performing data docking between software objects, classifying entity geometric data of the physical objects of the target deliverable, obtaining abstract objects by establishing the abstraction of the physical objects of the target deliverable to act, classifying the characteristics of the physical objects, completing data processing and application, and solving the technical problems that data chain breakage, data authority safety management and data structure difference cannot be converted due to the addition and replacement of source objects; the first abstract object is provided with abstract 'space boundary', although the first abstract object does not exist in a physical space, the first abstract object is represented as a geometric block in a data world, interactive calculation between the first abstract object data and the entity object data can be realized through the mapping relation and the constraint rule, meanwhile, the entity object data is mapped by the first abstract object data, the abstract geometric data is classified according to the functional partition of the target delivery object and is endowed with a functional label, and an abstract functional partition with a label is obtained, wherein the label comprises a functional characteristic and a boundary range: matching the first abstract object data according to the abstract function partition, endowing a label of the abstract function partition on the first abstract object data, acquiring second abstract object data with the label, and realizing 'configuration calculation rule combination' to replace 'object characteristic result exhaustion', so that the controllability of the number of objects, non-geometric functional labels and the like which do not need graph interactive operation are written in the second abstract object data, and source data can be deleted without loss; the method realizes clear business data boundary of each link, and the second abstract object data is deepened step by step according to project process and is dynamically associated and iterated to meet the technical transformation of business digital driving and cooperation in the building industry, thereby effectively solving the technical problems that when the business content is complex and the characteristic data is more and more, the number of 'exhaustion' objects is not controlled any more, and the difficulty for business personnel to select from a huge number of data objects is increased.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.

Fig. 1 is a flowchart of a method, a system, and an apparatus for generating feature data according to an embodiment of the present invention.

Fig. 2 is a system configuration diagram of a method, a system, and a device for generating feature data according to an embodiment of the present invention.

Fig. 3 is a device framework diagram of a method, a system, and a device for generating feature data according to an embodiment of the present invention.

Detailed Description

The embodiment of the invention provides a method, a system and equipment for generating characteristic data, which are used for solving the technical problems that when the service content is complex and the characteristic data is more and more, the number of 'exhaustion' objects is not controlled any more, and the difficulty of selecting from a huge number of data objects by service personnel is increased gradually.

In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which embodiments of the present invention belong. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

Before further detailed description of the embodiments of the present invention, terms and expressions referred to in the embodiments of the present invention will be described, and the terms and expressions referred to in the embodiments of the present invention will be explained as follows.

The generation of characteristic data is a technology for simply summarizing video content, extracting moving targets by performing algorithm analysis on the moving targets in the video in an automatic or semi-automatic mode, analyzing the motion tracks of all the targets, splicing different targets into a common background scene, and combining the different targets in a certain mode to generate a new concentrated video.

Referring to fig. 1, fig. 1 is a flowchart illustrating a method, a system and a device for generating feature data according to an embodiment of the present invention.

As shown in fig. 1, a method for generating feature data provided by the present invention includes:

acquiring entity geometric data of a target delivery object;

Wherein the target deliverable is typically a final as-built deliverable, typically determined on demand.

The target delivery data includes entity geometry data and business function labels of the target delivery, for example, the target delivery is a house, wherein the information of a series of geometry data of a house occupied area, length, width and height is involved, the entity geometry data also relates to geometry data of internal physical objects, for example: a series of data such as the number of rooms, the corresponding size of each room, the position and the size of a door of each room, the position and the size of a window of each room, and the number of layers of the room in a house can be directly expressed by setting a space coordinate system and coordinates of XYZ axes, and the corresponding data can be directly converted into a coordinate system and a geometric numerical value.

The business function label of the target delivery is generally the definition, such as material, structure, functional characteristics, etc., required to satisfy the function of the target delivery, for example: the target deliverables are schools, government buildings, residential buildings and the like, physical objects of the target deliverables comprise functional characteristics and boundary ranges which need to be met by the target deliverables, namely, materials which need to be adopted for the doors of the washrooms to meet the fireproof requirements, whether glass is arranged on the doors, handles of the doors and the like, or whether anti-theft doors are arranged, the labels are different for the target deliverables in the schools, the government buildings or the residential buildings, and the data which can be actually and directly generated and can generate the target deliverables can be formed by attaching the labels on the basis of the physical and geometric data of the target deliverables.

When the target delivery object is a building, the physical objects of the building, such as rooms, doors, windows, load-bearing beams, pipelines, stairs, and the like, are physical objects of the target delivery object;

after the entity geometric data of the target delivery object are divided according to the physical objects actually, the entity geometric data are divided into entity object data of the target delivery object, and the entity object data of the target delivery object, such as data of the height of a door and a window, the position of a door frame, the size of a room and the like, can be represented by adding numerical values to coordinates;

for convenience of processing the physical object data of the target delivery object, the physical object data may be defined and packaged, for example, the dimensions and coordinates of the door are defined and packaged, that is, the dimensions and coordinates of the door and window are defined by using the door and window as names, and then the information is packaged to form a database of the physical object for subsequent calling;

for further storage, the physical object data of the target delivery object includes the geometric data of the physical object, and also includes the production business label of the physical object, and also can give the production process business label (such as professional classification, material technology, manufacturer, etc.) on the defined and packaged physical object data;

and packaging and defining entity objects corresponding to various specific design drawings and graphs or model combinations in modeling software according to the physical object of the construction project delivery as a basic classification basis, wherein all major or business logics generated from concept design to completion delivery of the object are used as process classification attributes in a label mode, and the object-oriented multi-business logic dimension classification in the construction project industry is realized through the common characteristics of the label mode and physical functions.

For specific software such as REVIT: the window which is actually finished and delivered is classified as a foundation, the corresponding revit software belongs to a window family type, the boundary characteristic of the revit software is a hole based on a wall body, the positioning point of an independent system is an insertion point, the geometric characteristics of the rest windows, such as the height, width and depth of the window, relative to the hole boundary and the positioning point are determined, the self structure of the rest windows, such as the opening mode, the number of window sashes, hardware components and other components belong to geometric elements in the independent closed system, the parts can be freely replaced, the specific geometric parameters can be freely adjusted in a constraint range, and the series of elements are packaged and mapped to the solid object window of an external database to complete S1 operation, and accordingly; in AutoCAD software, the same encapsulation operation is implemented in the form of a dynamic property block.

Other professional business attributes of the entity object window, such as non-geometric attributes of materials, heat preservation coefficients, cost and the like, are hung on an external database in a label mode, and specific values are recorded in the subsequent process.

Other objects such as 'door', 'component', 'beam column', 'pipeline' and the like are input in the same way.

Performing abstract processing according to the entity geometric data of the target delivery object to generate abstract geometric data;

the abstract geometric data are polyhedrons, preferably cuboids or cubes in general, for example, when the target delivery object is a building, the target delivery object can be made to be an abstract cuboid, so that the cuboid can completely contain the building, and when the building is covered by the cuboid, the corresponding geometric relationship between the building and the cuboid can be generated, so that the abstract geometric data are formed.

classifying and cutting the cuboid of the abstract geometric data according to a physical object, such as a gate of a building, wherein a small cuboid is classified and cut out, and the small cuboid contains the gate;

these small cuboids, cubes together constitute a large cuboid of abstract geometric data, i.e. the first abstract object data is these small cuboids, which correspond to the physical object.

according to the physical object, a mutual mapping relation can be formed between first drawing object data and the first entity object data, for example, the physical object is a gate, and the entity object data of the gate can be directly and equivalently obtained according to the first abstract object data; generally, the mapping between the first abstract object data and the entity object data is one-to-many, or one-to-one;

the constraint rule is, for example, to put the gate corresponding to the entity object data into the cuboid corresponding to the first abstract object data, where when the related explicit actual entity object data is involved, it is necessary to constrain the two, for example, coordinates of a doorknob of the gate in the cuboid can form a plurality of constraints, and usually, the entity object data of one gate can correspond to the first abstract object data through a plurality of constraints, that is, the first abstract object data can be restored back to the entity object data according to a mapping relationship and a constraint relationship.

The first abstract object data can also be established in an independent external database and is mapped together with the entity object data; by the aid of the method, the characteristic data can be subjected to a series of iterations such as rule configuration and manual intervention through the property of the agent layer without being limited by a software format.

In the method for generating feature data of the embodiment, the first abstract object data of the physical object of the target delivery object is established for proxy, and data processing and application can be completed in an external database through object features, namely basic classification, space occupation, peripheral relation and the like; the technical problems that data chain breakage, data authority safety management and data structure difference between software cannot be converted due to source object addition and replacement are solved.

For example: an abstract mapping of a physical object can be established in an independent external database which is separated from concrete software, no matter the geometric size change of a window in entity object data in concrete business or the structural change in a boundary range is stored in mapping first abstract object data in the form of a feature set, a one-to-many relation is established, and all general non-geometric business attributes can be managed through the mapping first abstract object data so as to cover and influence all instances. (strong correlation independent of fixed order coding)

the abstract functional subareas such as the elevation of an axis network, the room function, the fire subarea or the service range are determined according to the target delivery object, if the target delivery object is constructed in a school, the target delivery object is determined according to the rules of the school building, the fire subareas to be set and the functions of the rooms to be set are determined, corresponding labels are arranged under the fire subareas, the labels are actually provided with values, the values are, for example, in the school, the required fire rating is required, the fireproof required material is embodied on the classroom door and can be directly limited to the required value, a plurality of cuboids with small functional characteristics are further divided in a large cuboid with abstract geometric data, the values of the corresponding labels are specifically included in the small cuboids, just like the example of a classroom, the door material can be directly limited to the door made of an alloy material, and the requirements and the limitations are determined when the target delivery object is functionally subarea, for example, the building is a school, a classroom in one classroom needs to be set to have a high fire-protection level, the material of a door of the classroom, a door handle, a door lock, whether the door is opened or not, and the like can be directly calibrated at the part with the high fire-protection level to form a label value, and the large cuboid of the abstract geometric data can be divided into a plurality of middle cuboids of functional partitions according to the functional partitions;

matching the first abstract object data according to the abstract function partition, endowing a label of the abstract function partition on the first abstract object data, and acquiring second abstract object data with the label;

the small cuboid of the first abstract object data is placed into a cuboid in a functional partition for matching and iteration, tags on the cuboid in the functional partition are endowed with corresponding values to the first abstract object data, so that the first abstract object data not only have geometric characteristics but also have functional characteristics, second abstract object data is generated, for example, the first drawing object data of which the physical object is a classroom door is placed into the functional partition with fireproof functional characteristics, the door, classroom paint and the like involved in the first abstract object data can obtain the fireproof tags under the corresponding fireproof functional characteristics, and values under the tags are obtained simultaneously or obtained, for example, the door material is fireproof material alloy, and the classroom paint is a certain material.

Since the same classroom may conform to a plurality of different functional characteristics, the abstract geometric data may be repeatedly classified and assigned with functional labels according to the functional partitions of the target deliverable to obtain abstract functional partitions with labels, the labels including functional characteristics, wherein the function of each involved abstract functional partition is different, for example, the first abstract functional partition formed for the first time is fireproof, the first string object data is placed in the first abstract partition to obtain the values of the labels of the materials of the doors of the classrooms, the doors made of the materials can take the fireproof effect, and when the second abstract functional partition formed for the second time is environment-friendly, the first abstract object data with the labels is placed in the second abstract partition to determine the materials meeting the environment again in the values of the labels, and determining the value of the exported material, and forming a unique value on the label of the second abstract object data through multiple iterations, wherein the unique value can meet the requirement of the target delivery object only when the door is determined to be made of a certain alloy material.

The window, the pipeline, the room and the like are automatically iterated in the mode, and if the window, the pipeline, the room and the like need to be adjusted, manual intervention is performed, so that final second abstract object data can be formed; the second abstract object data can be established in an independent external database, is defined and divided by a virtual space required by business logic or human activity function in the target delivery object, and builds an object mapping containing a geometric boundary in the function in specific business software;

the second abstract object data can intensively bear service data such as standard safety, functional parts, sensory effects, cost control, block management and the like from the spatial dimension, and quality control is realized by setting a lower limit requirement, so that the aim of finally delivering objects is fulfilled.

The first abstract object data definition is provided with abstract space boundaries, and although the abstract space boundaries do not exist in a physical space, the abstract space boundaries are regarded as geometric blocks in a data world, so that interactive computation with a physical object is realized.

When the first abstract object data enters the abstract function partition, if the physical object is subdivided by the computer to be small enough, the first abstract object data can be completely included in the abstract function partition, wherein when the concrete structure of the entity object data and the first abstract object data are restricted, inclusion, non-inclusion and intersection are generally adopted; when the first drawing object data is matched with the abstract function partition, Boolean operation logic is adopted, through Boolean operation logic, the relation of 'intersection fusion, intersection part reservation and intersection part split' between the first extraction object data and the abstract function partition can be further carried out to form a neural network, this is done in order to achieve a dynamic association of the first snapshot object data with the data of the abstract functional partition, which may be simultaneously contained by one abstract functional partition or a plurality of nested abstract functional partitions, thereby inheriting the tags within the abstract functional partitions carried by the series of first abstract object data and the values within the tags, the unique identity is formed through the self attribute of the entity object data corresponding to the first abstract object data and the service characteristic inherited by the abstract function partition, and the characteristic can dynamically change along with the space change of the object due to design coordination.

Protection of the rights: the abstract function partition has the condition of Boolean logic with the first abstract object data, and the functional characteristics of the first abstract object data are determined by the operation result between the first extraction object data and the abstract function partition instead of the original self attribute, so that the functional characteristics of the entity object data corresponding to the first extraction object data are ensured to dynamically change along with the change of design services such as graphics, and a mode of coding a single dimension by using multi-dimension object combination characteristics instead of self characteristics is ensured.

And realizing data dynamic association between entity object data corresponding to the first extraction object data and the abstract function partition concrete software format through mapping of an external database:

the second abstract object data is stored in an external database, and is articulated with a plurality of concrete graphic software by using the group of characteristics to extract key geometric information; the method uses the modes of feature mapping, key geometric information extraction and storage and an external database to realize the description of the same object among different software and avoid the problem of graph data structure difference.

In some embodiments of the application, the constraint rule comprises a geometric constraint and a boundary condition for the first abstract object data to satisfy a change in a state of an entity object in the entity object data.

The geometric constraint condition is the simplest coordinate, size and the like of the entity corresponding to the entity object data in the cuboid of the first abstract object corresponding to the first abstract object data, and the cuboid corresponding to the first abstract object data can be directly restored to the static geometric entity corresponding to the entity object data through the geometric constraint condition; however, since the entity object is often dynamic, such as a door, which has two states of opening and closing, if sufficient space cannot be reserved, the target deliverable cannot be formed efficiently by mapping the cuboid corresponding to the first abstract object data to the dynamic geometric entity corresponding to the entity object data; by setting boundary conditions, the first abstract object data meets the change of the state of the entity object in the entity object data, and enough space is reserved on the first abstract object, so that the entity object needing dynamic change can effectively move, other abstract objects can coordinate and link with each other, and the problem that the entity object can not be dynamically unfolded when a target delivered object is generated is solved.

The mapping of the entity object and the abstract object in the concrete software is carried out the spatial placement or the constraint of the object boundary condition, so as to carry out the conventional design service aiming at the graphic expression, and when the entity object is placed and the graphic design expression is finished, the boundary between the entity object and the abstract object generates the mesh relation.

The boundary condition comprises an object space occupation range, can not overlap with the geometric boundary of the entity object, and realizes the boundary condition constraint in the space of the abstract object, thereby realizing that all other objects are driven to change by moving the boundary of a certain entity object.

In order to improve the mapping relationship between the entity object data and the first abstract object data, for example, when the physical object is named, the physical object is directly named as an entity tag of a window, and the entity tag of the window corresponds to the first abstract tag of the window on the first abstract object data, so that a mapping relationship can be formed, and the mapping relationship is matched with the mapping relationship formed by the physical object, so that the mapping between the tag data can be effectively formed, the subsequent reference is facilitated, and the mapping relationship between two databases is formed.

In some embodiments of the present application, performing boolean operation logic on the abstract function partition and the first abstract object data, and obtaining the dynamic linkage relationship between the abstract function partition and the first abstract data specifically includes:

The method is mainly used for realizing that when some first abstract object data and abstract functional partitions are matched, difference data outside the abstract functional partitions exist, namely errors may occur in the process that the first abstract object abstractly classifies the abstract geometric data according to a physical object of a target delivered object, at the moment, the generated difference abstract data are mapped back to an entity object to be analyzed according to the mapping relation and the constraint rule, the difference abstract data and the entity object data are analyzed, if the difference exists, the first abstract object data are modified according to the entity object data, and if the difference exists, modification information is fed back; therefore, the first abstract object data can be adjusted and can be subjected to feedback repair, so that the purpose of finally generating second abstract object data and generating a target delivery object according to the second abstract data, the mapping relation and the constraint rule is achieved; if the first abstract object data, the abstract function partition and the entity object data are divided into different databases, object data collected from different databases are subjected to data cleaning by a BI tool, wherein the data cleaning comprises redundant data deletion, field splitting of multi-meaning data, warning and conversion of contradiction ambiguous data, data compliance review and business compliance review; the data cleansing rules are stored in an external database as a universal resource.

In addition to general data cleaning, business logic judgment is built in, for example, some types of data exceed a limit and are not met with business requirements, modification must be returned, or the naming of some types of data is not met with the requirements, the data are automatically modified within the identification range of a computer, business personnel are returned to modify the data in a manual intervention manner if the data are not within the range, and the special processing leaves marks on a database.

assigning the first label of the first abstract function partition and the value of the first label to corresponding first abstract data according to the dynamic linkage relation, and generating third abstract data with the first label;

based on the matching of the third abstract data with the second abstract functional partition,

if the third abstract data is matched with the third abstract data, determining a value in a first label of the third abstract data according to a first preset rule, generating fourth abstract data, and determining that the fourth abstract data is second abstract data with a label;

The first abstract function partition and the second abstract function partition may be in different databases as described above, for example, the a abstract function partition includes a11, a22, a33 … …, etc., the B abstract function partition includes B11, B22, B33 … …, etc., and C is first abstract object data C11, C22, C33 … …, where C is geometric data of an abstract object, for example, C11 is a window, B11 is a fireproof partition of a school door and window, in which a label of fireproof material is provided, a11 is an environmental protection partition of a school door and window, in which a value of environmental protection material is provided, C11 is matched with B interior, that is, C11 and B11 are matched with each other, and obtained according to an algorithm in B11, C11 obtains a label C111 of the fireproof material, which can limit the material of the door and window to several kinds of fireproof materials, then match C111 with a interior a, and obtain the fireproof material with a11 according to the algorithm, i.e. determining the value of the first tag; thus, matching processing of each first abstract object data is realized, and all second abstract object data are obtained;

if the matching is not successful, it is indicated that a value in the first tag may have a value that does not meet the first rule, that is, the value in the fireproof material does not meet the environmental protection requirement, and the value is obtained by information interaction through the service end, wherein the service end may directly perform manual intervention or obtain multiple algorithms through a big data algorithm to determine one of the algorithms to assign the first tag, for example, a value may be directly obtained after a certain material in all the fireproof materials is selected through big data or manual intervention.

When the third abstract object data is matched with a preset condition, new standard general service data can be automatically generated according to condition logic judgment, service specification conversion and professional calculation; in addition, a special interface is set for the configuration rule to realize manual intervention, the default algorithm can preferentially push best practice data (data adopted by the most people under the condition of statistical multi-feature), and the best practice data is manually judged whether to be directly adopted or new configuration data of the second preset rule form a new iteration statistical basis. Configuring a label management mode of the first abstract data of the rule for management and control; the configuration rule of the second preset rule from big data or manual intervention is stored in an external database as a universal resource;

and (3) performing higher-dimensionality management by adopting a label management mode, namely, in the generation process of each configuration rule, namely the second preset rule, giving a group of labels to each configuration rule in the background according to the identity of a configurator, the identity of an owner, the stage of the configuration rule, the characteristics of a project, the region of the configuration rule, the number of times of intervention and other factors. Algorithm optimization iteration is realized through technical means such as big data analysis, algorithm model building, neural network self-learning and the like. The rule configuration under the general characteristic simulates an objective rule, and after iteration, the rule can be intelligently judged under different image conditions, so that repeated intelligent labor of people is replaced.

In some implementations, the second preset rule is a preset rule with the largest number of times when the first tag and the second tag are selected to match, among a plurality of preset rules acquired by the service end.

As described in the above embodiments, when the value of the fire-retardant material in the first tag cannot be determined to be a proper value when matching with the second tag, it can be determined by external big data, wherein the most preferable determination is to determine the preset rule by determining the value which is used the most times as the result value and using the most preset rule, and when the same combination occurs the next time, the preset rule can be directly used without obtaining modification.

And determining the preset rule with the maximum adoption times as a corresponding second preset rule when the corresponding first label is matched with the corresponding second label in the second abstract partition.

Distributing new data according to the dynamically obtained combination logic characteristics between the abstract and the entity object to each concrete service software for use:

and the second abstract object data generated after the first label and the second label are matched and determined is stored in the first line drawing object data after comprehensive consideration of four dimensions of 'the self attribute of the object, the attribute of the environment where the object is located, a standard method and artificial subjective intervention', so that the requirement of a front-line service worker on the service data during graphic interactive design application is met.

The method can be flexibly mapped back to the business object which is in concrete software and accords with the characteristic entity object data, and the function of the rule is not influenced by the creation, deletion and editing of the concrete business object. And the information is accurately pushed to each service software according to rules or algorithms in the abstract function partition to interact with people, so that the processing difficulty of each service person on the information is simplified, and the process allows each service to further call other information as an auxiliary judgment basis according to authority setting.

Iteration of the second abstract object logarithm is completed in a circulation mode, and data-level cooperation is achieved;

in the process, related achievements can be recycled among the target deliveries, originally, the work of manually combing input conditions for judgment among different target deliveries is needed, the computer automatically identifies and matches the working conditions with similar conditions appearing in the past target deliveries from the data level of the minimum granularity, and pushes the working conditions to service personnel for decision making, and under the default condition, a standard general decision scheme can be directly selected and packaged into a combined decision packet, so that the workload of the service personnel is further simplified.

Besides the knowledge base iteration of various document resources of the finished target delivery object, the iteration of abstract resources with more general meaning such as a decision rule package is emphasized in a database mode. All manual intervention data are recorded and marked on the line in the iteration process, and responsibility traceability is realized; and the manual intervention result and the characteristic rule set are connected and iterated to the next stage for use, and a powerful algorithm rule base is formed step by step through platform and project data accumulation.

Most functional features have boundary conditions, abstract functional partitions are used as judgment conditions, and matching selection can be performed manually (for example, the front room where the door is located adopts the feature of a fireproof door 1, the front room is located at the first layer, and the feature that the door needs to be opened outwards 2 is obtained)

Information such as the working condition environment cannot be obtained, the object code can cause the loss of a subsequent data chain due to source deletion, and the characteristic judgment of the object needs to be combined with various conditions of the boundary range.

As shown in fig. 2, an embodiment of the present invention further provides a feature data generation system, including the following modules:

a first obtaining module 201, configured to obtain entity geometry data of a target delivery;

a first generating module 202, configured to classify the entity geometric data according to a physical object of the target delivery object, and generate entity object data of the target delivery object;

the second generating module 203 is configured to perform abstraction processing according to the geometric data of the target delivery object to generate abstract geometric data;

a third generating module 204, configured to perform abstract classification on the abstract geometric data according to a physical object of the target delivery object, so as to generate first abstract object data;

a second obtaining module 205, configured to map the first abstract object data and the entity object data, and obtain a mapping relationship between the first abstract object data and the entity object data and a constraint rule of the first abstract object data and the entity object data;

a third obtaining module 206, configured to classify the abstract geometric data according to a functional partition of the target deliverable and assign a functional label to the abstract geometric data, so as to obtain an abstract functional partition with a label, where the label includes a functional feature and a boundary range:

a fourth obtaining module 207, configured to match the first abstract object data according to the abstract function partition, and iterate the tag on the first abstract object data, so as to obtain second abstract object data with a tag;

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

As shown in fig. 3, an embodiment of the present invention further provides a feature data generation device, which includes a processor 300 and a memory 301;

the memory 301 is used for storing a program code 302 and transmitting the program code 302 to the processor;

the processor 300 is configured to execute the steps of one of the above-mentioned feature data generation methods according to the instructions in the program code 302.

Illustratively, the computer program 302 may be partitioned into one or more modules/units that are stored in the memory 301 and executed by the processor 300 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 302 in the terminal device 30.

The terminal device 30 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 300, a memory 301. Those skilled in the art will appreciate that fig. 3 is merely an example of a terminal device 30 and does not constitute a limitation of terminal device 30 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the terminal device may also include input-output devices, network access devices, buses, etc.

The processor 300 may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The storage 301 may be an internal storage unit of the terminal device 30, such as a hard disk or a memory of the terminal device 30. The memory 301 may also be an external storage device of the terminal device 30, such as a plug-in hard disk provided on the terminal device 30, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card (FlashCard), and the like. Further, the memory 301 may also include both an internal storage unit and an external storage device of the terminal device 30. The memory 301 is used for storing the computer program and other programs and data required by the terminal device. The memory 301 may also be used to temporarily store data that has been output or is to be output.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The embodiment of the invention sends a request for acquiring the image of the annotation task to the server of the annotation platform through the client; after the server side of the labeling platform receives the request, the distributed buffer area performs registration service to the coordination module;

after the registration service is completed, the coordination module reads and writes the picture index information with the timestamp in the index library and sends the picture index information to the distributed buffer area; the distributed buffer zone feeds back the concurrent access amount to the coordination module;

the coordination module adjusts and distributes the picture index information to the distributed buffer area according to the concurrent access amount; the client reads the picture index information of the distributed buffer area, and downloads the labeling task picture according to the picture index information;

and the client submits the annotation information to the annotation platform server, and the annotation platform server updates the annotation state of the index information of the annotation task picture. The embodiment of the invention solves the problem of high concurrent reading and writing by increasing the memory buffer area under the distributed storage of big data, provides a high-throughput concurrent labeling service method, and solves the technical problem that the data labeling service has high concurrent reading and writing due to the fact that a high concurrent reading and writing lock strategy is not provided during data labeling in the prior art.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. A method for generating feature data, comprising:

acquiring entity geometric data of a target delivery object;

classifying the abstract geometric data according to the functional partition of the target delivery object, giving a functional label to the abstract geometric data, and acquiring an abstract functional partition with the label, wherein the label comprises functional characteristics;

2. The method for generating feature data according to claim 1, wherein the first abstract object data is matched according to the abstract function partition, and a tag of the abstract function partition is assigned to the first abstract object data, so as to obtain second abstract object data with a tag:

3. The method for generating feature data according to claim 1, wherein the constraint rule includes a geometric constraint condition and a boundary condition, and the boundary condition is used to satisfy a change in a state of an entity object in the entity object data in the first abstract object data.

4. The method for generating feature data according to claim 1, wherein the entity object data includes an entity object tag, and the first abstract object data includes a first abstract tag, wherein the entity object tag and the first abstract tag are both tags classified as a physical object of the target delivery;

5. The method for generating feature data according to claim 2, wherein performing boolean operation logic on the abstract function partition and the first abstract object data, and obtaining the dynamic linkage relationship between the abstract function partition and the first abstract data specifically includes:

6. The method according to claim 2, wherein the abstract function partition includes a first abstract function partition and a second abstract function partition;

7. The method according to claim 6, wherein the second preset rule is a preset rule with the largest number of times when the first tag and the second tag are selected to match, among a plurality of preset rules obtained by the service end.

8. The method according to claim 7, wherein the preset rule with the largest number of times of adoption is determined as a second preset rule corresponding to the matching of the corresponding first tag and the corresponding second tag in the second abstract partition.

9. A system for generating feature data, comprising:

the second generation module is used for carrying out abstract processing according to the geometric data of the target delivery object to generate abstract geometric data;

10. A generation device of characteristic data is characterized by comprising a processor and a memory;

the processor is used for executing the characteristic data generation method of any one of claims 1 to 8 according to instructions in the program code.