CN116756839B - Intelligent management method for data of assembled building platform - Google Patents
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Abstract
The invention relates to the field of data management, in particular to an intelligent management method for data of an assembled building platform, which comprises the following steps of S1, establishing a subset of the types of assembled building components and inputting model data which is modeled into a corresponding subset according to the types; step S2, the central control module determines a judging mode aiming at the initial level of the model data according to the byte number of the model data of the input subset; step S3, when the central control module judges that the previous data level iteration is completed and the first preset time period passes, the data level iteration is carried out again, and the central control module judges whether the data level iteration is in accordance with a preset standard for the model data according to the frequency of the retrieval of the model data in the preset time period; step S4, when a second preset time length passes after single data level iteration, the central control module completes the processing of model data; the processing of the model data includes discarding of the data and compression of the data. The data redundancy problem of the fabricated building platform when facing big data environment is solved.
Description
Technical Field
The invention relates to the field of data management, in particular to an intelligent management method for data of an assembled building platform.
Background
At present, building information model technology (BIM for short) is becoming more and more popular, and building components or parts can be virtually presented through BIM modeling, so that information of the building components or parts in reality can be truly and vividly expressed through BIM. The fabricated building platform breaks various barriers and boundaries among persons, information, processes and the like related to the project, and efficient management of the building project is achieved.
Chinese patent publication No.: CN106600239a discloses a BIM-based cooperative management system for whole process data of building construction, comprising: the BIM data processing subsystem is used for storing an assembled building BIM model which can be split into a plurality of component models in a database, endowing each component model with unique coding information, and associating each component model with attribute information, including geometric information and material information; the production management subsystem is used for acquiring the geometric information and the material information of each component model so as to enable the production equipment to produce the corresponding components; the system comprises a tracking subsystem, an identity tag generating device and a plurality of code information identifying devices, wherein the tracking subsystem is used for generating a unique identity tag according to the code information of each component model, the code information identifying devices are distributed at a plurality of nodes in the production, storage, transportation and assembly links of the components, and the tracking subsystem is used for updating the state information of the component models. However, data optimization, data redundancy reduction and storage space optimization have limitations, so that big data environments faced by BIM cannot be effectively supported.
Disclosure of Invention
Therefore, the invention provides an intelligent management method for data of an assembled building platform, which is used for solving the problem of data redundancy of the assembled building platform in the prior art when the assembled building platform faces a big data environment.
In order to achieve the above purpose, the invention provides an intelligent management method for data of an assembled building platform, comprising the following steps:
step S1, building a subset of the types of the assembled building components and inputting model data which completes modeling into a corresponding subset according to the types;
step S2, aiming at a single component type subset, a central control module determines a judging mode aiming at an initial level of model data according to the byte number of the model data of an input subset;
step S3, aiming at the model data recorded by a single part type subset, the central control module carries out data level iteration again when a first preset time length is passed after the completion of the previous data level iteration is judged, the first preset time length is set to be 72h, the central control module judges whether the data level iteration of the model data accords with a preset standard according to the calling frequency of the model data in the preset time length, the preset time length is set to be 24h, when the preset standard is met, the data level iteration mode is determined to be grade degradation, the recording time interval of the detection module is controlled to detect whether the grade degradation of the model data accords with the judging mode of the preset standard, or the data level iteration mode is determined to be grade upgrading one stage, and when the preset standard is not met, the detection module is controlled to detect whether the model data is optimized in the preset time length, and the secondary judging mode of whether the data level iteration accords with the preset standard is determined according to the measured optimizing result;
Step S4, when the central control module judges that the second preset time length passes after finishing single data level iteration, the central control module finishes the processing of the model data; and setting the second preset time length to be 2h, wherein the processing of the model data comprises discarding of the data and compression of the data.
Further, in said step S2, said central control module determines a decision means for an initial level of model data based on the number of bytes of said model data entered into said subset, wherein,
the first level judging mode is that the central control module judges that the initial level of the model data is first-level data; the first level judgment mode satisfies that the byte number of the model data is smaller than a first preset byte number;
the second level judging mode is that the central control module judges that the initial level of the model data is second-level data; the second level judgment mode satisfies that the byte number of the model data is greater than or equal to the first preset byte number and less than a second preset byte number;
the third level judging mode is that the central control module judges that the initial level of the model data is three-level data; the third level judgment mode meets the condition that the byte number of the model data is larger than or equal to the second preset byte number and smaller than a third preset byte number;
The fourth level judging mode is that the central control module judges that the initial level of the model data is four-level data; the fourth level judgment mode satisfies that the byte number of the model data is greater than or equal to the third preset byte number and less than a fourth preset byte number;
the fifth level judging mode is that the central control module judges that the initial level of the model data is five-level data; the fifth level judgment mode satisfies that the byte number of the model data is greater than or equal to the fourth preset byte number.
Further, in the step S3, the central control module determines, according to the frequency of the retrieval of the model data within the preset duration, a determination mode for whether the data level iteration for the model data meets the preset standard, where,
the first judging mode is a judging mode that the central control module judges that the data level iteration of the model data accords with a preset standard and determines that the data level iteration mode is level degradation, and the central control module controls the detection module to detect the input time interval of the model data and determines whether the level degradation of the model data accords with the preset standard or not; the input time interval of the model data is the time length from the time node after the model data is input to the time node for iteration; the first judging mode meets the condition that the calling frequency of the model data in the preset time period is smaller than the first preset calling frequency, and the level of the model data before iteration is larger than or equal to two levels;
The second judging mode is a secondary judging mode that the central control module judges that the data level iteration of the model data does not accord with a preset standard, the central control module controls the detection module to detect whether the model data is optimized within the preset time period, and whether the data level iteration of the model data accords with the preset standard is determined according to the detected optimizing result; the second judging mode meets the condition that the calling frequency of the model data in the preset time length is more than or equal to the first preset calling frequency and less than the second preset calling frequency;
the third judging mode is that the central control module judges that the data level iteration of the model data accords with a preset standard, and the data level iteration mode is determined to be a level upgrading one level; the second judging mode meets the condition that the calling frequency of the model data in the preset time length is greater than or equal to the second preset calling frequency, and the level of the model data before iteration is less than or equal to four levels.
Further, the central control module controls the detection module to detect whether the model data is optimized within the preset time length under the second judging mode, and determines whether the data level iteration of the model data accords with a secondary judging mode of a preset standard according to the detected optimizing result, wherein,
The first secondary judgment mode is a judgment mode that the central control module judges that the data level iteration of the model data accords with a preset standard, and determines that the data level iteration mode is level upgrading, and the central control module further controls the detection module to detect the optimized byte number duty ratio of the model data and determines whether the level upgrading of the model data accords with the preset standard or not; the first secondary judgment mode meets the condition that the model data is optimized within the preset time length;
the second secondary judgment mode is that the central control module judges that the data level iteration of the model data does not meet the preset standard, and the data level iteration of the model data is not carried out; the second secondary judgment mode meets the condition that the model data is not optimized within the preset time.
Further, the central control module controls the detection module to detect the optimized byte number duty ratio of the model data under the first secondary judgment mode, and determines whether the level rise of the model data accords with the judgment mode of the preset standard according to the detected optimized byte number duty ratio, wherein,
the first upgrading judging mode is that the central control module judges that the grade increase of the model data accords with a preset standard, and the grade of the model data is increased by one step; the first upgrading judging mode meets the condition that the optimized byte number ratio is smaller than a preset byte number ratio, and the level of the model data before the level is increased is smaller than or equal to four levels;
The second upgrading judging mode is that the central control module judges that the grade rising of the model data accords with a preset standard, and the grade of the model data is increased by two stages; the second upgrading judging mode meets the conditions that the ratio of the optimized byte number is larger than or equal to the ratio of the preset byte number and the level of the model data before the level is increased is smaller than or equal to three levels.
Further, the central control module controls the detection module to detect the input time interval of the model data in the first judging mode, and determines whether the grade reduction of the model data accords with the judging mode of a preset standard according to the detected input time interval, wherein,
the first degradation judgment mode is that the central control module judges that the level reduction of the model data does not accord with a preset standard, and data level iteration is not carried out on the model data; the first degradation judgment mode meets the condition that the recording time interval is smaller than a preset recording time interval;
the second degradation judgment mode is that the central control module judges that the level reduction of the model data accords with a preset standard, and reduces the level of the model data by one step; the second degradation judgment mode meets the requirement that the recording time interval is larger than or equal to a preset recording time interval.
Further, in the step S4, the central control module determines, according to the data level after iteration, whether the processing of the model data meets a determination mode of a preset standard, where,
the first processing judgment mode is that the central control module judges that the processing of the model data accords with a preset standard and discards the model data; the first processing judgment mode satisfies that the data level after iteration is primary data;
the second processing judging mode is that the central control module judges that the processing of the model data accords with a preset standard and compresses the model data, and the adjusting module compresses the model data to a corresponding value according to the calling frequency of the model data in a preset duration; the second processing judgment mode satisfies that the data level after iteration is second-level data;
the third processing judgment mode is that the central control module judges that the processing of the model data does not accord with a preset standard and does not process the model data; and the third processing judgment mode satisfies that the data level after iteration is greater than or equal to three-level data.
Further, the central control module determines an adjustment mode for the compression of the model data according to the retrieval frequency of the model data in a preset duration under the second processing determination mode, wherein,
The first compression adjustment mode is that the adjustment module compresses the model data to a corresponding value by using a first preset compression ratio adjustment coefficient; the first compression adjustment mode meets the condition that the frequency of modulation and extraction is smaller than a third preset frequency of modulation and extraction;
the second compression adjustment mode is that the adjustment module compresses the model data to a corresponding value by using a second preset compression ratio adjustment coefficient; the second compression adjustment mode meets the condition that the modulation frequency is more than or equal to the third preset modulation frequency and less than the fourth preset modulation frequency;
the third compression adjustment mode is that the adjustment module compresses the model data to a corresponding value by using a third preset compression ratio adjustment coefficient; the third compression adjustment mode meets the requirement that the modulation frequency is greater than or equal to the fourth preset modulation frequency.
Further, the central control module calculates a difference value between a preset precision and the precision of the adjusted model under a first preset condition, and marks the difference value as a precision difference value, and the adjusting module determines a correction mode for the data compression of the model according to the precision difference value, wherein,
the first compression correction mode is that the adjusting module uses a first preset correction coefficient to increase the preset compression ratio adjusting coefficient to a corresponding value; the first compression correction mode meets the condition that the precision difference value is smaller than a first preset precision difference value;
The second compression correction mode is that the adjusting module uses a second preset correction coefficient to increase the preset compression ratio adjusting coefficient to a corresponding value; the second compression correction mode meets the condition that the precision difference value is larger than or equal to the first preset precision difference value and smaller than a second preset precision difference value;
the third compression correction mode is that the adjusting module uses a third preset correction coefficient to increase the preset compression ratio adjusting coefficient to a corresponding value; the third compression correction mode meets the condition that the precision difference value is larger than or equal to the second preset precision difference value;
the first preset condition is that the adjustment module completes adjustment of the model data compression and the precision of the adjusted model is smaller than preset precision.
Further, the fabricated building component type subset includes a superposition Liang Ziji, a prefabricated Liang Ziji, a superposition column subset, a prefabricated column subset, a superposition floor subset, a precast slab subset, a stair subset, a precast shear outer wall subset, a precast shear inner wall subset, a superposition shear outer wall subset, a superposition shear inner wall subset, a precast parapet wall subset, a precast air conditioning slab subset, a precast balcony slab subset, a precast bay window subset, a precast cladding wall slab subset, a precast embedded wall slab subset, a precast integral caisson subset, an ALC partition wall subset, a ceramsite partition wall slab subset, and other subsets.
Compared with the prior art, the method has the beneficial effects that through the step S1, the type subset of the assembled building components is established, and model data which is modeled is input into the corresponding subset according to the type; step S2, the central control module determines a judging mode aiming at the initial level of the model data according to the byte number of the model data of the input subset; step S3, when the central control module judges that the previous data level iteration is completed and the first preset time period passes, the data level iteration is carried out again, and the central control module judges whether the data level iteration is in accordance with a preset standard for the model data according to the frequency of the retrieval of the model data in the preset time period; step S4, when a second preset time length passes after single data level iteration, the central control module completes the processing of model data; the processing of the model data comprises discarding of the data and compression of the data, so that the problem of data redundancy of the fabricated building platform in the face of a large data environment is solved.
Further, the invention determines the judgment of the initial level of the model data by inputting the byte number of the model data of the subset, thereby laying a foundation for the hierarchical processing of the data.
Further, whether the model data is subjected to data level iteration is determined through the calling frequency in the preset time length, when the preset standard is met, the mode of determining the data level iteration is grade degradation, the detection module is controlled to detect the input time interval of the model data, and whether the grade degradation of the model data meets the preset standard is determined, or the mode of determining the data level iteration is grade upgrading, when the preset standard is not met, the detection module is controlled to detect whether the model data is optimized in the preset time length, and whether the data level iteration of the model data meets the preset standard is determined according to the measured optimization result, so that the accurate iteration of the data level is solved, and basis and execution standard are provided for later-period data optimization.
Further, when the central control module judges that the iteration of the data level of the model data does not meet the preset standard, whether the data is optimized is further detected, and when the data is optimized, the increasing mode of the data level is determined according to the optimized duty ratio, so that the level of the effective data is increased.
Further, the central control module judges that the mode of carrying out data level iteration on the model data accords with a preset standard and determines that the mode of data level iteration is level degradation, and the central control module further controls the detection module to detect the input time interval of the model data to determine whether the mode is further judged in degradation or not, so that the problem that the newly input data is misjudged due to the fact that the using time is short and the calling frequency is unqualified is avoided.
Further, after the central control module judges that the data iteration is finished, whether the processing of the model data accords with a preset standard is further determined according to the data level, and discarding or compressing of the data is finished according to the corresponding level, so that the data is optimized scientifically and reasonably, and the problem of data redundancy is solved.
Further, when the central control module judges that data needs to be compressed, the central control module compresses the model data to a corresponding value by using different preset compression ratio adjusting coefficients according to the data calling frequency, so that accurate optimization of the data is realized.
Further, when the adjustment module completes the adjustment of the data compression of the model and the precision of the adjusted model is smaller than the preset precision, the central control module corrects the preset compression ratio adjustment coefficient through the preset correction coefficient, so that the accuracy and the size balance of the data are ensured.
Furthermore, the invention classifies the assembled building components into component category subsets, and ensures reasonable storage and optimization of different data.
Drawings
FIG. 1 is a flow chart of an intelligent management method for data of an assembled building platform according to an embodiment of the invention;
FIG. 2 is a flowchart of a method for determining whether a data level iteration meets a preset standard for model data according to an embodiment of the present invention;
FIG. 3 is a flowchart of a method for determining whether the processing of model data meets the preset criteria according to the embodiment of the present invention;
FIG. 4 is a flow chart of an adjustment mode of model data compression according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; 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.
It should be noted that, the data in this embodiment are obtained by comprehensive analysis and evaluation of the historical detection data and the corresponding historical detection results of three months before the current data processing by the central control module. According to the invention, the central control module comprehensively determines the numerical value of each preset parameter standard aiming at the current processing according to 184420 times of platform data processing accumulated in the previous three months before the current data processing. It will be understood by those skilled in the art that the determination manner of the system according to the present invention for the parameters mentioned above may be that the value with the highest duty ratio is selected as the preset standard parameter according to the data distribution, the weighted summation is used to take the obtained value as the preset standard parameter, each history data is substituted into a specific formula, and the value obtained by using the formula is taken as the preset standard parameter or other selection manner, as long as different specific conditions in the single item determination process can be definitely defined by the obtained value by the system according to the present invention are satisfied.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
Referring to fig. 1, fig. 2, fig. 3, and fig. 4, flowcharts of an intelligent management method for data of an assembled building platform according to an embodiment of the invention are shown; the embodiment of the invention aims at a flow chart of a judging mode of whether the iteration of the data level accords with a preset standard or not; the embodiment of the invention discloses a flow chart of a judging mode of judging whether the processing of model data accords with a preset standard or not; the embodiment of the invention provides a flow chart of an adjusting mode of model data compression.
The embodiment of the invention discloses an intelligent management method for data of an assembled building platform, which comprises the following steps:
step S1, building a subset of the types of the assembled building components and inputting model data which completes modeling into a corresponding subset according to the types;
step S2, aiming at a single component type subset, a central control module determines a judging mode aiming at an initial level of model data according to the byte number of the model data of an input subset;
step S3, aiming at the model data recorded by a single part type subset, when the central control module judges that the previous data level iteration is completed and passes through a first preset time length 72h, carrying out data level iteration again, judging whether the data level iteration meets a preset standard or not according to the invoking frequency of the model data in the preset time length, setting the preset time length to be 24h, when the preset standard is met, determining the data level iteration mode to be grade degradation, controlling the detection module to detect the recording time interval of the model data to determine whether the grade degradation mode of the model data meets the preset standard or not, or determining the data level iteration mode to be grade upgrading one stage, and when the preset standard is not met, controlling the detection module to detect whether the model data is optimized in the preset time length, and determining whether the data level iteration meets the preset standard or not according to the measured optimizing result to carry out the secondary judgment mode of the data level iteration;
Step S4, when the central control module judges that the single data level iteration is completed and the second preset time length is 2 hours, the central control module completes the processing of the model data; the processing of the model data includes discarding of the data and compression of the data.
In particular, in said step S2, said central control module determines a decision means for an initial level of model data, based on the number of bytes of said model data entered in said subset, wherein,
the first level judging mode is that the central control module judges that the initial level of the model data is first-level data; the first level judgment mode meets the condition that the byte number of the model data is smaller than 150KB of a first preset byte number;
the second level judging mode is that the central control module judges that the initial level of the model data is second-level data; the second level judgment mode satisfies that the byte number of the model data is more than or equal to the first preset byte number and less than 200KB of a second preset byte number;
the third level judging mode is that the central control module judges that the initial level of the model data is three-level data; the third level judgment mode meets the condition that the byte number of the model data is more than or equal to the second preset byte number and less than the third preset byte number by 250KB;
The fourth level judging mode is that the central control module judges that the initial level of the model data is four-level data; the fourth level judgment mode satisfies that the byte number of the model data is more than or equal to the third preset byte number and less than 300KB of a fourth preset byte number;
the fifth level judging mode is that the central control module judges that the initial level of the model data is five-level data; the fifth level judgment mode satisfies that the byte number of the model data is greater than or equal to the fourth preset byte number.
Specifically, in the step S3, the central control module determines, according to the frequency of the retrieval of the model data within the preset duration 24h, a determination mode for whether the data level iteration for the model data meets the preset standard, where,
the first judging mode is a judging mode that the central control module judges that the data level iteration of the model data accords with a preset standard and determines that the data level iteration mode is level degradation, and the central control module controls the detection module to detect the input time interval of the model data and determines whether the level degradation of the model data accords with the preset standard or not; the input time interval of the model data is the time length from the time node after the model data is input to the time node for iteration; the first judging mode meets the condition that the calling frequency of the model data in the preset time period is smaller than the first preset calling frequency 24, and the level of the model data before iteration is larger than or equal to two levels;
The second judging mode is a secondary judging mode that the central control module judges that the data level iteration of the model data does not accord with a preset standard, the central control module controls the detection module to detect whether the model data is optimized within the preset time period, and whether the data level iteration of the model data accords with the preset standard is determined according to the detected optimizing result; the second judging mode meets the condition that the calling frequency of the model data in the preset time period is more than or equal to the first preset calling frequency and less than a second preset calling frequency 47;
the third judging mode is that the central control module judges that the data level iteration of the model data accords with a preset standard, and the data level iteration mode is determined to be a level upgrading one level; the second judging mode meets the condition that the calling frequency of the model data in the preset time length is greater than or equal to the second preset calling frequency, and the level of the model data before iteration is less than or equal to four levels.
Specifically, the central control module controls the detection module to detect whether the model data is optimized within the preset time length under the second judging mode, and determines whether the data level iteration of the model data accords with a secondary judging mode of a preset standard according to the detected optimizing result, wherein,
The first secondary judgment mode is a judgment mode that the central control module judges that the data level iteration of the model data accords with a preset standard, and determines that the data level iteration mode is level upgrading, and the central control module further controls the detection module to detect the optimized byte number duty ratio of the model data and determines whether the level upgrading of the model data accords with the preset standard or not; the first secondary judgment mode meets the condition that the model data is optimized within the preset time length;
the second secondary judgment mode is that the central control module judges that the data level iteration of the model data does not meet the preset standard, and the data level iteration of the model data is not carried out; the second secondary judgment mode meets the condition that the model data is not optimized within the preset time.
Specifically, the central control module controls the detection module to detect the optimized byte number duty ratio of the model data under the first secondary judgment mode, and determines whether the level rise of the model data meets the judgment mode of the preset standard according to the detected optimized byte number duty ratio, wherein,
the first upgrading judging mode is that the central control module judges that the grade increase of the model data accords with a preset standard, and the grade of the model data is increased by one step; the first upgrading judging mode meets the conditions that the ratio of the number of the optimized bytes is smaller than the ratio of the number of the preset bytes by 18 percent, and the level of the model data before the level is increased is smaller than or equal to four levels;
The second upgrading judging mode is that the central control module judges that the grade rising of the model data accords with a preset standard, and the grade of the model data is increased by two stages; the second upgrading judging mode meets the conditions that the ratio of the optimized byte number is larger than or equal to the ratio of the preset byte number and the level of the model data before the level is increased is smaller than or equal to three levels.
Specifically, the central control module controls the detection module to detect the input time interval of the model data in the first judging mode, and determines whether the grade reduction of the model data accords with the judging mode of a preset standard according to the detected input time interval, wherein,
the first degradation judgment mode is that the central control module judges that the level reduction of the model data does not accord with a preset standard, and data level iteration is not carried out on the model data; the first degradation judgment mode meets the condition that the input time interval is smaller than a preset input time interval 58h;
the second degradation judgment mode is that the central control module judges that the level reduction of the model data accords with a preset standard, and reduces the level of the model data by one step; the second degradation judgment mode meets the requirement that the recording time interval is larger than or equal to a preset recording time interval.
Specifically, in the step S4, the central control module determines, according to the data level after iteration, whether the processing of the model data meets a determination mode of a preset standard, where,
the first processing judgment mode is that the central control module judges that the processing of the model data accords with a preset standard and discards the model data; the first processing judgment mode satisfies that the data level after iteration is primary data;
the second processing judging mode is that the central control module judges that the processing of the model data accords with a preset standard and compresses the model data, and the adjusting module compresses the model data to a corresponding value according to the calling frequency of the model data in a preset duration; the second processing judgment mode satisfies that the data level after iteration is second-level data;
the third processing judgment mode is that the central control module judges that the processing of the model data does not accord with a preset standard and does not process the model data; and the third processing judgment mode satisfies that the data level after iteration is greater than or equal to three-level data.
Specifically, the central control module determines an adjustment mode for compression of the model data according to the calling frequency of the model data within a preset duration 24h in the second processing judgment mode, wherein,
The first compression adjustment mode is that the adjustment module compresses the model data to a corresponding value by using a first preset compression ratio adjustment coefficient of 0.90; the first compression adjustment mode satisfies that the frequency of modulation and extraction is smaller than a third preset frequency of modulation and extraction 12;
the second compression adjustment mode is that the adjustment module compresses the model data to a corresponding value by using a second preset compression ratio adjustment coefficient of 0.85; the second compression adjustment mode satisfies that the frequency of modulation is greater than or equal to the third preset frequency of modulation and less than the fourth preset frequency of modulation 18;
the third compression adjustment mode is that the adjustment module compresses the model data to a corresponding value by using a third preset compression ratio adjustment coefficient of 0.73; the third compression adjustment mode meets the requirement that the modulation frequency is greater than or equal to the fourth preset modulation frequency.
Specifically, the central control module calculates a difference value between a preset precision and an adjusted precision of the model under a first preset condition, and marks the difference value as a precision difference value, and the adjusting module determines a correction mode for data compression of the model according to the precision difference value, wherein,
the first compression correction mode is that the adjusting module uses a first preset correction coefficient 1.1 to increase the preset compression ratio adjusting coefficient to a corresponding value; the first compression correction mode meets the condition that the precision difference value is smaller than a first preset precision difference value of 0.02;
The second compression correction mode is that the adjusting module uses a second preset correction coefficient 1.2 to increase the preset compression ratio adjusting coefficient to a corresponding value; the second compression correction mode meets the condition that the precision difference value is larger than or equal to the first preset precision difference value and smaller than a second preset precision difference value by 0.08;
the third compression correction mode is that the adjusting module uses a third preset correction coefficient 1.3 to increase the preset compression ratio adjusting coefficient to a corresponding value; the third compression correction mode meets the condition that the precision difference value is larger than or equal to the second preset precision difference value;
the first preset condition is that the adjustment module completes adjustment of the model data compression and the precision of the adjusted model is smaller than 0.98 of preset precision.
Specifically, the fabricated building component type subset includes a superposition Liang Ziji, a prefabricated Liang Ziji, a superposition column subset, a prefabricated column subset, a superposition floor subset, a precast slab subset, a stair subset, a precast shear outer wall subset, a precast shear inner wall subset, a superposition shear outer wall subset, a superposition shear inner wall subset, a precast parapet wall subset, a precast air conditioner plate subset, a precast balcony slab subset, a precast bay window subset, a precast cladding wall slab subset, a precast embedded wall slab subset, a precast integral caisson subset, an ALC partition wall subset, a ceramsite partition wall slab subset, and other subsets.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An intelligent management method for data of an assembled building platform is characterized by comprising the following steps:
step S1, building a subset of the types of the assembled building components and inputting model data which completes modeling into a corresponding subset according to the types;
step S2, aiming at a single component type subset, a central control module determines a judging mode aiming at an initial level of model data according to the byte number of the model data of an input subset;
Step S3, aiming at the model data recorded by a single part type subset, the central control module carries out data level iteration again when a first preset time length is passed after the completion of the previous data level iteration is judged, the first preset time length is set to be 72h, the central control module judges whether the data level iteration of the model data accords with a preset standard according to the calling frequency of the model data in the preset time length, the preset time length is set to be 24h, when the preset standard is met, the data level iteration mode is determined to be grade degradation, the recording time interval of the detection module is controlled to detect whether the grade degradation of the model data accords with the judging mode of the preset standard, or the data level iteration mode is determined to be grade upgrading one stage, and when the preset standard is not met, the detection module is controlled to detect whether the model data is optimized in the preset time length, and the secondary judging mode of whether the data level iteration accords with the preset standard is determined according to the measured optimizing result;
step S4, when the central control module judges that the second preset time length passes after finishing single data level iteration, the central control module finishes the processing of the model data; and setting the second preset time length to be 2h, wherein the processing of the model data comprises discarding of the data and compression of the data.
2. The intelligent management method for data of fabricated building platform according to claim 1, wherein in the step S2, the central control module determines a decision mode for an initial level of model data according to the number of bytes of the model data entered into the subset, wherein,
the first level judging mode is that the central control module judges that the initial level of the model data is first-level data; the first level judgment mode satisfies that the byte number of the model data is smaller than a first preset byte number;
the second level judging mode is that the central control module judges that the initial level of the model data is second-level data; the second level judgment mode satisfies that the byte number of the model data is greater than or equal to the first preset byte number and less than a second preset byte number;
the third level judging mode is that the central control module judges that the initial level of the model data is three-level data; the third level judgment mode meets the condition that the byte number of the model data is larger than or equal to the second preset byte number and smaller than a third preset byte number;
the fourth level judging mode is that the central control module judges that the initial level of the model data is four-level data; the fourth level judgment mode satisfies that the byte number of the model data is greater than or equal to the third preset byte number and less than a fourth preset byte number;
The fifth level judging mode is that the central control module judges that the initial level of the model data is five-level data; the fifth level judgment mode satisfies that the byte number of the model data is greater than or equal to the fourth preset byte number.
3. The intelligent management method of fabricated building platform data according to claim 2, wherein in the step S3, the central control module determines, according to the frequency of the model data retrieving within a preset time period, a determination mode for determining whether the data level iteration for the model data meets a preset standard, wherein,
the first judging mode is a judging mode that the central control module judges that the data level iteration of the model data accords with a preset standard and determines that the data level iteration mode is level degradation, and the central control module controls the detection module to detect the input time interval of the model data and determines whether the level degradation of the model data accords with the preset standard or not; the input time interval of the model data is the time length from the time node after the model data is input to the time node for iteration; the first judging mode meets the condition that the calling frequency of the model data in the preset time period is smaller than the first preset calling frequency, and the level of the model data before iteration is larger than or equal to two levels;
The second judging mode is a secondary judging mode that the central control module judges that the data level iteration of the model data does not accord with a preset standard, the central control module controls the detection module to detect whether the model data is optimized within the preset time period, and whether the data level iteration of the model data accords with the preset standard is determined according to the detected optimizing result; the second judging mode meets the condition that the calling frequency of the model data in the preset time length is more than or equal to the first preset calling frequency and less than the second preset calling frequency;
the third judging mode is that the central control module judges that the data level iteration of the model data accords with a preset standard, and the data level iteration mode is determined to be a level upgrading one level; the second judging mode meets the condition that the calling frequency of the model data in the preset time length is greater than or equal to the second preset calling frequency, and the level of the model data before iteration is less than or equal to four levels.
4. The intelligent management method of fabricated building platform data according to claim 3, wherein the central control module controls the detection module to detect whether the model data is optimized within the preset time period under the second determination mode, and determines a secondary determination mode for performing data level iteration on the model data according to the detected optimization result, wherein,
The first secondary judgment mode is a judgment mode that the central control module judges that the data level iteration of the model data accords with a preset standard, and determines that the data level iteration mode is level upgrading, and the central control module further controls the detection module to detect the optimized byte number duty ratio of the model data and determines whether the level upgrading of the model data accords with the preset standard or not; the first secondary judgment mode meets the condition that the model data is optimized within the preset time length;
the second secondary judgment mode is that the central control module judges that the data level iteration of the model data does not meet the preset standard, and the data level iteration of the model data is not carried out; the second secondary judgment mode meets the condition that the model data is not optimized within the preset time.
5. The intelligent management method of fabricated building platform data according to claim 4, wherein the central control module controls the detection module to detect an optimized byte count ratio of the model data in the first secondary decision mode, and determines a decision mode for whether the level increase of the model data meets a preset standard according to the detected optimized byte count ratio, wherein,
The first upgrading judging mode is that the central control module judges that the grade increase of the model data accords with a preset standard, and the grade of the model data is increased by one step; the first upgrading judging mode meets the condition that the optimized byte number ratio is smaller than a preset byte number ratio, and the level of the model data before the level is increased is smaller than or equal to four levels;
the second upgrading judging mode is that the central control module judges that the grade rising of the model data accords with a preset standard, and the grade of the model data is increased by two stages; the second upgrading judging mode meets the conditions that the ratio of the optimized byte number is larger than or equal to the ratio of the preset byte number and the level of the model data before the level is increased is smaller than or equal to three levels.
6. The intelligent management method of fabricated building platform data according to claim 5, wherein the central control module controls the detection module to detect the entry time interval of the model data in the first determination mode, and determines whether the level decrease of the model data meets a preset standard or not according to the detected entry time interval, wherein,
the first degradation judgment mode is that the central control module judges that the level reduction of the model data does not accord with a preset standard, and data level iteration is not carried out on the model data; the first degradation judgment mode meets the condition that the recording time interval is smaller than a preset recording time interval;
The second degradation judgment mode is that the central control module judges that the level reduction of the model data accords with a preset standard, and reduces the level of the model data by one step; the second degradation judgment mode meets the requirement that the recording time interval is larger than or equal to a preset recording time interval.
7. The intelligent management method of fabricated building platform data according to claim 1, wherein in the step S4, the central control module determines whether the processing of the model data meets a preset standard according to the iterated data level, wherein,
the first processing judgment mode is that the central control module judges that the processing of the model data accords with a preset standard and discards the model data; the first processing judgment mode satisfies that the data level after iteration is primary data;
the second processing judging mode is that the central control module judges that the processing of the model data accords with a preset standard and compresses the model data, and the adjusting module compresses the model data to a corresponding value according to the calling frequency of the model data in a preset duration; the second processing judgment mode satisfies that the data level after iteration is second-level data;
The third processing judgment mode is that the central control module judges that the processing of the model data does not accord with a preset standard and does not process the model data; and the third processing judgment mode satisfies that the data level after iteration is greater than or equal to three-level data.
8. The intelligent management method of fabricated building platform data according to claim 7, wherein the central control module determines an adjustment mode for compression of the model data according to the frequency of the model data in a preset time period in the second processing determination mode, wherein,
the first compression adjustment mode is that the adjustment module compresses the model data to a corresponding value by using a first preset compression ratio adjustment coefficient; the first compression adjustment mode meets the condition that the frequency of modulation and extraction is smaller than a third preset frequency of modulation and extraction;
the second compression adjustment mode is that the adjustment module compresses the model data to a corresponding value by using a second preset compression ratio adjustment coefficient; the second compression adjustment mode meets the condition that the modulation frequency is more than or equal to the third preset modulation frequency and less than the fourth preset modulation frequency;
the third compression adjustment mode is that the adjustment module compresses the model data to a corresponding value by using a third preset compression ratio adjustment coefficient; the third compression adjustment mode meets the requirement that the modulation frequency is greater than or equal to the fourth preset modulation frequency.
9. The intelligent management method of fabricated building platform data according to claim 8, wherein the central control module calculates a difference between a preset precision and an adjusted precision of the model under a first preset condition, and marks the difference as a precision difference, and the adjusting module determines a correction mode for data compression of the model according to the precision difference, wherein,
the first compression correction mode is that the adjusting module uses a first preset correction coefficient to increase the preset compression ratio adjusting coefficient to a corresponding value; the first compression correction mode meets the condition that the precision difference value is smaller than a first preset precision difference value;
the second compression correction mode is that the adjusting module uses a second preset correction coefficient to increase the preset compression ratio adjusting coefficient to a corresponding value; the second compression correction mode meets the condition that the precision difference value is larger than or equal to the first preset precision difference value and smaller than a second preset precision difference value;
the third compression correction mode is that the adjusting module uses a third preset correction coefficient to increase the preset compression ratio adjusting coefficient to a corresponding value; the third compression correction mode meets the condition that the precision difference value is larger than or equal to the second preset precision difference value;
The first preset condition is that the adjustment module completes adjustment of the model data compression and the precision of the adjusted model is smaller than preset precision.
10. The intelligent management method of fabricated building platform data according to claim 1, wherein the subset of fabricated building component types comprises a superposition Liang Ziji, a prefabrication Liang Ziji, a superposition column subset, a prefabrication column subset, a superposition floor subset, a prefabrication panel subset, a stair subset, a prefabrication shear exterior wall subset, a prefabrication shear interior wall subset, a superposition shear exterior wall subset, a superposition shear interior wall subset, a prefabrication parapet subset, a prefabrication air conditioner panel subset, a prefabrication balcony subset, a prefabrication bay window subset, a prefabrication external wall panel subset, a prefabrication embedded wall panel subset, a prefabrication integral caisson subset, an ALC partition wall subset, a ceramsite partition wall subset, and other subsets.
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