CN114329746A - ETFE hyperbolic negative Gaussian cable membrane digital construction method and system based on big data - Google Patents
ETFE hyperbolic negative Gaussian cable membrane digital construction method and system based on big data Download PDFInfo
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Abstract
The invention provides an ETFE hyperbolic negative Gaussian cable membrane digital construction method and system based on big data, wherein the method comprises the following steps: designing a structural model based on an ETFE hyperbolic negative Gaussian cable membrane; constructing a tension change prediction model; acquiring first environment data based on coordinate position information of a building site; predicting tension change values of cable membranes made of different materials through a tension change prediction model based on the first environmental data; judging whether the tension change value exceeds a corresponding tension change limit value, and if not, adding a corresponding material cable film into a first queue to be selected; acquiring a light transmittance requirement, judging whether the light transmittance of each material cable film in the first queue to be selected meets the light transmittance requirement, and if so, adding the corresponding material cable film into a second queue to be selected; and selecting the cable membrane made of the target material based on the tension change resistance and the light transmittance of the cable membrane, and carrying out entity construction. The building method provided by the invention can improve the stability of the building structure and improve the user experience.
Description
Technical Field
The invention relates to the technical field of digital buildings, in particular to an ETFE hyperbolic negative Gaussian cable membrane digital construction method and system based on big data.
Background
The Membrane structure is also called a Tensioned Membrane structure (Tensioned Membrane structure), which is a novel building structure form developed in the middle of the 20 th century, and is a space structure form which is formed by a plurality of high-strength Membrane materials and reinforcing members (steel frames, steel columns or steel cables) in a certain way to generate certain pre-tensioning stress inside the Membrane structure so as to form a certain space shape, serve as a covering structure and can bear certain external load. The ETFE cable membrane is an excellent membrane material with a tension membrane structure, and is directly prepared from an ETFE (ethylene-tetrafluoroethylene copolymer) raw material. The ETFE has excellent shock resistance, electrical property, thermal stability and chemical corrosion resistance, and also has high mechanical strength and good processability. The ETFE membrane material can replace other products in many aspects and shows strong advantages and market prospects.
However, in the traditional tensioned membrane structure construction process, the construction is mostly directly completed based on the design drawing of a designer, the light transmittance of the membrane material and whether the membrane material is matched with the environmental factors of a building site are not fully considered, and therefore the constructed tensioned membrane structure is poor in stability and short in service life; if the film material with poor light transmittance is selected, the user experience is poor.
Disclosure of Invention
In order to solve at least one technical problem, the invention provides an ETFE hyperbolic negative Gaussian cable membrane digital construction method and system based on big data, which can select membrane materials matched with local environmental factors by combining big data and a machine learning method, thereby effectively improving the stability of a tensioned membrane structure and prolonging the service life; simultaneously, the stability is met, and the film material with better light transmittance is selected as far as possible, so that the experience of a user is further improved.
The invention provides an ETFE hyperbolic negative Gaussian cable membrane digital construction method based on big data, which comprises the following steps:
designing a structural model based on an ETFE hyperbolic negative Gaussian cable membrane by digital building design software and combining user requirements;
enumerating all preselected cable membranes, analyzing the tension change degrees of different cable membranes under various environmental factors, and constructing a tension change prediction model of the cable membrane material based on the environmental factors;
acquiring coordinate position information of a building site, and acquiring corresponding first environment data through a big data platform based on the coordinate position information;
predicting tension change values of the cable membranes made of different materials through the tension change prediction model based on the first environmental data;
judging whether the tension change values of the cable membranes made of different materials exceed corresponding tension change limit values, if so, rejecting the corresponding cable membranes made of different materials, and if not, adding the corresponding cable membranes made of different materials into a first queue to be selected;
acquiring the light transmittance requirement of a user on the ETFE hyperbolic negative Gaussian cable membrane, respectively acquiring the light transmittance of each material cable membrane in the first queue to be selected, judging whether the light transmittance of each material cable membrane in the first queue to be selected meets the light transmittance requirement, if not, rejecting the corresponding material cable membrane, and if so, adding the corresponding material cable membrane into the second queue to be selected;
and selecting a target material cable membrane from a second candidate queue based on the tension change resistance and the light transmittance of the cable membrane, manufacturing an ETFE hyperbolic negative Gaussian cable membrane by adopting the target material cable membrane, and then carrying out entity construction according to a structural model.
In this scheme, based on the tensile strength change ability and the luminousness of cable membrane, select out target material cable membrane from the second queue of awaiting choosing, specifically include:
the weight of the influence of the tension change resistance of the preset cable membrane material on the cable membrane selection is
And the weight of the influence of the light transmittance of the cable membrane material on the selection of the cable membrane is
；
Presetting a second candidate queue comprising n cable membranes made of different materials, wherein the cable membranes are based on the materials
Obtaining the corresponding light transmittance
And tension variation value in the first environment data
Wherein
，
Indicating the first in the second candidate queue
A cable membrane made of various materials;
cable membrane based on material
Tension change value in first environmental data
And acquiring the corresponding tension resistance change capacity grade by inquiring a preset tension resistance grade table
；
Cable membrane based on material
Obtaining the corresponding light transmittance
And acquiring corresponding light transmittance level by inquiring a preset light transmittance grade table
；
Grading the resistance to tension changes
And influence weight
Multiplying to obtain a first product, and grading the transmittance
And influence weight
Multiplying to obtain a second product, and adding the first product and the second product to obtain the material cord membrane
Composite score of
；
And respectively calculating the comprehensive scores of all the cable membranes made of the materials in the second queue to be selected, sequencing, and selecting the cable membrane made of the material with the highest comprehensive score as the cable membrane made of the target material.
In this embodiment, after predicting the tension variation values of the cable membranes made of different materials through the tension variation prediction model based on the first environmental data, the method further includes:
presetting m cable membranes made of all preselected materials based on the preselected materials
Obtaining the cable membrane adopting the pre-selected material in the preset area
All historical cable membrane buildings of construction, in which
，
Is shown as
A cable membrane made of a preselected material;
acquiring second environment data of each historical cable membrane building, and respectively performing characteristic calculation on the second environment data of each historical cable membrane building to obtain a characteristic value of each second environment data;
performing feature calculation on the first environment data to obtain a feature value of the first environment data;
comparing the difference rate between the characteristic value of the first environment data and the characteristic value of each second environment data, and adding the historical cable membrane buildings corresponding to the second environment data with the difference rate smaller than a first preset threshold value into a correction queue;
deep learning is carried out on the second environmental data of each historical cable membrane building in the correction queue, and a corresponding tension change prediction value is predicted through the tension change prediction model;
detecting corresponding tension change real values through tension change sensors pre-installed on various historical cable membrane buildings in a correction queue;
based on each historical cable membrane building in the correction queue, difference calculation is carried out respectively based on the real tension change value and the predicted tension change value, and the difference value of each historical cable membrane building in the correction queue is obtained;
calculating the average value of the difference values of all historical cable membrane buildings in the correction queue based on an average algorithm to obtain a correction value;
predicting a preselected material cable membrane in the tension change prediction model
On the basis of the tension change value, the correction value is added to obtain a preselected material cable membrane
Corrected tension variation value.
In the scheme, the method detects the corresponding true tension change value through the tension change sensors pre-installed on each historical cable membrane building in the correction queue, and specifically comprises the following steps:
respectively installing a plurality of tension change sensors at different positions based on a certain historical cable membrane building, and respectively acquiring a plurality of single-point tension change true values of the historical cable membrane building through the plurality of tension change sensors;
subtracting the true tension change value of each single point from the true tension change value of the remaining single points respectively to obtain a plurality of difference values, and taking an absolute value of the plurality of difference values to obtain a plurality of absolute values of the difference values;
judging whether a plurality of difference absolute values corresponding to the true value of each single-point tension change are larger than a second preset threshold value or not, and if so, marking the corresponding tension change sensor as a suspected invalid sensor;
after the real values of the tension changes of all the single points are compared, counting the number of marks of each tension change sensor as a suspected invalid sensor;
comparing the number of the marks of each tension change sensor which is a suspected invalid sensor with a third preset threshold, and judging the tension change sensor with the number of the marks larger than the third preset threshold as an invalid sensor;
and eliminating the single-point tension change true values acquired by the invalid sensors from the single-point tension change true values acquired by the plurality of tension change sensors, and carrying out average calculation on the remaining single-point tension change true values to obtain the tension change true value corresponding to the historical cable membrane building.
In this embodiment, after the entity is built according to the structural model, the method further includes:
forming a target building corresponding to the structural model;
collecting historical buildings made of the same materials as the cable membranes made of the target materials, wherein detection sensors are respectively arranged on each node of each historical building, and the detection sensors detect whether the corresponding nodes are abnormal in real time;
in historical time, when a detection sensor of a certain node detects abnormality, recording the characteristics of the node and the accumulated service time of a corresponding historical building when the abnormality occurs, forming historical abnormal data, and recording the abnormal data into a historical database;
constructing a node anomaly prediction model based on the current time, extracting all historical anomaly data in the historical database as training samples, and training the node anomaly prediction model by adopting the node characteristics in each piece of historical anomaly data and the accumulated use time of the corresponding historical building when the anomaly occurs so as to form an optimized node anomaly prediction model;
marking all nodes to be detected on the structure model, and acquiring characteristic data of all nodes to be detected;
respectively inputting the characteristic data of all nodes to be tested into the optimized node abnormity prediction model so as to predict the accumulated service time of the target building when each node to be tested is abnormal;
presetting the service life of a target building as h, judging whether the accumulated service time of the target building is less than h when each node to be detected is abnormal, and marking the corresponding node to be detected as a monitoring node if the accumulated service time of the target building is less than h;
and respectively arranging monitoring sensors at each monitoring node, monitoring abnormal states of the corresponding monitoring nodes in real time by the monitoring sensors, and timely alarming and reporting maintenance processing if the abnormal states occur.
In this scheme, after the ETFE hyperbolic negative gaussian cable membrane is manufactured by using the cable membrane made of the target material, the method further includes:
establishing a two-dimensional coordinate system, presetting the ETFE hyperbolic negative Gaussian cable membrane into multiple deformations, and respectively fixing the sides of a polygon on a steel framework of a structural model;
acquiring a first central point of the ETFE hyperbolic negative Gaussian cable membrane through a preset geometric algorithm;
connecting a first central point with each polygonal vertex of the ETFE hyperbolic negative Gaussian cable membrane to form a plurality of small triangles;
calculating to obtain a second central point of each small triangle through the geometric algorithm;
and the first central point and the second central points are respectively connected and extend to the steel framework to form a net, and the first central point and the second central points are locking points of the net to lock and pull the ETFE hyperbolic negative Gaussian cable membrane.
The second aspect of the present invention further provides an ETFE hyperbolic negative gaussian cable membrane digital construction system based on big data, which includes a memory and a processor, wherein the memory includes an ETFE hyperbolic negative gaussian cable membrane digital construction method program based on big data, and when being executed by the processor, the ETFE hyperbolic negative gaussian cable membrane digital construction method program based on big data realizes the following steps:
designing a structural model based on an ETFE hyperbolic negative Gaussian cable membrane by digital building design software and combining user requirements;
enumerating all preselected cable membranes, analyzing the tension change degrees of different cable membranes under various environmental factors, and constructing a tension change prediction model of the cable membrane material based on the environmental factors;
acquiring coordinate position information of a building site, and acquiring corresponding first environment data through a big data platform based on the coordinate position information;
predicting tension change values of the cable membranes made of different materials through the tension change prediction model based on the first environmental data;
judging whether the tension change values of the cable membranes made of different materials exceed corresponding tension change limit values, if so, rejecting the corresponding cable membranes made of different materials, and if not, adding the corresponding cable membranes made of different materials into a first queue to be selected;
acquiring the light transmittance requirement of a user on the ETFE hyperbolic negative Gaussian cable membrane, respectively acquiring the light transmittance of each material cable membrane in the first queue to be selected, judging whether the light transmittance of each material cable membrane in the first queue to be selected meets the light transmittance requirement, if not, rejecting the corresponding material cable membrane, and if so, adding the corresponding material cable membrane into the second queue to be selected;
and selecting a target material cable membrane from a second candidate queue based on the tension change resistance and the light transmittance of the cable membrane, manufacturing an ETFE hyperbolic negative Gaussian cable membrane by adopting the target material cable membrane, and then carrying out entity construction according to a structural model.
In this scheme, based on the tensile strength change ability and the luminousness of cable membrane, select out target material cable membrane from the second queue of awaiting choosing, specifically include:
the weight of the influence of the tension change resistance of the preset cable membrane material on the cable membrane selection is
And the weight of the influence of the light transmittance of the cable membrane material on the selection of the cable membrane is
；
Presetting a second candidate queue comprising n cable membranes made of different materials, wherein the cable membranes are based on the materials
Obtaining the corresponding light transmittance
And tension variation value in the first environment data
Wherein
，
Indicating the first in the second candidate queue
A cable membrane made of various materials;
cable membrane based on material
Tension change value in first environmental data
And acquiring the corresponding tension resistance change capacity grade by inquiring a preset tension resistance grade table
；
Cable membrane based on material
Obtaining the corresponding light transmittance
And acquiring corresponding light transmittance level by inquiring a preset light transmittance grade table
；
Grading the resistance to tension changes
And influence weight
Multiplying to obtain a first product, and transmitting lightRate class
And influence weight
Multiplying to obtain a second product, and adding the first product and the second product to obtain the material cord membrane
Composite score of
；
And respectively calculating the comprehensive scores of all the cable membranes made of the materials in the second queue to be selected, sequencing, and selecting the cable membrane made of the material with the highest comprehensive score as the cable membrane made of the target material.
In this scheme, after predicting the tension variation values of cable membranes made of different materials through the tension variation prediction model based on first environmental data, when executed by the processor, the ETFE hyperbolic negative gaussian cable membrane digital construction method based on big data further realizes the following steps:
presetting m cable membranes made of all preselected materials based on the preselected materials
Obtaining the cable membrane adopting the pre-selected material in the preset area
All historical cable membrane buildings of construction, in which
，
Is shown as
A preselected materialA cord membrane;
acquiring second environment data of each historical cable membrane building, and respectively performing characteristic calculation on the second environment data of each historical cable membrane building to obtain a characteristic value of each second environment data;
performing feature calculation on the first environment data to obtain a feature value of the first environment data;
comparing the difference rate between the characteristic value of the first environment data and the characteristic value of each second environment data, and adding the historical cable membrane buildings corresponding to the second environment data with the difference rate smaller than a first preset threshold value into a correction queue;
deep learning is carried out on the second environmental data of each historical cable membrane building in the correction queue, and a corresponding tension change prediction value is predicted through the tension change prediction model;
detecting corresponding tension change real values through tension change sensors pre-installed on various historical cable membrane buildings in a correction queue;
based on each historical cable membrane building in the correction queue, difference calculation is carried out respectively based on the real tension change value and the predicted tension change value, and the difference value of each historical cable membrane building in the correction queue is obtained;
calculating the average value of the difference values of all historical cable membrane buildings in the correction queue based on an average algorithm to obtain a correction value;
predicting a preselected material cable membrane in the tension change prediction model
On the basis of the tension change value, the correction value is added to obtain a preselected material cable membrane
Corrected tension variation value.
The third aspect of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a program of an ETFE hyperbolic negative gaussian-chordate membrane digital construction method based on big data, and when the program of the ETFE hyperbolic negative gaussian-chordate membrane digital construction method based on big data is executed by a processor, the steps of the ETFE hyperbolic negative gaussian-chordate membrane digital construction method based on big data are implemented.
According to the ETFE hyperbolic negative Gaussian cable membrane digital construction method and system based on the big data and the computer readable storage medium, disclosed by the invention, the membrane material matched with local environmental factors can be selected by combining the big data and a machine learning method, so that the stability of a tensioned membrane structure is effectively improved, and the service life is prolonged; simultaneously, the stability is met, and the film material with better light transmittance is selected as far as possible, so that the experience of a user is further improved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 shows a flow chart of an ETFE hyperbolic negative Gaussian mixture film digital construction method based on big data;
FIG. 2 shows a block diagram of an ETFE hyperbolic negative Gaussian cable membrane digital construction system based on big data.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
FIG. 1 shows a flow chart of an ETFE hyperbolic negative Gaussian mixture film digital construction method based on big data.
As shown in fig. 1, a first aspect of the present invention provides a method for digitally constructing an ETFE hyperbolic negative gaussian solimembrane based on big data, where the method includes:
s102, designing a structural model based on an ETFE hyperbolic negative Gaussian cable membrane by digital building design software and combining user requirements;
s104, enumerating all preselected cable membranes, analyzing tension change degrees of different cable membranes under various environmental factors, and constructing a tension change prediction model of the cable membranes based on the environmental factors;
s106, acquiring coordinate position information of a building site, and acquiring corresponding first environment data through a big data platform based on the coordinate position information;
s108, predicting tension change values of cable membranes made of different materials through the tension change prediction model based on the first environmental data;
s110, judging whether the tension change values of the cable membranes made of different materials exceed corresponding tension change limit values, if so, rejecting the corresponding cable membranes made of different materials, and if not, adding the corresponding cable membranes made of different materials into a first queue to be selected;
s112, obtaining the light transmittance requirement of the ETFE hyperbolic negative Gaussian cable membrane by a user, respectively obtaining the light transmittance of each material cable membrane in the first queue to be selected, judging whether the light transmittance of each material cable membrane in the first queue to be selected meets the light transmittance requirement, if not, rejecting the corresponding material cable membrane, and if so, adding the corresponding material cable membrane into the second queue to be selected;
s114, selecting a target material cable membrane from a second candidate queue based on the tension change resistance and the light transmittance of the cable membrane, manufacturing an ETFE hyperbolic negative Gaussian cable membrane by adopting the target material cable membrane, and then carrying out entity construction according to a structural model.
In particular, the environmental data may include wind power and temperature, which may cause the tension of the cable membrane to change, and once the wind power is high, the tension of the cable membrane may exceed a corresponding limit value, which may cause the cable membrane to be damaged. In order to avoid the problems, the invention screens out the cable membrane made of the adaptive material by analyzing the environmental data of the local building site.
The cord membranes made of different materials are specifically cord membranes with different thicknesses, for example, 0.3mm, 0.5mm and the like, and as the thickness of the cord membrane increases, the tension change value caused by environmental factors decreases. In other words, the larger the thickness of the cable film, the stronger the cable film can resist a severe environment (e.g., strong wind, high temperature), and the less likely to be broken. However, as the thickness increases, the light transmittance will decrease. The invention comprehensively considers the multidimensional factors such as local environment, light transmittance and the like, thereby selecting the optimal cable membrane.
It will be appreciated that when the membrane structure is deformed in the vicinity of the equilibrium position, two restoring forces may be generated: one is caused by geometric deformation; the other is caused by material strain. Generally, the geometric stiffness is much larger than the elastic stiffness, so that each diaphragm should have good stiffness, a negative gaussian curved surface should be formed as much as possible, that is, a "high point" and a "low point" are formed along the diagonal direction, respectively.
According to the specific embodiment of the invention, the method for preparing the ETFE hyperbolic negative Gaussian cable membrane by adopting the target cable membrane specifically comprises the following steps:
selecting the whole film corresponding to the target material cable film;
and cutting the whole film according to the structural model requirement to obtain each ETFE hyperbolic negative Gaussian cable film.
According to the embodiment of the invention, the method for selecting the cable membrane made of the target material from the second candidate queue based on the tension change resistance and the light transmittance of the cable membrane specifically comprises the following steps:
the weight of the influence of the tension change resistance of the preset cable membrane material on the cable membrane selection is
And the weight of the influence of the light transmittance of the cable membrane material on the selection of the cable membrane is
；
Presetting a second candidate queue comprising n cable membranes made of different materials, wherein the cable membranes are based on the materials
Obtaining the corresponding light transmittance
And tension variation value in the first environment data
Wherein
，
Indicating the first in the second candidate queue
A cable membrane made of various materials;
cable membrane based on material
Tension change value in first environmental data
And acquiring the corresponding tension resistance change capacity grade by inquiring a preset tension resistance grade table
；
Cable membrane based on material
Obtaining the corresponding light transmittance
And acquiring corresponding light transmittance level by inquiring a preset light transmittance grade table
；
Grading the resistance to tension changes
And influence weight
Multiplying to obtain a first product, and grading the transmittance
And influence weight
Multiplying to obtain a second product, and adding the first product and the second product to obtain the material cord membrane
Composite score of
；
And respectively calculating the comprehensive scores of all the cable membranes made of the materials in the second queue to be selected, sequencing, and selecting the cable membrane made of the material with the highest comprehensive score as the cable membrane made of the target material.
It should be noted that, when the comprehensive score of each material cable membrane is calculated based on the weighting mode of multiple factors, the transmittance needs to be adjusted
And tension variation value
Performing dimension reduction transposition based on corresponding transposition tables (such as tension resistance grade table and light transmittance grade table) to obtain the same dimension order (namely tension resistance change capability grade)
Light transmittance level) to realize the light transmittance for each materialAccurate comprehensive scoring is calculated by multiple influence factors of the cable membrane, so that the cable membrane made of more adaptive target materials can be selected, and the stability of the cable membrane structure is effectively improved.
It can be understood that when the corresponding light transmittance level is obtained by inquiring the preset light transmittance level table, because the light transmittance level table is established based on the mapping relation between the light transmittance level and the light transmittance range, the light transmittance range in which the light transmittance level table falls can be inquired based on the light transmittance, and the corresponding light transmittance level is determined based on the light transmittance range in which the light transmittance level table falls, and accordingly, the light transmittance is higher and the light transmittance level is higher. When the corresponding tension change resistance level is obtained by inquiring the preset tension change resistance level table, because the tension change resistance level table is established based on the mapping relation between the tension change resistance level and the tension change range, the tension change range in which the tension change resistance level table falls can be inquired based on the tension change value, and the corresponding tension change resistance level is determined based on the falling tension change range, and correspondingly, the larger the tension change value is, the more easily the tension change resistance level is influenced by the environmental factor, namely, the lower the tension change resistance level is.
According to an embodiment of the present invention, after predicting the tension variation value of the cord membranes of different materials through the tension variation prediction model based on the first environmental data, the method further includes:
presetting m cable membranes made of all preselected materials based on the preselected materials
Obtaining the cable membrane adopting the pre-selected material in the preset area
All historical cable membrane buildings of construction, in which
，
Is shown as
A cable membrane made of a preselected material;
acquiring second environment data of each historical cable membrane building, and respectively performing characteristic calculation on the second environment data of each historical cable membrane building to obtain a characteristic value of each second environment data;
performing feature calculation on the first environment data to obtain a feature value of the first environment data;
comparing the difference rate between the characteristic value of the first environment data and the characteristic value of each second environment data, and adding the historical cable membrane buildings corresponding to the second environment data with the difference rate smaller than a first preset threshold value into a correction queue;
deep learning is carried out on the second environmental data of each historical cable membrane building in the correction queue, and a corresponding tension change prediction value is predicted through the tension change prediction model;
detecting corresponding tension change real values through tension change sensors pre-installed on various historical cable membrane buildings in a correction queue;
based on each historical cable membrane building in the correction queue, difference calculation is carried out respectively based on the real tension change value and the predicted tension change value, and the difference value of each historical cable membrane building in the correction queue is obtained;
calculating the average value of the difference values of all historical cable membrane buildings in the correction queue based on an average algorithm to obtain a correction value;
predicting a preselected material cable membrane in the tension change prediction model
On the basis of the tension change value, the correction value is added to obtain a preselected material cable membrane
Corrected tension variation value.
Since the tension change prediction model is predicted based on the neural network, the predicted tension change value is greatly affected by the training degree. There will be some prediction error. In order to reduce the prediction error, the real tension change value and the predicted tension change value of the historical cable membrane building are analyzed to obtain the corresponding correction value, and the tension change value of the cable membrane made of the preselected material is corrected based on the correction value to obtain a more accurate tension change value, so that the cable membrane made of the preselected material can be conveniently and accurately screened subsequently.
According to the embodiment of the invention, the method for detecting the real value of the corresponding tension change by the tension change sensors pre-installed on each historical cable membrane building in the correction queue specifically comprises the following steps:
respectively installing a plurality of tension change sensors at different positions based on a certain historical cable membrane building, and respectively acquiring a plurality of single-point tension change true values of the historical cable membrane building through the plurality of tension change sensors;
subtracting the true tension change value of each single point from the true tension change value of the remaining single points respectively to obtain a plurality of difference values, and taking an absolute value of the plurality of difference values to obtain a plurality of absolute values of the difference values;
judging whether a plurality of difference absolute values corresponding to the true value of each single-point tension change are larger than a second preset threshold value or not, and if so, marking the corresponding tension change sensor as a suspected invalid sensor;
after the real values of the tension changes of all the single points are compared, counting the number of marks of each tension change sensor as a suspected invalid sensor;
comparing the number of the marks of each tension change sensor which is a suspected invalid sensor with a third preset threshold, and judging the tension change sensor with the number of the marks larger than the third preset threshold as an invalid sensor;
and eliminating the single-point tension change true values acquired by the invalid sensors from the single-point tension change true values acquired by the plurality of tension change sensors, and carrying out average calculation on the remaining single-point tension change true values to obtain the tension change true value corresponding to the historical cable membrane building.
It should be noted that, in order to further improve the detection accuracy of the true tension change value, the invention adopts a plurality of tension change sensors to perform simultaneous detection, and averages a plurality of detection results. However, since the tension change sensor is mostly placed outdoors, it is easily affected by external environmental factors, thereby causing a failure in normal operation or misalignment. The invention eliminates the invalid sensor from the plurality of tension change sensors by a difference comparison method, reserves the single-point tension change true value of the valid sensor, and then carries out average value calculation based on the single-point tension change true value of the valid sensor, thereby effectively improving the accuracy of acquiring the tension change true value and further improving the accuracy of subsequent correction values.
According to an embodiment of the invention, after the solid construction according to the structural model, the method further comprises:
forming a target building corresponding to the structural model;
collecting historical buildings made of the same materials as the cable membranes made of the target materials, wherein detection sensors are respectively arranged on each node of each historical building, and the detection sensors detect whether the corresponding nodes are abnormal in real time;
in historical time, when a detection sensor of a certain node detects abnormality, recording the characteristics of the node and the accumulated service time of a corresponding historical building when the abnormality occurs, forming historical abnormal data, and recording the abnormal data into a historical database;
constructing a node anomaly prediction model based on the current time, extracting all historical anomaly data in the historical database as training samples, and training the node anomaly prediction model by adopting the node characteristics in each piece of historical anomaly data and the accumulated use time of the corresponding historical building when the anomaly occurs so as to form an optimized node anomaly prediction model;
marking all nodes to be detected on the structure model, and acquiring characteristic data of all nodes to be detected;
respectively inputting the characteristic data of all nodes to be tested into the optimized node abnormity prediction model so as to predict the accumulated service time of the target building when each node to be tested is abnormal;
presetting the service life of a target building as h, judging whether the accumulated service time of the target building is less than h when each node to be detected is abnormal, and marking the corresponding node to be detected as a monitoring node if the accumulated service time of the target building is less than h;
and respectively arranging monitoring sensors at each monitoring node, monitoring abnormal states of the corresponding monitoring nodes in real time by the monitoring sensors, and timely alarming and reporting maintenance processing if the abnormal states occur.
It should be noted that the above-mentioned abnormality may be a phenomenon such as node breakage, fracture, deformation, etc., and if these phenomena occur, serious safety problems may be caused, so the present invention predicts the cumulative service time of each node of the target building when the abnormality occurs by analyzing the node characteristics of the historical building and the cumulative service time when the abnormality occurs, and performs enhanced monitoring on the nodes smaller than the service life of the target building to ensure stable operation of the target building in the whole life cycle.
According to a specific embodiment of the present invention, after the monitoring sensor monitors the abnormal state of the corresponding monitoring node in real time, the method further includes:
presetting an ID number of each monitoring sensor and an ID number of a target building, and associating the ID number of each monitoring sensor with corresponding monitoring node position information when the monitoring sensors are installed to form an association table of the target building and record the association table in a monitoring center;
if a certain monitoring sensor monitors that a corresponding monitoring node is abnormal, alarm information is generated and reported to a monitoring center;
extracting the ID number of the target building and the ID number of the monitoring sensor by the monitoring center according to the alarm information, finding out an association table corresponding to the target building based on the ID number of the target building, and finding out the position information of the corresponding monitoring node through the association table corresponding to the target building based on the ID number of the monitoring sensor;
and arranging related maintenance personnel to carry out maintenance treatment based on the position information of the monitoring node with the abnormality of the target building.
According to a specific embodiment of the present invention, the monitoring sensor monitors the abnormal state of the corresponding monitoring node in real time, specifically including:
the method comprises the steps that a preset target building is composed of s monomers with the same shape, for example, a plurality of repeated ginkgo leaf-shaped structures are spliced to form the preset target building, each monomer has the same monitoring node, monitoring sensors are respectively arranged on the same monitoring nodes of each monomer, and the monitoring sensors are image sensors;
based on the same monitoring node in the s monomers, acquiring s node images by the corresponding image sensors respectively, and segmenting each node image into a plurality of image areas according to the same segmentation mode;
selecting one node image from the s node images as a target node image;
comparing a first image area in the target node image with corresponding image areas of other node images based on pixel gray values to obtain a first difference rate of the first image area of the target node image;
judging whether the first difference rate is greater than a fourth preset threshold value, and if so, determining a first image area of the target node image as a suspected abnormal area;
comparing the gray value of a first image area in the target node image with corresponding image areas of the rest node images based on the pixel gray value, comparing the obtained first difference rate with a fourth preset threshold, and determining whether the first image area can be identified as a suspected abnormal area according to the comparison result;
counting the total times of the first image area in the target node image which is determined as a suspected abnormal area;
judging whether the total times are greater than a fifth preset threshold value or not, and if so, marking a first image area in the target node image as an abnormal area;
respectively comparing the residual image areas of the target node image with the image areas corresponding to the s-1 residual node images based on the pixel gray value to obtain all abnormal areas in the target node image;
and respectively comparing every two image areas of all the node images based on the pixel gray values to obtain all abnormal areas in all the node images.
It can be understood that once an abnormal area occurs in a certain monitoring node, an alarm can be given, and the position of the abnormal area is reported to the monitoring center.
According to a specific embodiment of the present invention, comparing the first image area in the target node image with the corresponding image areas of other node images based on the pixel grayscale value to obtain a first difference rate of the first image area of the target node image, specifically includes:
presetting that each image area has the same number of pixel points, performing gray value difference comparison on the pixel points at the same positions of the corresponding image areas of the first image area and other node images in the target node image, and if the difference value is greater than a sixth preset threshold value, determining that the pixel points are different;
and counting the number of the pixels with the difference in the first image area, and dividing the number of the pixels with the difference by the number of all the pixels in the first image area to obtain a first difference rate of the first image area of the target node image.
According to the embodiment of the invention, after the ETFE hyperbolic negative Gaussian cable membrane is manufactured by adopting the cable membrane made of the target material, the method further comprises the following steps:
establishing a two-dimensional coordinate system, presetting the ETFE hyperbolic negative Gaussian cable membrane into multiple deformations, and respectively fixing the sides of a polygon on a steel framework of a structural model;
acquiring a first central point of the ETFE hyperbolic negative Gaussian cable membrane through a preset geometric algorithm;
connecting a first central point with each polygonal vertex of the ETFE hyperbolic negative Gaussian cable membrane to form a plurality of small triangles;
calculating to obtain a second central point of each small triangle through the geometric algorithm;
and the first central point and the second central points are respectively connected and extend to the steel framework to form a net, and the first central point and the second central points are locking points of the net to lock and pull the ETFE hyperbolic negative Gaussian cable membrane.
It should be noted that, in order to further improve the stability of the ETFE hyperbolic negative gaussian cable membrane, a mesh design is required to draw the ETFE hyperbolic negative gaussian cable membrane. According to the invention, the first central point and the second central point are obtained through a preset algorithm, and the net distribution framework is designed based on the first central point and the second central point, so that the reasonability of net distribution is improved, and the stability of the whole building structure is further improved.
According to the specific embodiment of the present invention, obtaining the first central point of the ETFE hyperbolic negative gaussian cable membrane through a preset geometric algorithm specifically includes:
respectively obtaining polygon vertex coordinates of the ETFE hyperbolic negative Gaussian cable membrane, wherein the polygon vertex coordinates comprise a horizontal coordinate and a vertical coordinate;
adding the abscissa of all polygon vertexes of the ETFE hyperbolic negative Gaussian cable membrane to obtain an abscissa sum, and then dividing the abscissa sum by the number of the polygon vertexes to obtain the abscissa of the central point of the ETFE hyperbolic negative Gaussian cable membrane; adding the vertical coordinates of all polygon vertexes of the ETFE hyperbolic negative Gaussian cable membrane to obtain a vertical coordinate sum, and then dividing the vertical coordinate sum by the number of the polygon vertexes to obtain the vertical coordinate of the central point of the ETFE hyperbolic negative Gaussian cable membrane.
FIG. 2 shows a block diagram of an ETFE hyperbolic negative Gaussian cable membrane digital construction system based on big data.
As shown in fig. 2, the second aspect of the present invention further provides an ETFE hyperbolic negative gaussian soxhlet film digital construction system 2 based on big data, which includes a memory 21 and a processor 22, where the memory includes an ETFE hyperbolic negative gaussian soxhlet film digital construction method program based on big data, and when executed by the processor, the ETFE hyperbolic negative gaussian soxhlet film digital construction method program based on big data implements the following steps:
designing a structural model based on an ETFE hyperbolic negative Gaussian cable membrane by digital building design software and combining user requirements;
enumerating all preselected cable membranes, analyzing the tension change degrees of different cable membranes under various environmental factors, and constructing a tension change prediction model of the cable membrane material based on the environmental factors;
acquiring coordinate position information of a building site, and acquiring corresponding first environment data through a big data platform based on the coordinate position information;
predicting tension change values of the cable membranes made of different materials through the tension change prediction model based on the first environmental data;
judging whether the tension change values of the cable membranes made of different materials exceed corresponding tension change limit values, if so, rejecting the corresponding cable membranes made of different materials, and if not, adding the corresponding cable membranes made of different materials into a first queue to be selected;
acquiring the light transmittance requirement of a user on the ETFE hyperbolic negative Gaussian cable membrane, respectively acquiring the light transmittance of each material cable membrane in the first queue to be selected, judging whether the light transmittance of each material cable membrane in the first queue to be selected meets the light transmittance requirement, if not, rejecting the corresponding material cable membrane, and if so, adding the corresponding material cable membrane into the second queue to be selected;
and selecting a target material cable membrane from a second candidate queue based on the tension change resistance and the light transmittance of the cable membrane, manufacturing an ETFE hyperbolic negative Gaussian cable membrane by adopting the target material cable membrane, and then carrying out entity construction according to a structural model.
According to the embodiment of the invention, the method for selecting the cable membrane made of the target material from the second candidate queue based on the tension change resistance and the light transmittance of the cable membrane specifically comprises the following steps:
the weight of the influence of the tension change resistance of the preset cable membrane material on the cable membrane selection is
And the weight of the influence of the light transmittance of the cable membrane material on the selection of the cable membrane is
；
Presetting a second candidate queue comprising n different materialsCable membrane based on material
Obtaining the corresponding light transmittance
And tension variation value in the first environment data
Wherein
，
Indicating the first in the second candidate queue
A cable membrane made of various materials;
cable membrane based on material
Tension change value in first environmental data
And acquiring the corresponding tension resistance change capacity grade by inquiring a preset tension resistance grade table
；
Cable membrane based on material
Obtaining the corresponding light transmittance
And acquiring corresponding light transmittance level by inquiring a preset light transmittance grade table
；
Grading the resistance to tension changes
And influence weight
Multiplying to obtain a first product, and grading the transmittance
And influence weight
Multiplying to obtain a second product, and adding the first product and the second product to obtain the material cord membrane
Composite score of
；
And respectively calculating the comprehensive scores of all the cable membranes made of the materials in the second queue to be selected, sequencing, and selecting the cable membrane made of the material with the highest comprehensive score as the cable membrane made of the target material.
According to an embodiment of the present invention, after predicting the tension variation values of the cable membranes of different materials through the tension variation prediction model based on the first environmental data, when the processor executes the ETFE hyperbolic negative gaussian cable membrane digital building method program based on the big data, the following steps are further implemented:
presetting m cable membranes made of all preselected materials based on the preselected materials
Obtaining the cable membrane adopting the pre-selected material in the preset area
All historical cable membrane buildings of construction, in which
，
Is shown as
A cable membrane made of a preselected material;
acquiring second environment data of each historical cable membrane building, and respectively performing characteristic calculation on the second environment data of each historical cable membrane building to obtain a characteristic value of each second environment data;
performing feature calculation on the first environment data to obtain a feature value of the first environment data;
comparing the difference rate between the characteristic value of the first environment data and the characteristic value of each second environment data, and adding the historical cable membrane buildings corresponding to the second environment data with the difference rate smaller than a first preset threshold value into a correction queue;
deep learning is carried out on the second environmental data of each historical cable membrane building in the correction queue, and a corresponding tension change prediction value is predicted through the tension change prediction model;
detecting corresponding tension change real values through tension change sensors pre-installed on various historical cable membrane buildings in a correction queue;
based on each historical cable membrane building in the correction queue, difference calculation is carried out respectively based on the real tension change value and the predicted tension change value, and the difference value of each historical cable membrane building in the correction queue is obtained;
calculating the average value of the difference values of all historical cable membrane buildings in the correction queue based on an average algorithm to obtain a correction value;
predicting a preselected material cable membrane in the tension change prediction model
On the basis of the tension variation valueAdding the correction value to obtain a preselected material cable membrane
Corrected tension variation value.
The third aspect of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a program of an ETFE hyperbolic negative gaussian-chordate membrane digital construction method based on big data, and when the program of the ETFE hyperbolic negative gaussian-chordate membrane digital construction method based on big data is executed by a processor, the steps of the ETFE hyperbolic negative gaussian-chordate membrane digital construction method based on big data are implemented.
According to the ETFE hyperbolic negative Gaussian cable membrane digital construction method and system based on the big data and the computer readable storage medium, disclosed by the invention, the membrane material matched with local environmental factors can be selected by combining the big data and a machine learning method, so that the stability of a tensioned membrane structure is effectively improved, and the service life is prolonged; simultaneously, the stability is met, and the film material with better light transmittance is selected as far as possible, so that the experience of a user is further improved.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A big data-based ETFE hyperbolic negative Gaussian cable membrane digital construction method is characterized by comprising the following steps:
designing a structural model based on an ETFE hyperbolic negative Gaussian cable membrane by digital building design software and combining user requirements;
enumerating all preselected cable membranes, analyzing the tension change degrees of different cable membranes under various environmental factors, and constructing a tension change prediction model of the cable membrane material based on the environmental factors;
acquiring coordinate position information of a building site, and acquiring corresponding first environment data through a big data platform based on the coordinate position information;
predicting tension change values of the cable membranes made of different materials through the tension change prediction model based on the first environmental data;
judging whether the tension change values of the cable membranes made of different materials exceed corresponding tension change limit values, if so, rejecting the corresponding cable membranes made of different materials, and if not, adding the corresponding cable membranes made of different materials into a first queue to be selected;
acquiring the light transmittance requirement of a user on the ETFE hyperbolic negative Gaussian cable membrane, respectively acquiring the light transmittance of each material cable membrane in the first queue to be selected, judging whether the light transmittance of each material cable membrane in the first queue to be selected meets the light transmittance requirement, if not, rejecting the corresponding material cable membrane, and if so, adding the corresponding material cable membrane into the second queue to be selected;
and selecting a target material cable membrane from a second candidate queue based on the tension change resistance and the light transmittance of the cable membrane, manufacturing an ETFE hyperbolic negative Gaussian cable membrane by adopting the target material cable membrane, and then carrying out entity construction according to a structural model.
2. The ETFE hyperbolic negative Gaussian cable membrane digital construction method based on big data according to claim 1, characterized in that a cable membrane of a target material is selected from a second candidate queue based on the tension change resistance and the light transmittance of the cable membrane, and specifically comprises the following steps:
the weight of the influence of the tension change resistance of the preset cable membrane material on the cable membrane selection is
And the weight of the influence of the light transmittance of the cable membrane material on the selection of the cable membrane is
；
Presetting a second candidate queue comprising n cable membranes made of different materials, wherein the cable membranes are based on the materials
Obtaining the corresponding light transmittance
And tension variation value in the first environment data
Wherein
，
Indicating the first in the second candidate queue
A cable membrane made of various materials;
cable membrane based on material
Tension change value in first environmental data
And acquiring the corresponding tension resistance change capacity grade by inquiring a preset tension resistance grade table
；
Cable membrane based on material
Obtaining the corresponding light transmittance
And acquiring corresponding light transmittance level by inquiring a preset light transmittance grade table
；
Grading the resistance to tension changes
And influence weight
Multiplying to obtain a first product, and grading the transmittance
And influence weight
Multiplying to obtain a second product, and adding the first product and the second product to obtain the material cord membrane
Composite score of
；
And respectively calculating the comprehensive scores of all the cable membranes made of the materials in the second queue to be selected, sequencing, and selecting the cable membrane made of the material with the highest comprehensive score as the cable membrane made of the target material.
3. The ETFE hyperbolic negative Gaussian cable membrane digital construction method based on big data as claimed in claim 1, wherein after predicting tension change values of cable membranes made of different materials through the tension change prediction model based on first environmental data, the method further comprises:
presetting m cable membranes made of all preselected materials based on the preselected materials
Obtaining the cable membrane adopting the pre-selected material in the preset area
All historical cable membrane buildings of construction, in which
，
Is shown as
A cable membrane made of a preselected material;
acquiring second environment data of each historical cable membrane building, and respectively performing characteristic calculation on the second environment data of each historical cable membrane building to obtain a characteristic value of each second environment data;
performing feature calculation on the first environment data to obtain a feature value of the first environment data;
comparing the difference rate between the characteristic value of the first environment data and the characteristic value of each second environment data, and adding the historical cable membrane buildings corresponding to the second environment data with the difference rate smaller than a first preset threshold value into a correction queue;
deep learning is carried out on the second environmental data of each historical cable membrane building in the correction queue, and a corresponding tension change prediction value is predicted through the tension change prediction model;
detecting corresponding tension change real values through tension change sensors pre-installed on various historical cable membrane buildings in a correction queue;
based on each historical cable membrane building in the correction queue, difference calculation is carried out respectively based on the real tension change value and the predicted tension change value, and the difference value of each historical cable membrane building in the correction queue is obtained;
calculating the average value of the difference values of all historical cable membrane buildings in the correction queue based on an average algorithm to obtain a correction value;
predicting a preselected material cable membrane in the tension change prediction model
On the basis of the tension change value, the correction value is added to obtain a preselected material cable membrane
Corrected tension variation value.
4. The ETFE hyperbolic negative Gaussian cable membrane digital construction method based on big data as claimed in claim 3, wherein the corresponding true tension change value is detected by the tension change sensors pre-installed on each historical cable membrane building in the correction queue, and the method specifically comprises the following steps:
respectively installing a plurality of tension change sensors at different positions based on a certain historical cable membrane building, and respectively acquiring a plurality of single-point tension change true values of the historical cable membrane building through the plurality of tension change sensors;
subtracting the true tension change value of each single point from the true tension change value of the remaining single points respectively to obtain a plurality of difference values, and taking an absolute value of the plurality of difference values to obtain a plurality of absolute values of the difference values;
judging whether a plurality of difference absolute values corresponding to the true value of each single-point tension change are larger than a second preset threshold value or not, and if so, marking the corresponding tension change sensor as a suspected invalid sensor;
after the real values of the tension changes of all the single points are compared, counting the number of marks of each tension change sensor as a suspected invalid sensor;
comparing the number of the marks of each tension change sensor which is a suspected invalid sensor with a third preset threshold, and judging the tension change sensor with the number of the marks larger than the third preset threshold as an invalid sensor;
and eliminating the single-point tension change true values acquired by the invalid sensors from the single-point tension change true values acquired by the plurality of tension change sensors, and carrying out average calculation on the remaining single-point tension change true values to obtain the tension change true value corresponding to the historical cable membrane building.
5. The ETFE hyperbolic negative Gaussian mixture film digital construction method based on big data as claimed in claim 1, wherein after the physical construction according to the structural model, the method further comprises:
forming a target building corresponding to the structural model;
collecting historical buildings made of the same materials as the cable membranes made of the target materials, wherein detection sensors are respectively arranged on each node of each historical building, and the detection sensors detect whether the corresponding nodes are abnormal in real time;
in historical time, when a detection sensor of a certain node detects abnormality, recording the characteristics of the node and the accumulated service time of a corresponding historical building when the abnormality occurs, forming historical abnormal data, and recording the abnormal data into a historical database;
constructing a node anomaly prediction model based on the current time, extracting all historical anomaly data in the historical database as training samples, and training the node anomaly prediction model by adopting the node characteristics in each piece of historical anomaly data and the accumulated use time of the corresponding historical building when the anomaly occurs so as to form an optimized node anomaly prediction model;
marking all nodes to be detected on the structure model, and acquiring characteristic data of all nodes to be detected;
respectively inputting the characteristic data of all nodes to be tested into the optimized node abnormity prediction model so as to predict the accumulated service time of the target building when each node to be tested is abnormal;
presetting the service life of a target building as h, judging whether the accumulated service time of the target building is less than h when each node to be detected is abnormal, and marking the corresponding node to be detected as a monitoring node if the accumulated service time of the target building is less than h;
and respectively arranging monitoring sensors at each monitoring node, monitoring abnormal states of the corresponding monitoring nodes in real time by the monitoring sensors, and timely alarming and reporting maintenance processing if the abnormal states occur.
6. The digital construction method of the ETFE hyperbolic negative Gaussian cable membrane based on the big data as claimed in claim 1, wherein after the ETFE hyperbolic negative Gaussian cable membrane is manufactured by adopting the cable membrane made of the target material, the method further comprises:
establishing a two-dimensional coordinate system, presetting the ETFE hyperbolic negative Gaussian cable membrane into multiple deformations, and respectively fixing the sides of a polygon on a steel framework of a structural model;
acquiring a first central point of the ETFE hyperbolic negative Gaussian cable membrane through a preset geometric algorithm;
connecting a first central point with each polygonal vertex of the ETFE hyperbolic negative Gaussian cable membrane to form a plurality of small triangles;
calculating to obtain a second central point of each small triangle through the geometric algorithm;
and the first central point and the second central points are respectively connected and extend to the steel framework to form a net, and the first central point and the second central points are locking points of the net to lock and pull the ETFE hyperbolic negative Gaussian cable membrane.
7. The ETFE hyperbolic negative Gaussian mixture film digital construction system based on the big data is characterized by comprising a memory and a processor, wherein the memory comprises an ETFE hyperbolic negative Gaussian mixture film digital construction method program based on the big data, and the ETFE hyperbolic negative Gaussian mixture film digital construction method program based on the big data realizes the following steps when being executed by the processor:
designing a structural model based on an ETFE hyperbolic negative Gaussian cable membrane by digital building design software and combining user requirements;
enumerating all preselected cable membranes, analyzing the tension change degrees of different cable membranes under various environmental factors, and constructing a tension change prediction model of the cable membrane material based on the environmental factors;
acquiring coordinate position information of a building site, and acquiring corresponding first environment data through a big data platform based on the coordinate position information;
predicting tension change values of the cable membranes made of different materials through the tension change prediction model based on the first environmental data;
judging whether the tension change values of the cable membranes made of different materials exceed corresponding tension change limit values, if so, rejecting the corresponding cable membranes made of different materials, and if not, adding the corresponding cable membranes made of different materials into a first queue to be selected;
acquiring the light transmittance requirement of a user on the ETFE hyperbolic negative Gaussian cable membrane, respectively acquiring the light transmittance of each material cable membrane in the first queue to be selected, judging whether the light transmittance of each material cable membrane in the first queue to be selected meets the light transmittance requirement, if not, rejecting the corresponding material cable membrane, and if so, adding the corresponding material cable membrane into the second queue to be selected;
and selecting a target material cable membrane from a second candidate queue based on the tension change resistance and the light transmittance of the cable membrane, manufacturing an ETFE hyperbolic negative Gaussian cable membrane by adopting the target material cable membrane, and then carrying out entity construction according to a structural model.
8. The ETFE hyperbolic negative Gaussian cable membrane digital construction system based on big data according to claim 7, characterized in that a cable membrane of a target material is selected from a second candidate queue based on the tension change resistance and the light transmittance of the cable membrane, and specifically comprises:
the weight of the influence of the tension change resistance of the preset cable membrane material on the cable membrane selection is
And the weight of the influence of the light transmittance of the cable membrane material on the selection of the cable membrane is
；
Presetting a second candidate queue comprising n cable membranes made of different materials, wherein the cable membranes are based on the materials
Obtaining the corresponding light transmittance
And tension variation value in the first environment data
Wherein
，
Indicating the first in the second candidate queue
A cable membrane made of various materials;
cable membrane based on material
Tension change value in first environmental data
And acquiring the corresponding tension resistance change capacity grade by inquiring a preset tension resistance grade table
；
Cable membrane based on material
Obtaining the corresponding light transmittance
And acquiring corresponding light transmittance level by inquiring a preset light transmittance grade table
；
Grading the resistance to tension changes
And influence weight
Multiplying to obtain a first product, and grading the transmittance
And influence weight
Multiplying to obtain a second product, and adding the first product and the second product to obtain the material cord membrane
Composite score of
；
And respectively calculating the comprehensive scores of all the cable membranes made of the materials in the second queue to be selected, sequencing, and selecting the cable membrane made of the material with the highest comprehensive score as the cable membrane made of the target material.
9. The ETFE hyperbolic negative Gaussian cable membrane digital construction system based on big data as claimed in claim 7, wherein after predicting the tension variation values of cable membranes of different materials based on the first environmental data and by the tension variation prediction model, the ETFE hyperbolic negative Gaussian cable membrane digital construction method based on big data when being executed by the processor further realizes the following steps:
presetting m cable membranes made of all preselected materials based on the preselected materials
Obtaining the cable membrane adopting the pre-selected material in the preset area
All historical cable membrane buildings of construction, in which
，
Is shown as
A cable membrane made of a preselected material;
acquiring second environment data of each historical cable membrane building, and respectively performing characteristic calculation on the second environment data of each historical cable membrane building to obtain a characteristic value of each second environment data;
performing feature calculation on the first environment data to obtain a feature value of the first environment data;
comparing the difference rate between the characteristic value of the first environment data and the characteristic value of each second environment data, and adding the historical cable membrane buildings corresponding to the second environment data with the difference rate smaller than a first preset threshold value into a correction queue;
deep learning is carried out on the second environmental data of each historical cable membrane building in the correction queue, and a corresponding tension change prediction value is predicted through the tension change prediction model;
detecting corresponding tension change real values through tension change sensors pre-installed on various historical cable membrane buildings in a correction queue;
based on each historical cable membrane building in the correction queue, difference calculation is carried out respectively based on the real tension change value and the predicted tension change value, and the difference value of each historical cable membrane building in the correction queue is obtained;
calculating the average value of the difference values of all historical cable membrane buildings in the correction queue based on an average algorithm to obtain a correction value;
predicting a preselected material cable membrane in the tension change prediction model
On the basis of the tension change value, the correction value is added to obtain a preselected material cable membrane
Corrected tension variation value.
10. A computer-readable storage medium, wherein the computer-readable storage medium includes a big-data-based ETFE hyperbolic negative gaussian-spline film digital construction method program, and when the big-data-based ETFE hyperbolic negative gaussian-spline film digital construction method program is executed by a processor, the steps of the big-data-based ETFE hyperbolic negative gaussian-spline film digital construction method according to any one of claims 1 to 6 are implemented.
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