Hollow slab internal mold construction system

Abstract

The invention provides a hollow slab internal mold construction system, and relates to the technical field of building construction. A first construction result acquisition module; the initial construction parameter optimization module is used for scanning the first construction result through a laser scanner, analyzing and determining first concrete compactness deviation and first construction deviation, optimizing initial construction parameters and determining first optimal construction parameters; and the construction operation module is used for executing the construction operation of the second pouring interval according to the first optimal construction parameters until the preset construction period is completed. By means of the method, the technical problems that in an existing method, due to the fact that accuracy and timeliness of construction parameter regulation and control are insufficient, overall construction errors are seriously accumulated, and overall construction quality is affected can be solved, pouring parameters and vibration parameters can be rapidly and accurately adjusted, construction management and control fineness and intelligence are improved, and construction efficiency is improved. Therefore, the problem of construction regulation lag is effectively solved, and the construction precision and the construction quality are remarkably improved.

Description

Technical Field

[0001] The present application relates to the technical field of building construction, and in particular to a hollow slab inner formwork construction system. Background Art

[0002] As an important building structure, hollow slabs are widely used in bridges, factories, industrial and civil buildings. The casting construction of hollow slabs usually adopts a core mold support structure and forms an internal cavity by pouring concrete. However, in the existing hollow slab inner mold casting construction method, due to the lack of accuracy and timeliness in the control of construction parameters, various quality problems are prone to occur during the construction process.

[0003] First, during the concrete pouring process, parameter control relies on manual experience and it is difficult to respond to dynamic changes on the construction site in a timely manner. Second, the existing construction methods lack a real-time detection and feedback mechanism for construction errors. During the construction process, key indicators such as concrete density and core mold stress cannot be accurately monitored, resulting in construction quality problems (such as bubbles, cracks, local settlement, etc.) that are difficult to detect and deal with in a timely manner. As construction errors gradually accumulate, serious overall construction quality problems are eventually formed, which not only affects the service life of the hollow slab, but also increases the subsequent maintenance and rework costs.

[0004] In summary, the existing hollow slab inner mold casting construction method has significant deficiencies in the accuracy and timeliness of construction parameter control. Construction errors are difficult to correct in a timely manner, which seriously affects the overall construction quality. Summary of the invention

[0005] The purpose of this application is to provide a hollow slab inner mold construction system to solve the technical problems existing in the existing hollow slab inner mold casting construction method, which is caused by insufficient accuracy and timeliness of construction parameter control, resulting in serious accumulation of overall construction errors and affecting the overall construction quality.

[0006] In view of the above problems, the present application provides a hollow slab inner mold construction system, including: a target homemade core mold acquisition module, used to analyze the target construction requirements to determine the construction core mold attribute characteristics, prepare the core mold according to the core mold attribute characteristics, and obtain the target homemade core mold, wherein the target homemade core mold is spliced ​​by a plurality of hollow square steel pipes, including a supporting steel ring inside, and the outer surface is covered with a plastic sheet; a first construction result acquisition module, used to obtain the predetermined construction period of the target homemade core mold, and divide the predetermined construction period into a plurality of casting intervals, according to the initial construction parameters, perform the construction operation of the target homemade core mold in the first casting interval, and obtain the first construction result; an initial construction parameter optimization module, used to scan the first construction result through a laser scanner, analyze and determine the first concrete density deviation and the first construction deviation, and optimize the initial construction parameters according to the first concrete density deviation and the first construction deviation, and determine the first optimal construction parameters; a construction operation module, used to perform the construction operation of the target homemade core mold in the second casting interval according to the first optimal construction parameters until the predetermined construction period is completed.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The target construction requirements are analyzed to determine the construction core mold attribute characteristics, and the core mold is prepared according to the core mold attribute characteristics to obtain the target self-made core mold, wherein the target self-made core mold is spliced ​​by a plurality of hollow square steel pipes, including a supporting steel ring inside, and the outer surface is covered with a plastic sheet; then the predetermined construction period of the target self-made core mold is obtained, and the predetermined construction period is equally divided into a plurality of casting intervals, and the construction operation of the target self-made core mold in the first casting interval is performed according to the initial construction parameters to obtain the first construction result; the first construction result is further scanned by a laser scanner, and the first concrete density deviation and the first construction deviation are analyzed and determined, and the initial construction parameters are optimized according to the first concrete density deviation and the first construction deviation to determine the first optimal construction parameters; finally, according to the first optimal construction parameters, the construction operation of the target self-made core mold in the second casting interval is performed until the predetermined construction period is completed; the technical goal of quickly and accurately adjusting the casting parameters and the vibration parameters according to the actual construction status can be achieved, and the precision and intelligence of construction control can be improved, thereby effectively solving the problem of lagging construction regulation and control, and significantly improving the construction accuracy and construction quality.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0011] Figure 1 This is a structural schematic diagram of a hollow slab inner mold construction system for this application;

[0012] Figure 2 A schematic diagram of the process of executing construction operations in the first pouring interval in a hollow slab inner formwork construction system of the present application.

[0013] Description of reference numerals:

[0014] Target self-made core mold acquisition module 11, first construction result acquisition module 12, initial construction parameter optimization module 13, construction operation module 14. DETAILED DESCRIPTION

[0015] This application provides a hollow slab inner mold construction system to solve the technical problem that the existing hollow slab inner mold casting construction method has serious overall construction error accumulation and affects the overall construction quality due to the lack of accuracy and timeliness of construction parameter control. It can achieve the technical goal of quickly and accurately adjusting the casting parameters and vibration parameters according to the actual construction status, improve the precision and intelligence of construction control, thereby effectively solving the problem of lagging construction control and significantly improving construction accuracy and quality.

[0016] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0017] For examples, please see the attached Figure 1 The present application provides a hollow slab inner formwork construction system, comprising:

[0018] The target self-made core mold acquisition module 11 is used to analyze the target construction requirements to determine the property characteristics of the construction core mold, prepare the core mold according to the property characteristics of the core mold, and obtain the target self-made core mold, wherein the target self-made core mold is spliced ​​by multiple hollow square steel pipes, including a supporting steel ring inside, and the outer surface is covered with plastic cloth.

[0019] Specifically, according to the design requirements and specific working conditions of the construction project, the core mold size requirements, core mold structure requirements and construction environment adaptability are obtained; then, according to the construction requirements, the core mold attribute characteristics are clarified, and the core mold attribute characteristics include information such as size characteristics, structural characteristics and material type. Further, the core mold is prepared according to the core mold attribute characteristics to obtain the target self-made core mold, which includes steel pipe splicing, support steel ring installation and external plastic sheet covering. Steel pipe splicing refers to splicing multiple hollow square steel pipes into a frame structure of target size through welding or high-strength bolt connection; support steel ring installation refers to installing multiple layers of support steel rings inside the core mold, evenly arranged along the length direction, fixed by snap buckles or welding, and the arrangement spacing of the support steel rings is designed according to the core mold size and construction load to achieve the best load-bearing effect; external plastic sheet covering refers to covering the surface of the core mold with a layer of customized plastic sheet, and fixing the edge of the plastic sheet with adhesive or straps. By accurately analyzing construction requirements and customizing targeted self-made core molds, the applicability of the core molds and their stability during construction can be significantly improved, ensuring the quality of concrete pouring, improving construction efficiency and reducing rework and cost waste caused by mold problems.

[0020] The first construction result acquisition module 12 is used to obtain the predetermined construction period of the target self-made core mold, and divide the predetermined construction period into multiple casting intervals, and execute the construction operation of the target self-made core mold in the first casting interval according to the initial construction parameters to obtain the first construction result.

[0021] Specifically, the scheduled construction period of the target self-made core mold is obtained. The scheduled construction period refers to the total amount of time to complete the entire hollow slab concrete pouring and related operations, which can be set according to actual construction needs; then the construction period is divided into multiple equal pouring intervals, that is, the scheduled construction period is evenly divided into several time periods, each section corresponds to a pouring interval, which is helpful to control the pouring of concrete and quality monitoring in steps. After each pouring interval is completed, the system can collect construction data and adjust subsequent parameters.

[0022] Initial construction parameters are set according to the core mold attribute characteristics and concrete characteristics, wherein the initial construction parameters include pouring parameters and vibration parameters, the pouring parameters include pouring speed and layered pouring thickness, and the vibration parameters include vibration intensity and vibration frequency. For example, the pouring speed is set according to the core mold size and concrete fluidity to avoid concrete stratification due to too fast pouring or residual bubbles due to too slow pouring. Further, according to the initial construction parameters, concrete pouring and vibration operations are performed in the first pouring interval of the target self-made core mold to obtain a first construction result.

[0023] The initial construction parameter optimization module 13 is used to scan the first construction result through a laser scanner, analyze and determine the first concrete density deviation and the first construction deviation, and optimize the initial construction parameters according to the first concrete density deviation and the first construction deviation to determine the first optimal construction parameters.

[0024] Specifically, a laser scanner is used to scan the construction results of the first pouring interval to obtain the three-dimensional geometric information and other characteristic data of the concrete surface, wherein the scanning data content includes the flatness of the concrete surface, the volume filling condition and the interlayer bonding quality; then the first concrete density deviation and the first construction deviation are determined based on the analysis of the scanning results, wherein the concrete density deviation refers to the difference between the actual concrete density and the density required by the design, usually expressed by the void ratio or the degree of compaction; the construction deviation refers to the deviation between the construction results and the expected project quantity, that is, the difference between the actual completed project quantity and the design drawing or the planned project quantity, for example, insufficient or excessive concrete pouring.

[0025] Then, the initial construction parameters are optimized according to the first concrete density deviation and the first construction deviation, that is, the initial construction parameters are optimized and corrected for the purpose of eliminating the first concrete density deviation and the first construction deviation to obtain the first optimal construction parameters.

[0026] The construction operation module 14 is used to perform the construction operation of the target self-made core mold in the second casting interval according to the first optimal construction parameters until the predetermined construction period is completed.

[0027] Specifically, finally, according to the first optimal construction parameter, the target self-made core mold is constructed in the second pouring interval, the second construction result is obtained, and the second concrete density deviation and the second construction deviation of the second construction result are analyzed and determined; then, the first optimal construction parameter is optimized according to the second concrete density deviation and the second construction deviation, and the second optimal construction parameter is obtained to execute the construction of the third pouring interval, and the same method is used for iterative operation until the predetermined construction cycle is completed. By combining a closed-loop control system with real-time monitoring and dynamic adjustment, the entire pouring process gradually eliminates construction deviations, ensuring construction accuracy and quality.

[0028] By providing a hollow slab inner formwork construction system, the technical problem existing in the existing hollow slab inner formwork casting construction method, that is, the lack of accuracy and timeliness in the control of construction parameters, resulting in serious accumulation of overall construction errors and affecting the overall construction quality, can be solved. The target construction requirements are analyzed to determine the construction core mold attribute characteristics, and the core mold is prepared according to the core mold attribute characteristics to obtain the target self-made core mold, wherein the target self-made core mold is spliced ​​by a plurality of hollow square steel pipes, including a supporting steel ring inside, and the outer surface is covered with a plastic sheet; then the predetermined construction period of the target self-made core mold is obtained, and the predetermined construction period is equally divided into a plurality of casting intervals, and the construction operation of the target self-made core mold in the first casting interval is performed according to the initial construction parameters to obtain the first construction result; the first construction result is further scanned by a laser scanner, and the first concrete density deviation and the first construction deviation are analyzed and determined, and the initial construction parameters are optimized according to the first concrete density deviation and the first construction deviation to determine the first optimal construction parameters; finally, according to the first optimal construction parameters, the construction operation of the target self-made core mold in the second casting interval is performed until the predetermined construction period is completed; the technical goal of quickly and accurately adjusting the casting parameters and the vibration parameters according to the actual construction status can be achieved, and the precision and intelligence of construction control can be improved, thereby effectively solving the problem of lagging construction regulation and control, and significantly improving the construction accuracy and construction quality.

[0029] Furthermore, the present application includes:

[0030] The initial construction parameters include pouring parameters and vibration parameters, wherein the pouring parameters include pouring speed and layered pouring thickness, and the vibration parameters include vibration intensity and vibration frequency.

[0031] Specifically, the initial construction parameters are key control indicators set during concrete pouring construction, including pouring parameters and vibration parameters, which are used to control the pouring process and vibration process of concrete respectively. The setting of these parameters directly affects the molding quality, density and construction efficiency of concrete. Among them, the pouring parameters include pouring speed and layered pouring thickness. The pouring speed refers to the amount of concrete supplied per unit time, usually expressed in volume units. Controlling the pouring speed can ensure that the concrete can fill the core mold in time, while preventing stratification or segregation caused by too fast supply; the layered pouring thickness refers to the vertical thickness of each concrete pouring, usually in centimeters or millimeters. Controlling the layered pouring thickness can ensure that each layer of concrete can reach the designed density, while avoiding deformation of the core mold or local force concentration caused by layered superposition.

[0032] Vibration parameters include vibration intensity and vibration frequency. Vibration intensity refers to the mechanical force applied to concrete by the vibrator, usually represented by the power or amplitude of the vibrator. Controlling the vibration intensity can provide sufficient vibration intensity to eliminate bubbles and increase density without destroying the aggregate structure. Vibration frequency refers to the number of vibrations of the vibrator per second, usually expressed in Hertz (Hz). Controlling the vibration frequency can adjust the density of concrete and avoid concrete segregation caused by excessively high frequency.

[0033] Further, as attached Figure 2 As shown, this application includes:

[0034] When executing construction work, real-time temperature data and real-time pressure data are obtained through fixed-point monitoring by temperature sensors and pressure sensors deployed inside the target self-made core mold; the real-time temperature data and real-time pressure data are mapped and converted to determine concrete temperature data and concrete pressure data; the concrete temperature data and concrete pressure data are input into a parameter adaptive corrector, and first parameter compensation data is output to adaptively fine-tune the initial construction parameters, wherein the parameter adaptive corrector is constructed based on the fuzzy control principle.

[0035] Specifically, temperature sensors and pressure sensors are arranged inside the target self-made core mold to collect key data in the concrete pouring construction at fixed points. The temperature sensors are arranged at different heights, lengths and areas of the core mold to comprehensively monitor the temperature distribution inside the concrete; the pressure sensors are arranged on the inner surface of the core mold, focusing on monitoring the key areas (such as the middle and ends) where the core mold is stressed, and are used to collect pressure data during the concrete pouring process at fixed points to reflect the uniformity and distribution of concrete filling. Then, when performing construction work, the temperature sensors and pressure sensors deployed inside the target self-made core mold are used to monitor and obtain real-time temperature data and real-time pressure data at fixed points. The real-time temperature data and real-time pressure data are further mapped and converted, that is, the original real-time temperature data and real-time pressure data collected from the sensor are normalized and feature extracted, and converted into concrete temperature data and concrete pressure data with engineering significance, and the concrete temperature data and concrete pressure data are obtained.

[0036] Then, a parameter adaptive corrector is constructed based on the fuzzy control principle, that is, the fuzzy control theory is used to transform the complex nonlinear relationship between temperature and pressure into fuzzy rules, and the compensation data of the construction parameters are obtained through reasoning. Among them, the parameter adaptive corrector includes fuzzification, fuzzy rule base, reasoning mechanism and defuzzification. Fuzzification refers to fuzzifying the input data (such as temperature gradient and pressure uniformity) and dividing them into multiple levels; the fuzzy rule base is established based on construction experience and engineering requirements; the reasoning mechanism is used to calculate the output results of the fuzzy rules using fuzzy reasoning methods (such as the maximum and minimum method or the weighted average method); defuzzification refers to converting the fuzzy reasoning results into specific values ​​and outputting the first parameter compensation data. Then, the concrete temperature data and concrete pressure data are input into the parameter adaptive corrector, and the first parameter compensation data is output, wherein the parameter compensation data includes the pouring speed adjustment value, the layered pouring thickness adjustment value, the vibration intensity adjustment value and the vibration frequency adjustment value, and the initial construction parameters are adaptively fine-tuned according to the first parameter compensation data. For example, if the temperature difference ΔT=5°C (high temperature difference), the pressure uniformity σ=20% (severe unevenness), the output is: reduce the pouring speed by 10%, reduce the layered thickness by 5cm, increase the vibration intensity by 15%, and increase the vibration frequency by 10%. By inputting the concrete temperature data and pressure data into the parameter adaptive corrector based on fuzzy control, outputting the first parameter compensation data, and adaptively fine-tuning the initial construction parameters according to these compensation data, an efficient and intelligent construction optimization system is constructed, which not only significantly improves the level of refined management of the construction process, but also greatly improves the concrete molding quality and construction efficiency.

[0037] Furthermore, the present application includes:

[0038] Acquire concrete property characteristics, use the core mold property characteristics and concrete property characteristics as constraints, retrieve historical construction logs, and obtain a sample monitoring temperature set and a sample monitoring pressure set; randomly select a first sample monitoring temperature and a first sample monitoring pressure, and retrieve and obtain a sample concrete temperature set and a sample concrete pressure set based on the first sample monitoring temperature and the first sample monitoring pressure, and obtain the first sample concrete temperature and the first sample concrete pressure after mean calculation; sequentially obtain a sample concrete temperature set of the sample monitoring temperature set and a sample concrete pressure set of the sample monitoring pressure set, and establish a temperature mapping based on the sample monitoring temperature set and the sample concrete temperature set, and establish a pressure mapping based on the sample monitoring pressure set and the sample concrete pressure set; based on the temperature mapping and the pressure mapping, map and convert the real-time temperature data and the real-time pressure data.

[0039] Specifically, the concrete attribute characteristics are obtained, and the concrete attribute characteristics include parameters such as concrete mix ratio, strength, and fluidity; then, the core mold attribute characteristics and concrete attribute characteristics are used as constraints, and the historical construction logs are retrieved to obtain the sample monitoring temperature set and the sample monitoring pressure set monitored by the sensor. Then, any monitoring temperature is randomly selected from the sample monitoring temperature set as the first sample monitoring temperature, and any monitoring pressure is randomly selected from the sample monitoring pressure set as the first sample monitoring pressure. Then, the core mold attribute characteristics, concrete attribute characteristics, the first sample monitoring temperature, and the first sample monitoring pressure are used as constraints, and the sample concrete temperature set and the sample concrete pressure set are retrieved based on big data, and the sample concrete temperature set and the sample concrete pressure set are averaged to obtain the first sample concrete temperature and the first sample concrete pressure.

[0040] Using the same method, the sample concrete temperature set of the sample monitoring temperature set and the sample concrete pressure set of the sample monitoring pressure set are obtained in turn; further, the correlation feature analysis is performed based on the sample monitoring temperature set and the sample concrete temperature set to establish a temperature mapping; the correlation feature analysis is performed based on the sample monitoring pressure set and the sample concrete pressure set to establish a pressure mapping. Finally, the real-time temperature data and the real-time pressure data are mapped and converted based on the temperature mapping and the pressure mapping.

[0041] Furthermore, the present application includes:

[0042] The first construction result is scanned by a laser scanner to obtain a first construction concrete density and a first construction quantity; the first expected quantity of the first pouring interval is obtained, and the deviation of the first construction concrete density and the first construction quantity is calculated according to the benchmark concrete density and the first expected quantity, so as to obtain the first concrete density deviation and the first construction deviation.

[0043] Specifically, the geometric information, volume and density distribution of the concrete surface after the completion of the first pouring interval are obtained through high-precision scanning technology, the compaction degree of the concrete inside the core mold is inferred from the scanning results, the void ratio and density index are calculated, and the first construction concrete density is obtained; the actual concrete volume of the first pouring interval is calculated based on the scanning data to obtain the first construction engineering quantity. Then, the first expected engineering quantity of the first pouring interval is obtained, and the deviation of the first construction concrete density is calculated according to the reference concrete density to obtain the first concrete density deviation; the deviation of the first construction engineering quantity is calculated according to the first expected engineering quantity to obtain the first construction deviation.

[0044] Furthermore, the present application includes:

[0045] Obtain a second expected engineering quantity for a second pouring interval, wherein the second pouring interval is the next adjacent pouring interval of the first pouring interval; sum the first construction deviation and the second expected engineering quantity to obtain a second actual engineering quantity; simulate the initial construction parameters in the pouring twin space with the second actual engineering quantity as a constraint to obtain first simulated construction parameters; optimize the first simulated construction parameters according to the first concrete density deviation, and output the first optimal construction parameters.

[0046] Specifically, the next adjacent pouring interval of the first pouring interval among the multiple pouring intervals is set as the second pouring interval, and the second expected engineering quantity of the second pouring interval is obtained, and the second expected engineering quantity refers to the concrete volume required to be completed in the second pouring interval; then the first construction deviation and the second expected engineering quantity are summed, and the sum of the two is used as the second actual engineering quantity, that is, the expected construction target of the second pouring interval. Then, a pouring twin space is constructed. The pouring twin space is a digital virtual model of the construction site. By inputting the core mold structure, concrete characteristics, construction parameters, etc. into the digital twin system, the dynamic changes in the real construction process are simulated. Then, based on the constraint of meeting the second actual engineering quantity, the initial construction parameters are simulated in the pouring twin space, that is, according to the second actual engineering quantity and the expected construction quality, the effects of different construction parameter combinations are tested in the twin space, the initial construction parameters are optimized, and the first simulated construction parameters are obtained, wherein the first simulated construction parameters include the first simulated pouring speed, the first simulated layered pouring thickness, the first simulated vibration intensity, and the first simulated vibration frequency. Finally, the first simulated construction parameters are optimized according to the first concrete density deviation to obtain the first optimal construction parameters.

[0047] Furthermore, the present application includes:

[0048] The first concrete density deviation is input into the parameter optimization channel for analysis, and the first pouring speed correction coefficient, the first layered pouring thickness correction coefficient, the first vibration intensity correction coefficient and the first vibration frequency correction coefficient are output; the first simulation construction parameter is optimized and corrected according to the first pouring speed correction coefficient, the first layered pouring thickness correction coefficient, the first vibration intensity correction coefficient and the first vibration frequency correction coefficient, and the first optimal construction parameter is output.

[0049] Specifically, first, the first concrete density deviation is input into a parameter optimization channel for analysis, and a first pouring speed correction coefficient, a first layered pouring thickness correction coefficient, a first vibration intensity correction coefficient and a first vibration frequency correction coefficient are output, wherein the parameter optimization channel is constructed based on a BP neural network; then, the first simulated pouring speed, the first simulated layered pouring thickness, the first simulated vibration intensity and the first simulated vibration frequency are optimized and corrected according to the first pouring speed correction coefficient, the first layered pouring thickness correction coefficient, the first vibration intensity correction coefficient and the first vibration frequency correction coefficient, and the first optimal construction parameter is output.

[0050] Furthermore, the present application includes:

[0051] Query historical construction logs, collect sample density deviation sets and sample pouring speed correction coefficient sets, sample layered pouring thickness correction coefficient sets, sample vibration strength correction coefficient sets and sample vibration frequency correction coefficient sets, and construct a sample training set; divide the sample training set into M equal parts, select M times with replacement, construct a first training set, iterate and select M times to obtain M training sets; take the sample density deviation as input, take the sample pouring speed correction coefficient, sample layered pouring thickness correction coefficient, sample vibration strength correction coefficient and sample vibration frequency correction coefficient as output, use the M training sets to supervise the training and cross-validate the BP neural network, and obtain M convergence parameter optimization branches; integrate and construct the parameter optimization channel based on the M convergence parameter optimization branches, wherein the output of the parameter optimization channel is the mode of the outputs of the M convergence parameter optimization branches.

[0052] Specifically, first, query the historical construction log to retrieve sample data, collect the sample density deviation set and the sample pouring speed correction coefficient set, the sample layered pouring thickness correction coefficient set, the sample vibration strength correction coefficient set and the sample vibration frequency correction coefficient set, and construct a sample training set, wherein the sample density deviation and the sample pouring speed correction coefficient, the sample layered pouring thickness correction coefficient, the sample vibration strength correction coefficient, and the sample vibration frequency correction coefficient correspond one to one. Then divide the sample training set into M parts to obtain M data sets, and select M times with replacement from the M data sets to construct the first training set, and use the same method to iteratively select M times to obtain M training sets.

[0053] Further, M parameter optimization branches are constructed based on the BP neural network, wherein the parameter optimization branch includes an input layer, a hidden layer, and an output layer; then, the sample density deviation is used as input, and the sample pouring speed correction coefficient, the sample layered pouring thickness correction coefficient, the sample vibration intensity correction coefficient, and the sample vibration frequency correction coefficient are used as output, and the M parameter optimization branches are supervised trained and cross-validated using the M training sets, for example, based on the gradient descent method, the weights and biases of the neural network are updated to complete the training, and M convergence parameter optimization branches are obtained. Finally, the parameter optimization channel is constructed based on the integration of the M convergence parameter optimization branches, wherein the output of the parameter optimization channel is the mode of the outputs of the M convergence parameter optimization branches.

[0054] In summary, the hollow slab inner formwork construction system provided by the present application has the following technical effects:

[0055] The target construction requirements are analyzed to determine the construction core mold attribute characteristics, and the core mold is prepared according to the core mold attribute characteristics to obtain the target self-made core mold, wherein the target self-made core mold is spliced ​​by a plurality of hollow square steel pipes, including a supporting steel ring inside, and the outer surface is covered with a plastic sheet; then the predetermined construction period of the target self-made core mold is obtained, and the predetermined construction period is equally divided into a plurality of casting intervals, and the construction operation of the target self-made core mold in the first casting interval is performed according to the initial construction parameters to obtain the first construction result; the first construction result is further scanned by a laser scanner, and the first concrete density deviation and the first construction deviation are analyzed and determined, and the initial construction parameters are optimized according to the first concrete density deviation and the first construction deviation to determine the first optimal construction parameters; finally, according to the first optimal construction parameters, the construction operation of the target self-made core mold in the second casting interval is performed until the predetermined construction period is completed; the technical goal of quickly and accurately adjusting the casting parameters and the vibration parameters according to the actual construction status can be achieved, and the precision and intelligence of construction control can be improved, thereby effectively solving the problem of lagging construction regulation and control, and significantly improving the construction accuracy and construction quality.

[0056] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0057] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A hollow slab inner formwork construction system, characterized in that: include: A target self-made core mold acquisition module is used to analyze the target construction requirements to determine the property characteristics of the construction core mold, prepare the core mold according to the property characteristics of the core mold, and obtain the target self-made core mold, wherein the target self-made core mold is spliced ​​by a plurality of hollow square steel pipes, including a supporting steel ring inside, and the outer surface is covered with plastic cloth; A first construction result acquisition module is used to acquire a predetermined construction period of the target self-made core mold, and divide the predetermined construction period into multiple casting intervals, and perform the construction operation of the target self-made core mold in the first casting interval according to the initial construction parameters to acquire a first construction result; an initial construction parameter optimization module, configured to scan the first construction result by means of a laser scanner, analyze and determine a first concrete density deviation and a first construction deviation, and optimize the initial construction parameters according to the first concrete density deviation and the first construction deviation to determine a first optimal construction parameter; A construction operation module is used to perform the construction operation of the target self-made core mold in the second casting interval according to the first optimal construction parameters until the predetermined construction period is completed.

2. A hollow slab inner formwork construction system according to claim 1, characterized in that: The initial construction parameters include pouring parameters and vibration parameters, wherein the pouring parameters include pouring speed and layered pouring thickness, and the vibration parameters include vibration intensity and vibration frequency.

3. A hollow slab inner formwork construction system according to claim 2, characterized in that: The construction operation of executing the target self-made core mold in the first pouring interval includes: When performing construction work, the temperature sensor and pressure sensor deployed inside the target self-made core mold are used for fixed-point monitoring to obtain real-time temperature data and real-time pressure data; Mapping and converting the real-time temperature data and the real-time pressure data to determine concrete temperature data and concrete pressure data; The concrete temperature data and the concrete pressure data are input into a parameter adaptive corrector, and the first parameter compensation data is output to adaptively fine-tune the initial construction parameters, wherein the parameter adaptive corrector is constructed based on the fuzzy control principle.

4. A hollow slab inner formwork construction system according to claim 3, characterized in that: Mapping and converting the real-time temperature data and the real-time pressure data includes: Acquire concrete property characteristics, use the core mold property characteristics and concrete property characteristics as constraints, retrieve historical construction logs, and acquire a sample monitoring temperature set and a sample monitoring pressure set; Randomly select a first sample monitoring temperature and a first sample monitoring pressure, retrieve and obtain a sample concrete temperature set and a sample concrete pressure set based on the first sample monitoring temperature and the first sample monitoring pressure, and obtain a first sample concrete temperature and a first sample concrete pressure after average calculation; Sequentially obtaining a sample concrete temperature set of the sample monitoring temperature set and a sample concrete pressure set of the sample monitoring pressure set, and establishing a temperature mapping according to the sample monitoring temperature set and the sample concrete temperature set, and establishing a pressure mapping according to the sample monitoring pressure set and the sample concrete pressure set; Based on the temperature mapping and the pressure mapping, the real-time temperature data and the real-time pressure data are mapped and converted.

5. A hollow slab inner formwork construction system according to claim 1, characterized in that: Analyze and determine the first concrete density deviation and the first construction deviation, including: Scan the first construction result by a laser scanner to obtain a first construction concrete density and a first construction engineering quantity; The first expected engineering quantity of the first pouring interval is obtained, and the deviation of the first construction concrete density and the first construction engineering quantity is calculated according to the reference concrete density and the first expected engineering quantity to obtain the first concrete density deviation and the first construction deviation.

6. A hollow slab inner formwork construction system according to claim 5, characterized in that: Optimizing the initial construction parameters according to the first concrete density deviation and the first construction deviation to determine the first optimal construction parameters includes: Obtaining a second expected engineering quantity of a second pouring interval, wherein the second pouring interval is a next pouring interval adjacent to the first pouring interval; Summing the first construction deviation and the second expected engineering quantity to obtain a second actual engineering quantity; Taking the satisfaction of the second actual engineering quantity as a constraint, simulating the initial construction parameters in the casting twin space to obtain first simulated construction parameters; The first simulation construction parameter is optimized according to the first concrete density deviation, and the first optimal construction parameter is output.

7. A hollow slab inner formwork construction system according to claim 6, characterized in that: Optimizing the first simulation construction parameter according to the first concrete density deviation includes: The first concrete density deviation is input into the parameter optimization channel for analysis, and a first pouring speed correction coefficient, a first layered pouring thickness correction coefficient, a first vibration intensity correction coefficient, and a first vibration frequency correction coefficient are output; The first simulation construction parameter is optimized and corrected according to the first pouring speed correction coefficient, the first layered pouring thickness correction coefficient, the first vibration intensity correction coefficient and the first vibration frequency correction coefficient, and the first optimal construction parameter is output.

8. A hollow slab inner formwork construction system according to claim 7, characterized in that: Construct parameter optimization channel, including: Query historical construction logs, collect sample density deviation sets and sample pouring speed correction coefficient sets, sample layered pouring thickness correction coefficient sets, sample vibration intensity correction coefficient sets, and sample vibration frequency correction coefficient sets, and construct a sample training set; Divide the sample training set into M equal parts, select M parts with replacement, construct a first training set, and iterate M times to obtain M training sets; Taking the sample density deviation as input, taking the sample pouring speed correction coefficient, the sample layered pouring thickness correction coefficient, the sample vibration intensity correction coefficient and the sample vibration frequency correction coefficient as output, using the M training sets to perform supervised training and cross-validation on the BP neural network, and obtaining M convergence parameter optimization branches; The parameter optimization channel is constructed based on the integration of the M convergent parameter optimization branches, wherein the output of the parameter optimization channel is the mode of the outputs of the M convergent parameter optimization branches.

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