CN113392441A - Household data processing method and device, electronic equipment and storage medium - Google Patents
Household data processing method and device, electronic equipment and storage medium Download PDFInfo
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- G06N3/12—Computing arrangements based on biological models using genetic models
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Abstract
The embodiment of the application discloses a house type data processing method and device, electronic equipment and a storage medium. The method comprises the steps of acquiring combination data of the house types to be processed, wherein the combination data of the house types to be processed comprises a plurality of house type combinations, demand proportions of the house type combinations and total house type numbers, inputting the combination data of the house types to be processed into a house type graph processing algorithm, acquiring the combination data of the house types output by the house type graph processing algorithm, wherein the house type graph processing algorithm is obtained based on a genetic algorithm, and then taking the combination data of the house types as target house type combination data. Therefore, after the user type combination data to be processed is input into the user type graph processing algorithm, the user type graph processing algorithm outputs the user type combination data which is in line with the user expectation, the user type combination graph which is superior is automatically calculated through the algorithm, manual operation is reduced, and the design efficiency of the user type graph is improved.
Description
Technical Field
The present application relates to the field of architectural design analysis technologies, and in particular, to a method and an apparatus for processing house type data, an electronic device, and a storage medium.
Background
With the rapid development of the real estate industry, more and more residential buildings are built around the country, and the design demand for residential house types is larger and larger. In the building design, basic schemes of buildings are arranged according to planning indexes of plots, building outlines are arranged as much as possible according to building mandatory specifications, and house types with different areas need to be mixed and spliced so as to meet the requirements of cost measurement and profit rate, meet development conditions, support marketing planning, facilitate development of later-period work and the like. However, the existing design method is time-consuming, labor-consuming and inaccurate by performing manual design and iterative improvement according to the experience of a designer.
Disclosure of Invention
In view of the above problems, the present application provides a house type data processing method, apparatus, electronic device and storage medium to improve the above problems.
In a first aspect, an embodiment of the present application provides a house type data processing method, where the method includes: acquiring to-be-processed house type combination data, wherein the to-be-processed house type combination data comprises a plurality of house type combinations, demand proportions of the house type combinations and total house type number; inputting the house type combination data to be processed into a house type graph processing algorithm to obtain the house type combination data output by the house type graph processing algorithm, wherein the house type graph processing algorithm is obtained based on a genetic algorithm; and taking the house type combination data as target house type combination data.
In a second aspect, an embodiment of the present application provides a house type data processing apparatus, including: the device comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring to-be-processed house type combination data, and the to-be-processed house type combination data comprises a plurality of house type combinations, demand proportions of the house type combinations and total house type number; the second acquisition module is used for inputting the user type combination data to be processed into a user type graph processing algorithm to acquire the user type combination data output by the user type graph processing algorithm, wherein the user type graph processing algorithm is obtained based on a genetic algorithm; and the processing module is used for taking the house type combination data as target house type combination data.
In a third aspect, an embodiment of the present application provides an electronic device, including one or more processors and a memory; one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to perform the method of the first aspect described above.
In a fourth aspect, the present application provides a computer-readable storage medium, in which a program code is stored, where the program code executes the method of the first aspect.
The application provides a house type data processing method and device, electronic equipment and a storage medium. The method comprises the steps of acquiring combination data of the house types to be processed, wherein the combination data of the house types to be processed comprises a plurality of house type combinations, demand proportions of the house type combinations and total house type numbers, inputting the combination data of the house types to be processed into a house type graph processing algorithm, acquiring the combination data of the house types output by the house type graph processing algorithm, wherein the house type graph processing algorithm is obtained based on a genetic algorithm, and then taking the combination data of the house types as target house type combination data. Therefore, after the user type combination data to be processed is input into the user type graph processing algorithm, the user type graph processing algorithm outputs the user type combination data which is in line with the user expectation, the user type combination graph which is superior is automatically calculated through the algorithm, manual operation is reduced, and the design efficiency of the user type graph is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart illustrating a method for processing house type data according to an embodiment of the present application.
Fig. 2 is a flowchart illustrating a method for processing house type data according to another embodiment of the present application.
Fig. 3 is a diagram illustrating an example of a house type data processing procedure proposed in an embodiment of the present application.
Fig. 4 shows a block diagram of a house type data processing apparatus according to an embodiment of the present application.
Fig. 5 shows another block diagram of the house-type data processing apparatus according to the embodiment of the present application.
Fig. 6 shows a block diagram of an electronic device for executing the house type data processing method according to the embodiment of the present application.
Fig. 7 is a memory unit for storing or carrying program codes for implementing the house type data processing method according to the embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, 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 application.
With the rapid development of the real estate industry, more and more residential buildings are built around the country, and the design demand for residential house types is larger and larger. In the building design, basic schemes of buildings are arranged according to planning indexes of plots, building outlines are arranged as much as possible according to building mandatory specifications, and house types with different areas need to be mixed and spliced so as to meet the requirements of cost measurement and profit rate, meet development conditions, support marketing planning, facilitate development of later-period work and the like. However, the existing design method is time-consuming, labor-consuming and inaccurate by performing manual design and iterative improvement according to the experience of a designer.
Therefore, the inventor proposes a house type data processing method, a device, an electronic device and a storage medium for improving the house type graph design efficiency in the application.
Embodiments of the present application will be described in detail below with reference to the accompanying drawings.
Referring to fig. 1, an embodiment of the present application provides a house type data processing method, which is applicable to an electronic device, and the method includes:
step S110: and acquiring the household type combination data to be processed.
In this embodiment, the to-be-processed house type combination data may include a plurality of house type combinations, a demand proportion of the plurality of house type combinations, and a total house type number. Each house type combination can comprise a plurality of house types, and the number of each house type in the plurality of house types included in each house type combination can be set according to actual requirements. For example, assuming that there are a house type a, a house type b, a house type c, and a house type d, a plurality of house type combinations may be configured by an arbitrary number of house type types. Optionally, the house type combinations may be [ a, a, b, b ], [ a, a, c, c ], [ c, c, d ], and [ d, d, a ], respectively, in which manner, the demand ratios of the house type combinations may be set according to actual situations, for example, the demand ratios of the house type combinations [ a, a, b, b ], [ a, a, c, c ], [ c, c, d ], and [ d, d, a ] may be set to 6: 1: 3: 1.
as a mode, a plurality of types of house type combination data may be pre-stored, so that when acquiring the to-be-processed house type combination data, a certain number of types of house type combination data may be randomly selected from the stored house type combination data as the to-be-processed house type combination data, and optionally, the corresponding type of house type combination data may also be selected as the to-be-processed house type combination data according to a building plan (including capital, planned number of house types, and the like). As another mode, the user-selected user type combination data may be acquired as the user type combination data to be processed.
Step S120: and inputting the user type combination data to be processed into a user type graph processing algorithm to obtain the user type combination data output by the user type graph processing algorithm.
The house type graph processing algorithm is obtained based on a genetic algorithm, and can be used for performing cross and variation on the house type combination data to be processed to obtain a house type combination with high fitness as output. The specific implementation principle of the genetic algorithm can refer to the prior art, and is not described herein again. As a mode, the user type combination data to be processed can be input into the user type graph processing algorithm, and then the user type combination data output by the user type graph processing algorithm can be obtained.
Step S130: and taking the house type combination data as target house type combination data.
As one way, the house type combination data may be the target house type combination data. For example, in the above example, assuming that the plurality of house type combinations may be [ a, a, b, b ], [ a, a, c, c ], [ c, c, d ], and [ d, d, a ], if the house type combination data output by the house type graph processing algorithm is [ a, b, b, c, d ], the house type combination data may be [ a, b, b, c, d ] as the target house type combination data.
According to the house type data processing method, the house type combination data to be processed are obtained, the house type combination data to be processed comprise a plurality of house type combinations, demand proportions of the house type combinations and total house type numbers, then the house type combination data to be processed are input into a house type graph processing algorithm, the house type combination data output by the house type graph processing algorithm are obtained, the house type graph processing algorithm is obtained based on a genetic algorithm, and then the house type combination data are used as target house type combination data. Therefore, after the user type combination data to be processed is input into the user type graph processing algorithm, the user type graph processing algorithm outputs the user type combination data which is in line with the user expectation, the user type combination graph which is superior is automatically calculated through the algorithm, manual operation is reduced, and the design efficiency of the user type graph is improved.
Referring to fig. 2, another embodiment of the present application provides a house type data processing method, which can be applied to an electronic device, the method including:
step S210: and acquiring the household type combination data to be processed.
Step S220: and acquiring initial house type combination data from the house type combination data to be processed, and performing blending processing on the initial house type combination data based on the house type graph processing algorithm to obtain reference house type combination data.
Optionally, a set of optimal solutions may be selected from the household type combination data to be processed as initial household type combination data, where the selection criteria of the optimal solutions may be a household type combination including as many household numbers as possible under the condition of satisfying the cost measurement and profit margin. In this way, the initial house type combination data can be mixed and spliced based on the house type graph processing algorithm to obtain the reference house type combination data. Alternatively, the initial subscriber type combination data may include a plurality of initial subscriber type combinations, and the reference subscriber type combination data may include a plurality of reference subscriber type combinations. Optionally, in the process of the mashup processing, the fitness of each of the households included in the initial household combination data may be measured and calculated according to the fitness condition of the initial household combination data, and optionally, some candidate solutions may be retained according to the fitness measurement and calculation result, and other candidate solutions are discarded.
As a mode, a plurality of initial house type combinations may be combined and generated based on a house type graph processing algorithm to obtain reference house type combination data. For example, as an embodiment, a first house type combination may be generated by selecting several house type types from a plurality of initial house type combinations based on a house type graph processing algorithm and combining the several house type types. For example, if the initial house type combinations are "[ a, a, c, b ], [ a, b, c, c ], [ a, c, d ] and [ b, d, a ]", and the house type types included therein are "a, b, c and d", then any number of house type types "a, b, c and/or d" may be combined, and optionally, if the combination results in "[ a, b, c, c, d ]", then the house type combination "[ a, b, c, c, d ]" may be used as the first house type combination. In this embodiment, the number of the selected house types and the specific number of different house types are not limited. It will be appreciated that in this manner, the reference house type combination data output by the house type graph processing algorithm may comprise the first house type combination.
Step S230: and acquiring a convergence parameter of the user pattern processing algorithm in the current round processing process.
Optionally, after the initial house type combination data is subjected to blending processing based on the house type graph processing algorithm to obtain the reference house type combination data, error parameters of the proportion of the multiple reference house type combinations and the demand proportion of the multiple house type combinations can be obtained. If the error parameter meets the preset threshold, the error parameter can be used as a convergence parameter of the user-type graph processing algorithm in the current round processing process; optionally, if the error parameter does not satisfy the preset threshold, the initial house type combination data may be combined and generated based on the house type graph processing algorithm continuously until the error parameter satisfies the preset threshold.
In one embodiment, the first error parameter corresponding to the first house type combination may be obtained, or the first error parameter corresponding to the demand ratio of the first house type combination and the plurality of house type combinations may be obtained. Optionally, if the first error parameter satisfies the first threshold, the house type in the first house type combination may be subjected to cross processing to obtain the second house type combination. Optionally, if the first error parameter does not satisfy the first threshold, it may be determined whether the first error parameter satisfies the second threshold, where a magnitude relationship between the first threshold and the second threshold may be determined according to an actual situation, and is not particularly limited. Optionally, if the first error parameter satisfies the second threshold, the house type in the first house type combination may be subjected to mutation processing to obtain a third house type combination.
As one way, in order to improve the accuracy of the house type combination, after the second house type combination is obtained, the second error parameter corresponding to the second house type combination may be acquired. Optionally, if the second error parameter does not satisfy the second threshold, the second error parameter may be used as a convergence parameter of the user-type graph processing algorithm in the current round of processing.
As a way, the third error parameter corresponding to the second house type combination may be continuously obtained, and then the third error parameter may be used as a convergence parameter of the house type graph processing algorithm in the current round processing process.
It should be noted that, in some possible embodiments, the fineness of the second house type combination in this embodiment may be greater than the fineness of the first house type combination, and the fineness of the third house type combination may be greater than the fineness of the second house type combination, wherein the fineness may be understood as the degree of crossing, mutation or propagation of the house type combinations.
Step S241: and if the convergence parameter meets a preset condition, outputting the reference house type combination data as the house type combination data.
The preset condition may be that the user pattern processing algorithm tends to converge when the error of the demand proportion of the reference user pattern combination data output by the user pattern processing algorithm and the input multiple user pattern combinations is small. As a mode, if the convergence parameter of the house type map processing algorithm in the current round processing process satisfies the preset condition, the reference house type combination data may be output as the house type combination data.
Step S242: and if the convergence parameter does not meet the preset condition, performing the next round of blending processing on the reference house type combination data.
Optionally, if the convergence parameter of the user pattern processing algorithm in the current round of processing does not satisfy the preset condition, then the next round of blending processing may be performed on the reference user pattern combined data, and optionally, the processing flow of the next round of blending processing may refer to the foregoing processing flow, which is not described herein again.
Step S250: and taking the house type combination data as target house type combination data.
The following takes fig. 3 as an example to illustrate the embodiments of the present application:
as shown in fig. 3, in a specific application scenario, a syncretized house type desired by a user may be encoded into a chromosome or an individual having a certain structure to form an encoding dictionary, and optionally, the encoding dictionary in this embodiment may be represented as { 'H1': a, 'H2': B, 'H3': C, 'H4': D } where "H1, H2, H3, and H4" in the dictionary represent the syncretized types requiring the syncretization, and "A, B, C, D" in the dictionary represents the number of the syncretized house types. As a mode, an initial population may be randomly generated from the coding dictionary, and the initial population may be understood as an initial house type combination, so that the house type graph processing algorithm may perform a blending process on input house type combination data to be processed based on the initial house type combination to obtain and output reference house type combination data.
Optionally, in this manner, a convergence parameter of the user-type graph processing algorithm in the current round of processing may be obtained, and whether the convergence criterion is satisfied is determined. Optionally, if the convergence criterion is satisfied, the reference house type combination data may be directly output; optionally, if the convergence criterion is not satisfied, the selection operator may be continuously executed on the currently obtained house type combination data, and it is determined whether the result obtained after the execution satisfies the first set threshold (as shown in fig. 5, whether Random [0, 1] < Pc is satisfied, optionally, if so, it may be determined that the result obtained after the execution of the selection operator satisfies the first set threshold, and if not, it may be determined that the structure obtained after the execution of the selection operator does not satisfy the first set threshold). Optionally, if the result obtained after the execution meets the first set threshold, the crossover operator may be executed on the result to improve the accuracy of the house type combination data, and it is determined whether the result after the crossover operator is executed meets the second threshold (for example, whether Random [0, 1] < Pm is true as shown in fig. 5, optionally, if true, it may be determined that the result after the selection operator is executed meets the second set threshold, and if not, it may be determined that the structure after the selection operator is executed does not meet the second set threshold). Optionally, if the result obtained after the execution does not satisfy the first set threshold, it may be directly determined whether the result satisfies the second threshold, and the subsequent determination process may refer to a processing flow in a case where the result after the execution of the selected operator satisfies the second set threshold.
As a way, if the structure after executing the selection operator meets the second set threshold, the mutation operator can be continuously executed on the current result, and after the mutation operator is executed, whether the convergence parameter of the corresponding floor plan processing algorithm meets the convergence criterion under the current condition is determined, so that the accuracy and reliability of the determination are increased.
According to the house type data processing method, the house type combination data to be processed are obtained, the house type combination data to be processed comprise a plurality of house type combinations, demand proportions of the house type combinations and total house type numbers, then the house type combination data to be processed are input into a house type graph processing algorithm, the house type combination data output by the house type graph processing algorithm are obtained, the house type graph processing algorithm is obtained based on a genetic algorithm, and then the house type combination data are used as target house type combination data. Therefore, after the user type combination data to be processed is input into the user type graph processing algorithm, the user type graph processing algorithm outputs the user type combination data which is in line with the user expectation, the user type combination graph which is superior is automatically calculated through the algorithm, manual operation is reduced, and the design efficiency of the user type graph is improved.
Referring to fig. 4, an embodiment of the present application provides a house-type data processing apparatus 300, operating on an electronic device, the apparatus 300 including:
the first obtaining module 310 is configured to obtain to-be-processed house type combination data, where the to-be-processed house type combination data includes a plurality of house type combinations, demand ratios of the plurality of house type combinations, and a total number of house types.
A second obtaining module 320, configured to input the user type combination data to be processed into a user type graph processing algorithm, and obtain the user type combination data output by the user type graph processing algorithm, where the user type graph processing algorithm is obtained based on a genetic algorithm.
As one way, referring to fig. 5, the second obtaining module 320 may include:
the first processing unit 321 is configured to obtain initial house type combination data from the to-be-processed house type combination data, and perform blending processing on the initial house type combination data based on the house type map processing algorithm to obtain reference house type combination data.
Optionally, the initial user type combination data includes a plurality of initial user type combinations, and the reference user type combination data includes a plurality of reference user type combinations. In one manner, the first processing unit 321 may be configured to combine and generate the initial subscriber type combinations based on the subscriber type map processing algorithm, so as to obtain reference subscriber type combination data. For example, as an implementation manner, the first processing unit 321 may be configured to select several types of houses from the initial types of houses based on the house pattern processing algorithm, and combine the several types of houses to generate the first type of house combination.
An obtaining unit 322, configured to obtain a convergence parameter of the user-type graph processing algorithm in the current round of processing.
Optionally, the obtaining unit 322 may be configured to obtain an error parameter of a ratio of the plurality of reference house type combinations and a requirement ratio of the plurality of house type combinations. If the error parameter meets a preset threshold value, taking the error parameter as a convergence parameter of the user pattern processing algorithm in the current round processing process; and if the error parameter does not meet the preset threshold value, combining and generating the initial house type combination data continuously based on the house type graph processing algorithm until the error parameter meets the preset threshold value.
As an embodiment, the obtaining unit 322 may be configured to obtain a first error parameter corresponding to the first user type combination; optionally, if the first error parameter meets a first threshold, performing cross processing on the house type in the first house type combination to obtain a second house type combination; optionally, if the first error parameter does not satisfy the first threshold, if the first error parameter satisfies the second threshold, performing mutation processing on the house type in the first house type combination to obtain a third house type combination.
As another embodiment, the obtaining unit 322 may be further configured to obtain a second error parameter corresponding to the second user type combination, and optionally, if the second error parameter does not satisfy the second threshold, use the second error parameter as a convergence parameter of the user type graph processing algorithm in the current round of processing.
As another embodiment, the obtaining unit 322 may be further configured to obtain a third error parameter corresponding to the second user type combination; and taking the third error parameter as a convergence parameter of the user pattern processing algorithm in the current round processing process.
A second processing unit 323, configured to output the reference house type combination data as the house type combination data if the convergence parameter meets a preset condition; and if the convergence parameter does not meet the preset condition, performing the next round of blending processing on the reference house type combination data.
And the processing module 330 is configured to use the house type combination data as target house type combination data.
According to the house type data processing device, the house type combination data to be processed are obtained, the house type combination data to be processed comprise a plurality of house type combinations, demand proportions of the house type combinations and total house type numbers, then the house type combination data to be processed are input into a house type graph processing algorithm, the house type combination data output by the house type graph processing algorithm are obtained, the house type graph processing algorithm is obtained based on a genetic algorithm, and then the house type combination data are used as target house type combination data. Therefore, after the user type combination data to be processed is input into the user type graph processing algorithm, the user type graph processing algorithm outputs the user type combination data which is in line with the user expectation, the user type combination graph which is superior is automatically calculated through the algorithm, manual operation is reduced, and the design efficiency of the user type graph is improved.
It should be noted that the device embodiment and the method embodiment in the present application correspond to each other, and specific principles in the device embodiment may refer to the contents in the method embodiment, which is not described herein again.
An electronic device provided by the present application will be described with reference to fig. 6.
Referring to fig. 6, based on the above-mentioned house type data processing method and apparatus, another electronic device 100 capable of executing the house type data processing method is provided in the embodiment of the present application. The electronic device 100 includes one or more processors 102 (only one shown) and a memory 104 coupled to each other. The memory 104 stores therein a program that can execute the content in the foregoing embodiments, and the processor 102 can execute the program stored in the memory 104, and the memory 104 includes the apparatus 300 described in the foregoing embodiments.
The Memory 104 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). The memory 104 may be used to store instructions, programs, code sets, or instruction sets. The memory 104 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playing function, a video image playing function, etc.), instructions for implementing the various method embodiments described above, and the like. The data storage area may also store data created by the electronic device 100 during use (e.g., phone book, audio-video data, chat log data), and the like.
Referring to fig. 7, a block diagram of a computer-readable storage medium according to an embodiment of the present application is shown. The computer-readable medium 400 has stored therein a program code that can be called by a processor to execute the method described in the above-described method embodiments.
The computer-readable storage medium 400 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. Alternatively, the computer-readable storage medium 400 includes a non-volatile computer-readable storage medium. The computer readable storage medium 400 has storage space for program code 410 for performing any of the method steps of the method described above. The program code can be read from or written to one or more computer program products. Program code 410 may be compressed, for example, in a suitable form.
According to the house type data processing method, the house type data processing device, the electronic equipment and the storage medium, the house type combination data to be processed are obtained and comprise a plurality of house type combinations, demand proportions of the house type combinations and total house type numbers, then the house type combination data to be processed are input into a house type graph processing algorithm, the house type combination data output by the house type graph processing algorithm are obtained, the house type graph processing algorithm is obtained based on a genetic algorithm, and then the house type combination data are used as target house type combination data. Therefore, after the user type combination data to be processed is input into the user type graph processing algorithm, the user type graph processing algorithm outputs the user type combination data which is in line with the user expectation, the user type combination graph which is superior is automatically calculated through the algorithm, manual operation is reduced, and the design efficiency of the user type graph is improved.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application 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; such modifications and substitutions do not necessarily depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A house type data processing method, characterized in that the method comprises:
acquiring to-be-processed house type combination data, wherein the to-be-processed house type combination data comprises a plurality of house type combinations, demand proportions of the house type combinations and total house type number;
inputting the house type combination data to be processed into a house type graph processing algorithm to obtain the house type combination data output by the house type graph processing algorithm, wherein the house type graph processing algorithm is obtained based on a genetic algorithm;
and taking the house type combination data as target house type combination data.
2. The method of claim 1, wherein inputting the user type combination data to be processed into a user type graph processing algorithm to obtain the user type combination data output by the user type graph processing algorithm comprises:
acquiring initial house type combination data from the house type combination data to be processed, and performing blending processing on the initial house type combination data based on the house type graph processing algorithm to obtain reference house type combination data;
acquiring convergence parameters of the user pattern processing algorithm in the current round processing process;
if the convergence parameter meets a preset condition, outputting the reference house type combination data as house type combination data;
and if the convergence parameter does not meet the preset condition, performing the next round of blending processing on the reference house type combination data.
3. The method of claim 2, wherein the initial subscriber type combination data comprises a plurality of initial subscriber type combinations, the reference subscriber type combination data comprises a plurality of reference subscriber type combinations, and the pugging processing is performed on the initial subscriber type combination data based on the subscriber type pattern processing algorithm to obtain reference subscriber type combination data, comprising:
combining and generating the plurality of initial house type combinations based on the house type graph processing algorithm to obtain reference house type combination data;
the acquiring of the convergence parameter of the user pattern processing algorithm in the current round processing process includes:
acquiring error parameters of the proportion of the plurality of reference house type combinations and the demand proportion of the plurality of house type combinations;
if the error parameter meets a preset threshold value, taking the error parameter as a convergence parameter of the user pattern processing algorithm in the current round processing process;
and if the error parameter does not meet the preset threshold value, combining and generating the initial house type combination data continuously based on the house type graph processing algorithm until the error parameter meets the preset threshold value.
4. The method of claim 3, wherein the combining and generating the plurality of initial house type combinations based on the house type graph processing algorithm comprises:
selecting a plurality of house type types from the initial house type combinations based on the house type graph processing algorithm, and combining the house type types to generate a first house type combination;
the obtaining of the error parameter of the ratio of the plurality of reference house type combinations and the demand ratio of the plurality of house type combinations includes:
acquiring a first error parameter corresponding to the first house type combination;
if the first error parameter meets a first threshold value, performing cross processing on the house type in the first house type combination to obtain a second house type combination;
and if the first error parameter does not meet a first threshold value, if the first error parameter meets a second threshold value, performing mutation processing on the house type in the first house type combination to obtain a third house type combination.
5. The method of claim 4, further comprising:
and acquiring a second error parameter corresponding to the second house type combination, and if the second error parameter does not meet the second threshold, taking the second error parameter as a convergence parameter of the house type graph processing algorithm in the current round processing process.
6. The method of claim 4, further comprising:
acquiring a third error parameter corresponding to the second house type combination;
and taking the third error parameter as a convergence parameter of the user pattern processing algorithm in the current round processing process.
7. A house-type data processing apparatus, characterized in that the apparatus comprises:
the device comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring to-be-processed house type combination data, and the to-be-processed house type combination data comprises a plurality of house type combinations, demand proportions of the house type combinations and total house type number;
the second acquisition module is used for inputting the user type combination data to be processed into a user type graph processing algorithm to acquire the user type combination data output by the user type graph processing algorithm, wherein the user type graph processing algorithm is obtained based on a genetic algorithm;
and the processing module is used for taking the house type combination data as target house type combination data.
8. The apparatus of claim 7, wherein the second obtaining module comprises a first processing unit, a obtaining unit, and a second processing unit:
the first processing unit is used for acquiring initial house type combination data from the house type combination data to be processed, and performing mixed splicing processing on the initial house type combination data based on the house type graph processing algorithm to obtain reference house type combination data;
the acquisition unit is used for acquiring convergence parameters of the user pattern processing algorithm in the current round processing process;
the second processing unit is used for outputting the reference house type combination data as the house type combination data if the convergence parameter meets a preset condition; and if the convergence parameter does not meet the preset condition, performing the next round of blending processing on the reference house type combination data.
9. An electronic device, comprising a memory;
one or more processors;
one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to perform the method of any of claims 1-6.
10. A computer-readable storage medium, having a program code stored therein, wherein the program code when executed by a processor performs the method of any of claims 1-6.
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