A method and system for coupled control of a functional concrete processing cycle
By collecting multi-dimensional features and analyzing functional requirements of concrete raw materials, a joint control mechanism is constructed, which solves the problem that existing technologies cannot intelligently match processing control strategies, and realizes precise matching of concrete processing strategies and optimization of functional control.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHENZHEN HUIJI CONCRETE CO LTD
- Filing Date
- 2025-07-15
- Publication Date
- 2026-05-15
AI Technical Summary
Existing technologies cannot intelligently match processing control strategies based on the characteristics of concrete raw materials, resulting in unstable functional control effects during concrete processing and difficulty in dynamically optimizing control strategies.
By collecting multi-dimensional features of concrete raw materials, a joint control database is constructed, and reference databases are divided into those with and without admixtures. Functional requirements are analyzed, the optimal control strategy is obtained through optimization traversal, and joint control is implemented.
It achieves precise matching and functional control optimization of concrete processing strategies, improving the stability and efficiency of the concrete processing process.
Smart Images
Figure CN120972507B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent control technology, specifically to a method and system for co-drive control of the functional concrete processing cycle. Background Technology
[0002] The workability and final functionality of concrete are influenced by the type, quality, and proportions of raw materials. In actual production, processing parameters are often determined by fixed proportions or empirical rules, lacking a systematic mechanism for collecting and analyzing the multidimensional characteristics of raw materials. Because different raw materials vary in strength, particle shape, mud content, and storage conditions, traditional methods struggle to accurately respond to concrete performance requirements, leading to unstable functional control during processing and making it difficult to dynamically optimize and match control strategies to specific raw materials. Summary of the Invention
[0003] This application provides a method and system for controlling the processing cycle of functional concrete, which addresses the technical problem in the prior art that it is impossible to intelligently match processing control strategies based on the characteristics of concrete raw materials.
[0004] In view of the above problems, this application provides a method and system for controlling the processing cycle of functional concrete.
[0005] The first aspect of this application provides a method for co-driving control of the processing cycle of functional concrete, the method comprising:
[0006] Multi-dimensional feature collection of concrete raw materials yields raw material characteristic information; using this raw material characteristic information as a screening constraint, the joint-drive control database is filtered and analyzed to obtain a control reference database; the control reference database is divided into a partitioning result, which includes a reference library without admixtures and a reference library with admixtures; the functional requirements of the concrete are analyzed, and a functional evaluation plan is determined; based on the functional evaluation plan, the reference library without admixtures is optimized to obtain a first optimal control strategy, and the reference library with admixtures is optimized to obtain a second optimal control strategy; the first optimal control strategy and the second optimal control strategy are compared to obtain a target control strategy, and the concrete is subjected to joint-drive control according to the target control strategy.
[0007] A second aspect of this application provides a co-drive control system for the functional concrete processing cycle, the system comprising:
[0008] The system comprises the following modules: a feature collection module for collecting multi-dimensional features of concrete raw materials to obtain raw material feature information; a screening and analysis module for using the raw material feature information as a screening constraint to screen and analyze the joint-drive control database to obtain a control reference database; a partitioning module for partitioning the control reference database to obtain partitioning results, wherein the partitioning results include a reference library without admixtures and a reference library with admixtures; an analysis module for analyzing the functional requirements of the concrete and determining a functional evaluation plan; an optimization traversal module for performing an optimization traversal on the reference library without admixtures based on the functional evaluation plan to obtain a first optimal control strategy, and performing an optimization traversal on the reference library with admixtures to obtain a second optimal control strategy; and a joint-drive control module for comparing the first optimal control strategy and the second optimal control strategy to obtain a target control strategy, and performing joint-drive control on the concrete according to the target control strategy.
[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:
[0010] This application collects multi-dimensional features of concrete raw materials to obtain raw material feature information; uses the raw material feature information as a screening constraint to screen and analyze the joint-drive control database to obtain a control reference database; divides the control reference database to obtain a division result, wherein the division result includes a reference library without admixtures and a reference library with admixtures; analyzes the functional requirements of the concrete and determines a functional evaluation plan; based on the functional evaluation plan, performs an optimization traversal on the reference library without admixtures to obtain a first optimal control strategy, and performs an optimization traversal on the reference library with admixtures to obtain a second optimal control strategy; compares the first optimal control strategy and the second optimal control strategy to obtain a target control strategy, and performs joint-drive control on the concrete according to the target control strategy. This invention solves the technical problem in the prior art that it is impossible to intelligently match processing control strategies based on the characteristics of concrete raw materials. By constructing a joint-drive control mechanism based on raw material characteristics and functional requirements, it achieves the technical effect of accurate matching of concrete processing strategies and optimization of functional control. Attached Figure Description
[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0012] Figure 1 A schematic diagram of a method for controlling the processing cycle of functional concrete provided in this application embodiment;
[0013] Figure 2 This is a schematic diagram of a combined drive control system for the processing cycle of functional concrete, provided as an embodiment of this application.
[0014] Figure labeling: Feature collection module 11, screening and analysis module 12, partitioning module 13, analysis module 14, optimization traversal module 15, and linkage control module 16. Detailed Implementation
[0015] This application provides a method and system for the combined control of the functional concrete processing cycle, which addresses the technical problem in the prior art that it is impossible to intelligently match processing control strategies based on the characteristics of concrete raw materials. By constructing a combined control mechanism based on raw material characteristics and functional requirements, it achieves the technical effect of accurately matching concrete processing strategies and optimizing functional control.
[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0017] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.
[0018] Example 1, as Figure 1 As shown, this application provides a method for co-driving control of the processing cycle of functional concrete, the method comprising:
[0019] Step S100: Collect multi-dimensional features of the raw materials of concrete to obtain raw material feature information.
[0020] In this embodiment, multi-dimensional feature collection is performed on the raw materials of concrete. Specifically, this includes sequentially introducing a category feature collection mechanism, a hazardous feature collection mechanism, and a storage feature collection mechanism to extract multi-dimensional parameters for each category in the constructed raw material category set. The category feature collection mechanism is used to collect the basic performance indicators of the raw materials to form the first arbitrary feature parameter, such as the strength, fineness, and setting time of cement, and the particle shape, gradation, and mud content of aggregates. The hazardous feature collection mechanism is used to identify components that adversely affect the performance of concrete to form the second arbitrary feature parameter, such as impurity content and the proportion of reactive minerals. The storage feature collection mechanism collects storage and management-related indicators to form the third arbitrary feature parameter, such as the storage time of the raw materials, temperature and humidity environment, and packaging integrity. Finally, the above three types of parameters are integrated to construct structured raw material feature information.
[0021] Step S200: Using the raw material characteristic information as a screening constraint, the joint drive control database is screened and analyzed to obtain a control reference database.
[0022] In this embodiment, when using raw material characteristic information as a screening constraint to perform screening analysis on the joint drive control database, any joint drive control data group in the joint drive control database is extracted, and the similarity coefficient between the raw material characteristic information and the characteristic information in the data group is calculated. If the similarity coefficient reaches a predetermined similarity limit, the data group is included in the control reference database. Through this process, the control reference database is obtained.
[0023] Furthermore, in the method provided in the application embodiments, using the raw material characteristic information as a screening constraint to perform screening and analysis on the co-drive control database to obtain a control reference database, it further includes:
[0024] Extract any set of joint drive control data from the joint drive control database; obtain the similarity coefficient between the raw material characteristic information and any characteristic information in the arbitrary joint drive control data set; if the similarity coefficient reaches a predetermined similarity limit, add the arbitrary joint drive control data set to the control reference database; wherein, the process includes: constructing a category set of the raw materials; introducing a category feature collection mechanism to collect category features from any category in the category set to obtain a first arbitrary feature parameter; introducing a harmful feature collection mechanism to collect harmful features from any category to obtain a second arbitrary feature parameter; introducing a storage feature collection mechanism to collect storage features from any category to obtain a third arbitrary feature parameter; and constructing the raw material characteristic information based on the first arbitrary feature parameter, the second arbitrary feature parameter, and the third arbitrary feature parameter.
[0025] In this embodiment, an arbitrary set of coupled-drive control data is first extracted from the coupled-drive control database. The coupled-drive control database stores different combinations of raw materials and corresponding processing parameters and control strategies used in historical concrete processing. The database is then accessed to extract the arbitrary set of coupled-drive control data. Using the Euclidean distance algorithm, the similarity coefficient between the current raw material feature information and the feature information in the extracted arbitrary set of coupled-drive control data is calculated. All features are normalized before calculation to ensure dimensional consistency and eliminate extreme value interference. If the similarity coefficient is less than a predetermined similarity limit of 0.35, the corresponding coupled-drive control data set is added to the control reference database.
[0026] To construct raw material characteristic information, a category set of raw materials was first established, including cement, aggregates, and admixtures. A category characteristic collection mechanism was introduced to collect category characteristics. For cement, 28-day strength was measured using a compressive strength tester, fineness was measured using a specific surface area analyzer, and initial and final setting times were obtained using an automatic setting time meter. The obtained data were normalized to form the first arbitrary characteristic parameter of cement. For aggregates, particle shape was obtained using an image particle size analysis system, particle size distribution was obtained using a standard sieve method, and mud content was obtained using a washing sedimentation method. The values were recorded and normalized to form the first arbitrary characteristic parameter of aggregates. For admixtures, viscosity indices were measured using a viscometer, uniformly coded, and normalized, and included in the first arbitrary characteristic parameter.
[0027] Subsequently, a hazardous characteristic collection mechanism was introduced to collect hazardous characteristics of any category. For cement samples, the content of heavy metal elements such as Pb and Cd was detected by X-ray fluorescence spectrometry, and the concentration of radionuclides Ra and Th was detected by gamma-ray spectrometry. The measured data were normalized and included in the second arbitrary characteristic parameter. For aggregate samples, the organic matter content was detected by colorimetric tubes, and the proportion of mud lumps was determined by sedimentation method. For admixtures, the concentration of chloride ions and alkali ions was detected by ion chromatography. All test results were converted to standard units and normalized as the second arbitrary characteristic parameter.
[0028] Subsequently, a storage feature collection mechanism was introduced to collect storage features for any category. By setting the relative humidity threshold of the material storage area to 60%, temperature and humidity sensors were used to automatically record data 24 hours a day. An RFID system was used to set storage tags for different raw materials and record the arrival time and batch. Manual inspections were arranged every 7 days to remove damaged or expired materials, forming storage data such as the pass rate, storage period and environmental stability. These data were then standardized and coded to form a third arbitrary feature parameter.
[0029] Finally, by summarizing the first, second, and third arbitrary feature parameters after normalization, the raw material feature information for the current batch is output.
[0030] Furthermore, the method provided in the application embodiments also includes:
[0031] If the arbitrary category is a cement category, then the arbitrary category is subjected to feature collection according to the predetermined cement characteristics in the category feature collection mechanism to obtain cement feature parameters, and the cement feature parameters are used as the first arbitrary feature parameter, wherein the predetermined cement characteristics include at least strength, fineness and setting time.
[0032] In this embodiment of the application, if any category is cement, the cement category features are collected according to the predetermined cement characteristics in the category feature collection mechanism. The predetermined cement characteristics include at least strength, fineness and setting time.
[0033] Specifically, the cement samples are first prepared using a standard 40mm×40mm×160mm mold to create cement mortar specimens, which are then cured for 28 days under constant temperature and humidity conditions. The cured specimens are then physically tested using an electric compressive and flexural strength testing machine to obtain their flexural and compressive strength values (in MPa), thus characterizing the cement's load-bearing capacity. These results constitute the strength parameters of the cement category. The above operations are executed by the strength testing module, which is scheduled by the category feature collection mechanism.
[0034] Subsequently, two methods were used in combination to collect data on the fineness characteristics. First, a laser particle size analyzer was used to measure the particle size distribution of the cement samples, obtaining particle size parameters such as D10, D50, and D90. Second, a gas permeation surface area analyzer (such as a Blaine surface area analyzer) was used to determine the specific surface area per unit mass of the cement samples, expressed in m² / kg. The particle size distribution parameters and specific surface area results were then combined and processed to obtain the cement fineness parameters.
[0035] Subsequently, based on the setting time characteristics, standard consistency cement slurry was prepared and injected into Vicat molds with a central hole. The molds were then placed at 20°C for time recording. An automatic setting time meter was used to monitor the entire process. The time when the probe first sank to a position 6 mm above the bottom surface of the mold was recorded as the initial setting time, and the time when the probe completely stopped sinking was recorded as the final setting time. Both times were recorded as the cement setting time parameter.
[0036] Finally, the obtained cement strength parameters, cement fineness parameters, and cement setting time parameters are normalized to eliminate dimensional differences and then summarized to form structured cement characteristic parameters, which are then used as the first arbitrary characteristic parameters.
[0037] Furthermore, the method provided in the application embodiments also includes:
[0038] If any category is an aggregate category, then the arbitrary category is subjected to feature collection according to the predetermined aggregate characteristics in the category feature collection mechanism to obtain aggregate feature parameters, and the aggregate feature parameters are used as the first arbitrary feature parameter, wherein the predetermined aggregate characteristics include at least particle shape, gradation and mud content.
[0039] In this embodiment of the application, if any category is an aggregate category, then the features of any category are collected according to the predetermined aggregate characteristics in the category feature collection mechanism. The predetermined aggregate characteristics include at least particle shape, gradation, and mud content.
[0040] Specifically, the first step, regarding particle shape, is to use image-based particle shape analysis. Aggregate samples are evenly laid on a black background, and high-resolution images are captured under a stable light source. Edge recognition technology is used to extract the contour of each particle, and the lengths of the major and minor axes are measured to calculate their aspect ratios. The number of particles with a major-to-minor axis ratio greater than 3:1 is counted, and their percentage of the total number of particles is calculated as the content of needle-like and flaky particles, used to characterize the aggregate particle shape parameters.
[0041] Next, a standard sieving method was used to determine the gradation. 1000g of the dried aggregate sample was weighed and sequentially placed through four sieves of 5.0mm, 10mm, 20mm, and 31.5mm, and continuously sieved for 5 minutes using a vibrating sieve. Each sieve was then removed, and the mass of the material remaining on the sieve was weighed using an electronic balance. The percentage of each particle size range was calculated, and the maximum, minimum, and concentrated particle sizes were determined as aggregate gradation parameters.
[0042] Next, the washing and sedimentation method was used to determine the mud content. 500g of aggregate sample was weighed and placed in a mixing container, 1L of water was added, and the mixture was stirred thoroughly. After standing for 15 minutes, the supernatant water was poured off, and the remaining aggregate was dried using a forced-air drying oven until constant weight was achieved. The residual mass after drying was recorded. The mud content parameter was obtained using the formula: Mud Content = (Original Sample Mass - Drying Mass) / Original Sample Mass × 100%.
[0043] Finally, the obtained aggregate particle shape parameters, aggregate gradation parameters, and aggregate mud content parameters are normalized to form structured aggregate characteristic parameters, which are then used as the first arbitrary characteristic parameters.
[0044] Step S300: Divide the control reference database to obtain the division result, wherein the division result includes a reference library without admixtures and a reference library with admixtures.
[0045] In this embodiment, when dividing the control reference database, the process records in each co-drive control data group are first extracted, and then classified and identified based on whether they contain admixture dosage information. Specifically, the raw material ratio field in the data group is read to determine whether there are valid records of admixture types and dosages. If the record contains admixture names and corresponding dosage ratios such as water-reducing agents, retarders, and air-entraining agents, the data group is classified into the admixture-containing reference database; if no admixture usage information is recorded or all admixture dosages are zero, it is classified into the admixture-free reference database. This method achieves a structured split of the control reference database, resulting in the division.
[0046] Step S400: Analyze the functional requirements of the concrete and determine the functional evaluation plan.
[0047] In this embodiment of the application, a functional index set containing multiple functional indicators with demand degree identifiers is first formed based on the functional requirements of concrete. Then, a first index is randomly selected from it and adjusted according to the weight based on its corresponding first demand degree to form a functional evaluation plan.
[0048] Furthermore, in the method provided in the application embodiments, analyzing the functional requirements of the concrete and determining a functional evaluation plan further includes:
[0049] The functional requirements are analyzed to obtain a set of functional indicators, wherein the set of functional indicators includes multiple functional indicators with demand degree identifiers; a first indicator is extracted from the multiple functional indicators with demand degree identifiers, and the first indicator corresponds to a first demand degree; the first indicator is adjusted with the first demand degree as a weight to form the functional evaluation plan.
[0050] In this embodiment, a manual checklist method is first used to list the performance indicators that the concrete should meet based on the engineering structural design specifications, the usage environment, and the concrete performance requirements, thus constructing a set of functional indicators, such as compressive strength, workability, impermeability, frost resistance, and durability. Each indicator is followed by a value set by technical experts according to on-site needs, called a demand level indicator. This indicator uses a decimal between 0 and 1 to represent the importance of the indicator; for example, compressive strength is indicated by 0.4, workability by 0.3, impermeability by 0.2, and frost resistance by 0.1.
[0051] Then, select any indicator from the set of functional indicators as the first indicator, for example, choose compressive strength, record its target value as 40 MPa, and read its corresponding first demand level, which is 0.4. Next, use a weighted calculation method to multiply the target value of the first indicator by the first demand level to calculate the weighted indicator value, that is, weighted value = 40 × 0.4 = 16.
[0052] Finally, this weighted value is used as the first evaluation value, and the above weighted calculation process is repeated for the other functional indicators in turn. After combining all the weighted values, a comprehensive evaluation structure for the current concrete performance requirements is formed, namely the functional evaluation plan.
[0053] Step S500: Based on the functional evaluation plan, perform an optimization traversal on the reference library without admixtures to obtain a first optimal control strategy, and perform an optimization traversal on the reference library with admixtures to obtain a second optimal control strategy.
[0054] In this embodiment, based on the functional evaluation plan, functional evaluation analysis is performed on each reference set in both the admixture-free and admixture-containing reference libraries, and the functional index is calculated and then sorted in descending order. The co-drive control strategy corresponding to the data set with the highest functional index in the admixture-free reference library is selected as the first optimal control strategy. If a reference set with a higher functional index exists in the admixture-containing reference library, it is included in the target reference platform and sorted. Finally, the co-drive control strategy with the highest functional index is selected as the second optimal control strategy.
[0055] Furthermore, in the method provided in the application embodiments, the first optimal control strategy is obtained by performing an optimization traversal of the additive-free reference library based on the functional evaluation plan. This further includes:
[0056] Based on the aforementioned functional evaluation plan, a functional evaluation analysis is performed on the first reference set in the admixture-free reference library to obtain a first functional index; the admixture-free reference library is then sorted in descending order based on the first functional index to obtain an admixture-free reference list; the first first reference set in the admixture-free reference list is obtained, and the co-drive control strategy in the first first reference set is taken as the first optimal control strategy.
[0057] In this embodiment of the application, when performing functional evaluation analysis on the first reference set in the admixture-free reference library based on the functional evaluation plan, firstly, a performance index extraction method is used to traverse and extract each reference set (i.e., the first reference set) from the admixture-free reference library, and the concrete performance indices recorded therein are read, including compressive strength, workability, impermeability, and frost resistance. These performance indices correspond one-to-one with the set of functional indices listed in the functional evaluation plan.
[0058] Subsequently, based on the target index values and demand indicators defined in the functional evaluation plan, a functional evaluation analysis was conducted on the first reference set. Specifically, each performance index was first normalized to eliminate the influence of dimensions. After normalization, a linear weighting method was used, that is, each normalized value was multiplied by its corresponding demand indicator and summed to obtain the first functional index corresponding to the first reference set.
[0059] Next, based on the functional indices corresponding to all reference sets, the admixture-free reference library is rearranged using a descending sorting method to form an admixture-free reference list.
[0060] Finally, the first-ranked reference set, namely the first-ranked reference set, is extracted from the no-admixture reference list. This reference set contains a complete set of control parameters, including water-cement ratio, sand ratio, mixing time, temperature control range, etc. This set of parameters is the joint-drive control strategy in this reference set. This control strategy is taken as the optimal strategy for the current concrete performance under no-admixture conditions and is determined as the first optimal control strategy.
[0061] Furthermore, in the method provided in the application embodiments, the optimization traversal of the reference library with admixtures to obtain the second optimal control strategy further includes:
[0062] Extract the second reference set from the admixture reference library; perform functional evaluation analysis on the second reference set based on the functional evaluation plan to obtain a second functional index; determine whether the second functional index is greater than the first first functional index of the first first reference set; if it is greater, add the second reference set to the target reference platform; sort the target reference platform in descending order based on the second functional index to obtain an admixture reference list; obtain the second first reference set in the admixture reference list, and use the co-drive control strategy in the second first reference set as the second optimal control strategy.
[0063] In this embodiment, the admixture reference library is first traversed, and reference sets are extracted sequentially as the current second reference set. Each second reference set contains concrete performance data with admixture participation, including compressive strength, workability, impermeability, and frost resistance, and records the admixture type and dosage, such as the dosage of polycarboxylate superplasticizer and air-entraining agent.
[0064] Subsequently, using the performance target values and demand indicators set in the functional evaluation plan, a functional evaluation analysis was conducted on the second reference set. Specifically, a normalization method was used to standardize each performance indicator to the range of 0 to 1, and then a linear weighted method was used to calculate the functional score, that is, multiplying the normalized performance value by the corresponding demand weight and summing the results to obtain the second functional index of the reference set.
[0065] Next, the second functional index is compared with the first functional index of the first primary reference set to determine if it is superior. If the second functional index is greater than the first primary functional index, the second reference set is added to the target reference platform, thus constructing a candidate strategy pool. All second reference sets added to the target reference platform are sorted in descending order according to their corresponding functional indices from high to low, generating a reference list with additives.
[0066] Finally, the first-ranked reference set, i.e. the second-best reference set, is obtained from the list of references with admixtures. This reference set contains complete control parameters for the combined drive, including water-cement ratio, admixture dosage, feeding sequence, and stirring time. It is extracted as the second optimal control strategy.
[0067] Step S600: Compare the first optimal control strategy with the second optimal control strategy to obtain the target control strategy, and perform co-drive control on the concrete according to the target control strategy.
[0068] In this embodiment, when comparing the first optimal control strategy and the second optimal control strategy, predetermined comparison features are first read, including admixture cost, co-drive processing time, and functionality index. Based on these comparison features, the first control record in the first primary reference set is analyzed, and its comprehensive performance matching degree is calculated using a normalized scoring method to obtain the first control fitness. Similarly, the second control record in the second primary reference set is scored in the same way to obtain the second control fitness. Then, the first control fitness and the second control fitness are compared, and the final target control strategy is determined based on the higher score.
[0069] Then, based on the linkage parameters (such as water-cement ratio, admixture dosage, mixing time, and feeding sequence) included in the target control strategy, the data is sent to the control execution system to automate and link the processes of raw material metering, admixture addition, equipment speed adjustment, and temperature-controlled curing, thereby completing the linkage control of the concrete processing cycle.
[0070] Furthermore, in the method provided in the application embodiments, comparing the first optimal control strategy and the second optimal control strategy to obtain the target control strategy further includes:
[0071] Read predetermined comparison features; perform a traversal analysis of the first control record in the first first-position reference set based on the predetermined comparison features to obtain a first control fitness; perform a traversal analysis of the second control record in the second first-position reference set based on the predetermined comparison features to obtain a second control fitness; determine the target control strategy by comparing the first control fitness with the second control fitness; wherein, the predetermined comparison features include at least admixture cost, co-drive processing time, and functionality index.
[0072] In this embodiment, predetermined comparison features are first read, including at least admixture cost, combined processing time, and functionality index. The admixture cost is calculated item by item using the formula: admixture cost = dosage × unit price, by extracting the types and corresponding percentages of admixtures from the control records and combining them with unit price data. The combined processing time is calculated by reading the timestamps of the start and end times of mixing, and the time difference between the two is calculated in seconds. The functionality index is directly retrieved from the weighted score obtained based on the aforementioned functionality evaluation plan.
[0073] After obtaining the three comparative features, the first control record in the first primary reference set is analyzed. Specifically, this includes extracting the corresponding admixture cost, combined drive processing time, and functionality index from the control record. To eliminate dimensional differences and ensure comparability of indicators within the same numerical range, the admixture cost and combined drive processing time are standardized using a min-max normalization method, compressing them to the [0, 1] interval. The functionality index is retained as its original value as a performance weight. Subsequently, the three indicators are multiplied by preset weighting coefficients (e.g., functionality index weight 0.5, combined drive processing time weight 0.3, and admixture cost weight 0.2), and the products are summed to obtain the first control fitness, which represents the overall superiority or inferiority of the control strategy.
[0074] Similarly, the same process is performed on the second control record in the second primary reference set. The corresponding admixture cost, co-drive processing time, and functionality index are extracted. The first two values are normalized, then multiplied by their respective weights, and summed to obtain the second control fitness.
[0075] Finally, by comparing the fitness of the first control with that of the second control, the control strategy corresponding to the higher score is selected as the final target control strategy.
[0076] Furthermore, in the method provided in the application embodiments, after performing co-drive control on the concrete according to the target control strategy, it further includes:
[0077] Obtain the strength grade parameters of the concrete; collect the application environment parameters of the concrete; collect the application climate parameters of the concrete; establish curing constraints based on the strength grade parameters, the application environment parameters, and the application climate parameters; match a target curing scheme in the curing database based on the curing constraints, and cure the concrete according to the target curing scheme.
[0078] In this embodiment of the application, after the concrete pouring construction is completed, the strength grade parameter of the concrete is first obtained. This parameter comes from the technical requirements in the construction drawings, such as grade markings like C30 and C40.
[0079] Then, environmental parameters for the concrete application are collected. These parameters describe the service environment of the concrete structure, specifically including its location (e.g., ground level, underground, elevated), usage location (e.g., indoor, outdoor), and whether it is exposed to corrosive media (e.g., chlorides, sulfates). These parameters are collected by reviewing structural design drawings, construction site environmental records, or safety technical briefing documents.
[0080] Next, the application climate parameters for the concrete are collected. These parameters include external meteorological data, such as average daily temperature (°C), relative humidity (%), wind speed (m / s), and the presence of strong sunlight or rainfall. This data is obtained through on-site meteorological data acquisition equipment or a third-party meteorological platform.
[0081] Subsequently, maintenance constraints are established based on strength grade parameters, application environment parameters, and application climate parameters. Maintenance constraints are a set of maintenance conditions tailored to the specific conditions of the current project, used to define maintenance methods, duration, and moisture retention techniques. For example, if the strength grade is C40, the application environment is outdoor exposure to direct sunlight, and the climate conditions are high temperature and low humidity, then the maintenance constraints will explicitly require the use of comprehensive measures such as shading, water spraying, and covering to prevent early cracking and moisture loss.
[0082] Subsequently, a target maintenance scheme is matched against the maintenance database based on maintenance constraints. The maintenance database contains multiple pre-set typical maintenance schemes, each including the applicable intensity level range, environmental classification, and climate range. For example, "For C30-C50, exposed environment, temperature > 30℃ and humidity < 50%, spraying + plastic film maintenance is recommended." A conditional matching algorithm is used to find the maintenance method that perfectly matches the current maintenance constraints, thus determining the target maintenance scheme.
[0083] Finally, the concrete is cured according to the target curing plan.
[0084] In summary, the embodiments of this application have at least the following technical effects:
[0085] This application collects multi-dimensional features of concrete raw materials to obtain raw material feature information; uses the raw material feature information as a screening constraint to screen and analyze the joint-drive control database to obtain a control reference database; divides the control reference database to obtain a division result, wherein the division result includes a reference library without admixtures and a reference library with admixtures; analyzes the functional requirements of the concrete and determines a functional evaluation plan; based on the functional evaluation plan, performs an optimization traversal on the reference library without admixtures to obtain a first optimal control strategy, and performs an optimization traversal on the reference library with admixtures to obtain a second optimal control strategy; compares the first optimal control strategy and the second optimal control strategy to obtain a target control strategy, and performs joint-drive control on the concrete according to the target control strategy. This invention solves the technical problem in the prior art that it is impossible to intelligently match processing control strategies based on the characteristics of concrete raw materials. By constructing a joint-drive control mechanism based on raw material characteristics and functional requirements, it achieves the technical effect of accurate matching of concrete processing strategies and optimization of functional control.
[0086] Example 2, based on the same inventive concept as the co-drive control method for the processing cycle of functional concrete in the foregoing examples, such as... Figure 2 As shown, this application provides a co-drive control system for the functional concrete processing cycle. The system and method embodiments in this application are based on the same inventive concept. The system includes:
[0087] The system comprises the following modules: Feature Collection Module 11, for collecting multi-dimensional features of concrete raw materials to obtain raw material feature information; Screening and Analysis Module 12, for using the raw material feature information as a screening constraint to screen and analyze the joint drive control database to obtain a control reference database; Division Module 13, for dividing the control reference database to obtain a division result, wherein the division result includes a reference library without admixtures and a reference library with admixtures; Analysis Module 14, for analyzing the functional requirements of the concrete and determining a functional evaluation plan; Optimization Traversal Module 15, for performing optimization traversal on the reference library without admixtures based on the functional evaluation plan to obtain a first optimal control strategy, and performing optimization traversal on the reference library with admixtures to obtain a second optimal control strategy; and Joint Drive Control Module 16, for comparing the first optimal control strategy and the second optimal control strategy to obtain a target control strategy, and performing joint drive control on the concrete according to the target control strategy.
[0088] Furthermore, the system is also used to implement the following functions:
[0089] Extract any set of joint drive control data from the joint drive control database; obtain the similarity coefficient between the raw material characteristic information and any characteristic information in the arbitrary joint drive control data set; if the similarity coefficient reaches a predetermined similarity limit, add the arbitrary joint drive control data set to the control reference database; wherein, the process includes: constructing a category set of the raw materials; introducing a category feature collection mechanism to collect category features from any category in the category set to obtain a first arbitrary feature parameter; introducing a harmful feature collection mechanism to collect harmful features from any category to obtain a second arbitrary feature parameter; introducing a storage feature collection mechanism to collect storage features from any category to obtain a third arbitrary feature parameter; and constructing the raw material characteristic information based on the first arbitrary feature parameter, the second arbitrary feature parameter, and the third arbitrary feature parameter.
[0090] Furthermore, the system is also used to implement the following functions:
[0091] If the arbitrary category is a cement category, then the arbitrary category is subjected to feature collection according to the predetermined cement characteristics in the category feature collection mechanism to obtain cement feature parameters, and the cement feature parameters are used as the first arbitrary feature parameter, wherein the predetermined cement characteristics include at least strength, fineness and setting time.
[0092] Furthermore, the system is also used to implement the following functions:
[0093] If any category is an aggregate category, then the arbitrary category is subjected to feature collection according to the predetermined aggregate characteristics in the category feature collection mechanism to obtain aggregate feature parameters, and the aggregate feature parameters are used as the first arbitrary feature parameter, wherein the predetermined aggregate characteristics include at least particle shape, gradation and mud content.
[0094] Furthermore, the system is also used to implement the following functions:
[0095] The functional requirements are analyzed to obtain a set of functional indicators, wherein the set of functional indicators includes multiple functional indicators with demand degree identifiers; a first indicator is extracted from the multiple functional indicators with demand degree identifiers, and the first indicator corresponds to a first demand degree; the first indicator is adjusted with the first demand degree as a weight to form the functional evaluation plan.
[0096] Furthermore, the system is also used to implement the following functions:
[0097] Based on the aforementioned functional evaluation plan, a functional evaluation analysis is performed on the first reference set in the admixture-free reference library to obtain a first functional index; the admixture-free reference library is then sorted in descending order based on the first functional index to obtain an admixture-free reference list; the first first reference set in the admixture-free reference list is obtained, and the co-drive control strategy in the first first reference set is taken as the first optimal control strategy.
[0098] Furthermore, the system is also used to implement the following functions:
[0099] Extract the second reference set from the admixture reference library; perform functional evaluation analysis on the second reference set based on the functional evaluation plan to obtain a second functional index; determine whether the second functional index is greater than the first first functional index of the first first reference set; if it is greater, add the second reference set to the target reference platform; sort the target reference platform in descending order based on the second functional index to obtain an admixture reference list; obtain the second first reference set in the admixture reference list, and use the co-drive control strategy in the second first reference set as the second optimal control strategy.
[0100] Furthermore, the system is also used to implement the following functions:
[0101] Read predetermined comparison features; perform a traversal analysis of the first control record in the first first-position reference set based on the predetermined comparison features to obtain a first control fitness; perform a traversal analysis of the second control record in the second first-position reference set based on the predetermined comparison features to obtain a second control fitness; determine the target control strategy by comparing the first control fitness with the second control fitness; wherein, the predetermined comparison features include at least admixture cost, co-drive processing time, and functionality index.
[0102] Furthermore, the system is also used to implement the following functions:
[0103] Obtain the strength grade parameters of the concrete; collect the application environment parameters of the concrete; collect the application climate parameters of the concrete; establish curing constraints based on the strength grade parameters, the application environment parameters, and the application climate parameters; match a target curing scheme in the curing database based on the curing constraints, and cure the concrete according to the target curing scheme.
[0104] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.
[0105] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
[0106] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.
Claims
1. A method for controlling the processing cycle of functional concrete, characterized in that, include: Multi-dimensional feature collection of concrete raw materials yields feature information. Using the raw material characteristic information as a screening constraint, the joint drive control database is screened and analyzed to obtain a control reference database; The control reference database is divided to obtain a division result, wherein the division result includes a reference library without admixtures and a reference library with admixtures; Analyze the functional requirements of the concrete and determine a functional evaluation plan; Based on the aforementioned functional evaluation plan, the first optimal control strategy is obtained by performing an optimization traversal on the reference library without admixtures, and the second optimal control strategy is obtained by performing an optimization traversal on the reference library with admixtures. By comparing the first optimal control strategy with the second optimal control strategy, a target control strategy is obtained, and the concrete is subjected to joint drive control according to the target control strategy. Based on the aforementioned functional evaluation plan, an optimization traversal is performed on the admixture-free reference library to obtain a first optimal control strategy, including: Based on the aforementioned functional evaluation plan, a functional evaluation analysis is performed on the first reference set in the additive-free reference library to obtain a first functional index; The additive-free reference library is sorted in descending order based on the first functional index to obtain an additive-free reference list; Obtain the first first reference set in the additive-free reference list, and use the co-drive control strategy in the first first reference set as the first optimal control strategy; An optimization traversal is performed on the aforementioned reference library of admixtures to obtain a second optimal control strategy, including: Extract the second reference set from the admixture reference library; Based on the aforementioned functional evaluation plan, a functional evaluation analysis is performed on the second reference set to obtain a second functional index; Determine whether the second functional index is greater than the first first functional index of the first first reference set; If it is greater than, add the second reference set to the target reference platform; The target reference platform is sorted in descending order based on the second functional index to obtain a reference list with admixtures; Obtain the second first reference set from the admixture reference list, and use the co-drive control strategy in the second first reference set as the second optimal control strategy; By comparing the first optimal control strategy with the second optimal control strategy, a target control strategy is obtained, including: Read the predefined comparison features; Based on the predetermined comparison features, a traversal analysis of the first control record in the first first reference set is performed to obtain the first control fitness. Based on the predetermined comparison features, a traversal analysis of the second control record in the second first reference set is performed to obtain the second control fitness. The target control strategy is determined by comparing the first control fitness with the second control fitness. The predetermined comparative features include at least the cost of admixtures, the duration of combined drive processing, and the functionality index.
2. The method for controlling the processing cycle of functional concrete as described in claim 1, characterized in that, Using the raw material characteristic information as a screening constraint, the co-drive control database is screened and analyzed to obtain a control reference database, including: Extract any group of joint drive control data from the joint drive control database; Obtain the similarity coefficient between the raw material characteristic information and any characteristic information in any joint drive control data group; If the similarity coefficient reaches a predetermined similarity limit, then the arbitrary joint drive control data group is added to the control reference database; This includes: Construct a category set for the aforementioned raw materials; A category feature collection mechanism is introduced to collect category features for any category in the category set to obtain the first arbitrary feature parameter; A harmful feature collection mechanism is introduced to collect harmful features of the arbitrary product category to obtain a second arbitrary feature parameter; A storage feature collection mechanism is introduced to collect storage features for any category of product to obtain a third arbitrary feature parameter. The raw material feature information is constructed based on the first arbitrary feature parameter, the second arbitrary feature parameter, and the third arbitrary feature parameter.
3. The method for controlling the processing cycle of functional concrete as described in claim 2, characterized in that, If the arbitrary category is a cement category, then the arbitrary category is subjected to feature collection according to the predetermined cement characteristics in the category feature collection mechanism to obtain cement feature parameters, and the cement feature parameters are used as the first arbitrary feature parameter, wherein the predetermined cement characteristics include at least strength, fineness and setting time.
4. The method for controlling the processing cycle of functional concrete as described in claim 2, characterized in that, If any category is an aggregate category, then the arbitrary category is subjected to feature collection according to the predetermined aggregate characteristics in the category feature collection mechanism to obtain aggregate feature parameters, and the aggregate feature parameters are used as the first arbitrary feature parameter, wherein the predetermined aggregate characteristics include at least particle shape, gradation and mud content.
5. The method for controlling the processing cycle of functional concrete as described in claim 1, characterized in that, Analyze the functional requirements of the concrete and determine a functional evaluation plan, including: The functional requirements are analyzed to obtain a set of functional indicators, wherein the set of functional indicators includes multiple functional indicators with a requirement degree identifier. Extract the first indicator from the plurality of functional indicators with demand indicators, and the first indicator corresponds to the first demand. The first indicator is adjusted with the first demand level as the weight to form the functional evaluation plan.
6. The method for controlling the processing cycle of functional concrete as described in claim 1, characterized in that, After performing co-drive control on the concrete according to the target control strategy, the method further includes: Obtain the strength grade parameters of the concrete; Collect the application environment parameters of the concrete; Collect the application climate parameters of the concrete; Maintenance constraints are established based on the intensity level parameters, the application environment parameters, and the application climate parameters. Based on the maintenance constraints, a target maintenance scheme is matched in the maintenance database, and the concrete is maintained according to the target maintenance scheme.
7. A co-drive control system for the functional concrete processing cycle, characterized in that, The system is used to execute a co-drive control method for the processing cycle of functional concrete as described in any one of claims 1-6, the system comprising: The feature collection module is used to collect multi-dimensional features of concrete raw materials to obtain raw material feature information; The screening and analysis module is used to perform screening and analysis on the joint drive control database using the raw material characteristic information as screening constraints, so as to obtain a control reference database. A partitioning module is used to partition the control reference database to obtain partitioning results, wherein the partitioning results include a reference library without admixtures and a reference library with admixtures; The analysis module is used to analyze the functional requirements of the concrete and determine the functional evaluation plan. The optimization traversal module is used to perform optimization traversal on the reference library without admixtures based on the functional evaluation plan to obtain the first optimal control strategy, and to perform optimization traversal on the reference library with admixtures to obtain the second optimal control strategy. The co-drive control module is used to compare the first optimal control strategy with the second optimal control strategy to obtain a target control strategy, and to perform co-drive control on the concrete according to the target control strategy.