CN115982830B - Indoor design node adaptation method, device, computer equipment and storage medium - Google Patents
Indoor design node adaptation method, device, computer equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of data processing, and provides an adaptation method, a device, computer equipment and a storage medium of an indoor design node, which comprise the following steps: acquiring an indoor design model from a design model database; acquiring a design requirement text input by a customer; identifying a design requirement text based on a preset first neural network model, and identifying a design requirement keyword in the design requirement text; identifying design nodes to which the design requirement keywords belong; identifying an indoor design model based on a preset second neural network model, and identifying each design node of the indoor design model; based on the design node to which the design requirement keyword belongs, the design requirement keyword is added into a design template of the design node corresponding to the indoor design model, so that the adaptive design of the indoor design node is completed. The invention adds the text adaptation of the design requirement of the customer to the design template of the indoor design model based on the neural network model without relying on manpower.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to an adaptation method, an adaptation device, a computer device, and a storage medium for an indoor design node.
Background
At present, when enterprises move to change office places and individual houses move to change living places, decoration design is usually needed; users in places usually do not have the indoor design capability, so that professional design companies are required to assist in indoor design; the user puts forward design requirements to the design company, and the design company performs indoor design according to the design requirements.
However, at present, only the design requirements can be manually recorded by the staff of a design company, then the planning and design of each node of the indoor design are manually carried out, and the design requirements are added into a design model to obtain a preliminary indoor design model; the degree of automation is low, too dependent on staff.
Disclosure of Invention
The invention mainly aims to provide an adaptation method, a device, computer equipment and a storage medium of an indoor design node, and aims to overcome the defect that the current indoor design model generation is too dependent on manpower.
In order to achieve the above purpose, the present invention provides an adaptation method of an indoor design node, comprising the following steps:
Acquiring an indoor design model from a design model database; the indoor design model comprises a design template of each indoor design node;
acquiring a design requirement text input by a customer; the design requirement text comprises the design requirement of the customer on indoor design;
identifying the design requirement text based on a preset first neural network model, and identifying a design requirement keyword in the design requirement text;
identifying design nodes to which the design requirement keywords belong;
identifying the indoor design model based on a preset second neural network model, and identifying each design node of the indoor design model;
and adding the design requirement keywords into the design templates of the corresponding design nodes of the indoor design model based on the design nodes to which the design requirement keywords belong so as to complete the adaptive design of the indoor design nodes.
Further, the step of identifying each design node of the indoor design model based on the preset second neural network model includes:
acquiring node texts corresponding to all design nodes in the indoor design model;
Identifying a target feature text from the node text, inputting the target feature text into the second neural network model, and generating a classification list corresponding to the target feature text based on the second neural network model; the second neural network model is a Bert model, and the target feature text is a named entity text; the classification list comprises classification of the target feature text and corresponding classification probability;
screening out the target classification with the maximum classification probability based on the classification list as a classification result of the target feature text; and taking the target classification as a design node of the indoor design model.
Further, the step of generating a classification list corresponding to the target feature text based on the second neural network model includes:
extracting hidden vectors corresponding to the target feature text based on the hidden layer of the second neural network model;
extracting weights corresponding to the hidden vectors from a classification layer based on the second neural network model; the weights are used for describing the importance level of the hidden vector;
calculating the scores of the target feature texts belonging to all classifications according to the hidden vectors and the weights based on the classification layer of the second neural network model;
Obtaining corresponding coding vectors of all the classifications, and correspondingly calculating loss function values of all the classifications of the target feature text according to the coding vectors and scores of all the classifications of the target feature text;
calculating the classification probability of the target feature text belonging to each class based on the loss function value of the target feature text belonging to each class;
and generating a classification list corresponding to the target feature text based on the classification and the classification probability.
Further, the step of adding the design requirement keyword to a design template of a design node corresponding to the indoor design model based on the design node to which the design requirement keyword belongs to complete the adaptive design of the indoor design node includes:
creating a unique customer identification code for said customer; the customer identification code is used for identifying the customer;
acquiring the work number of a designer of a design task in a processing chamber; after receiving the indoor design task of the client, the management user distributes the indoor design task to a designer for processing;
acquiring the work number of the management user;
creating a unique task code for the customer's indoor design task;
Generating a client task code based on the client identification code and the task code;
generating a design code based on the work number of the management user and the work number of the designer;
and generating a marking code of the indoor design task based on the task code and the design code, wherein the marking code is used for marking the indoor design task.
Further, the step of generating a marking code for the indoor design task based on the task code and the design code includes:
judging whether the task codes and the design codes have the same characters or not;
if yes, the same number of the characters is obtained and is recorded as a target number; if not, a preset number is obtained and is used as the target number;
acquiring a standard Base64 coding table, and uniformly and circularly moving codes in the standard Base64 coding table backwards by a preset number of bits to obtain a new Base64 coding table; wherein, the value of the preset digit is equal to the value of the target number;
concatenating the task code and the design code to obtain a concatenated code;
and adopting the new Base64 coding table to code the serial codes to obtain the marking codes of the indoor design tasks.
Further, the step of generating a marking code for the indoor design task based on the task code and the design code, for marking the indoor design task, further includes:
converting the format of the design requirement text into a specific format;
adding a marking code of the indoor design task at a specified position of the converted design requirement text;
carrying out hash calculation on the design requirement text to obtain a corresponding standard hash value, storing the standard hash value and the design requirement text in a database, and establishing an index relation between the standard hash value and the marking code in the database; and when the standard hash value in the database is called, recording the called times and time of the standard hash value.
Further, after the step of establishing the index relation between the hash value and the tag code in the database, the method includes:
when task verification is needed in the processing process of the indoor design task, acquiring a design requirement text stored in a database, acquiring a marking code corresponding to the task to be verified, and marking the marking code as a verification marking code;
Carrying out hash calculation on the design requirement text stored in the database to obtain a corresponding hash value;
according to the verification marking code, acquiring a standard hash value corresponding to the verification marking code based on the index relation between the standard hash value and the marking code established in the database;
verifying whether the hash value is identical to the standard hash value; if the tasks are different, verifying that the tasks are tampered; if the number of times and the time of the call of the standard hash value are the same, verifying whether the number of times and the time of the task verification are completely consistent; if the tasks are not completely consistent, judging that the tasks are tampered; and if the tasks are completely consistent, judging that the tasks are not tampered.
The invention also provides an adapting device of the indoor design node, which comprises:
the first acquisition unit is used for acquiring an indoor design model from the design model database; the indoor design model comprises a design template of each indoor design node;
the second acquisition unit is used for acquiring the design requirement text input by the client; the design requirement text comprises the design requirement of the customer on indoor design;
The first recognition unit is used for recognizing the design requirement text based on a preset first neural network model and recognizing design requirement keywords in the design requirement text;
the second identification unit is used for identifying the design nodes to which the design requirement keywords belong;
the third recognition unit is used for recognizing the indoor design model based on a preset second neural network model and recognizing each design node of the indoor design model;
and the adaptation unit is used for adding the design requirement keywords into the design templates of the design nodes corresponding to the indoor design model based on the design nodes to which the design requirement keywords belong so as to complete the adaptation design of the indoor design nodes.
The invention also provides a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of any of the methods described above when the computer program is executed.
The invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method of any of the preceding claims.
The invention provides an adaptation method, a device, computer equipment and a storage medium of an indoor design node, wherein an indoor design model is obtained from a design model database; acquiring a design requirement text input by a customer; the design requirement text comprises the design requirement of the customer on indoor design; identifying the design requirement text by identifying the design requirement text based on a preset first neural network model; identifying design nodes to which the design requirement keywords belong; identifying the indoor design model based on a preset second neural network model, and identifying each design node of the indoor design model; and adding the design requirement keywords into the design templates of the corresponding design nodes of the indoor design model based on the design nodes to which the design requirement keywords belong so as to complete the adaptive design of the indoor design nodes. The invention adds the text adaptation of the design requirement of the customer to the design template of the design node corresponding to the indoor design model based on the neural network model, and automatically carries out the adaptation design of the indoor design node without relying on manpower.
Drawings
FIG. 1 is a schematic diagram illustrating steps of an adaptation method of an indoor design node according to an embodiment of the present invention;
FIG. 2 is a block diagram illustrating an adapting device of an indoor design node according to an embodiment of the present invention;
fig. 3 is a block diagram schematically illustrating a structure of a computer device according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, in one embodiment of the present invention, there is provided a method for adapting an indoor design node, including the steps of:
step S1, acquiring an indoor design model from a design model database; the indoor design model comprises a design template of each indoor design node;
s2, obtaining a design requirement text input by a customer; the design requirement text comprises the design requirement of the customer on indoor design;
Step S3, identifying the design requirement text based on a preset first neural network model, and identifying design requirement keywords in the design requirement text;
s4, identifying design nodes to which the design requirement keywords belong;
step S5, identifying the indoor design model based on a preset second neural network model, and identifying each design node of the indoor design model;
and S6, adding the design requirement keywords into the design templates of the design nodes corresponding to the indoor design model based on the design nodes to which the design requirement keywords belong so as to complete the adaptive design of the indoor design nodes.
In this embodiment, the above-described scheme is applied to the adaptation of the indoor design nodes of the indoor design model for the design requirement text input by the customer; namely, the design requirements of customers are automatically added to the design templates of the corresponding design nodes of the indoor design model, manual input of designers is not needed, labor is not needed, and the office process is lighter and more convenient.
As described in the above step S1, the indoor design model is stored in the design model database, and the indoor design model includes the design templates of each design node of the indoor design, where the design nodes include each design module in the indoor design, such as a design module of an overall space and an overall color, a design module of a wall surface, a window, a curtain, a design module of a household appliance, and a furniture design module. The design nodes may be different for different design requirements of customers.
As described in the above step S2, the customer may input a specific design requirement text, and the customer inputs the design requirement for the interior design in the form of text, which may be recognized by the computer.
As described in the step S3, the first neural network model is a text recognition model, which can perform keyword recognition, and is used for recognizing the design requirement text and recognizing the design requirement keywords in the design requirement text. The above design requires keywords such as layout of wall surfaces, style of furniture, overall color tone, etc.
After the design requirement keywords are identified, the design nodes to which they respectively belong need to be identified as described in step S4; specifically, the semantic recognition is performed on the keywords of the design requirement, and the design node to which the keywords belong can be obtained according to the meaning of the keywords. Alternatively, the corresponding relation between the keywords and the design nodes may be preset in the database, and according to the corresponding relation, the design nodes to which the design requirement keywords belong may be obtained.
As described in the step S5, the second neural network model is obtained by training a deep learning model, and is used for identifying an indoor design model and identifying each design node of the indoor design model; identifying individual design nodes of the indoor design model facilitates subsequent addition of the design requirement keywords to a design template.
As described in the above step S6, the design requirement keyword has a design node to which the design requirement keyword belongs, and the indoor design model has a plurality of design nodes, so that the indoor design model forms a preliminary indoor design model including the design requirement of the customer after the design requirement keyword is added only by adding the design requirement keyword to the same design node in the indoor design model. In the process, the neural network model is adopted to adapt the indoor design nodes of the indoor design model, and labor is not needed, so that the office is lighter, and the design efficiency is improved.
In an embodiment, the step S5 of identifying each design node of the indoor design model based on the preset second neural network model includes:
step S51, node texts corresponding to all design nodes in the indoor design model are obtained;
step S52, identifying a target feature text from the node text, inputting the target feature text into the second neural network model, and generating a classification list corresponding to the target feature text based on the second neural network model; the second neural network model is a Bert model, and the target feature text is a named entity text; the classification list comprises classification of the target feature text and corresponding classification probability;
Step S53, screening out the target classification with the maximum classification probability as the classification result of the target feature text based on the classification list; and taking the target classification as a design node of the indoor design model.
In this embodiment, each design node has a simple text describing the type of the design node, i.e. the node text includes some feature texts, which may be named entities, typically nouns, etc. in the text; for example, the node text is wall surface design specification and space design requirement; the named entity is a wall surface and a space; the wall surface and the space are feature texts in the node texts, namely target feature texts in the embodiment. After the target feature text is identified, the computer can only identify the text, but cannot determine the classification to which it belongs, and therefore classification by the second neural network model is required. The second neural network model is a Bert model, which is one of natural speech models, and can predict words to be recognized using context information. The Bert model can classify the target feature text, namely, can recognize the target feature text as machine-readable classification; and according to the classification result, the design nodes to which the design requirement keywords belong can be adapted.
In an embodiment, the step S52 of generating the classification list corresponding to the target feature text based on the second neural network model includes:
step S521, extracting hidden vectors corresponding to the target feature text based on the hidden layer of the second neural network model;
step S522, extracting weights corresponding to the hidden vectors from the classification layer based on the second neural network model; the weights are used for describing the importance level of the hidden vector; in this embodiment, the second neural network model includes a feature extraction layer, a hidden layer, and a classification layer; the feature extraction layer is used for extracting feature vectors of the text, the hidden layer is used for extracting hidden vectors, the classification layer is used for classifying, and the classification layer further comprises weights corresponding to the hidden layer.
Step S523, calculating the scores of the target feature texts belonging to various classifications according to the hidden vectors and the weights based on the classification layers of the second neural network model; the above score calculation was performed using a softmax function.
Step S524, obtaining coding vectors corresponding to all the classifications, and correspondingly calculating loss function values of the target feature text belonging to all the classifications according to the coding vectors and the scores of the target feature text belonging to all the classifications; the encoded vector is a one-hot encoded vector, and the loss function value is calculated by using a log function. I.e. the loss function value = s x log (score), where s is the encoding vector.
Step S525, calculating the classification probability of the target feature text belonging to each classification based on the loss function value of the target feature text belonging to each classification; the classification probability characterizes the likelihood that the target feature text belongs to each classification.
And step S526, generating a classification list corresponding to the target feature text based on the classification and the classification probability. The classification list comprises all classifications, and the target feature text belongs to the classification probability corresponding to all classifications. And selecting the classification with the highest classification probability from the set of the classification probabilities as a classification result of the target feature text, wherein the classification result is the design node of the indoor design model.
In an embodiment, after the step S6 of adding the design requirement keyword to the design template of the design node corresponding to the indoor design model based on the design node to which the design requirement keyword belongs to complete the adaptive design of the indoor design node, the method includes:
step S7, creating a unique customer identification code for the customer; the customer identification code is used for identifying the customer; the creation of the customer identification code may be generated in combination with a customer name and a number. For example, the customer identification code, such as KF01, is generated using a customer abbreviated association number.
S8, acquiring the work number of a designer of the indoor design task; after receiving the indoor design task of the client, the management user distributes the indoor design task to a designer for processing;
step S9, the work number of the management user is obtained; the work number of the management user and the designer is a unique number inside the design company.
Step S10, creating a unique task code for the indoor design task of the client; the task code can be generated by combining a task number and a task attribute, and the task attribute can mark the attribute characteristics of the task.
Step S11, generating a client task code based on the client identification code and the task code; specifically, the client identification code and the task code may be concatenated to obtain the client task code.
Step S12, generating a design code based on the work number of the management user and the work number of the designer; specifically, the job number of the management user and the job number of the designer may be concatenated, or a specific character may be added between the job number of the management user and the job number of the designer, and concatenated to obtain the unique design code.
And step S13, generating a marking code of the indoor design task based on the task code and the design code, wherein the marking code is used for marking the indoor design task.
In this embodiment, after the design requirements of the clients are adaptively added to the indoor design model, an indoor design task needs to be created and specific designers are allocated to process the indoor design task, and in order to facilitate the follow-up of the task, the indoor design task needs to be marked by using a marking code. Meanwhile, in order to correlate and mark the indoor design task with the customer, the designer and the management user during marking, the marking code for marking the indoor design task can be combined with the customer identification code, the work number of the designer, the work number of the management user and the task code of the indoor design task during generating. The information is combined in the marking code to generate, so that personnel information and task information related to the indoor design task can be conveniently identified from the marking code; the generation of the marking code is convenient for task tracking and subsequent inspection.
In an embodiment, the step S13 of generating the marking code for the indoor design task based on the task code and the design code includes:
S131, judging whether the task codes and the design codes have the same characters;
s132, if so, acquiring the same number of the characters and recording the same number as a target number; if not, a preset number is obtained and is used as the target number; for example, the preset number is 3.
S133, acquiring a standard Base64 coding table, and uniformly and circularly moving codes in the standard Base64 coding table backwards by a preset number of bits to obtain a new Base64 coding table; wherein, the value of the preset digit is equal to the value of the target number; it will be appreciated that the code arranged at the end of the standard Base64 code table is then moved to the head of the code table.
S134, the task codes and the design codes are connected in series to obtain serial codes;
s135, coding the serial codes by adopting the new Base64 coding table to obtain the marking codes of the indoor design task.
In this embodiment, a unique scheme for generating the above-mentioned tag code is proposed; in this embodiment, the Base64 encoding table is used to generate the tag code, and if the standard Base64 encoding table is used, the tag code is easily tampered, so that the standard Base64 encoding table can be rearranged. When the Base64 coding table is rearranged, in order to correlate the arrangement mode with the task code and the design code, the same character number in the task code and the design code can be obtained; and moving the standard Base64 coding table based on the numerical value of the same character number to rearrange. It can be understood that even if the Base64 code table moves only by one position, the code table will change significantly, and the code results will be completely different, so that the new Base64 code table obtained by rearrangement has uniqueness, and the serial codes obtained by serial connection of the task code and the design code are also unique, so that the task code and the design code cannot be easily tampered, and the security of the data is ensured.
In another embodiment, the generating the marking code for the indoor design task based on the task code and the design code, after the step S13 for marking the indoor design task, further includes:
step S14, converting the format of the design requirement text into a specific format;
step S15, adding a marking code of the indoor design task at a specified position of the converted design requirement text;
s16, carrying out hash calculation on the design requirement text to obtain a corresponding standard hash value, storing the standard hash value and the design requirement text in a database, and establishing an index relation between the standard hash value and the marking code in the database; and when the standard hash value in the database is called, recording the called times and time of the standard hash value. The above design requires that the text be text after the addition of the marker code.
In this embodiment, after the step S16 of establishing the index relationship between the hash value and the tag code in the database, the method includes:
step S17, when task verification is needed in the processing process of the indoor design task, acquiring a design requirement text stored in a database, and acquiring a mark code corresponding to the task to be verified, and marking the mark code as a verification mark code;
Step S18, carrying out hash calculation on the design requirement text stored in the database to obtain a corresponding hash value;
step S19, according to the verification marking code, acquiring a standard hash value corresponding to the verification marking code based on the index relation between the standard hash value and the marking code established in the database;
step S19a, verifying whether the hash value is the same as the standard hash value; if the tasks are different, verifying that the tasks are tampered; if the number of times and the time of the call of the standard hash value are the same, verifying whether the number of times and the time of the task verification are completely consistent; if the tasks are not completely consistent, judging that the tasks are tampered; and if the tasks are completely consistent, judging that the tasks are not tampered.
In this embodiment, a scheme for judging whether the design task is tampered is also provided. Specifically, the text of the design requirement corresponding to the design task can be converted into a specific format (storage format, content editing format, picture attribute, etc.), a marking code is added at a designated position, hash calculation is further performed, and a corresponding standard hash value is obtained, wherein the standard hash value is used as a basis for judging whether the design task is tampered or not later.
When the task verification is performed subsequently and whether tampering occurs, only the design requirement text stored in the database is required to be obtained, and hash calculation is performed to obtain a corresponding hash value; further, obtaining a standard hash value corresponding to the text marking code of the design requirement from a database; verifying whether the hash value is identical to the standard hash value; if the tasks are different, verifying that the tasks are tampered; if the number of times and the time of the call of the standard hash value are the same, verifying whether the number of times and the time of the task verification are completely consistent; if the tasks are not completely consistent, judging that the tasks are tampered; and if the tasks are completely consistent, judging that the tasks are not tampered.
The adaptation method of the indoor design node provided by the embodiment of the invention is that an indoor design model is obtained from a design model database; acquiring a design requirement text input by a customer; the design requirement text comprises the design requirement of the customer on indoor design; identifying the design requirement text by identifying the design requirement text based on a preset first neural network model; identifying design nodes to which the design requirement keywords belong; identifying the indoor design model based on a preset second neural network model, and identifying each design node of the indoor design model; and adding the design requirement keywords into the design templates of the corresponding design nodes of the indoor design model based on the design nodes to which the design requirement keywords belong so as to complete the adaptive design of the indoor design nodes. The invention adds the text adaptation of the design requirement of the customer to the design template of the design node corresponding to the indoor design model based on the neural network model, and automatically carries out the adaptation design of the indoor design node without relying on manpower.
Referring to fig. 2, in an embodiment of the present invention, there is further provided an adapting apparatus for an indoor design node, including:
the first acquisition unit is used for acquiring an indoor design model from the design model database; the indoor design model comprises a design template of each indoor design node;
the second acquisition unit is used for acquiring the design requirement text input by the client; the design requirement text comprises the design requirement of the customer on indoor design;
the first recognition unit is used for recognizing the design requirement text based on a preset first neural network model and recognizing design requirement keywords in the design requirement text;
the second identification unit is used for identifying the design nodes to which the design requirement keywords belong;
the third recognition unit is used for recognizing the indoor design model based on a preset second neural network model and recognizing each design node of the indoor design model;
and the adaptation unit is used for adding the design requirement keywords into the design templates of the design nodes corresponding to the indoor design model based on the design nodes to which the design requirement keywords belong so as to complete the adaptation design of the indoor design nodes.
In an embodiment, the third identifying unit includes:
the acquisition subunit is used for acquiring node texts corresponding to all design nodes in the indoor design model;
the recognition subunit is used for recognizing a target feature text from the node text, inputting the target feature text into the second neural network model, and generating a classification list corresponding to the target feature text based on the second neural network model; the second neural network model is a Bert model, and the target feature text is a named entity text; the classification list comprises classification of the target feature text and corresponding classification probability;
the screening subunit is used for screening out the target classification with the maximum classification probability based on the classification list as a classification result of the target feature text; and taking the target classification as a design node of the indoor design model.
In an embodiment, the identifying subunit generates, based on the second neural network model, a classification list corresponding to the target feature text, and specifically includes:
extracting hidden vectors corresponding to the target feature text based on the hidden layer of the second neural network model;
Extracting weights corresponding to the hidden vectors from a classification layer based on the second neural network model; the weights are used for describing the importance level of the hidden vector;
calculating the scores of the target feature texts belonging to all classifications according to the hidden vectors and the weights based on the classification layer of the second neural network model;
obtaining corresponding coding vectors of all the classifications, and correspondingly calculating loss function values of all the classifications of the target feature text according to the coding vectors and scores of all the classifications of the target feature text;
calculating the classification probability of the target feature text belonging to each class based on the loss function value of the target feature text belonging to each class;
and generating a classification list corresponding to the target feature text based on the classification and the classification probability.
In an embodiment, the adapting device of the indoor design node further includes:
a first creation unit for creating a unique customer identification code for the customer; the customer identification code is used for identifying the customer;
a third obtaining unit for obtaining the work number of the designer of the processing indoor design task; after receiving the indoor design task of the client, the management user distributes the indoor design task to a designer for processing;
A fourth obtaining unit, configured to obtain a job number of the management user;
a second creating unit, configured to create a unique task code for the indoor design task of the customer;
the first generation unit is used for generating a client task code based on the client identification code and the task code;
a second generation unit configured to generate a design code based on the work number of the management user and the work number of the designer;
and the third generating unit is used for generating a marking code of the indoor design task based on the task code and the design code and marking the indoor design task.
In an embodiment, the third generating unit includes:
a judging subunit, configured to judge whether the task code and the design code have the same character;
a number acquisition subunit, configured to acquire the same number of characters if any, and record the number as a target number; if not, a preset number is obtained and is used as the target number;
the coding table generation subunit is used for acquiring a standard Base64 coding table, uniformly and circularly moving codes in the standard Base64 coding table backwards by a preset number of bits to obtain a new Base64 coding table; wherein, the value of the preset digit is equal to the value of the target number;
The serial connection subunit is used for connecting the task code and the design code in series to obtain a serial connection code;
and the coding subunit is used for coding the serial codes by adopting the new Base64 coding table to obtain the marking codes of the indoor design tasks.
In another embodiment, the adapting device of the indoor design node further includes:
a conversion unit for converting the format of the design requirement text into a specific format;
an adding unit, configured to add a marking code of the indoor design task at a specified position of the converted design requirement text;
the first calculation unit is used for carrying out hash calculation on the design requirement text to obtain a corresponding standard hash value, storing the standard hash value and the design requirement text in a database, and establishing an index relation between the standard hash value and the marking code in the database; and when the standard hash value in the database is called, recording the called times and time of the standard hash value.
In yet another embodiment, the adapting device of the indoor design node includes:
a fifth obtaining unit, configured to obtain a design requirement text stored in a database when task verification is required in a processing process of the indoor design task, and obtain a mark code corresponding to the task to be verified, and record the mark code as a verification mark code;
The second calculation unit is used for carrying out hash calculation on the design requirement text stored in the database to obtain a corresponding hash value;
a sixth obtaining unit, configured to obtain, according to the verification marking code, a standard hash value corresponding to the verification marking code based on an index relationship between the standard hash value and the marking code established in the database;
a verification unit, configured to verify whether the hash value is the same as the standard hash value; if the tasks are different, verifying that the tasks are tampered; if the number of times and the time of the call of the standard hash value are the same, verifying whether the number of times and the time of the task verification are completely consistent; if the tasks are not completely consistent, judging that the tasks are tampered; and if the tasks are completely consistent, judging that the tasks are not tampered.
In this embodiment, for specific implementation of each unit and subunit in the embodiment of the foregoing apparatus, please refer to the description in the embodiment of the foregoing method, and no further description is given here.
Referring to fig. 3, in an embodiment of the present invention, there is further provided a computer device, which may be a server, and an internal structure thereof may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the computer is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing data such as design requirement text. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method of adapting an indoor design node.
It will be appreciated by those skilled in the art that the architecture shown in fig. 3 is merely a block diagram of a portion of the architecture in connection with the present inventive arrangements and is not intended to limit the computer devices to which the present inventive arrangements are applicable.
An embodiment of the present invention further provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor implements a method for adapting an indoor design node. It is understood that the computer readable storage medium in this embodiment may be a volatile readable storage medium or a nonvolatile readable storage medium.
In summary, the adaptation method, the device, the computer equipment and the storage medium for the indoor design node provided by the embodiment of the invention acquire an indoor design model from a design model database; acquiring a design requirement text input by a customer; the design requirement text comprises the design requirement of the customer on indoor design; identifying the design requirement text by identifying the design requirement text based on a preset first neural network model; identifying design nodes to which the design requirement keywords belong; identifying the indoor design model based on a preset second neural network model, and identifying each design node of the indoor design model; and adding the design requirement keywords into the design templates of the corresponding design nodes of the indoor design model based on the design nodes to which the design requirement keywords belong so as to complete the adaptive design of the indoor design nodes. The invention adds the text adaptation of the design requirement of the customer to the design template of the design node corresponding to the indoor design model based on the neural network model, and automatically carries out the adaptation design of the indoor design node without relying on manpower.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium provided by the present invention and used in embodiments may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent structures or equivalent processes using the descriptions and drawings of the present invention or direct or indirect application in other related technical fields are included in the scope of the present invention.
Claims (6)
1. An adaptation method of an indoor design node is characterized by comprising the following steps:
acquiring an indoor design model from a design model database; the indoor design model comprises a design template of each indoor design node;
Acquiring a design requirement text input by a customer; the design requirement text comprises the design requirement of the customer on indoor design;
identifying the design requirement text based on a preset first neural network model, and identifying a design requirement keyword in the design requirement text;
identifying design nodes to which the design requirement keywords belong;
identifying the indoor design model based on a preset second neural network model, and identifying each design node of the indoor design model;
based on the design nodes to which the design requirement keywords belong, adding the design requirement keywords into the design templates of the corresponding design nodes of the indoor design model so as to complete the adaptive design of the indoor design nodes;
creating a unique customer identification code for said customer; the customer identification code is used for identifying the customer;
acquiring the work number of a designer of a design task in a processing chamber; after receiving the indoor design task of the client, the management user distributes the indoor design task to a designer for processing;
acquiring the work number of the management user;
creating a unique task code for the customer's indoor design task;
Generating a client task code based on the client identification code and the task code;
generating a design code based on the work number of the management user and the work number of the designer;
generating a marking code of the indoor design task based on the task code and the design code, wherein the marking code is used for marking the indoor design task;
the step of generating a marking code for the indoor design task based on the task code and the design code includes:
judging whether the task codes and the design codes have the same characters or not;
if yes, the same number of the characters is obtained and is recorded as a target number; if not, a preset number is obtained and is used as the target number;
acquiring a standard Base64 coding table, and uniformly and circularly moving codes in the standard Base64 coding table backwards by a preset number of bits to obtain a new Base64 coding table; wherein, the value of the preset digit is equal to the value of the target number;
concatenating the task code and the design code to obtain a concatenated code;
coding the serial codes by adopting the new Base64 coding table to obtain the marking codes of the indoor design tasks;
The step of generating a marking code for the indoor design task based on the task code and the design code, wherein the marking code is used for marking the indoor design task, and the method further comprises the following steps:
converting the format of the design requirement text into a specific format;
adding a marking code of the indoor design task at a specified position of the converted design requirement text;
carrying out hash calculation on the design requirement text to obtain a corresponding standard hash value, storing the standard hash value and the design requirement text in a database, and establishing an index relation between the standard hash value and the marking code in the database; recording the number of times and time that the standard hash value is called when the standard hash value in the database is called;
after the step of establishing the index relation between the hash value and the marking code in the database, the method comprises the following steps:
when task verification is needed in the processing process of the indoor design task, acquiring a design requirement text stored in a database, acquiring a marking code corresponding to the task to be verified, and marking the marking code as a verification marking code;
carrying out hash calculation on the design requirement text stored in the database to obtain a corresponding hash value;
According to the verification marking code, acquiring a standard hash value corresponding to the verification marking code based on the index relation between the standard hash value and the marking code established in the database;
verifying whether the hash value is identical to the standard hash value; if the tasks are different, verifying that the tasks are tampered; if the number of times and the time of the call of the standard hash value are the same, verifying whether the number of times and the time of the task verification are completely consistent; if the tasks are not completely consistent, judging that the tasks are tampered; and if the tasks are completely consistent, judging that the tasks are not tampered.
2. The adaptation method of indoor design nodes according to claim 1, wherein the step of identifying each design node of the indoor design model by identifying the indoor design model based on a preset second neural network model comprises:
acquiring node texts corresponding to all design nodes in the indoor design model;
identifying a target feature text from the node text, inputting the target feature text into the second neural network model, and generating a classification list corresponding to the target feature text based on the second neural network model; wherein the second neural network model is a Bert model; the classification list comprises classification of the target feature text and corresponding classification probability;
Screening out the target classification with the maximum classification probability based on the classification list as a classification result of the target feature text; and taking the target classification as a design node of the indoor design model.
3. The adaptation method of an indoor design node according to claim 2, wherein the step of generating the classification list corresponding to the target feature text based on the second neural network model comprises:
extracting hidden vectors corresponding to the target feature text based on the hidden layer of the second neural network model;
extracting weights corresponding to the hidden vectors from a classification layer based on the second neural network model; the weights are used for describing the importance level of the hidden vector;
calculating the scores of the target feature texts belonging to all classifications according to the hidden vectors and the weights based on the classification layer of the second neural network model;
obtaining corresponding coding vectors of all the classifications, and correspondingly calculating loss function values of all the classifications of the target feature text according to the coding vectors and scores of all the classifications of the target feature text;
calculating the classification probability of the target feature text belonging to each class based on the loss function value of the target feature text belonging to each class;
And generating a classification list corresponding to the target feature text based on the classification and the classification probability.
4. An adaptation device of an indoor design node, comprising:
the first acquisition unit is used for acquiring an indoor design model from the design model database; the indoor design model comprises a design template of each indoor design node;
the second acquisition unit is used for acquiring the design requirement text input by the client; the design requirement text comprises the design requirement of the customer on indoor design;
the first recognition unit is used for recognizing the design requirement text based on a preset first neural network model and recognizing design requirement keywords in the design requirement text;
the second identification unit is used for identifying the design nodes to which the design requirement keywords belong;
the third recognition unit is used for recognizing the indoor design model based on a preset second neural network model and recognizing each design node of the indoor design model;
the adaptation unit is used for adding the design requirement keywords into the design templates of the corresponding design nodes of the indoor design model based on the design nodes to which the design requirement keywords belong so as to complete the adaptation design of the indoor design nodes;
A first creation unit for creating a unique customer identification code for the customer; the customer identification code is used for identifying the customer;
a third obtaining unit for obtaining the work number of the designer of the processing indoor design task; after receiving the indoor design task of the client, the management user distributes the indoor design task to a designer for processing;
a fourth obtaining unit, configured to obtain a job number of the management user;
a second creating unit, configured to create a unique task code for the indoor design task of the customer;
the first generation unit is used for generating a client task code based on the client identification code and the task code;
a second generation unit configured to generate a design code based on the work number of the management user and the work number of the designer;
a third generating unit, configured to generate a marking code of the indoor design task based on the task code and the design code, and to mark the indoor design task;
the third generation unit includes:
a judging subunit, configured to judge whether the task code and the design code have the same character;
A number acquisition subunit, configured to acquire the same number of characters if any, and record the number as a target number; if not, a preset number is obtained and is used as the target number;
the coding table generation subunit is used for acquiring a standard Base64 coding table, uniformly and circularly moving codes in the standard Base64 coding table backwards by a preset number of bits to obtain a new Base64 coding table; wherein, the value of the preset digit is equal to the value of the target number;
the serial connection subunit is used for connecting the task code and the design code in series to obtain a serial connection code;
the coding subunit is used for coding the serial codes by adopting the new Base64 coding table to obtain the marking codes of the indoor design tasks;
the adapting device of the indoor design node further comprises:
a conversion unit for converting the format of the design requirement text into a specific format;
an adding unit, configured to add a marking code of the indoor design task at a specified position of the converted design requirement text;
the first calculation unit is used for carrying out hash calculation on the design requirement text to obtain a corresponding standard hash value, storing the standard hash value and the design requirement text in a database, and establishing an index relation between the standard hash value and the marking code in the database; recording the number of times and time that the standard hash value is called when the standard hash value in the database is called;
The adapting device of the indoor design node further comprises:
a fifth obtaining unit, configured to obtain a design requirement text stored in a database when task verification is required in a processing process of the indoor design task, and obtain a mark code corresponding to the task to be verified, and record the mark code as a verification mark code;
the second calculation unit is used for carrying out hash calculation on the design requirement text stored in the database to obtain a corresponding hash value;
a sixth obtaining unit, configured to obtain, according to the verification marking code, a standard hash value corresponding to the verification marking code based on an index relationship between the standard hash value and the marking code established in the database;
a verification unit, configured to verify whether the hash value is the same as the standard hash value; if the tasks are different, verifying that the tasks are tampered; if the number of times and the time of the call of the standard hash value are the same, verifying whether the number of times and the time of the task verification are completely consistent; if the tasks are not completely consistent, judging that the tasks are tampered; and if the tasks are completely consistent, judging that the tasks are not tampered.
5. A computer device comprising a memory and a processor, the memory having stored therein a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 3.
6. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 3.
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