A method and system for detecting and evaluating the compaction degree of concrete pouring in a hollow floor with a core inner form

Abstract

The present invention provides a method and system for detecting and evaluating the compaction degree of concrete in a hollow floor with a core inner form, which relates to the field of building construction technology. The present invention collects the process data and physical data of concrete, extracts features from the process data to obtain the whole building feature data, temperature feature data and pressure feature data, analyzes the vibration feature data to generate a pouring vibration coefficient; performs mathematical analysis on the temperature data to generate a pouring temperature coefficient, and generates a pouring property coefficient according to the pouring vibration coefficient and the pouring temperature coefficient; generates a void index according to the pressure feature data and the pouring property coefficient; performs statistical analysis on the void index to obtain a void distribution coefficient, and performs mathematical analysis on the physical data and the void distribution coefficient to generate the pouring compaction degree.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction, and specifically provides a method and system for detecting and evaluating the compaction degree of concrete pouring in a hollow floor with a core formwork Background Technique

[0002] In modern construction projects, concrete, as a major building material, its construction quality directly affects the structural safety and service life of buildings. Especially in buildings with a hollow floor structure using a core formwork, the compaction degree of concrete pouring is particularly important. The hollow floor structure has the advantages of light weight, high strength, and good heat insulation and sound insulation effects. However, its construction process is complex. Especially during the concrete pouring process, how to ensure the compaction degree of concrete is a technical problem. Traditional detection methods mainly rely on manual observation, empirical judgment, and simple physical tests. These methods are not only time-consuming and laborious, but also due to the influence of human factors, it is difficult to guarantee the accuracy and consistency of the detection results. With the progress of building construction technology, data acquisition and analysis technology has gradually been applied to the monitoring of concrete construction quality. However, existing technical means lack a systematic and scientific analysis method when dealing with a large amount of complex dynamic data and multiple physical parameters, resulting in the reliability and accuracy of the detection results needing to be further improved.

[0003] In the prior art, the publication number CN117890434A discloses a method for detecting the compaction degree of concrete vibration. In this prior art, a piezoelectric sensor is placed at the monitoring position of the compaction degree of concrete vibration. After pouring the concrete, vibration is carried out, and the signal of the piezoelectric sensor is monitored during the vibration. After the vibration ends, signal data is output; the obtained signal data is respectively subjected to graphic operations with the monitoring signal data when the piezoelectric sensor is in the air and the monitoring signal data when the piezoelectric sensor is in the concrete to determine the compaction degree of the concrete vibration. Based on the characteristics that the sensor response under frequency signal scanning excitation is related to the external environmental constraints, the compaction degree during the vibration after concrete pouring can be identified through signals, and the quality defects of concrete pouring can be discovered in time and remedial measures can be taken, thereby improving the quality of concrete pouring. However, the prior art still has defects. There are various factors affecting the compaction degree, and vibration data is only one of them. Relying solely on vibration data to evaluate the compaction degree is obviously lacking in comprehensiveness and will make the results unconvincing.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for detecting and evaluating the compaction degree of concrete pouring in a hollow floor with a core formwork to solve the problems raised in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method and system for detecting and evaluating the compactness of concrete pouring in a hollow floor with a core formwork, the specific steps include:

[0008] Step 1: Collect the process data and physical data of the concrete, and normalize the process data. The process data includes amplitude time series data of vibration, temperature time series data, and pressure time series data. The physical data includes the mass and volume of the core formwork, and the mass and volume of the concrete after pouring is completed;

[0009] Step 2: Extract the feature data of the amplitude time series data of vibration to obtain vibration time domain feature data and vibration frequency domain feature data, analyze the vibration time domain feature data and vibration frequency domain feature data, and generate a pouring vibration coefficient; the vibration time domain feature data is the maximum value of autocorrelation, and the vibration frequency domain feature data includes the main frequency and power spectral density;

[0010] Step 3: Extract the feature data of the temperature time series data to obtain temperature feature data, perform mathematical analysis on the temperature data to generate a pouring temperature coefficient, and generate a pouring property coefficient according to the pouring vibration coefficient and the pouring temperature coefficient; the temperature feature data includes the maximum temperature, average temperature, and average fluctuation amplitude;

[0011] Step 4: Extract the feature data of the pressure time series data to obtain pressure feature data, generate a void index according to the pressure feature data and the pouring property coefficient; perform statistical analysis on the void index to obtain a void distribution coefficient. The pressure feature data includes the maximum pressure and the maximum pressure change rate;

[0012] Step 5: Perform mathematical analysis on the physical data and the void distribution coefficient to generate the pouring compactness.

[0013] Further, the specific logic for collecting the process data is: divide the core formwork into N parts with equal areas, and collect the amplitude time series data of vibration, temperature time series data, and pressure time series data of each part.

[0014] Further, the specific logic for extracting the vibration time domain feature data and vibration frequency domain feature data is: obtain the maximum value of autocorrelation according to the autocorrelation function, perform Fourier transform on the amplitude time series data of vibration to transform it into the form of the sum of a series of complex numbers, obtain the spectrum X(k), and obtain the main frequency and power spectral density through the spectrum. The specific formula for calculating the maximum value of autocorrelation is:

[0015]

[0016] Wherein, R sp(0) is the autocorrelation maximum value, and P(t) is the amplitude time series data of the vibration;

[0017] The specific formula for calculating the power spectral density is as follows:

[0018]

[0019] Among them, P(k) is the power spectral density, n is the number of frequencies, L is the length of the vibration signal, X(k) is the vibration spectrum; l is the frequency index;

[0020] The main frequency is the frequency corresponding to the maximum amplitude in the frequency domain.

[0021] Furthermore, the specific logic for generating the pouring vibration coefficient is: analyzing the vibration time-domain characteristic data and the vibration frequency-domain characteristic data to generate the pouring vibration coefficient, and the specific formula is:

[0022]

[0023] Among them, Jp is the pouring vibration coefficient, R sp (0) is the autocorrelation maximum value, P(k) is the power spectral density, f m is the main frequency, and f0 is the material resonance frequency.

[0024] Furthermore, the specific logic for generating the pouring property coefficient is: extracting the characteristics of the temperature time series data to obtain the maximum temperature, average temperature, and average fluctuation amplitude, performing mathematical analysis on the maximum temperature, average temperature, and average fluctuation amplitude to generate the pouring temperature coefficient, and generating the pouring property coefficient based on the pouring vibration coefficient and the pouring temperature coefficient. The specific formula for calculating the pouring temperature coefficient is:

[0025]

[0026] Among them, Jt is the pouring temperature coefficient, is the average temperature, T F is the average fluctuation amplitude, T max is the maximum temperature;

[0027] The specific formula for generating the pouring property coefficient is:

[0028] Ja = Jp * Jt

[0029] Among them, Ja is the pouring property coefficient, Jp is the pouring vibration coefficient, and Jt is the pouring temperature coefficient.

[0030] Further, the specific logic for generating the void distribution coefficient is as follows: Feature extraction is performed on the pressure time-series data to obtain pressure feature data, and a void index is generated based on the pressure feature data and the pouring property coefficient; Statistical analysis is carried out on the void index to obtain the void distribution coefficient. The specific logic for generating the void index is as follows:

[0031]

[0032] where Ks is the void index, Ja is the pouring property coefficient, Pr max is the maximum pressure, is the maximum pressure change rate;

[0033] The specific formula for generating the void distribution coefficient is as follows:

[0034]

[0035] where, is the void distribution coefficient, Ks i is the void index of the i-th region, and N is the number of regions.

[0036] Further, mathematical analysis is performed on the physical data and the void distribution coefficient to generate the pouring compactness; The specific formula is as follows:

[0037]

[0038] where ρ is the pouring compactness, is the void distribution coefficient, M h is the mass of the core inner formwork, M t is the mass of the concrete after pouring, V h is the volume of the core inner formwork, V t is the volume of the concrete after pouring.

[0039] The present invention further provides a detection and evaluation system for the pouring compactness of the concrete of the hollow floor with a core inner formwork, which is used to implement the above-mentioned detection method for the pouring compactness of the concrete of the hollow floor with a core inner formwork, and specifically includes:

[0040] A data acquisition module, which is used to collect the process data and physical data of the concrete, and perform normalization processing on the process data. The process data includes the amplitude time-series data, temperature time-series data, and pressure time-series data of vibration, and the physical data includes the mass and volume of the core inner formwork, and the mass and volume of the concrete after pouring;

[0041] A vibration analysis module, which is used to extract features from the amplitude time-series data of vibration to obtain vibration time-domain feature data and vibration frequency-domain feature data, analyze the vibration time-domain feature data and vibration frequency-domain feature data, and generate a pouring vibration coefficient; the vibration time-domain feature data is the maximum autocorrelation value, and the vibration frequency-domain feature data includes the main frequency and the power spectral density;

[0042] A temperature analysis module, which is used to extract features from the temperature time-series data to obtain temperature feature data, perform mathematical analysis on the temperature data to generate a pouring temperature coefficient, and generate a pouring property coefficient according to the pouring vibration coefficient and the pouring temperature coefficient; the temperature feature data includes the maximum temperature, the average temperature, and the average fluctuation amplitude;

[0043] A pressure analysis module, which is used to extract features from the pressure time-series data to obtain pressure feature data, generate a void index according to the pressure feature data and the pouring property coefficient; perform statistical analysis on the void index to obtain a void distribution coefficient, and the pressure feature data includes the maximum pressure and the maximum pressure change rate;

[0044] A comprehensive analysis module, which is used to perform mathematical analysis on the physical data and the void distribution coefficient to generate a pouring density.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] The present invention comprehensively considers the vibration data, temperature data, and pressure data of pouring, generates a void distribution coefficient that can reflect the pouring effect of concrete and the generation of voids, and then combines the physical data of concrete to generate a pouring density that directly reflects the size of the density. The generation of the pouring density comprehensively considers a variety of key process parameters and physical data, significantly improving the comprehensiveness and accuracy of density evaluation.

[0047] The present invention also divides the concrete into multiple regions, first analyzes each region, then performs a comprehensive analysis on all regions, and finally obtains the result. Regional analysis can provide a more comprehensive perspective, making the comprehensive evaluation more scientific and reliable. If there is a problem in a certain region, it can be quickly located and targeted measures can be taken. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0049] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the following further describes the present invention in detail with reference to specific embodiments.

[0050] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar terms used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connected" or "linked" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to represent relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0051] Embodiment:

[0052] Please refer to Figure 1 , the present invention provides a technical solution:

[0053] A method for detecting and evaluating the compaction degree of concrete in a hollow floor with a core inner mold, the specific steps include:

[0054] Step 1: Collect the process data and physical data of the concrete, and normalize the process data. The process data includes amplitude time series data of vibration, temperature time series data, and pressure time series data. The physical data includes the mass and volume of the core inner mold, and the mass and volume of the concrete after pouring is completed;

[0055] The specific logic for collecting process data is: divide the core inner mold into N parts with equal areas, install vibration sensors, temperature sensors, and pressure sensors at the center of each area, and collect the amplitude time series data of vibration, temperature time series data, and pressure time series data of each part.

[0056] Step 2: Extract the feature data of the amplitude time series data of vibration to obtain the vibration time domain feature data and vibration frequency domain feature data, analyze the vibration time domain feature data and vibration frequency domain feature data, and generate a pouring vibration coefficient; the vibration time domain feature data is the maximum value of autocorrelation, and the vibration frequency domain feature data includes the main frequency and power spectral density;

[0057] The specific logic for extracting the vibration time domain feature data and vibration frequency domain feature data is: obtain the maximum value of autocorrelation according to the autocorrelation function, perform Fourier transform on the amplitude time series data of vibration to transform it into the form of the sum of a series of complex numbers, obtain the frequency spectrum X(k), obtain the main frequency and power spectral density through the frequency spectrum, and the specific formula for calculating the maximum value of autocorrelation is:

[0058]

[0059] Among them, R sp (0) is the autocorrelation maximum value, and P(t) is the amplitude time-series data of vibration; the autocorrelation maximum value R sp (0) reflects the periodic stability of the signal. The larger its value, the more regular and stable the vibration small signal is; the better the pouring effect is;

[0060] The specific formula based on which the power spectral density is calculated is:

[0061]

[0062] Among them, P(k) is the power spectral density, n is the number of frequencies, L is the length of the vibration signal, and X(k) is the vibration spectrum; l is the frequency index; the power spectral density P(k) reflects the energy of the vibration signal. The larger its value, the stronger the vibration energy and the better the pouring effect;

[0063] The main frequency is the frequency corresponding to the maximum amplitude in the frequency domain. The main frequency is representative and plays an important role in vibration analysis.

[0064] The specific logic based on which the pouring vibration coefficient is generated is: Analyze the vibration time-domain characteristic data and vibration frequency-domain characteristic data to generate the pouring vibration coefficient. The specific formula based on which is:

[0065]

[0066] Among them, Jp is the pouring vibration coefficient, R sp (0) is the autocorrelation maximum value, P(k) is the power spectral density, f m is the main frequency, f0 is the material resonance frequency. The pouring vibration coefficient Jp reflects the influence of the provided vibration on the pouring effect. The larger its value, the better the pouring effect. The generation of this coefficient can provide an important basis for the evaluation of the pouring effect. f m -f0 is the vibration deviation value. The larger its value, the worse the pouring effect. Each material has a resonance frequency. When vibrating at the resonance frequency, the pouring effect is the best. On the contrary, the larger the deviation, the worse the vibration effect.

[0067] Step 3: Extract the temperature characteristic data from the temperature time-series data, perform mathematical analysis on the temperature data to generate the pouring temperature coefficient, and generate the pouring property coefficient based on the pouring vibration coefficient and the pouring temperature coefficient; the temperature characteristic data includes the maximum temperature, the average temperature, and the average fluctuation amplitude;

[0068] The specific logic based on which the average fluctuation amplitude is extracted is: Define the difference between adjacent extreme values as the fluctuation amplitude, and calculate the average value of all the fluctuation amplitudes in the temperature time-series data to obtain the average fluctuation amplitude

[0069] The specific logic for generating the pouring property coefficient is as follows: Feature extraction is performed on the temperature time-series data to obtain the maximum temperature, average temperature, and average fluctuation amplitude. Mathematical analysis is carried out on the maximum temperature, average temperature, and average fluctuation amplitude to generate the pouring temperature coefficient. The pouring property coefficient is generated based on the pouring vibration coefficient and the pouring temperature coefficient. The specific formula for calculating the pouring temperature coefficient is:

[0070]

[0071] Among them, Jt is the pouring temperature coefficient, is the average temperature, T F is the average fluctuation amplitude, T max is the maximum temperature; the pouring temperature coefficient Jt reflects the influence of temperature on the pouring effect. The larger its value, the better the pouring effect;

[0072] The specific formula for generating the pouring property coefficient is:

[0073] Ja = Jp * J

[0074] Among them, Ja is the pouring property coefficient, Jp is the pouring vibration coefficient, and Jt is the pouring temperature coefficient. The pouring property coefficient Ja comprehensively reflects the influence of temperature and vibration on the pouring effect. The larger its value, the better the pouring effect; the generation of this coefficient can provide an important basis for the evaluation of the comprehensive pouring effect.

[0075] Step 4: Feature extraction is performed on the pressure time-series data to obtain pressure feature data. The void index is generated based on the pressure feature data and the pouring property coefficient; statistical analysis is carried out on the void index to obtain the void distribution coefficient. The pressure feature data includes the maximum pressure and the maximum pressure change rate;

[0076] Feature extraction is performed on the pressure time-series data to obtain pressure feature data. The void index is generated based on the pressure feature data and the pouring property coefficient; statistical analysis is carried out on the void index to obtain the void distribution coefficient. The specific logic for generating the void index is:

[0077]

[0078] Among them, Ks is the void index, Ja is the pouring property coefficient, Pr max is the maximum pressure, is the maximum pressure change rate. The void index Ks comprehensively reflects the generation of voids in the cast concrete. The larger its value, the fewer voids are generated in the concrete. The amount of voids directly determines the degree of compaction. The generation of this index can provide an important basis for the detection of compaction. The pouring property coefficient Ja comprehensively reflects the influence of temperature and vibration on the pouring effect. The larger its value, the better the pouring effect; the better the pouring effect, the fewer voids are generated; the fewer voids, the greater the mass, and the greater the maximum pressure on the core formwork; the maximum pressure change rate is smaller, the more uneven the pouring is, and the easier it is to generate voids.

[0079] The specific formula for generating the void distribution coefficient is as follows:

[0080]

[0081] wherein, is the void distribution coefficient, Ks i is the void index of the i-th region, and N is the number of regions. The void distribution coefficient comprehensively reflects the generation of voids in each region. The larger its value, the fewer voids are generated in the concrete of each region, and the better the overall pouring effect.

[0082] Step 5: Perform mathematical analysis on the physical data and the void distribution coefficient to generate the pouring compaction.

[0083] Perform mathematical analysis on the physical data and the void distribution coefficient to generate the pouring compaction; the specific formula is as follows:

[0084]

[0085] wherein, ρ is the pouring compaction, is the void distribution coefficient, M h is the mass of the core formwork, M t is the mass of the concrete after pouring, V h is the volume of the core formwork, V t The volume of the concrete after pouring.

[0086] Please refer to Figure 2 , the present invention further provides a detection and evaluation system for the compaction of concrete in a hollow floor with a core formwork, which is used to implement the above-mentioned detection method for the compaction of concrete in a hollow floor with a core formwork, and specifically includes:

[0087] A data acquisition module, which is used to collect the process data and physical data of the concrete, and perform normalization processing on the process data. The process data includes the amplitude time series data of vibration, the temperature time series data, and the pressure time series data. The physical data includes the mass and volume of the core formwork, the mass and volume of the concrete after pouring;

[0088] A vibration analysis module, configured to extract features from the amplitude time-series data of vibration to obtain vibration time-domain feature data and vibration frequency-domain feature data, analyze the vibration time-domain feature data and the vibration frequency-domain feature data, and generate a pouring vibration coefficient; the vibration time-domain feature data is the maximum autocorrelation value, and the vibration frequency-domain feature data includes the main frequency and the power spectral density;

[0089] A temperature analysis module, configured to extract features from the temperature time-series data to obtain temperature feature data, perform mathematical analysis on the temperature data to generate a pouring temperature coefficient, and generate a pouring property coefficient according to the pouring vibration coefficient and the pouring temperature coefficient; the temperature feature data includes the maximum temperature, the average temperature, and the average fluctuation amplitude;

[0090] A pressure analysis module, configured to extract features from the pressure time-series data to obtain pressure feature data, generate a void index according to the pressure feature data and the pouring property coefficient; perform statistical analysis on the void index to obtain a void distribution coefficient, and the pressure feature data includes the maximum pressure and the maximum pressure change rate;

[0091] A comprehensive analysis module, configured to perform mathematical analysis on the physical data and the void distribution coefficient to generate a pouring compactness.

[0092] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0093] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by the combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0094] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application.

Claims

1. A method for testing and evaluating the density of concrete pouring of hollow floor slabs based on a core inner mold, characterized in that: The specific steps include: Step 1: Collect the process data and physical data of concrete, and normalize the process data, wherein the process data includes the amplitude time series data of vibration, the temperature time series data and the pressure time series data, and the physical data includes the mass and volume of the inner mold of the cylinder core, and the mass and volume of the concrete after pouring; Step 2: Extract the characteristics of the vibration amplitude time series data to obtain vibration time domain characteristic data and vibration frequency domain characteristic data, analyze the vibration time domain characteristic data and vibration frequency domain characteristic data, and generate a pouring vibration coefficient; the vibration time domain characteristic data is the maximum value of the autocorrelation, and the vibration frequency domain characteristic data includes the main frequency and power spectrum density; Step 3: Extract the characteristics of the temperature time series data to obtain temperature characteristic data, perform mathematical analysis on the temperature data to generate a casting temperature coefficient, and generate a casting property coefficient according to the casting vibration coefficient and the casting temperature coefficient; the temperature characteristic data includes the maximum temperature, the average temperature and the average fluctuation amplitude; Step 4: extracting features from the pressure time series data to obtain pressure feature data, generating a void index based on the pressure feature data and the casting property coefficient; performing statistical analysis on the void index to obtain a void distribution coefficient, wherein the pressure feature data includes the maximum pressure and the maximum pressure change rate; Step 5: Perform mathematical analysis on the physical data and void distribution coefficient to generate the casting density.

2. A method for detecting and evaluating the pouring density of hollow floor concrete based on a cylinder core inner mold according to claim 1, characterized in that: The specific logic for collecting process data is: divide the inner mold of the cylinder core into N parts of equal area, and collect the vibration amplitude time series data, temperature time series data and pressure time series data of each part.

3. A method for detecting and evaluating the pouring density of hollow floor concrete based on a cylinder core inner mold according to claim 1, characterized in that: The specific logic for extracting vibration time domain feature data and vibration frequency domain feature data is as follows: the maximum autocorrelation value is obtained according to the autocorrelation function, the amplitude time series data of the vibration is Fourier transformed to transform it into the form of the sum of a series of complex numbers to obtain the spectrum X(k), the main frequency and power spectrum density are obtained through the spectrum, and the specific formula for calculating the maximum autocorrelation value is as follows: Among them, R sp (0) is the maximum value of autocorrelation, P(t) is the amplitude time series data of vibration; The specific formula for calculating the power spectral density is: Where P(k) is the power spectrum density, n is the number of frequencies, L is the length of the vibration signal, X(k) is the vibration spectrum, and l is the frequency index; The main frequency is the frequency corresponding to the maximum frequency domain amplitude.

4. The method for detecting and evaluating the pouring density of hollow floor concrete based on a cylinder core inner mold according to claim 1 is characterized in that: The specific logic for generating the pouring vibration coefficient is: analyze the vibration time domain characteristic data and the vibration frequency domain characteristic data to generate the pouring vibration coefficient. The specific formula is: Among them, Jp is the pouring vibration coefficient, R sp (0) is the maximum value of autocorrelation, P(k) is the power spectrum density, f m is the main frequency and f0 is the resonant frequency of the material.

5. The method for detecting and evaluating the density of concrete pouring of hollow floor slabs based on a core inner mold according to claim 1 is characterized in that: The specific logic for generating the casting property coefficient is as follows: extract the features of the temperature time series data to obtain the maximum temperature, average temperature and average fluctuation range, perform mathematical analysis on the maximum temperature, average temperature and average fluctuation range to generate the casting temperature coefficient, generate the casting property coefficient based on the casting vibration coefficient and the casting temperature coefficient, and calculate the casting temperature coefficient based on the specific formula: Where Jt is the pouring temperature coefficient, is the average temperature, T F is the average fluctuation range, T max is the maximum temperature; The specific formula for generating the casting property coefficient is: Ja=Jp*Jt Among them, Ja is the casting property coefficient, Jp is the casting vibration coefficient, and Jt is the casting temperature coefficient.

6. The method for testing and evaluating the density of concrete pouring of hollow floor slabs based on a core inner mold according to claim 1 is characterized in that: The specific logic for generating the void distribution coefficient is as follows: extract the characteristics of the pressure time series data to obtain pressure characteristic data, and generate the void index based on the pressure characteristic data and the casting property coefficient; perform statistical analysis on the void index to obtain the void distribution coefficient. The specific logic for generating the void index is as follows: Among them, Ks is the void index, Ja is the casting property coefficient, Pr max is the maximum pressure, is the maximum pressure change rate; The specific formula based on which the void distribution coefficient is generated is: in, is the void distribution coefficient, Ks i is the void index of the ith region, and N is the number of regions.

7. The method for testing and evaluating the density of concrete pouring of hollow floor slabs based on a core inner mold according to claim 1 is characterized in that: The physical data and void distribution coefficient are mathematically analyzed to generate the pouring density; the specific formula based on this is: Among them, ρ is the casting density, is the void distribution coefficient, M h is the mass of the inner mold of the cylinder core, M t V is the mass of concrete after pouring. h is the volume of the inner mold of the cylinder core, V t The volume of concrete after pouring is completed.

8. A system for detecting and evaluating the density of concrete pouring of hollow floor slabs based on a core inner mold, which is used to implement the method for detecting the density of concrete pouring of hollow floor slabs based on a core inner mold according to any one of claims 1 to 7, specifically comprising: A data acquisition module is used to collect the process data and physical data of concrete and normalize the process data. The process data includes the vibration amplitude time series data, temperature time series data and pressure time series data. The physical data includes the mass and volume of the inner mold of the cylinder core, and the mass and volume of the concrete after pouring. A vibration analysis module is used to extract features from the vibration amplitude time series data to obtain vibration time domain feature data and vibration frequency domain feature data, analyze the vibration time domain feature data and vibration frequency domain feature data, and generate a pouring vibration coefficient; the vibration time domain feature data is the maximum value of the autocorrelation, and the vibration frequency domain feature data includes the main frequency and power spectrum density; The temperature analysis module is used to extract the characteristics of the temperature time series data to obtain temperature characteristic data, perform mathematical analysis on the temperature data to generate a casting temperature coefficient, and generate a casting property coefficient according to the casting vibration coefficient and the casting temperature coefficient; the temperature characteristic data includes the maximum temperature, the average temperature and the average fluctuation amplitude; The pressure analysis module is used to extract the characteristics of the pressure time series data to obtain pressure characteristic data, generate a void index according to the pressure characteristic data and the casting property coefficient; perform statistical analysis on the void index to obtain a void distribution coefficient, wherein the pressure characteristic data includes the maximum pressure and the maximum pressure change rate; Comprehensive analysis module, used to generate casting density by mathematical analysis of physical data and void distribution coefficient.

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