Concrete full lifecycle green quality control method based on internet of things and big data

Abstract

Provided is a concrete full lifecycle green quality control method based on Internet of Things and big data, comprising the following steps: step 1, on the basis of an information platform, selecting raw materials required by concrete, so as to achieve the control of the selection of the raw materials of concrete; step 2, on the basis of the information platform, performing engineering concrete mix ratio intelligent design, and obtaining the mix ratio of the raw materials required by concrete corresponding to a project under construction; step 3, on the basis of the information platform, taking into account the obtained concrete mix ratio to mix and prepare concrete by means of an intelligent concrete factory, and automatically testing and controlling the quality of the prepared concrete at the same time; and step 4, on the basis of the information platform, using the mixed and prepared concrete to perform intelligent construction. Automatic, intelligent and green control of concrete throughout the full lifecycle from raw materials, mix ratio design, production, and construction to the service life of the structure are achieved by controlling each stage of the full lifecycle of the concrete.

Description

Green quality control method for concrete throughout its life cycle based on big data of the Internet of Things Technical Field

[0001] The present invention relates to the field of engineering construction technology. More specifically, the present invention relates to a green quality control method for the entire life cycle of concrete based on Internet of Things big data. Background Art

[0002] Concrete is the most fundamental building material, playing a vital role in all types of buildings and structures. However, concrete can exacerbate environmental pollution during its production and use, such as the pollution and energy consumption caused by its primary raw material, cement, and the water and soil damage and resource scarcity caused by river sand mining. Greening concrete throughout its entire lifecycle encompasses every stage of the entire industrial process, from raw materials and mixture testing, production, and molding into components to the operational life of the structure. Automated and intelligent technologies must be employed throughout concrete production, pouring, and maintenance to implement energy-saving and environmentally friendly improvements, reduce or eliminate noise, sewage, dust, and solid waste emissions, and achieve greening and quality control throughout the entire material conversion process. Currently, no relevant technical research has been conducted in this field.

[0003] Summary of the Invention

[0004] One purpose of the present invention is to provide a green quality control method for the entire life cycle of concrete based on big data of the Internet of Things. By controlling each stage of the entire life cycle of concrete, it can achieve automated, intelligent and green control of concrete from raw materials, mix design, production, construction to the entire life cycle of the structure.

[0005] To achieve these objectives and other advantages of the present invention, a method for green quality control of concrete throughout its life cycle based on IoT big data is provided. Green quality control of concrete throughout its life cycle is centrally managed and scheduled via an information platform, specifically comprising the following steps:

[0006] Step 1: Select the raw materials required for concrete based on the information platform to achieve control of the selection of concrete raw materials;

[0007] Step 2: Based on the information platform and the selected concrete raw materials, intelligently design the engineering concrete mix ratio to obtain the mix ratio of each raw material required for the concrete of the project to be constructed;

[0008] Step 3: Based on the information platform, the concrete mix ratio obtained by the above design is combined with the concrete intelligent factory to mix and prepare the concrete, and the quality of the prepared concrete is automatically tested and controlled;

[0009] Step 4: Based on the information platform, the concrete prepared by the above mixing is used for intelligent construction.

[0010] Preferably, the step one is specifically as follows: first, obtaining the resource distribution, performance characteristics, and service conditions corresponding to the raw materials of different projects in different regions across the country, and forming a database within the information platform; second, based on the concrete design requirements and standard specifications of the project to be constructed, and based on the characteristics of the area of ​​the project to be constructed, screening and comparing in the above database to obtain standardized grading sand and gravel aggregates, standardized mixed component cement and admixtures, and standardized raw material services of high-efficiency admixtures that are highly adaptable to standardized cementitious materials.

[0011] Preferably, the step 2 specifically includes:

[0012] S21. Establish a concrete performance gene library based on historical project data acquired and recorded in advance on the information platform. The concrete performance gene library includes: project area, amount of each raw material, glue-water ratio, cement-mortar strength, concrete strength, concrete workability, net paste surplus, and mortar surplus; wherein net paste surplus = net paste volume - fine aggregate pore volume = net paste volume - fine aggregate void ratio * fine aggregate volume / (1 - fine aggregate void ratio), mortar surplus = mortar volume - coarse aggregate pore volume = mortar volume - coarse aggregate void ratio * coarse aggregate volume / (1 - coarse aggregate void ratio);

[0013] The net pulp surplus V ey Calculate by the following formula: V ey =V e -P s ×V s / (1-P s )

[0014] Where V e is the net pulp volume, P s is the porosity of fine aggregate, V s is the volume of fine aggregate;

[0015] The mortar surplus V my Calculate by the following formula: V my =V m -P g ×V g / (1-P g )

[0016] Where: V m is the mortar volume, P g is the porosity of coarse aggregate, V g is the volume of coarse aggregate;

[0017] S22. The linear relationship between the glue ratio and the concrete mix strength is obtained by the Paul-Rhys formula: y = af cu,o +b

[0018] Among them, y is the glue ratio, f cu,o is the concrete mix strength, a and b are the new regression coefficients;

[0019] Establish the composite cementitious material mortar strength model,

[0020] Where, f b is the strength of composite cementitious material mortar, U k is the dosage of cementitious material k; i k A is the dosage in the test method of k activity index of cementitious materials; k - activity index of the cementitious material k; f ce is the strength of cement mortar;

[0021] The amount of each cementitious material and the cement mortar strength data in the concrete performance gene library are brought into the composite cementitious material mortar strength model to obtain the composite cementitious material mortar strength corresponding to different concrete strengths in different regions;

[0022] Combining the composite cementitious material mortar strength data corresponding to different concrete strengths in different regions obtained in the above step and the glue-water ratio data corresponding to different concrete strengths in different regions in the concrete performance gene library, the linear relationship is introduced into the linear relationship to perform data fitting to obtain a and b values ​​corresponding to different concrete strengths in different regions, thereby forming a new regression coefficient gene library with regional characteristics;

[0023] S23. Determine the design targets for concrete mix strength and workability;

[0024] S24. Determine the values ​​of the mortar surplus and the net paste surplus according to the concrete mix strength and workability design targets and in combination with the concrete performance gene library, and calculate the amount of coarse aggregate and fine aggregate per unit volume of concrete;

[0025] S25. Determine the new regression coefficients a and b based on the concrete mix strength and workability design targets and the new regression coefficient gene library, and then determine the glue-to-water ratio, the amount of each cementitious material used per unit volume of concrete, and the amount of water used;

[0026] S26. Summarize the amounts of coarse aggregate, fine aggregate, various cementitious materials, and water consumption in a unit volume of concrete to obtain specific concrete mix ratio data.

[0027] Preferably, step S24 specifically includes:

[0028] S241. Determine the values ​​of the mortar surplus and the net paste surplus according to the concrete mix strength and workability design targets and in combination with the concrete performance gene library;

[0029] S242. Calculate the volume of coarse aggregate V g and fine aggregate volume V s , and then calculate the amount of coarse aggregate and fine aggregate in unit volume of concrete by solving the density of coarse aggregate and fine aggregate;

[0030] S243, screening the various coarse aggregates at the construction site;

[0031] S244. Construct an objective function by combining the close packing model and the least squares algorithm to determine the mass ratio of each subdivided coarse aggregate under the condition of close packing of aggregates; and calculate the mass of each subdivided coarse aggregate per unit volume of concrete based on the amount of coarse aggregate obtained in step S243.

[0032] Preferably, step S25 specifically includes:

[0033] S251. Collecting physical and chemical property data of various gelling materials;

[0034] S252, according to the concrete mix strength and workability design targets, combined with the new regression coefficient gene library, determine new regression coefficients a and b, and calculate the value of the glue-water ratio;

[0035] S253, determining the dosage of each cementitious material based on the glue-water ratio and the concrete mix strength and workability design targets, combined with the concrete performance gene library;

[0036] S254. Calculate the density of the composite cementitious material and the amount of the composite cementitious material per unit volume of concrete based on the dosage and density of each cementitious material, thereby obtaining the amount of each cementitious material per unit volume of concrete;

[0037] S255. Calculate the amount of water used per unit volume of concrete based on the glue-water ratio and the amount of composite cementitious material used per unit volume of concrete.

[0038] Preferably, the concrete production and preparation in step 3 is achieved by an intelligent concrete factory, and the intelligent concrete factory is data-connected with the information platform. Specifically, the intelligent concrete factory includes a powder storage management workshop, an aggregate storage management workshop, a concrete production management workshop, and a concrete delivery management workshop, and each management workshop is equipped with an intelligent robot and an intelligent control system.

[0039] The powder silo in the powder storage management workshop is provided with a powder electronic access control and an intelligent material level system. The amount of powder required is input through the information platform and transmitted to the intelligent control system, which controls the powder electronic access control to open and automatically samples the powder through the powder sampling device to the unattended floor scale for weighing until the weight reaches the set powder dosage value. Then, the powder electronic access control is controlled to close through the intelligent control system, and the intelligent material level system automatically obtains the total amount of powder in the powder silo and displays it in real time. The powder sampling device is an automatic sampling and autonomous walking robot, and the unattended floor scale is equipped with a machine vision sensor, which obtains the powder sampling dosage value and compares it with the unattended floor scale weighing value, and transmits the result to the powder sampling device until the unattended floor scale weighs to the set powder dosage value;

[0040] The AI ​​warehousing system in the aggregate storage management workshop obtains the amount of aggregate required input by the information platform, and controls the aggregate sampling robot to take samples in the aggregate silo, and then weighs them on the unattended scale until the set powder amount value is reached. The unattended scale is equipped with a machine vision sensor, which obtains the amount value of the aggregate sample and compares it with the weighing value of the unattended scale, and transmits the result to the aggregate sampling robot until the unattended scale weighs the set aggregate amount value; the powder and aggregate weighed by the unattended scale of the powder storage management workshop and the aggregate storage management workshop are guided into the mixing building by controlling the silo guidance system;

[0041] The mixing plant in the concrete production management workshop is equipped with a multi-machine centralized control system, an unmanned loader, and a machine vision system. The machine vision system monitors the mixing plant in real time and transmits the results to the multi-machine centralized control system to determine whether there is material transportation. If material transportation is available, the multi-machine centralized control system controls the unmanned loader to dock the transported materials and input them into the mixing plant. The multi-machine centralized control system controls mixing and preparing concrete. An online monitoring system for concrete working performance is installed in the mixing plant to monitor various working performances of the concrete in real time until they meet the design requirements.

[0042] The concrete delivery management workshop loads the concrete prepared by the concrete production management workshop through an unmanned mixer truck, and controls the unmanned mixer truck to the designed position through the intelligent scheduling system, and uses the slump detection and test block making robot to detect the slump of the concrete and make test blocks.

[0043] Preferably, the step 4 is specifically as follows:

[0044] First, during the concrete pouring process, a transparent visual template equipped with an industrial camera is deployed for construction.

[0045] Secondly, during the construction process, a concrete appearance defect mapping algorithm is established based on image morphology analysis, HSV color gamut segmentation, and the Hough detection algorithm to achieve intelligent judgment of the concrete surface bubble rate. At the same time, by establishing a relationship model between the vibrator current and voltage information and the concrete vibration state, concrete vibration quality defects can be predicted.

[0046] Then, based on the above-obtained concrete surface bubble rate analysis and concrete vibration quality defect analysis results, combined with the concrete vibration construction quality requirements, on-site construction conditions, and construction technology, an in-depth analysis was conducted to determine the vibration time, frequency, and location process parameters. Combined with the concrete vibration curve to guide the determination of the vibration endpoint, combined with the structural and environmental information of the construction site, a three-dimensional visualization model of the vibration effect was formed on the platform.

[0047] Finally, all the concrete pouring construction data obtained above are recorded in the database of the information platform, and the data corresponding to the relevant construction projects are accumulated in the database of the information platform until a concrete vibration quality standard database is formed.

[0048] Preferably, the information platform further includes a data warehouse, which includes a visualization decision platform and a concrete gene library, wherein the visualization decision platform realizes the timely collection, benchmarking analysis and long-term storage of platform data throughout the entire life cycle of concrete. The data uploaded by users or devices is first subjected to anomaly detection and classification by the isolation forest algorithm, automatically eliminating outliers and deviations to avoid the influence of error data; secondly, the principal component analysis algorithm and the t-distributed random neighbor embedding algorithm are used to perform linear and nonlinear transformations on multi-dimensional data respectively, reducing the data dimension and realizing dimensionality reduction analysis and visualization processing;

[0049] The concrete gene library includes a strength gene library, a work performance gene library, a durability gene library and an appearance quality gene library; based on the data of the entire life cycle of concrete, an artificial neural network model optimized by genetic algorithms is used to establish a nonlinear mapping model between raw material quality, raw material dosage, environmental factors, concrete type and concrete mechanical properties, namely the concrete strength gene library, to achieve accurate prediction of concrete strength; using the random forest algorithm, a nonlinear mapping model is established between concrete type, pouring method, strength, slump and concrete mortar surplus and net paste surplus, namely the concrete work performance gene library, to achieve optimal recommendation of surplus paste dosage, and assist in the intelligent prediction of concrete mix proportion in step two. Design; using the least squares support vector machine algorithm based on random forest, a nonlinear mapping model between raw material quality, raw material usage, environmental factors and durability performance was established, which is the concrete durability performance gene library, to achieve accurate prediction of concrete frost resistance and anti-permeability performance; using the collected concrete appearance pictures, through grayscale conversion and standard deviation calculation, first established an evaluation method for appearance color difference: standard deviation 0-7.5 is level one, 7.5-14 is level two, 14-21 is level three, >21 is level four, and then using the random forest algorithm, a nonlinear mapping model between raw material quality, raw material usage, environmental factors and concrete appearance grade was established, which is the appearance gene library, to achieve accurate prediction of concrete appearance color difference.

[0050] Preferably, the information platform also includes an evaluation index system, which is based on the collection of data on the entire life cycle of concrete, including concrete raw materials, mix proportions, production, construction and physical testing, and proposes a calculation method for indicators including five aspects: concrete raw material stability index, physical homogeneity index, production standardization index, construction refinement index, and mix proportion greening index. The comprehensive evaluation of concrete is achieved in the form of a five-dimensional graph, and the area of ​​the five-dimensional graph is calculated by calculus derivation to achieve comprehensive benchmarking of concrete products between different units.

[0051] Preferably, the information platform also includes a comprehensive coordination system, which includes multiple modules, specifically:

[0052] The demand planning coordination module collects the overall volume and volume of each part of the construction project, the construction project schedule, and the construction organization plan of the project department, and systematically schedules concrete production. It also feeds back the information to the site selection coordination module and the raw material organization coordination module. At the same time, it can also optimize and adjust the concrete production schedule and schedule according to the raw material organization situation and equipment production situation.

[0053] The station selection coordination module coordinates the selection of mixing station equipment by matching the daily production volume peak, the hourly production volume peak and the equipment production capacity, and combining the distribution of mixing stations around the project. It also coordinates the selection of mixing station equipment by matching road conditions and transportation conditions. At the same time, it can also propose adjustment suggestions for demand plans based on equipment conditions and transportation conditions.

[0054] The raw material organization and coordination module receives the concrete production plan from the demand planning and coordination module. Based on the theoretical mix ratio information collected by the platform and the production mix ratio information collected by the industrial control system, it forms a raw material demand plan and provides it to the procurement department and suppliers, thus enabling the advance organization of raw materials. At the same time, it can also propose adjustment suggestions for the production plan based on the actual progress of the raw material organization.

[0055] The production task coordination module receives the concrete production plan from the demand planning coordination module, and schedules production based on equipment production capacity, equipment status, and raw material preparation. This production scheduling plan is then provided to the raw material organization coordination module and the equipment scheduling module. Furthermore, the module can also propose production plan adjustment suggestions based on the raw material preparation and equipment preparation status.

[0056] The equipment scheduling coordination module accepts the production scheduling plan of the production task coordination module, analyzes the production capacity and equipment status of the production equipment, and forms production equipment scheduling and equipment maintenance requirements and provides them to the equipment department. It analyzes the production scheduling plan to form tank truck requirements and tank truck scheduling plans and provides them to the fleet to realize equipment scheduling. At the same time, it can also make adjustment suggestions to the production plan based on the actual preparation of production equipment and transportation equipment.

[0057] The present invention has at least the following beneficial effects:

[0058] 1. The present invention builds a green intelligent platform for the entire life cycle of concrete industry by comprehensively applying BIM technology, Beidou technology, and distributed microservice architecture asynchronous mixing station equipment acquisition and control technology. It comprehensively applies mixing station equipment acquisition and control technology and adopts information technology to realize the automation, intelligence and greenness of the entire life cycle of concrete from raw materials, mix design, production, construction to the service of the structure.

[0059] 2. The greening of concrete raw materials in the present invention is beneficial to reducing the comprehensive cost of engineering concrete in various regions, steadily improving the quality of concrete structures, and early prediction of characteristics such as appearance, thereby improving the scientific nature of concrete mix ratios and ensuring engineering quality.

[0060] 3. The intelligent design of engineering concrete mix ratio of the present invention can greatly reduce the workload of manual concrete mix ratio trial mixing, shorten the engineering concrete mix ratio verification cycle, and is conducive to improving the green level of concrete throughout its life cycle.

[0061] 4. The greening of the entire concrete production process of the present invention realizes intelligent management of the entire concrete production process through a concrete smart factory based on machine perception and execution, greatly reducing on-site production personnel, improving concrete quality, and reducing safety risks.

[0062] 5. The greening of the concrete construction process of the present invention is achieved through the integration and joint application of the intelligent concrete vibration system, formwork visualization technology, and large-volume concrete intelligent temperature control and anti-cracking technology during the concrete construction process, so as to achieve controllable quality during the concrete construction process and ensure that the concrete project is green, economical and durable.

[0063] Other advantages, objectives and features of the present invention will be reflected in part from the following description and will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] FIG1 is a flowchart of the implementation of the green quality control method for concrete throughout its life cycle according to the present invention;

[0065] FIG2 is a flow chart of raw material management of the smart concrete factory according to the present invention;

[0066] FIG3 is a fitting diagram of a close packing curve of coarse aggregate according to the present invention;

[0067] FIG4 is a flow chart of bubble detection on the surface of concrete according to the present invention;

[0068] FIG5 is a concrete vibration curve of the present invention. DETAILED DESCRIPTION

[0069] The present invention will be described in further detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0070] It should be noted that, in the description of the present invention, the terms "horizontal", "longitudinal", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0071] As shown in FIG1 , the present invention provides a method for green quality control of concrete throughout its life cycle based on IoT big data. Green quality control of concrete throughout its life cycle is centrally managed and scheduled via an information platform, specifically comprising the following steps:

[0072] Step 1: Select the raw materials required for concrete based on the information platform to achieve control of the selection of concrete raw materials;

[0073] Step 2: Based on the information platform and the selected concrete raw materials, intelligently design the engineering concrete mix ratio to obtain the mix ratio of each raw material required for the concrete of the project to be constructed;

[0074] Step 3: Based on the information platform, the concrete mix ratio obtained by the above design is combined with the concrete intelligent factory to mix and prepare the concrete, and the quality of the prepared concrete is automatically tested and controlled;

[0075] Step 4: Based on the information platform, the concrete prepared by the above mixing is used for intelligent construction.

[0076] The intelligent information platform for green quality control throughout the concrete lifecycle utilizes BIM, Beidou, and asynchronous mixing station equipment data collection within a distributed microservices architecture, creating a highly available containerized cluster deployment model. Through data collaboration, the platform integrates key technologies such as intelligent concrete testing and preparation, green concrete production and intelligent construction, and intelligent perception and resource utilization of concrete structures, achieving resource coordination, technical collaboration, and optimal greening of the concrete lifecycle.

[0077] In a specific embodiment.

[0078] The specific steps of step one are as follows: first, obtaining the resource distribution, performance characteristics, and service conditions corresponding to the raw materials of different projects in different regions across the country, and forming a database within the information platform; second, based on the concrete design requirements and standard specifications of the project to be constructed, and based on the characteristics of the area of ​​the project to be constructed, screening and comparing in the above database to obtain standardized grading sand and gravel aggregates, standardized mixed component cement and admixtures, and standardized raw material services of high-efficiency admixtures that are highly adaptable to standardized cementitious materials.

[0079] Greening concrete raw materials specifically refers to comprehensively analyzing the resource distribution, performance characteristics, and service availability of raw materials from projects in different regions across the country during the raw material selection phase of concrete's entire life cycle (including the resource distribution of concrete raw materials, the physical and chemical performance data of raw materials, and the suppliers' logistics and transportation services, after-sales service data, etc.). This is combined with the project's concrete design requirements and standard specifications, and based on the characteristics of each area, standardized raw material services such as sand and gravel aggregates with standardized gradations, cement and admixtures with standardized mixing components, and high-efficiency admixtures that are highly compatible with standardized cementitious materials are provided. This will reduce the overall cost of concrete projects in various regions, steadily improve the quality of concrete structures, and enable early prediction of properties such as appearance, thereby improving the scientific nature of concrete mix proportions and ensuring project quality.

[0080] In a specific embodiment.

[0081] The intelligent design of engineering concrete mix ratios is based on a large amount of statistical data on concrete mix ratios of different strength grades, different working performances, and different application fields from actual engineering projects. It then uses the closest packing optimization algorithm and the improved Paul-Mi formula to perform artificial intelligence analysis on the statistical concrete mix ratio big data. The big data algorithm is then used to establish a mapping model between raw materials, mix ratios, environment, and concrete performance, thereby constructing a growing concrete mix ratio gene library. The second step specifically includes:

[0082] S21. Establish a concrete performance gene library based on historical project data acquired and recorded in advance on the information platform. The concrete performance gene library includes: project area, amount of each raw material, glue-water ratio, cement-mortar strength, concrete strength, concrete workability, net paste surplus, and mortar surplus; wherein net paste surplus = net paste volume - fine aggregate pore volume = net paste volume - fine aggregate void ratio * fine aggregate volume / (1 - fine aggregate void ratio), mortar surplus = mortar volume - coarse aggregate pore volume = mortar volume - coarse aggregate void ratio * coarse aggregate volume / (1 - coarse aggregate void ratio);

[0083] The net pulp surplus V ey Calculate by the following formula: V ey =V e -P s ×V s / (1-P s ) (2)

[0084] Where V e is the net pulp volume, P s is the porosity of fine aggregate, V s is the volume of fine aggregate;

[0085] The mortar surplus V my Calculate by the following formula: V my =V m -P g ×V g / (1-P g ) (3)

[0086] Where: V m is the mortar volume, P g is the porosity of coarse aggregate, V g is the volume of coarse aggregate;

[0087] A large amount of concrete mix data from actual engineering projects, covering different concrete strength grades, performance characteristics, and application areas, was collected and used to establish a concrete performance gene library using a big data algorithm. The concrete mix design method based on big data was demonstrated using the 100 sets of concrete mix data from engineering projects shown in Table 1 as an example.

[0088] Table 1 Concrete mix ratio data in 100 engineering projects

[0089] S22. The linear relationship between the glue ratio and the concrete mix strength is obtained by the Paul-Rhys formula: y = af cu，o +b (1)

[0090] Among them, y is the glue ratio, f cu，o is the concrete mix strength, a and b are the new regression coefficients;

[0091] The data in the concrete performance gene library is brought into the linear relationship formula (1) for data fitting to obtain the a and b values ​​corresponding to different concrete strengths in different regions, thereby forming a new regression coefficient gene library with regional characteristics;

[0092] Establishing the new regression coefficient gene library specifically includes:

[0093] Establish the composite cementitious material mortar strength model,

[0094] Where, f b is the strength of composite cementitious material mortar, U k is the dosage of cementitious material k; i k A is the dosage in the test method of k activity index of cementitious materials; k - activity index of the cementitious material k; f ce is the strength of cement mortar;

[0095] The amount of each cementitious material and the cement mortar strength data in the concrete performance gene library are brought into the composite cementitious material mortar strength model to obtain the composite cementitious material mortar strength corresponding to different concrete strengths in different regions;

[0096] For the same composite cementitious material, the water-binder ratio Mixed with concrete strength f cu,0 There is a nonlinear inverse proportional relationship. Take the reciprocal of both sides of the Bolomy formula to get the glue ratio With f cu,0 、f b The relationship is as follows:

[0097] Then we get:

[0098] b=ab (7)

[0099] Among them, a a 、a b is the regression coefficient in the Paul-Richard formula;

[0100] Combined with the composite cementitious material mortar strength data corresponding to different concrete strengths in different regions obtained in the above steps, the mix ratios of composite cementitious material mortars with similar strengths are regarded as the same type of composite cementitious materials, and the glue-water ratio data corresponding to different concrete strengths in different regions in the concrete performance gene library are brought into formula (1), formula (6) and formula (7) for data fitting to obtain the a corresponding to different concrete strengths in different regions. a 、a b The values ​​of a and b corresponding to different concrete strengths in different regions are obtained, forming a new regression coefficient gene library with regional characteristics. Table 2 takes the new regression coefficients corresponding to the concrete mix ratios of 100 groups of engineering projects as an example as the concrete performance gene library.

[0101] Table 2 New regression coefficient gene library corresponding to concrete mix ratios of 100 engineering projects

[0102] S23. Determine the concrete mix strength and workability design targets. Determine the concrete mix strength and workability design targets based on the raw material resource distribution and performance characteristics of projects in different regions across the country, combined with the project concrete design requirements and standards and specifications. This example uses a concrete mix ratio with a design slump of 200 mm and a 28-day compressive strength of 43 MPa as an example.

[0103] S24. Determine the values ​​of the mortar surplus and the net paste surplus according to the concrete mix strength and workability design targets and in combination with the concrete performance gene library, and calculate the amount of coarse aggregate and fine aggregate per unit volume of concrete;

[0104] Step S24 specifically includes:

[0105] S241. Determine the values ​​of the mortar surplus and the net paste surplus based on the concrete mix strength and work performance design targets and in combination with the concrete performance gene library. Based on the design targets of a concrete slump of 220 mm and a 28-day compressive strength of 40 MPa, determine in the concrete performance gene library that the mortar surplus has a value range of 280-320 L and the net paste surplus has a value range of 68-92 L. The values ​​of the mortar surplus and the net paste surplus are selected within the above ranges. In this embodiment, the net paste surplus volume V ey Take 70L, the excess volume of mortar is V my Take 280L.

[0106] S242. Calculate the volume of coarse aggregate V g and fine aggregate volume V s , and then calculate the amount of coarse aggregate and fine aggregate in unit volume of concrete by solving the density of coarse aggregate and fine aggregate;

[0107] According to formula (2) and formula (3), it can be deduced that: V g =(1000-V k -V my ) / (P g / (1-P g )+1) (8) V s =(V m -V ey ) / (P s / (1-P s )+1) (9)

[0108] Formula (8), V k is the volume of air; in this embodiment, V k The value is 20L, P g The value is 43%, P s The value is 40%;

[0109] Solving equations (8) and (9) yields Vg = 399L, Vs = 306.6L.

[0110] In this embodiment, the density of coarse aggregate is 2650 kg / m 3 , fine aggregate density is 2650kg / m 3 , from this solution, we can get the amount of coarse aggregate in unit volume of concrete is 1057kg, and the amount of fine aggregate is 797kg.

[0111] S243, screening the various coarse aggregates at the construction site;

[0112] Assume that when D = Ds, CFPT = 0, and when D = D L When CFPT=100, the formula is:

[0113] In formula (10): CPFT-content percentage of particles smaller than particle size D, %;

[0114] D-sieve hole size, mm;

[0115] Ds - diameter of the smallest particle in the mixed aggregate, mm;

[0116] D L - diameter of the largest particle in the mixed aggregate, mm;

[0117] n-distribution modulus, ranging from 0.2 to 0.4.

[0118] In this embodiment, the data information of each subdivided coarse aggregate in the coarse aggregate is shown in Table 3:

[0119] Table 3 Coarse aggregate screening results

[0120] S244. Construct an objective function by combining the close packing model and the least squares algorithm to determine the mass ratio of each subdivided coarse aggregate under the condition of close packing of aggregates; and calculate the mass of each subdivided coarse aggregate per unit volume of concrete based on the amount of coarse aggregate obtained in step S243.

[0121] The formula for establishing the residual sum of squares between the multivariate mixed aggregate accumulation curve and the target gradation curve is as follows:

[0122] In formula (11): Ss is the residual sum of squares, P mix and P tar They are the actual stacking curve and the target gradation curve respectively.

[0123] A computer program calculates the particle composition that minimizes Ss, bringing the mixed aggregate's particle composition closest to the target curve. Finally, the particle composition closest to the target gradation is output. In this embodiment, after inputting the screening data for coarse aggregate 1 and coarse aggregate 2, a close packing calculation is performed, resulting in a coarse aggregate mass ratio closest to the theoretical packing curve of: coarse aggregate 1:coarse aggregate 2 = 19:81. The close packing curve fitting results are shown in Figure 3. Based on the coarse aggregate usage per unit volume of concrete calculated in S42, the usage of coarse aggregate 1 per unit volume of concrete is 201 kg, and the usage of coarse aggregate 2 per unit volume of concrete is 856 kg.

[0124] S25. Determine the new regression coefficients a and b based on the concrete mix strength and workability design targets and the new regression coefficient gene library, and then determine the glue-to-water ratio, the amount of each cementitious material used per unit volume of concrete, and the amount of water used;

[0125] Step S25 specifically includes:

[0126] S251. Collecting physical and chemical property data of various gelling materials;

[0127] In this embodiment, cement and fly ash are used as examples for explanation. The physical and chemical properties of the two are shown in Table 4 and Table 5:

[0128] Table 4 Cement performance data

[0129] Table 5 Fly ash performance data

[0130] S252, according to the concrete mix strength and work performance design goals, combined with the new regression coefficient gene library to determine the new regression coefficient a, b values, calculate the glue ratio According to the design target of concrete slump of 220mm and 28-day compressive strength of 40MPa, the new regression coefficients a and b are determined in the new regression coefficient gene library, and then the corresponding glue ratio value is calculated according to formula (1); in this embodiment, corresponding to row 29 in Table 2, a is taken as 0.06, b is taken as 0.29, and the glue ratio is calculated.

[0131] S253. Determine the dosage of each cementitious material based on the cement-water ratio and the concrete mix strength and workability design targets, combined with the concrete performance gene library. Based on the concrete slump of 220 mm and the design targets of 40 MPa in 28 days, and the calculated cement-water ratio, select the fly ash dosage that meets the requirements from Table 1 as 15%-50%. In actual selection, the design target of 28-day compressive strength of concrete can be expanded to a certain range close to 40 MPa, such as 40 ± 5 MPa. In this embodiment, the fly ash dosage is selected as 45%, and the corresponding cement dosage is 55%. At this time, f is calculated according to the composite cementitious material strength calculation formula (4): b The value is 32.62MPa.

[0132] S254. Calculate the density ρ of the composite cementitious material and the amount M of the composite cementitious material per unit volume of concrete based on the dosage and density of each cementitious material, and then obtain the amount M of each cementitious material per unit volume of concrete. i ;

[0133] In formula (12), U1 is the cement content, U2 is the fly ash content, ρ1 is the density of cement, and ρ2 is the density of fly ash. The calculated value is ρ = 2740 kg / m 3 , M = 363kg, of which the amount of cement M1 = 200kg, and the amount of fly ash M2 = 163kg.

[0134] S255. Calculate the water content M per unit volume of concrete based on the glue-water ratio and the amount of composite cementitious material per unit volume of concrete. w ,

[0135] S26. Summarize the amounts of coarse aggregate, fine aggregate, various cementitious materials, and water consumption in a unit volume of concrete to obtain specific concrete mix ratio data.

[0136] In this example, the admixture dosage is calculated based on the 1.2% recommended dosage of Sika water reducer manufacturer, i.e., the dosage is 1.2% of the dosage of composite cementitious material M, which is 4.36 kg. The specific concrete mix ratio data is shown in Table 6:

[0137] Table 6

[0138] For the same concrete mix strength and workability design goals, the concrete mix ratios obtained using the traditional concrete mix design method are shown in Table 7:

[0139] Table 7

[0140] The concrete mix ratio P1 obtained by the big data-based concrete mix ratio design method provided by the present invention and the concrete mix ratio P2 obtained by the traditional concrete mix ratio design method were tested and verified. The verification results are shown in Table 8:

[0141] Table 8

[0142] The design goal of this embodiment is a concrete slump of 220mm and a 28-day compressive strength of 40MPa. The test verification results of the concrete mix P1 obtained by the concrete mix design method based on big data provided by the present invention and the concrete mix P2 obtained by the traditional concrete mix design method are equivalent to the design value results, and both can meet the design requirements. However, a detailed comparison of the concrete mixes obtained by the two methods shows that when the concrete 28-day strength value and slump value of P1 are close, the amount of cement in the mix is ​​100kg lower; and the amount of net paste composed of cement, fly ash and water is 506kg, while the amount of net paste in P2 is 535kg. From the comparison, it can be seen that the concrete mix obtained by the concrete mix design method based on big data provided by the present invention is more economical than the concrete mix obtained by the traditional concrete mix design method, under the premise of meeting the design requirements, and is more in line with the requirements of green and low carbon.

[0143] In a specific embodiment.

[0144] As shown in Figure 2, the greening of the entire concrete production process is achieved through a concrete smart factory based on machine perception and execution. By comprehensively applying robot technology, intelligent sensing technology, image recognition technology, machine vision technology, autonomous driving technology, AI algorithms and other intelligent technologies based on machine perception and execution in workshops such as the powder storage management workshop, aggregate storage management workshop, concrete production management workshop, and concrete delivery management workshop, intelligent management of the entire concrete production process is achieved, significantly reducing the number of on-site production personnel. The concrete production preparation in step three is achieved through a concrete smart factory, and the concrete smart factory is data-connected with the information platform. Specifically, the concrete smart factory includes a powder storage management workshop, an aggregate storage management workshop, a concrete production management workshop, and a concrete delivery management workshop. Each management workshop is equipped with intelligent robots and is equipped with an intelligent control system.

[0145] The powder silo in the powder storage management workshop is provided with a powder electronic access control and an intelligent material level system. The amount of powder required is input through the information platform and transmitted to the intelligent control system, which controls the powder electronic access control to open and automatically samples the powder through the powder sampling device to the unattended floor scale for weighing until the weight reaches the set powder dosage value. Then, the powder electronic access control is controlled to close through the intelligent control system, and the intelligent material level system automatically obtains the total amount of powder in the powder silo and displays it in real time. The powder sampling device is an automatic sampling and autonomous walking robot, and the unattended floor scale is equipped with a machine vision sensor, which obtains the powder sampling dosage value and compares it with the unattended floor scale weighing value, and transmits the result to the powder sampling device until the unattended floor scale weighs to the set powder dosage value;

[0146] The AI ​​warehousing system in the aggregate storage management workshop obtains the amount of aggregate required input by the information platform, and controls the aggregate sampling robot to take samples in the aggregate silo, and then weighs them on the unattended scale until the set powder amount value is reached. The unattended scale is equipped with a machine vision sensor, which obtains the amount value of the aggregate sample and compares it with the weighing value of the unattended scale, and transmits the result to the aggregate sampling robot until the unattended scale weighs the set aggregate amount value; the powder and aggregate weighed by the unattended scale of the powder storage management workshop and the aggregate storage management workshop are guided into the mixing building by controlling the silo guidance system;

[0147] The mixing plant in the concrete production management workshop is equipped with a multi-machine centralized control system, an unmanned loader, and a machine vision system. The machine vision system monitors the mixing plant in real time and transmits the results to the multi-machine centralized control system to determine whether there is material transportation. If material transportation is available, the multi-machine centralized control system controls the unmanned loader to dock the transported materials and input them into the mixing plant. The multi-machine centralized control system controls mixing and preparing concrete. An online monitoring system for concrete working performance is installed in the mixing plant to monitor various working performances of the concrete in real time until they meet the design requirements.

[0148] The concrete delivery management workshop loads the concrete prepared by the concrete production management workshop through an unmanned mixer truck, and controls the unmanned mixer truck to the designed position through the intelligent scheduling system, and uses the slump detection and test block making robot to detect the slump of the concrete and make test blocks.

[0149] The intelligent green platform for the entire life cycle of concrete realizes the intelligent and green concrete production process through the combined application of the intelligent concrete scheduling system, the intelligent concrete silo management and control system, the intelligent detection system for the performance of fresh concrete, and the comprehensive utilization system of wastewater and waste residue from concrete stations.

[0150] In a specific embodiment.

[0151] The intelligent concrete construction module realizes the digitization of the concrete vibration construction process. Due to the huge workload of concrete on-site construction, it is difficult to achieve quantitative and precise control by manual operation, which may lead to vibration quality defects and even cause structural safety problems such as cracking in key parts. The specific steps of step 4 are:

[0152] First, during the concrete pouring process, a transparent visual template equipped with an industrial camera is deployed for construction.

[0153] Secondly, during the construction process, a concrete appearance defect map algorithm is established based on image morphology analysis, HSV color gamut segmentation, and Hough detection algorithm to achieve intelligent judgment of the concrete surface bubble rate. Specifically:

[0154] (1) Image preprocessing, data annotation, and division into training and validation sets;

[0155] (2) Establish the YOLO-v5 deep network model, as shown in Figure 4;

[0156] (3) Train the model and load the trained model for target detection.

[0157] The YOLO-v5 network mainly includes Input (input layer), Backbone (backbone network), Neck (bottleneck end) and Detect Head (detection head). Input refers to the network input, which is usually a fixed-size image; Backbone is the backbone network in the YOLOv5s network structure, and is the core network architecture for extracting features from the original image. The Backbone backbone network is usually composed of convolutional layers and pooling layers, which gradually reduce the size of the feature map and increase its abstraction level. As data flows through the network, each layer processes it to better capture the key features of the input data; the Neck network connects the Backbone and Detect, and is responsible for further extracting higher-level features from the features extracted by the convolutional layer and pooling layer, and performing feature fusion; the Detect Head network is the detection head of the YOLOv5 network, which accepts the deep features of the Neck, predicts the type and location of the target, and generates a Bounding box.

[0158] At the same time, by establishing a relationship model between the vibrating rod current and voltage information and the concrete vibration state, concrete vibration quality defects can be predicted. As shown in Figure 5, by conducting laboratory vibration tests on concrete in different states and plotting curves of current, vibration time, and vibration density, intelligent concrete vibration construction can be achieved in specific scenarios (prefabricated component sites and tower cranes).

[0159] Then, based on the above-obtained concrete surface bubble rate analysis and concrete vibration quality defect analysis results, combined with the concrete vibration construction quality requirements, on-site construction conditions, and construction technology, an in-depth analysis was conducted to establish the vibration time, frequency, and location process parameters. Combined with the concrete vibration curve to guide the determination of the vibration endpoint, as shown in Figure 5, combined with the structural and environmental information of the construction site, a three-dimensional visualization model of the vibration effect was formed on the platform.

[0160] Finally, all the concrete pouring construction data obtained above are recorded in the database of the information platform to realize intelligent and real-time monitoring of the quality of concrete vibration construction, guide operators to reasonably adjust their work behavior, and accumulate a large amount of production data and corresponding data of relevant construction projects in the database of the information platform until a concrete vibration quality standard database is formed.

[0161] In a specific embodiment.

[0162] The information platform comprises a platform foundation, an evaluation index system, a data warehouse, and a comprehensive coordination system. The foundation utilizes a distributed, microservices architecture, asynchronous batching plant equipment acquisition and control technology, and a containerized deployment model. It also establishes comprehensive concrete data standards and supports a variety of product forms, including PCs and mobile devices. The system's strong adaptability effectively reduces the technical complexity of platform development and facilitates rapid deployment and subsequent iterations.

[0163] The information platform also includes a data warehouse, which includes a visualization decision-making platform and a concrete gene library. The visualization decision-making platform realizes the timely collection, benchmarking analysis and long-term storage of platform data throughout the entire life cycle of concrete. The data uploaded by users or devices is first subjected to anomaly detection and classification by the isolation forest algorithm (iForest), and outliers and deviations are automatically eliminated to avoid the influence of erroneous data; secondly, the principal component analysis algorithm (PCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithm are used to perform linear and nonlinear transformations on multi-dimensional data respectively to reduce data dimensions and realize dimensionality reduction analysis and visualization processing; it provides users with high-efficiency and high-accuracy data presentation under massive data, realizes self-benchmarking, peer benchmarking, and industry benchmarking of data, and assists management decision-making.

[0164] The concrete gene library includes a strength gene library, a work performance gene library, a durability gene library and an appearance quality gene library; based on the data of the entire life cycle of concrete, an artificial neural network model optimized by genetic algorithms is used to establish a nonlinear mapping model between raw material quality, raw material dosage, environmental factors, concrete type and concrete mechanical properties, namely the concrete strength gene library, to achieve accurate prediction of concrete strength; using the random forest algorithm, a nonlinear mapping model is established between concrete type, pouring method, strength, slump and concrete mortar surplus and net paste surplus, namely the concrete work performance gene library, to achieve optimal recommendation of surplus paste dosage, and assist in the intelligent prediction of concrete mix proportion in step two. Design; using the least squares support vector machine algorithm based on random forest, a nonlinear mapping model between raw material quality, raw material usage, environmental factors and durability performance was established, which is the concrete durability performance gene library, to achieve accurate prediction of concrete frost resistance and anti-permeability performance; using the collected concrete appearance pictures, through grayscale conversion and standard deviation calculation, first established an evaluation method for appearance color difference: standard deviation 0-7.5 is level one, 7.5-14 is level two, 14-21 is level three, >21 is level four, and then using the random forest algorithm, a nonlinear mapping model between raw material quality, raw material usage, environmental factors and concrete appearance grade was established, which is the appearance gene library, to achieve accurate prediction of concrete appearance color difference.

[0165] The information platform also includes an evaluation index system, which is based on the collection of data on the entire life cycle of concrete, including concrete raw materials, mix proportions, production, construction and physical testing. It proposes a calculation method for indicators in five aspects: concrete raw material stability index, physical homogeneity index, production standardization index, construction refinement index, and mix proportion greening index. It also realizes comprehensive evaluation of concrete in the form of a five-dimensional graph, and calculates the area of ​​the five-dimensional graph by calculus derivation to achieve comprehensive benchmarking of concrete products between different units. Raw material indicators include: physical and chemical indicators of raw materials such as cement, sand, stone, fly ash, mineral powder, and silica fume (such as the 28-day strength of cement mortar, the fineness modulus of sand, the porosity of stone, the particle size range, the fly ash activity index, the mineral powder activity index, the silica fume activity index and other parameters, all of which can be obtained through experimental testing).

[0166] Raw material stability index:

[0167] Green index of the ratio:

[0168] Production Standardization Index

[0169] Construction refinement index:

[0170] Entity homogeneity index:

[0171] The information platform also includes a comprehensive coordination system. The big data center is a data application system, which is a two-way optimized comprehensive coordination system. It includes a demand planning coordination module, a site selection coordination module, a raw material organization coordination module, a production task coordination module and an equipment scheduling coordination module. By linking the demand side, equipment side, material side and application side demands, it realizes the overall optimization of production resources and drives the rational operation of each link.

[0172] It includes multiple modules, specifically:

[0173] The demand planning coordination module collects the overall volume and volume of each part of the construction project, the construction project schedule, and the construction organization plan of the project department, and systematically schedules concrete production. It also feeds back the information to the site selection coordination module and the raw material organization coordination module. At the same time, it can also optimize and adjust the concrete production schedule and schedule according to the raw material organization situation and equipment production situation.

[0174] The station selection coordination module coordinates the selection of mixing station equipment by matching the daily production volume peak, the hourly production volume peak and the equipment production capacity, and combining the distribution of mixing stations around the project. It also coordinates the selection of mixing station equipment by matching road conditions and transportation conditions. At the same time, it can also propose adjustment suggestions for demand plans based on equipment conditions and transportation conditions.

[0175] The raw material organization and coordination module receives the concrete production plan from the demand planning and coordination module. Based on the theoretical mix ratio information collected by the platform and the production mix ratio information collected by the industrial control system, it forms a raw material demand plan and provides it to the procurement department and suppliers, thus enabling the advance organization of raw materials. At the same time, it can also propose adjustment suggestions for the production plan based on the actual progress of the raw material organization.

[0176] The production task coordination module receives the concrete production plan from the demand planning coordination module, and schedules production based on equipment production capacity, equipment status, and raw material preparation. This production scheduling plan is then provided to the raw material organization coordination module and the equipment scheduling module. Furthermore, the module can also propose production plan adjustment suggestions based on the raw material preparation and equipment preparation status.

[0177] The equipment scheduling coordination module accepts the production scheduling plan of the production task coordination module, analyzes the production capacity and equipment status of the production equipment, and forms production equipment scheduling and equipment maintenance requirements and provides them to the equipment department. It analyzes the production scheduling plan to form tank truck requirements and tank truck scheduling plans and provides them to the fleet to realize equipment scheduling. At the same time, it can also make adjustment suggestions to the production plan based on the actual preparation of production equipment and transportation equipment.

[0178] Specific embodiments

[0179] The present invention proposes a method for greening and quality control of the entire life cycle of concrete based on Internet of Things big data. This method has been deployed and applied in more than 300 actual engineering projects across the country. A bridge engineering project in Jiangsu area is taken as an example to illustrate the implementation steps of the present invention application.

[0180] 1. In terms of greening concrete raw materials, we comprehensively analyzed the raw material resource distribution, performance characteristics, and service conditions of projects in the same region of Jiangsu. Combined with the project concrete design requirements and standard specifications, we provide the following raw material optimization based on the characteristics of the Jiangsu region:

[0181] (1) Standardized graded sand and gravel aggregates are uniformly allocated to reduce the porosity of concrete aggregate by 3%, thereby saving an average of 20 kg of cement per cubic meter of concrete.

[0182] (2) Based on the distribution of cementitious materials in the Jiangsu area, standardized cement and admixtures and high-efficiency admixtures with strong adaptability to standardized cementitious materials were formulated, which reduced the comprehensive cost of concrete per cubic meter of the bridge project by 20 yuan. At the same time, the quality, appearance and other characteristics of the concrete structure project were effectively guaranteed.

[0183] 2. Regarding the intelligent design and implementation of engineering concrete mix ratios, this example uses a concrete mix ratio with a designed slump of 200mm and a 28-day compressive strength of 43MPa as an example. Based on the graded screening data of sand and gravel aggregates at the construction site, the MAA close packing model and the least squares method (LSM) are combined to construct an objective function to achieve close packing design and determine the amount of sand and stone aggregate required for close packing. After inputting the screening data of coarse aggregate 1 and coarse aggregate 2, a computer-generated close packing calculation was performed, resulting in a coarse aggregate mass ratio that most closely approximates the theoretical packing curve: coarse aggregate 1:coarse aggregate 2 = 19:81. Using the workability gene library, the range for excess mortar was determined to be 280-320L, with 280L being used in this example. The range for excess mortar was 68-92L, with 70L being used in this example. This solution yields a fine aggregate mass of 797kg and a coarse aggregate mass of 1057kg per cubic meter of concrete.

[0184] The water-binder ratio and admixture dosages were determined by combining on-site cement performance indicators, admixture type, dosage, and activity index with the "equivalent cement" model and a and b value gene libraries. The resulting composite density of the cementitious material is 2740 kg / m³. The total mass of the cementitious material is 363 kg, including 200 kg of cement and 163 kg of fly ash. The water consumption = water-binder ratio * total mass of the cementitious material = 142 kg.

[0185] The concrete mix ratio data are shown in Table 9 below, which is derived from the volumetric method and the density data of each material. Compared to traditional mix design methods, this method increases the amount of mineral admixtures by 50-10 kg per cubic meter of concrete, while ensuring the concrete design target and workability. This reduces cement usage by 50-100 kg per cubic meter of concrete, thus achieving green, low-carbon, energy-saving, and emission-reduction goals.

[0186] Table 9

[0187] 3. In terms of the implementation of intelligent concrete production, the greening of the entire concrete production process is achieved through a smart concrete factory based on machine perception and execution.

[0188] 4. In terms of intelligent concrete construction implementation, intelligent and real-time monitoring of the quality of concrete vibration construction is realized, and operators are guided to reasonably adjust their work behaviors. After accumulating a large amount of production data, a database of concrete vibration quality standards is formed.

[0189] 5. In terms of information platform implementation, the information platform used in the bridge engineering project in this embodiment includes a platform base, an evaluation index system, a data warehouse, and a big data center module.

[0190] The bridge engineering project in this embodiment comprehensively uses a green and quality control system and method for the entire life cycle of concrete based on the Internet of Things big data of the present invention, which can achieve a significant reduction in carbon emissions throughout the entire life cycle of concrete, while achieving the comprehensive optimization of the unit cost of concrete. The specific efficiency comparison is shown in Table 10 below. Compared with before the application of the present invention, the carbon emissions of the entire life cycle of concrete were reduced by 30.25%, the comprehensive cost of concrete per cubic meter was reduced by 30 yuan, the one-time qualified rate of concrete structure engineering was increased by 3.13%, and the total calculated construction period was saved by 12%. Therefore, the application of a green and quality control system and method for the entire life cycle of concrete based on the Internet of Things big data of the present invention can achieve cost savings for engineering projects, significantly reduce carbon emissions throughout the production process, and can improve the one-time qualified rate of concrete structure engineering and save engineering construction period.

[0191] Table 10 Benefit Comparison

[0192] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A green quality control method for the entire life cycle of concrete based on Internet of Things big data, characterized in that: The green quality control of concrete throughout its life cycle is centrally managed and scheduled through an information platform, which includes the following steps: Step 1: Select the raw materials required for concrete based on the information platform to achieve control over the selection of raw materials for concrete; Step 2: Based on the information platform and the selected concrete raw materials, intelligently design the engineering concrete mix ratio to obtain the mix ratio of each raw material required for the concrete of the project to be constructed; Step 3: Based on the information platform, the concrete is mixed and prepared through the concrete intelligent factory in combination with the concrete mix ratio obtained by the above design, and the quality of the prepared concrete is automatically detected and controlled at the same time; Step 4: Based on the information platform, the concrete prepared by the above mixing is used for intelligent construction.

2. The green quality control method for the entire life cycle of concrete based on Internet of Things big data as claimed in claim 1 is characterized in that: The step one is specifically as follows: first, obtaining the resource distribution, performance characteristics, and service conditions corresponding to the raw materials of different projects in different regions across the country, and forming a database in the information platform; second, based on the concrete design requirements and standard specifications of the projects to be constructed, and based on the characteristics of the areas of the projects to be constructed, screening and comparing in the above database to obtain standardized grading sand and gravel aggregates, standardized mixed component cement and admixtures, and standardized raw material services of high-efficiency admixtures that are highly adaptable to standardized cementitious materials.

3. The green quality control method for the entire life cycle of concrete based on Internet of Things big data as claimed in claim 1 is characterized in that: The step 2 specifically includes: S21. Establish a concrete performance gene library based on historical engineering project data acquired and recorded in advance in the information platform, wherein the concrete performance gene library includes: project area, amount of each raw material, glue-water ratio, cement mortar strength, concrete strength, concrete working performance, net paste surplus and mortar surplus; wherein, net paste surplus=net paste volume-fine aggregate pore volume=net paste volume-fine aggregate void ratio*fine aggregate volume / (1-fine aggregate void ratio), mortar surplus=mortar volume-coarse aggregate pore volume=mortar volume-coarse aggregate void ratio*coarse aggregate volume / (1-coarse aggregate void ratio); The net pulp surplus V ey Calculated by the following formula: In ey =V e -P s ×V s / (1-P s ) Where V e is the net pulp volume, P s is the porosity of fine aggregate, V s is the volume of fine aggregate; The mortar surplus V my Calculated by the following formula: In my =V m -P g ×V g / (1-P g ) Where: V m is the mortar volume, P g is the porosity of coarse aggregate, V g is the volume of coarse aggregate; S22. The linear relationship between the glue ratio and the concrete strength is obtained by the Paul-Rhizome formula: y=of cu，o +b Among them, y is the glue ratio, f cu，o is the strength of concrete mix, a and b are new regression coefficients; Establish the strength model of composite cementitious material mortar. In the formula, f b is the strength of composite cementitious material mortar, U k is the dosage of cementitious material k; i k A is the dosage in the test method of the k activity index of cementitious materials; k —activity index of cementitious material k; f ce is the strength of cement mortar; The amount of each cementitious material and the cement mortar strength data in the concrete performance gene library are brought into the composite cementitious material mortar strength model to obtain the composite cementitious material mortar strength corresponding to different concrete strengths in different regions; Combine the composite cementitious material mortar strength data corresponding to different concrete strengths in different regions obtained in the above steps and the glue-water ratio data corresponding to different concrete strengths in different regions in the concrete performance gene library, bring them into the linear relationship to perform data fitting to obtain a and b values ​​corresponding to different concrete strengths in different regions, and form a new regression coefficient gene library with regional characteristics; S23. Determine the design objectives of concrete mix strength and workability; S24, according to the concrete mix strength and work performance design targets, combined with the concrete performance gene library, determine the values ​​of the mortar surplus and the net paste surplus, and calculate the amount of coarse aggregate and fine aggregate in a unit volume of concrete; S25, according to the concrete mix strength and work performance design goals and the new regression coefficient gene library, determine the new regression coefficient a, b values, and then determine the glue-water ratio, the amount of each cementitious material in the unit volume of concrete, and the amount of water; S26. Summarize the amounts of coarse aggregate, fine aggregate, various cementitious materials and water consumption in a unit volume of concrete to obtain specific concrete mix ratio data.

4. The green quality control method for the entire life cycle of concrete based on Internet of Things big data as claimed in claim 3 is characterized in that: Step S24 specifically includes: S241, according to the concrete preparation strength and work performance design targets, combined with the concrete performance gene library, determine the values ​​of the mortar surplus and the net paste surplus; S242. Calculate the volume of coarse aggregate V g and fine aggregate volume V s , and then calculate the amount of coarse aggregate and fine aggregate in unit volume of concrete by solving the density of coarse aggregate and fine aggregate; S243, screening the various subdivided coarse aggregates at the construction site; S244. Construct an objective function by combining the close packing model and the least squares algorithm to determine the mass ratio of each subdivided coarse aggregate under the condition of close packing of aggregates; and calculate the mass of each subdivided coarse aggregate in a unit volume of concrete according to the amount of coarse aggregate obtained in step S243.

5. The green quality control method for the entire life cycle of concrete based on Internet of Things big data as claimed in claim 4 is characterized in that: Step S25 specifically includes: S251. Collect the physical and chemical performance data of each cementitious material; S252, according to the concrete mix strength and work performance design targets, combined with the new regression coefficient gene library, determine the new regression coefficient a, b values, and calculate the value of the glue ratio; S253, determining the dosage of each cementitious material according to the glue-water ratio and the concrete preparation strength and workability design targets in combination with the concrete performance gene library; S254, calculating the density of the composite cementitious material and the amount of the composite cementitious material in the unit volume of concrete according to the dosage and density of each cementitious material, and then obtaining the amount of each cementitious material in the unit volume of concrete; S255. Calculate the amount of water used in unit volume of concrete based on the glue-water ratio and the amount of composite cementitious material used in unit volume of concrete.

6. The green quality control method for the entire life cycle of concrete based on Internet of Things big data as claimed in claim 1 is characterized in that: The concrete production and preparation in step 3 is realized by a concrete intelligent factory, and the concrete intelligent factory is connected with the information platform to achieve data connectivity. Specifically, the concrete intelligent factory includes a powder storage management workshop, an aggregate storage management workshop, a concrete production management workshop, and a concrete delivery management workshop. Each management workshop is equipped with an intelligent robot and an intelligent control system. The powder silo in the powder storage management workshop is provided with a powder electronic access control and an intelligent material level system. The amount of powder required is input through the information platform and transmitted to the intelligent control system, which controls the powder electronic access control to open and automatically samples the powder to the unattended scale for weighing through the powder sampling device until it is weighed to the set powder usage value, and then controls the powder electronic access control to close through the intelligent control system, and the intelligent material level system automatically obtains the total amount of powder in the powder silo and displays it in real time. The powder sampling device is an automatic sampling and autonomous walking robot, and the unattended scale is equipped with a machine vision sensor, which obtains the powder sampling usage value and compares it with the unattended scale weighing value, and transmits the result to the powder sampling device until the unattended scale weighs to the set powder usage value; The AI ​​storage system in the aggregate storage management workshop obtains the amount of aggregate required input by the information platform, and controls the aggregate sampling robot to take samples in the aggregate silo, and then weighs them on the unattended scale until the set powder amount value is reached. The unattended scale is equipped with a machine vision sensor, which obtains the amount value of the aggregate sampling and compares it with the weighing value of the unattended scale, and transmits the result to the aggregate sampling robot until the unattended scale weighs the set aggregate amount value; the powder and aggregate weighed by the unattended scale of the powder storage management workshop and the aggregate storage management workshop are guided to the mixing building by controlling the silo guidance system; The mixing plant of the concrete production management workshop is equipped with a multi-machine centralized control system, an unmanned loader, and a machine vision system. The machine vision system monitors the mixing plant in real time and transmits the results to the multi-machine centralized control system to determine whether there is material transportation. If there is material transportation, the multi-machine centralized control system controls the unmanned loader to dock the transported materials and input them into the mixing plant. The multi-machine centralized control system controls the mixing and preparation of concrete. An online monitoring system for concrete working performance is set in the mixing plant to monitor various working performances of the concrete in real time until the design requirements are met; The concrete delivery management workshop loads the concrete prepared by the concrete production management workshop through an unmanned mixer truck, and controls the unmanned mixer truck to the designed position through an intelligent scheduling system, and detects the slump of the concrete and makes test blocks through a slump detection and test block making robot.

7. The green quality control method for the entire life cycle of concrete based on Internet of Things big data as claimed in claim 1, characterized in that: The step 4 is specifically as follows: First, during the concrete pouring process, a transparent visual template equipped with an industrial camera is used for construction. Secondly, during the construction process, a concrete appearance defect map algorithm is established based on image morphology analysis, HSV color gamut segmentation and Hough detection algorithm to realize intelligent judgment of concrete surface bubble rate; at the same time, by establishing a relationship model between the vibrating rod current, voltage information and concrete vibration state, concrete vibration quality defects can be predicted; Then, through the above-obtained concrete surface bubble rate analysis and concrete vibration quality defect analysis results, combined with the concrete vibration construction quality requirements, on-site construction conditions, and construction technology, an in-depth analysis is conducted to determine the vibration time, frequency, and location process parameters. Combined with the concrete vibration curve that guides the judgment of the vibration endpoint, combined with the structural information and environmental information of the construction site, a three-dimensional visualization model of the vibration effect is formed on the platform; Finally, all the data of concrete pouring construction obtained above are recorded in the database of the information platform, and the data corresponding to the relevant construction projects are accumulated in the database of the information platform until a concrete vibration quality standard database is formed.

8. The green quality control method for the entire life cycle of concrete based on Internet of Things big data as claimed in claim 3, characterized in that: The information platform also includes a data warehouse, which includes a visualization decision platform and a concrete gene library. The visualization decision platform realizes the timely collection, benchmarking analysis and long-term storage of platform data of the entire life cycle of concrete. The data uploaded by users or devices are firstly subjected to anomaly detection and classification by an isolation forest algorithm, and outliers and deviations are automatically removed to avoid the influence of error data; secondly, the principal component analysis algorithm and the t-distribution random neighborhood embedding algorithm are used to perform linear and nonlinear transformations on multi-dimensional data respectively, so as to reduce the data dimension and realize dimensionality reduction analysis and visualization processing; The concrete gene library includes a strength gene library, a work performance gene library, a durability gene library and an appearance quality gene library; for the data of the entire life cycle of concrete, an artificial neural network model optimized by genetic algorithm is used to establish a nonlinear mapping model between raw material quality, raw material dosage, environmental factors, concrete type and concrete mechanical properties, namely the concrete strength gene library, to achieve accurate prediction of concrete strength; a nonlinear mapping model between concrete type, pouring method, strength, slump and concrete mortar surplus and net paste surplus is established by using random forest algorithm, namely the concrete work performance gene library, to achieve optimal recommendation of surplus paste dosage, and to assist in the intelligent prediction of concrete mix proportion in step 2. Design; using the least squares support vector machine algorithm based on random forest, a nonlinear mapping model between raw material quality, raw material dosage, environmental factors and durability performance was established, which is the concrete durability performance gene library, to achieve accurate prediction of concrete frost resistance and anti-permeability performance; using the collected concrete appearance pictures, through grayscale conversion and standard deviation calculation, firstly, an evaluation method for appearance color difference was established: the standard deviation 0-7.5 is level one, 7.5-14 is level two, 14-21 is level three, and >21 is level four, and then using the random forest algorithm, a nonlinear mapping model between raw material quality, raw material dosage, environmental factors and concrete appearance grade was established, which is the appearance gene library, to achieve accurate prediction of concrete appearance color difference.

9. The green quality control method for the entire life cycle of concrete based on Internet of Things big data as claimed in claim 3, characterized in that: The information platform also includes an evaluation index system, which is based on the collection of data on the entire life cycle of concrete, including concrete raw materials, mix proportions, production, construction and physical testing. It proposes a calculation method for five indicators, including concrete raw material stability index, physical homogeneity index, production standardization index, construction refinement index and mix proportion greening index. It also implements a comprehensive evaluation of concrete in the form of a five-dimensional graph, and calculates the area of ​​the five-dimensional graph by calculus derivation, to achieve comprehensive benchmarking of concrete products between different units.

10. The green quality control method for the entire life cycle of concrete based on Internet of Things big data as claimed in claim 3, characterized in that: The information platform also includes a comprehensive coordination system, which includes multiple modules, specifically: The demand planning coordination module collects the overall volume and volume of each part of the construction project, the progress plan of the construction project, and the construction organization plan of the project department, and systematically schedules the concrete production, and feeds back the information to the site selection coordination module and the raw material organization coordination module. At the same time, it can also optimize and adjust the concrete production plan and progress plan according to the raw material organization situation and equipment production situation; The station selection coordination module coordinates the selection of mixing station equipment by matching the daily production volume peak, the hourly production volume peak and the production capacity of the equipment, and combining the distribution of mixing stations around the project. It coordinates the selection of mixing stations by matching road conditions and transportation conditions. At the same time, it can also make adjustment suggestions for demand plans based on equipment conditions and transportation conditions. The raw material organization and coordination module accepts the concrete production plan of the demand planning and coordination module, forms a raw material demand plan based on the theoretical mix ratio information collected by the platform and the production mix ratio information collected by the industrial control, and provides it to the procurement department and suppliers to realize the advance organization of raw materials. At the same time, it can also propose adjustment suggestions for the production plan based on the actual progress of the raw material organization; The production task coordination module accepts the concrete production plan from the demand planning coordination module, and schedules production based on the equipment production capacity, equipment status, and raw material preparation status. It forms a production scheduling plan and provides it to the raw material organization coordination module and the equipment scheduling module. At the same time, it can also make suggestions for adjusting the production plan based on the raw material preparation status and equipment preparation status. The equipment scheduling coordination module accepts the production scheduling plan of the production task coordination module, analyzes the production capacity and equipment status of the production equipment, and forms the production equipment scheduling and equipment maintenance requirements and provides them to the equipment department. It analyzes the production scheduling plan to form the tank truck demand and tank truck scheduling plan and provides them to the fleet to realize equipment scheduling. At the same time, it can also make suggestions for adjusting the production plan according to the actual preparation of the production equipment and transportation equipment.

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