A cloud platform-based concrete production analysis system

Abstract

This invention discloses a cloud-based concrete production analysis system. Based on big data on raw material usage for concrete strength grades, this system addresses issues such as untimely monitoring, incomplete monitoring information, and inaccurate problem responses in existing concrete production systems. The system specifically includes: a data acquisition unit, a cloud storage unit, a mixing data analysis unit, a concrete quality analysis unit, an early warning release unit, and a production display terminal. This method uses a cloud platform to store, integrate, calculate, analyze, and visualize concrete mixing information, reducing the manpower input cost for concrete mixing management, providing real-time responses to production exceeding standards and quality issues, and improving the risk control of concrete product quality problems, the intensity of production process monitoring, and management efficiency.

Description

Technical Field

[0001] This invention relates to the field of concrete production management technology, and in particular to a cloud-based concrete production analysis system. Background Technology

[0002] Concrete, as a fundamental raw material in construction engineering, directly impacts the quality and lifespan of buildings. Large-scale construction projects involve multiple concrete mixing plants, and individual projects typically use numerous batches of finished concrete. The quality of finished concrete is influenced by a variety of factors. By analyzing the concrete production process, we can predict concrete quality risks and thereby control the reliability of construction projects.

[0003] Current technologies primarily focus on pre-construction management and single-object management. Regarding concrete quality, the assessment of production quality mainly relies on whether strength test results meet standards to determine the quality of a single batch of concrete, lacking a comprehensive analysis of the overall concrete quality of all finished products used in a building project. In terms of the concrete production process, there is a lack of real-time monitoring of raw materials and mix proportions, and a lack of in-process analysis for real-time risk warnings, post-production control and guidance, and multi-level, multi-dimensional, and multi-object comprehensive and structured management. This lack of timeliness, risk control, and deep penetration in management directly impacts the comprehensiveness of concrete and building construction quality management.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a cloud-based concrete production analysis system to solve the problems existing in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] This invention provides a cloud-based concrete production analysis system, comprising: a data acquisition unit, a cloud storage unit, a mixing data analysis unit, a concrete quality analysis unit, an early warning release unit, and a display terminal; wherein,

[0008] The data acquisition unit is used to collect information on building structure units, concrete mixing equipment, production batch identification information, design usage of raw materials for concrete of various strength standards, actual raw material usage for each batch, design strength standard for each batch of concrete, and strength test information for each batch of concrete, and transmits the collected data to the cloud storage unit in the cloud platform for storage.

[0009] The cloud storage unit is used to store all the collected data;

[0010] The mixing status analysis unit is used to analyze the production deviation ratio (CMPD Ratio) between the actual and planned usage of each batch of concrete. Taking the production label sequence of each batch of concrete from each machine as the root node of the sequence, the CMPD Ratio is assigned a deviation ratio label sequence. The deviation ratio label is mapped to the machine with the production label and the number of batches produced by the machine. The deviation ratio label is set as a qualified label if the production deviation ratio (CPD Ratio) does not exceed the production deviation ratio error range (ER), and the deviation ratio label is set as an unqualified label if the production deviation ratio (CPD Ratio) exceeds the production deviation ratio error range (ER). All unqualified labels are sent to the early warning release unit.

[0011] The concrete quality analysis unit is used to analyze the overall concrete quality of the building structure. The number of concrete strength test samples for the analyzed building structure is determined by the equipment identification and the number of batches of concrete per batch. The overall concrete strength confidence test is performed on all the concrete used for the analyzed object. The building structure is marked with a no-risk label if it meets the confidence requirements, and marked with a risk label if it does not meet the confidence requirements. All risk labels are sent to the early warning release unit.

[0012] The early warning issuing unit sends an identifier data retrieval request to the cloud storage unit based on the aforementioned received identifier and identifier sequence, generates real-time early warning and risk display charts for concrete production exceeding standards and for concrete strength and quality early warning and risk display charts for physical structural units, and sends them to the display terminal for visualization.

[0013] Furthermore, the specific data acquisition method of the data acquisition unit is as follows:

[0014] Add entity structural unit identifiers to the sampled building entities;

[0015] Add equipment identification to each concrete mixing plant that was collected;

[0016] Add a batch number label to each batch of concrete produced by each concrete mixing plant.

[0017] Add an identifier sequence for the actual quantity of raw materials used in each batch of concrete.

[0018] Add a sequence of identifiers for the designed mix quantities of raw materials for each batch of concrete.

[0019] Add a standard identification sequence for concrete strength grade, and add a test compressive strength identification sequence for the tested concrete value.

[0020] Furthermore, the specific analysis process of the mixing data analysis unit is as follows:

[0021] Obtain the design strength standard for each batch of concrete to determine the design mix proportion of raw materials for each batch of concrete;

[0022] Based on the actual mix proportion of raw materials for each batch of concrete and the designed mix proportion of raw materials for each batch of concrete, calculate the production deviation ratio of each raw material for each batch of concrete, and assign an identification sequence to the production deviation ratio of raw materials for each batch of concrete.

[0023] According to the formula CR n =(CMAP) n -CMDP n ) / CMDP n ×100% to calculate the deviation rate of single raw material production for concrete, where CP n For each batch of concrete being extracted, CMAP n The actual quantity of each raw material in each batch of concrete, CMDP n Design the mix quantity for each batch of concrete using a single raw material;

[0024] Determine whether the deviation rate of the single raw material production of concrete falls within the preset error range of concrete production deviation rate;

[0025] When the deviation rate of a single raw material production falls within the preset error range of the concrete production deviation rate, the rate is marked with a qualified label.

[0026] When the deviation rate of a single raw material production is not within the preset error range of concrete production deviation rate, an unqualified label shall be marked for that rate.

[0027] When the set of actual mix proportions of raw materials for each batch of concrete contains a single raw material with a production deviation rate marked as unqualified, the batch of concrete is marked as exceeding the production standard.

[0028] Furthermore, the specific analysis process of the concrete quality analysis unit is as follows:

[0029] Obtain the equipment identification and the number of concrete batches per batch;

[0030] The sample size range of the analyzed entity structural unit and the concrete strength standard are determined based on the obtained equipment identification and the number of batches of concrete per batch.

[0031] The total number of concrete compressive strength test samples for the analyzed object is calculated using the formula TS = n × i, where TS is the total number of concrete compressive strength test samples, n is the number of concrete batches, and i is the number of times the compressive strength of each batch of concrete is tested.

[0032] In the labeling of concrete compressive strength testing, a single concrete test sample is a random variable, determined by the formula... Calculate the average compressive strength of the tested concrete, where μ is the average compressive strength of the tested concrete. The formula represents the sum of the compressive strength test values ​​for each batch of concrete, where n is the number of concrete batches and i is the number of compressive strength tests per batch.

[0033] Calculate the standard deviation of the tested concrete compressive strength, where σ is the standard deviation of the tested concrete compressive strength. The sum of the deviations between each compressive strength test and the mean compressive strength test for each batch of concrete, where n is the number of concrete batches and i is the number of compressive strength tests for each batch of concrete.

[0034] ; through formula Calculate the density function values ​​of all random variable concrete compressive strength test samples, and use the normal distribution principle in mathematical statistics to calculate and analyze the area distribution of the density function of the concrete test samples, where T i n Let f(T) be a random variable representing the concrete compressive strength test value. i n ) represents the density function value of the concrete compressive strength test sample, σ represents the standard deviation of the tested concrete compressive strength, μ represents the average value of the tested concrete compressive strength, and other symbols are defined in conventional mathematics.

[0035] The proportion of the normal distribution area of ​​the above concrete compressive strength identification sequence CTCS is obtained from the normal distribution area table.

[0036] When the area ratio is less than the confidence level, the building entity represented by the concrete compressive strength identification sequence of the solid structural unit has a high probability of concrete quality risk, and the concrete compressive strength identification sequence is marked with a risk label.

[0037] When the area ratio is greater than the confidence level, the building entity represented by the concrete compressive strength identification sequence of the solid structural unit has a low probability of concrete quality risk, and the concrete compressive strength identification sequence is marked with no risk label.

[0038] By adopting the above technical solution, the present invention has the following beneficial effects:

[0039] (1) By identifying concrete production equipment and production batches, production process information and production status information from concrete production equipment can be collected or automatically received and then summarized and managed. It is applicable to data collection scenarios of low-intelligence production equipment and edge intelligent equipment with good data processing and computing performance, and has universality in business application scenarios.

[0040] (2) The concrete mixing analysis unit of this invention analyzes the deviation ratio of concrete production between the actual mix ratio of each piece of concrete raw materials and the designed mix ratio of concrete raw materials for each piece of equipment and each piece of concrete for each piece of equipment. It also assigns identification sequences to each piece of equipment, each piece of concrete and each piece of concrete raw materials for each piece of concrete in a unit building project. This provides production management personnel with a multi-dimensional, multi-level, three-dimensional and structured way to monitor the concrete production process. At the same time, it provides production management personnel with guidance on production data analysis results, which facilitates the production management personnel to discover and adjust problems after the fact, rationally plan the allocation of subsequent production tasks, improve the quality of concrete production management and reduce production costs.

[0041] (3) The concrete quality analysis unit of this invention conducts a concrete quality risk analysis by comparing the strength of each piece of concrete tested on each piece of equipment with the concrete strength standard; it conducts an overall strength test analysis on the concrete used in a unit building project (by batch), and judges the overall quality qualification of the concrete in the unit building project by the normal distribution density function of the strength of each batch of concrete tested; it provides data science support for the prediction of the service life of concrete and building projects; it provides reference support for production management personnel and building project management personnel for the prediction of production risks and problem analysis of concrete and building projects, which facilitates the in-depth management penetration, can accurately trace the source of quality problems, and improve management efficiency.

[0042] (4) The early warning release unit of this invention provides real-time early warning reminders and problem feedback on concrete mixing status and concrete quality risks, thereby improving the intensity and timeliness of concrete production process monitoring; timely early warning enables rapid personnel response to concrete production problems, reducing the redundancy of the process for managers to analyze information to identify problems during production and after finished product management, and achieving the benefits of controllable risks, cost reduction and efficiency improvement in production management.

[0043] (5) This invention achieves highly penetrating and comprehensive management and monitoring of the concrete production process by collecting, preprocessing and cleaning, multi-dimensional analysis, early warning distribution and result visualization of concrete production data, presenting an intelligent closed loop in management; by pushing risk and abnormal data, it provides business operation management guidance for managers, transforming the management model from "users using the system to manage business" to "system leading user management". Attached Figure Description

[0044] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 This is a diagram illustrating the overall model architecture of a cloud-based concrete production analysis method and system proposed in this invention.

[0046] Figure 2 This is an illustration of the concrete mixing analysis method proposed in this invention;

[0047] Figure 3 This is an illustration of a concrete mixing analysis method proposed in another embodiment of the present invention. Detailed Implementation

[0048] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0050] like Figure 1 As shown, this invention proposes a cloud-based concrete production analysis system, which includes:

[0051] The data acquisition unit collects information on the building's structural units, concrete mixing equipment, production batch number identification information, design usage of raw materials for concrete of various strength standards, actual raw material usage for each batch, design strength standard for each batch of concrete, and strength test information for each batch of concrete.

[0052] The data acquisition unit adds a solid structure unit identifier (CP) to the collected building entity units. n .

[0053] The data acquisition unit adds an equipment identifier (EI) to each concrete mixing plant that is collected. n .

[0054] The data acquisition unit adds a batch number identifier (CP) to each batch of concrete produced by each concrete mixing plant. n .

[0055] The data acquisition unit collects data on each batch of concrete CP. n Add a sequence of identifiers, CMAP, for each batch of concrete, representing the actual quantity of raw materials used in the mix, where CMAP = {CMAP1, CMAP2, ..., CMAP}. n}, where CMAP n This refers to the actual quantity of a single raw material used in each batch of concrete.

[0056] The data acquisition unit collects data on each batch of concrete CP. n The raw material design mix quantity is added to the sequence of raw material design mix quantity identifiers for each batch of concrete, CMDP = {CMDP1, CMDP2, ..., CMDP}. n}, where CMDP n Design the mix quantity for each batch of concrete using a single raw material.

[0057] The data acquisition unit adds a concrete strength standard identifier sequence CSS to the concrete strength grade, where CSS = {CSS1, CSS2, ..., CSS3}. n}, where CSS n This refers to a grade of concrete strength standard.

[0058] The data acquisition unit adds a concrete compressive strength identifier sequence CTCS to the measured compressive strength value of the concrete sample, CTCS = {CTCS1, CTCS2, ..., CTCS}. n}, in Each concrete compressive strength test sample contains the same number of concrete compressive strength test samples.

[0059] The data acquisition unit sends all the above-mentioned collected information to the cloud storage unit for storage.

[0060] In this application, the mixing analysis unit obtains the design strength standard (CSS) for each batch of concrete. nDetermine the design mix proportions (CMDP) for each batch of concrete, where CMDP = {CMDP1, CMDP2, ..., CMDP}. n}

[0061] The mixing analysis unit calculates the production deviation ratio (CMPD) of each raw material in each batch of concrete based on the actual mix proportion (CMAP) and the design mix proportion (CMDP) of the raw materials in each batch of concrete. The CMPD ratio is: CMPD = {CR1, CR2, ..., CR...} n}, where CR n The deviation rate for the production of a single raw material for each batch of concrete.

[0062] According to the formula CR n =(CMAP) n -CMDP n ) / CMDP n ×100% Calculation of the deviation rate (CR) for single raw material production of concrete n .

[0063] Determine the deviation ratio (CR) of the single raw material production of concrete. n Does it fall within the preset deviation rate error range ER for concrete production? ER = (ER min ER max ).

[0064] When the CR n When ∈ER, for CR n Marking the qualified label QL n .

[0065] When the At that time, for CR n UQL (Unqualified Labeling) n .

[0066] If the actual mix proportions of each batch of concrete raw materials in the CMAP set contain the marked non-compliant label UQL n Element CR n At that time, for each batch of concrete CP n PEL label for marking production exceeding standards n .

[0067] If each batch of concrete CP n PEL label marked for exceeding production limits n Then, the equipment identifier (EI) of the concrete batch is sent to the early warning issuing unit. n CP (Concrete Batches Per Batch) nThe following are the raw material quantity identification sequences for each batch of concrete: Actual Mix Quantity Identifier Sequence (CMAP), Design Mix Quantity Identifier Sequence (CMDP), Production Deviation Ratio (CMPDRatio) for each raw material in each batch of concrete, and Production Deviation Ratio (CR) for each single raw material in each batch of concrete. n QL qualified label n With non-compliant label UQL n .

[0068] The early warning issuing unit in this application sends an identifier data retrieval request to the cloud storage unit based on the aforementioned received identifier and identifier sequence, and generates a real-time early warning and risk status display chart for concrete production exceeding standards.

[0069] The early warning release unit sends real-time early warnings and risk status charts of excessive concrete production to the status display terminal for visual display.

[0070] The concrete quality analysis unit in this application is based on the equipment identification number EI. n CP (Concrete Batches Per Batch) n Determine the structural element SU of the entity being analyzed n The range of sample sizes and the concrete strength standard CSS n .

[0071] The concrete quality analysis unit in this application retrieves the solid structure unit SU from the cloud storage unit. n The concrete compressive strength identification sequence CTCS is used to calculate the total number of concrete compressive strength test samples for the analysis object using the formula TS = n × i.

[0072] The concrete compressive strength identification CTCS n Medium single concrete test sample T i n As a random variable, the principle of normal distribution in mathematical statistics is used to calculate and analyze the overall distribution of concrete test samples under the design strength standard requirements;

[0073] Through formula Calculate the average compressive strength of the tested concrete;

[0074] Furthermore, through the formula Calculate the standard deviation of the tested concrete compressive strength.

[0075] Through formula Calculate all random variables in the concrete compressive strength test samples The density function value is used to generate a normal probability distribution curve of concrete compressive strength test based on the density function value and the concrete compressive strength test samples. The area of ​​the unit interval of the horizontal axis of the normal distribution curve represents the proportion of the number of concrete compressive strength test samples to the total number of samples in that interval. The total area of ​​the horizontal axis interval of the normal distribution curve is 1, and the confidence level is C.

[0076] Based on the normal distribution area table, the normal distribution area ratio AR of the above concrete compressive strength identification sequence CTCS is obtained;

[0077] When AR < confidence level C, the solid structural unit SU n The building unit represented by the Concrete Compressive Strength Marking Sequence (CTCS) has a high probability of experiencing concrete quality risks. The CTCS is then labeled with a risk tag RL. n .

[0078] When AR > confidence level C, the solid structural unit SU n The building unit represented by the Concrete Compressive Strength Marking Sequence (CTCS) has a low probability of experiencing concrete quality risks, and the CTCS marker has no risk label URL. n .

[0079] If the solid structural unit SU n Risk label RL n Then, the solid structural unit SU of the concrete block is sent to the early warning issuing unit. n Normal distribution area ratio (AR), risk label (RL) n Send the EI identifier of the concrete mixing equipment involved in the physical structure. n CP (Concrete Batches Per Batch) n Concrete strength standard label (CSS) n Concrete compressive strength identification sequence (CTCS), concrete sample identification

[0080] The early warning issuing unit in this application sends an identifier data retrieval request to the cloud storage unit based on the aforementioned received identifier and identifier sequence, and generates a chart displaying the early warning and risk status of the concrete strength quality of the physical structure unit;

[0081] The early warning release unit sends the early warning and risk status chart of the concrete strength quality of the physical structure unit to the status display terminal for visual display.

[0082] Example 1

[0083] like Figure 2As shown, the data acquisition unit collects information on the building structure unit, concrete mixing equipment, production batch number identification information, design usage of raw materials for concrete of various strength standards, actual raw material usage for each batch, design strength standard for each batch of concrete, and strength test information for each batch of concrete.

[0084] The data acquisition unit adds a solid structural unit identifier (SU) to the collected building entity units. n ;

[0085] Each concrete mixing plant is labeled with an equipment identifier (EI) using a data acquisition unit. n ;

[0086] The data acquisition unit adds a batch number identifier (CP) to each batch of concrete produced by each concrete mixing plant. n ;

[0087] The data acquisition unit analyzes the CP of each batch of concrete sampled. n Add the actual quantity of raw materials used to each batch of concrete, using a sequence of identifiers CMP, where CMP = {CMAP1, CMAP2, ..., CMAP...}. n};

[0088] The data acquisition unit analyzes the CP of each batch of concrete sampled. n The raw material design mix quantity is added to the sequence of raw material design mix quantity identifiers for each batch of concrete, CMDP = {CMDP1, CMDP2, ..., CMDP}. n}, where CMDP n Design the mix quantity for each batch of concrete using a single raw material;

[0089] The concrete strength grade is added to the concrete strength standard identifier sequence CSS by the data acquisition unit. CSS = {C15, C20, C25, C30, C35, C40, C45, C50, C55, C60, C70, C80}.

[0090] The mixing analysis unit determines the design mix proportion (CMDP) of raw materials for each batch of concrete by obtaining the design strength standard C35 for each batch of concrete from the cloud storage unit. CMDP = {840, 0, 150, 120, 367, 0, 1542, 1902, 810, 0, 1.5, 33.9, 0, 0}.

[0091] The mixing analysis unit obtains the design mix proportions (CMDP, CMP) of each batch of concrete raw materials from the cloud storage unit. CMP = {839, 0, 148.8, 119.3, 365.9, 0, 1532, 1512, 1907, 794, 0, 1.5, 33.82, 0, 0}.

[0092] The mixing analysis unit calculates the production deviation ratio (CMPD Ratio) of each raw material in each batch of concrete based on the actual mix proportion (CMP) and the design mix proportion (CMDP) of each batch of concrete raw materials obtained from the cloud storage unit. This deviation is then expressed using the formula CR. n =(CMAP) n -CMDP n ) / CMDP n ×100% Calculation of the deviation rate (CR) for single raw material production of concrete n , CMPDRatio={-0.12%, 0.00%, -1.07%, -0.58%, -0.30%, 0.00%, -0.65%, -1.95%, 0.26%, -1.98%, 0.00%, 0.00%, -0.24%, 0.00%, 0.00%};

[0093] The mixing analysis unit uses ER = (-1.00%, 1.00%) to determine the deviation rate (CR) of the single raw material used in the concrete production. n Does it fall within the preset deviation rate error range (ER) for concrete production?

[0094] When the CR n When ∈ER, for CR n Marking the qualified label QL n ;

[0095] When the At that time, for CR n UQL (Unqualified Labeling) n ;

[0096] The deviation error of this batch of concrete production is ES={QL1, QL2, UQL3, QL4, QL5, QL6, QL7, UQL8, QL9, UQL 10 QL 11 QL 12 QL 13 QL 14 QL 15};

[0097] Each batch of concrete raw materials' actual mix proportions includes the UQL label marked as non-compliant in the CMAP collection. n Element CR n Therefore, for each batch of concrete CP n PEL label for marking production exceeding standards n ;

[0098] Each batch of concrete CP n PEL labels indicating production exceeding standards exist. nSend the equipment identification number (EI) of the concrete batch to the early warning issuing unit. n CP (Concrete Batches Per Batch) n The following are the raw material quantity identification sequences for each batch of concrete: Actual Mix Quantity Identifier Sequence (CMAP), Design Mix Quantity Identifier Sequence (CMDP), Production Deviation Ratio (CMPDRatio) for each raw material in each batch of concrete, and Production Deviation Ratio (CR) for each single raw material in each batch of concrete. n QL qualified label n With non-compliant label UQL n ;

[0099] Based on the received identifier and identifier sequence, the early warning issuing unit sends an identifier data retrieval request to the cloud storage unit to generate a real-time early warning and risk status display chart for concrete production exceeding standards.

[0100] The early warning release unit sends real-time early warnings and risk status charts of excessive concrete production to the status display terminal for visual display.

[0101] Example 2

[0102] like Figure 3 As shown, the data acquisition unit collects information on the building's structural units, concrete mixing equipment, production batch number identification, design strength standard for each batch of concrete, and strength test information for each batch of concrete.

[0103] The data acquisition unit adds a solid structural unit identifier (SU) to the collected building entity units. n ;

[0104] Each concrete mixing plant is labeled with an equipment identifier (EI) using a data acquisition unit. n ;

[0105] The data acquisition unit adds a batch number identifier (CP) to each batch of concrete produced by each concrete mixing plant. n ;

[0106] The concrete strength grade is added to the concrete strength standard identifier sequence CSS by the data acquisition unit. CSS = {C15, C20, C25, C30, C35, C40, C45, C50, C55, C60, C70, C80}.

[0107] The data acquisition unit adds a concrete compressive strength identification sequence CTCS to the tested concrete compressive strength values. CTCS = {CTCS1, CTCS2, CTCS3}, CTCS1 = {44.6, 43.6, 45.5}, ​​CTCS2 = {47.6, 47.1, 49.1}, CTCS3 = {48.1, 46.7, 51.1}. Each concrete compressive strength identification contains the same number of concrete compressive strength test samples.

[0108] The data acquisition unit sends all the above-mentioned collected information to the cloud storage unit for storage.

[0109] The concrete quality analysis unit is based on the equipment identification number (EI). n CP (Concrete Batches Per Batch) n Determine the structural element SU of the entity being analyzed n The range of sample sizes and the concrete strength standard CSS n ;

[0110] The concrete quality analysis unit retrieves the solid structure unit SU from the cloud storage unit. n The concrete compressive strength identification sequence CTCS is used to calculate the total number of concrete compressive strength test samples for the analysis object using the formula TS=n×i, which is TS=9;

[0111] Through formula The average compressive strength of the tested concrete was calculated to be μ = 47.04;

[0112] Furthermore, through the formula The standard deviation of the tested concrete compressive strength, σ, is calculated to be 4.72.

[0113] Through formula Calculate all random variables in the concrete compressive strength test samples The density function value is used to generate a normal probability distribution curve of concrete compressive strength test based on the density function value and the concrete compressive strength test samples. The area of ​​the unit interval on the horizontal axis of the normal distribution curve represents the proportion of the number of concrete compressive strength test samples to the total number of samples in that interval. The total area of ​​the horizontal axis interval of the normal distribution curve is 1, and the confidence level is C = 0.95.

[0114] Based on the normal distribution area table, the normal distribution area ratio AR of the above concrete compressive strength identification sequence CTCS is obtained, AR(μ-1.96σ,μ+1.96σ)=0.9485;

[0115] When AR < confidence level C, the solid structural unit SU nThe building unit represented by the Concrete Compressive Strength Marking Sequence (CTCS) has a high probability of experiencing concrete quality risks. The CTCS is then labeled with a risk tag RL. n ;

[0116] When AR > confidence level C, the solid structural unit SU n The building unit represented by the Concrete Compressive Strength Marking Sequence (CTCS) has a low probability of experiencing concrete quality risks, and the CTCS marker has no risk label URL. n ;

[0117] If the solid structural unit SU n Risk label RL n Then, the solid structural unit SU of the concrete block is sent to the early warning issuing unit. n Normal distribution area ratio (AR), risk label (RL) n Send the EI identifier of the concrete mixing equipment involved in the physical structure. n CP (Concrete Batches Per Batch) n Concrete strength standard label (CSS) n Concrete compressive strength identification sequence CTCS, concrete sample identification T i n ;

[0118] Based on the aforementioned received identifier and identifier sequence, the early warning issuing unit sends an identifier data retrieval request to the cloud storage unit to generate a chart displaying the early warning and risk status of the concrete strength and quality of the physical structure unit;

[0119] The early warning release unit sends the early warning and risk status chart of the concrete strength quality of the physical structure unit to the status display terminal for visual display.

[0120] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A cloud-based concrete production analysis system, characterized in that, include: The system includes a data acquisition unit, a cloud storage unit, a mixing data analysis unit, a concrete quality analysis unit, an early warning release unit, and a display terminal; among which, The data acquisition unit is used to collect information on building structure units, concrete mixing equipment, production batch identification information, design usage of raw materials for concrete of various strength standards, actual raw material usage for each batch, design strength standard for each batch of concrete, and strength test information for each batch of concrete, and transmits the collected data to the cloud storage unit in the cloud platform for storage. The cloud storage unit is used to store all the collected data; The mixing data analysis unit is used to analyze the production deviation ratio (CMPD Ratio) between the actual and planned usage of each batch of concrete. Taking the production label sequence of each batch of concrete from each machine as the root node of the sequence, the CMPD Ratio is assigned a deviation ratio label sequence. The deviation ratio label is mapped to the machine with the production label and the number of batches produced by the machine. The deviation ratio label is set as a qualified label if the production deviation ratio (CMPD Ratio) does not exceed the production deviation ratio error range (ER), and the deviation ratio label is set as an unqualified label if the production deviation ratio (CMPD Ratio) exceeds the production deviation ratio error range (ER). All unqualified labels are sent to the early warning release unit. The concrete quality analysis unit is used to analyze the overall concrete quality of the building structure. It determines the number of concrete strength test samples for the analyzed building structure through equipment identification and the number of batches of concrete per batch. It performs a comprehensive concrete strength confidence test on all concrete used in the analysis. Building structure units that meet the confidence requirements are marked with a no-risk label, while building structure units that do not meet the confidence requirements are marked with a risk label. All risk labels are sent to the early warning release unit. The early warning issuing unit sends an identification data retrieval request to the cloud storage unit based on the received tags and tag sequences, generates real-time early warning and risk display charts for concrete production exceeding standards and concrete strength and quality early warning and risk display charts for physical structural units, and sends them to the display terminal for visualization. The specific analysis process of the concrete quality analysis unit is as follows: Obtain the equipment identification and the number of concrete batches per batch; The sample size range of the analyzed entity structural unit and the concrete strength standard are determined based on the obtained equipment identification and the number of batches of concrete per batch. The total number of concrete compressive strength test samples for the analysis object is calculated using the formula TS = n×i, where TS is the total number of concrete compressive strength test samples, n is the number of concrete batches, and i is the number of compressive strength tests per batch of concrete. In the labeling of concrete compressive strength testing, a single concrete test sample is a random variable, determined by the formula μ = Calculate the average compressive strength of the tested concrete, where μ is the average compressive strength of the tested concrete. Let σ = the sum of the compressive strength test values ​​for each batch of concrete, n be the number of batches of concrete, and i be the number of compressive strength tests per batch of concrete; σ = Calculate the standard deviation of the tested concrete compressive strength, where σ is the standard deviation of the tested concrete compressive strength. The sum of the deviations between each compressive strength test and the mean compressive strength test for each batch of concrete, where n is the number of concrete batches and i is the number of compressive strength tests for each batch of concrete; Through formula f( )= Calculate the density function values ​​of all random variable concrete compressive strength test samples, and use the normal distribution principle in mathematical statistics to calculate and analyze the area distribution of the density function of the concrete test samples. Let f be a random variable representing the concrete compressive strength test value. ) represents the density function value of the concrete compressive strength test sample, σ represents the standard deviation of the tested concrete compressive strength, μ represents the average value of the tested concrete compressive strength, and other symbols are defined in conventional mathematics. The proportion of the normal distribution area of ​​the above concrete compressive strength identification sequence CTCS is obtained from the normal distribution area table. When the area ratio is less than the confidence level, the building entity represented by the concrete compressive strength identification sequence of the solid structural unit has a high probability of concrete quality risk, and the concrete compressive strength identification sequence is marked with a risk label. When the area ratio is greater than the confidence level, the building entity represented by the concrete compressive strength identification sequence of the solid structural unit has a low probability of concrete quality risk, and the concrete compressive strength identification sequence is marked with no risk label.

2. The cloud-based concrete production analysis system according to claim 1, characterized in that, The specific data acquisition method of the data acquisition unit is as follows: Add entity structural unit identifiers to the sampled building entities; Add equipment identification to each concrete mixing plant that was collected; Add a batch number label to each batch of concrete produced by each concrete mixing plant. Add an identifier sequence for the actual quantity of raw materials used in each batch of concrete. Add a sequence of identifiers for the designed mix quantities of raw materials for each batch of concrete. Add a standard identification sequence for concrete strength grade, and add a test compressive strength identification sequence for the tested concrete value.

3. The cloud-based concrete production analysis system according to claim 1, characterized in that, The specific analysis process of the mixing data analysis unit is as follows: To determine the design mix proportions of raw materials for each batch of concrete, the standard design strength for each batch of concrete must be obtained. Based on the actual mix proportion of raw materials for each batch of concrete and the designed mix proportion of raw materials for each batch of concrete, calculate the production deviation ratio of each raw material for each batch of concrete, and assign an identification sequence to the production deviation ratio of raw materials for each batch of concrete. According to the formula =( - ) / × 100% to calculate the deviation rate of single raw material production in concrete, where For each batch of concrete being mined, The actual quantity of each raw material in each batch of concrete; Design the mix quantity for each batch of concrete using a single raw material; Determine whether the deviation rate of the single raw material production of concrete falls within the preset error range of concrete production deviation rate; When the deviation rate of a single raw material production falls within the preset error range of the concrete production deviation rate, the rate is marked with a qualified label. When the deviation rate of a single raw material production is not within the preset error range of concrete production deviation rate, an unqualified label shall be marked for that rate. When the set of actual mix proportions of raw materials for each batch of concrete contains a single raw material with a production deviation rate marked as unqualified, the batch of concrete is marked as exceeding the production standard.

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