Method and system for detecting and evaluating concrete pouring compactness of hollow floor system based on cylinder core internal mold

Abstract

The invention provides a concrete pouring compactness detection and evaluation method and system based on a cylinder core internal mold hollow floor, and relates to the technical field of building construction.The method comprises the steps that process data and physical data of concrete are collected, feature extraction is conducted on the process data, and whole-building feature data, temperature feature data and pressure feature data are obtained; analyzing the vibration characteristic data to generate a pouring vibration coefficient; performing mathematical analysis on the temperature data to generate a pouring temperature coefficient, and generating a pouring property coefficient according to the pouring vibration coefficient and the pouring temperature coefficient; generating a void index according to the pressure characteristic data and the pouring property coefficient; performing statistical analysis on the void index to obtain a void distribution coefficient, and performing mathematical analysis on the physical data and the void distribution coefficient to generate the pouring compactness.

Description

Technical Field

[0001] The invention relates to the technical field of building construction, and in particular to a method and system for detecting and evaluating the compactness of concrete pouring of hollow floor slabs based on a cylinder core inner mold. Background Art

[0002] In modern construction projects, concrete is a major building material, and its construction quality directly affects the structural safety and service life of the building. Especially in buildings using hollow floor structures with core inner molds, the density of concrete pouring is particularly important. The hollow floor structure is light, high-strength and has good thermal insulation and sound insulation effects, but its construction process is complicated. Especially in the concrete pouring process, how to ensure the density 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, the accuracy and consistency of the test results are difficult to guarantee. With the advancement of construction technology, data acquisition and analysis technology has gradually been applied to the monitoring of concrete construction quality, but the existing technical means lack systematic and scientific analysis methods when processing a large amount of complex dynamic data and multiple physical parameters, resulting in the reliability and accuracy of the test results needing to be further improved.

[0003] In the prior art, the publication number CN117890434A discloses a method for detecting the vibration density of concrete. The prior art places a piezoelectric sensor at the vibration density monitoring position of the concrete, vibrates after pouring the concrete, monitors the signal of the piezoelectric sensor while vibrating, and outputs signal data after the vibration is completed; the obtained signal data and 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 are respectively subjected to graphical calculation to determine the vibration density of the concrete. The method is based on the characteristics of the sensor response related to the external environment constraints under the frequency signal scanning excitation. Through the signal identification of the density of the concrete after pouring and vibrating, the quality defects of the concrete pouring can be discovered in time and remedial measures can be taken to improve the quality of the concrete pouring. However, the prior art still has defects. There are many factors that affect the pouring density, and the vibration data is only one of them. Relying only on the vibration data to evaluate the density is obviously lacking in comprehensiveness, which will make the results unconvincing.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one 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 pouring density of hollow floor concrete based on a cylinder core inner mold, so as to solve the problems raised in the above-mentioned background technology.

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

[0007] A method and system for detecting and evaluating the density of concrete pouring of hollow floor slabs based on a core inner mold, the specific steps of which include:

[0008] 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;

[0009] 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;

[0010] 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;

[0011] 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;

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

[0013] Furthermore, the specific logic for collecting process data is: dividing the inner mold of the cylinder core into N parts of equal area, and collecting the vibration amplitude time series data, temperature time series data and pressure time series data of each part.

[0014] Furthermore, the specific logic for extracting the vibration time domain characteristic data and the vibration frequency domain characteristic 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:

[0015]

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

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

[0018]

[0019] 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 k is the frequency index;

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

[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 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.

[0024] Furthermore, the specific logic for generating the casting property coefficient is as follows: extracting features from the temperature time series data to obtain the maximum temperature, average temperature and average fluctuation range, performing mathematical analysis on the maximum temperature, average temperature and average fluctuation range to generate the casting temperature coefficient, and generating the casting property coefficient according to the casting vibration coefficient and the casting temperature coefficient. The specific formula for calculating the casting temperature coefficient is as follows:

[0025]

[0026] Where Jt is the pouring temperature coefficient, is the average temperature, T F is the average fluctuation range, T max is the maximum temperature;

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

[0028] Ja=Jp*Jt

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

[0030] Furthermore, the specific logic for generating the void distribution coefficient is as follows: extracting the characteristics of the pressure time series data to obtain pressure characteristic data, and generating the void index according to the pressure characteristic data and the casting property coefficient; performing statistical analysis on the void index to obtain the void distribution coefficient, and the specific logic for generating the void index is as follows:

[0031]

[0032] 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;

[0033] The specific formula based on which the void distribution coefficient is generated is:

[0034]

[0035] in, is the void distribution coefficient, Ks i is the void index of the ith region, and N is the number of regions.

[0036] Furthermore, the physical data and void distribution coefficient are mathematically analyzed to generate the casting density; the specific formula based on it is:

[0037]

[0038] 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 is the volume of the inner mold of the cylinder core, V h V is the mass of concrete after pouring. t The volume of concrete after pouring is completed.

[0039] The present invention further provides 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, and specifically comprises:

[0040] 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.

[0041] 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;

[0042] 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;

[0043] 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;

[0044] Comprehensive analysis module, used to generate casting density by mathematical analysis of physical data and void distribution coefficient.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention comprehensively considers the vibration data, temperature data and pressure data of pouring, and generates a void distribution coefficient that can reflect the concrete pouring effect and the void generation situation. Then, combined with the physical data of concrete, a pouring density that directly reflects the density is generated. The generation of pouring density comprehensively considers multiple key process parameters and physical data, which significantly improves the comprehensiveness and accuracy of density evaluation.

[0047] The present invention also divides the concrete into multiple regions, analyzes each region first, then conducts a comprehensive analysis of all regions, and finally obtains the results. Regional analysis can provide a more comprehensive perspective, making the comprehensive evaluation more scientific and reliable. If a problem occurs in a certain area, 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. Figure 2 It is a schematic diagram of the overall system structure of the present invention. DETAILED DESCRIPTION

[0049] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0050] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0051] Example:

[0052] See also Figure 1 , the present invention provides a technical solution:

[0053] A method for testing and evaluating the density of concrete pouring of hollow floor slabs based on a core inner mold, the specific steps comprising:

[0054] 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;

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

[0056] 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;

[0057] 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:

[0058]

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

[0060] The specific formula for calculating the power spectral density is:

[0061]

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

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

[0064] 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:

[0065]

[0066] 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, f0 is the material resonance frequency, and the casting vibration coefficient Jp reflects the influence of the provided vibration on the casting effect. The larger its value, the better the casting effect. The generation of this coefficient can provide an important basis for the evaluation of the casting effect. m -f0 is the vibration deviation value. The larger the value, the worse the casting effect. Each material has a resonance frequency. The casting effect is best when vibrating at the resonance frequency. Conversely, the larger the deviation, the worse the vibration effect.

[0067] 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;

[0068] The specific logic for extracting the average fluctuation amplitude is: the difference between adjacent extreme values ​​is defined as the fluctuation amplitude, and the average fluctuation amplitude of all fluctuation amplitudes in the temperature time series data is calculated to obtain the average fluctuation amplitude.

[0069] 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:

[0070]

[0071] 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 pouring temperature coefficient Jt reflects the influence of temperature on the pouring effect. The larger its value is, the better the pouring effect is.

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

[0073] Ja=Jp*Jt

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

[0075] 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;

[0076] The pressure characteristic data is obtained by extracting the characteristics of the pressure time series data, and the void index is generated according to the pressure characteristic data and the casting property coefficient; the void index is statistically analyzed to obtain the void distribution coefficient. The specific logic for generating the void index is as follows:

[0077]

[0078] Among them, Ks is the void index, Ja is the casting property coefficient, Pr max is the maximum pressure, The maximum pressure change rate. The void index Ks comprehensively reflects the voids generated in the poured concrete. The larger its value, the fewer voids are generated in the concrete. The amount of voids directly determines the density. The generation of this index can provide an important basis for the density test. 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 inner mold of the cylinder core; the maximum pressure change rate The smaller it is, the more uneven the pouring will be and the easier it will be to create gaps.

[0079] The specific formula based on which the void distribution coefficient is generated is:

[0080]

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

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

[0083] The physical data and void distribution coefficient are mathematically analyzed to generate the pouring density; the specific formula based on this is:

[0084]

[0085] 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 is the volume of the inner mold of the cylinder core, V h V is the mass of concrete after pouring. t The volume of concrete after pouring is completed.

[0086] See also Figure 2 The present invention further provides a hollow floor concrete pouring density detection and evaluation system based on a cylinder core inner mold, which is used to implement the hollow floor concrete pouring density detection method based on a cylinder core inner mold, specifically comprising:

[0087] 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.

[0088] 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;

[0089] 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;

[0090] 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;

[0091] Comprehensive analysis module, used to generate casting density by mathematical analysis of physical data and void distribution coefficient.

[0092] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0093] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed 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, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in 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 k 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 is the volume of the inner mold of the cylinder core, v h V is the mass of concrete after pouring. 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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