A method and system for optimizing carbon emissions of dry powder mortar

Abstract

This application provides a method and system for optimizing carbon emissions in dry mortar. The method involves collecting the particle size distribution, crushing value, and replacement rate of recycled aggregate in dry mortar to form a set of performance indicators, establishing a mapping relationship between these indicators and carbon emissions; acquiring acoustic signals of recycled aggregate mixing and collision through multiple sensing units within the mixing chamber; converting the acoustic signals into collision characteristic spectra reflecting the intensity of particle interaction, extracting time-varying characteristics to identify the mixing uniformity state; dynamically predicting the critical triggering state that causes abrupt changes in mortar performance due to recycled aggregate using a time-series analysis model; and forming optimized balance parameters that meet carbon emission constraints through coordinated analysis of the mapping relationship and the critical triggering state, thereby generating dynamic control commands for the recycled aggregate addition ratio in real time. This application achieves a second-level dynamic balance between recycled aggregate content and carbon emission intensity, improving mixing uniformity and reducing carbon emissions.

Description

Technical Field

[0001] This application relates to the field of carbon emission optimization technology, and in particular to a method and system for optimizing carbon emissions from dry powder mortar. Background Technology

[0002] Against the backdrop of the construction industry's accelerated pursuit of "dual carbon" goals, the carbon emissions from the production process of dry-mix mortar, as an important basic building material, are receiving increasing attention. Cement, as its main binder, accounts for a large proportion of the total carbon footprint of mortar. Therefore, the core technological requirement for the application of dry-mix mortar lies in significantly reducing greenhouse gas emissions per unit product through in-depth optimization of material formulations and process improvements, while ensuring or even improving key product performance (such as mechanical strength, workability, and durability). Specifically, it is necessary to develop comprehensive technical solutions that can accurately balance the substitution of high-carbon components (such as cement), the extensive use of low / zero-carbon auxiliary binders or industrial solid waste (such as fly ash, slag powder, and tailings powder), and the introduction of functional additives to reduce cement usage or optimize production energy consumption, while strictly ensuring that the mortar meets the quality requirements of different application scenarios (masonry, plastering, flooring, etc.).

[0003] One existing targeted solution is an intelligent mix design optimization system based on a life-cycle assessment database. This system integrates a database covering the carbon footprint information of various raw materials and combines it with a mortar performance prediction model (based on historical experimental data or mechanistic models). By constructing multi-objective optimization algorithms (such as minimizing carbon emissions while constraining performance indicators such as compressive strength, consistency, and water retention), the system can automatically explore and calculate mortar formulation combinations that meet specific performance requirements and have lower overall implicit carbon emissions, providing data-driven decision support for production mix design. However, the shortcomings of this existing solution lie in the fact that the accuracy of its optimization results is highly dependent on the completeness and real-time nature of the underlying database, and its adaptability to real-world production environments with complex raw material sources and dynamically changing carbon footprint data is limited. Summary of the Invention

[0004] This application provides a method and system for optimizing carbon emissions of dry mortar, which solves the problems of static setting of recycled aggregate dosage, inability to coordinate carbon emission performance, and failure of mixed abrupt response in the prior art.

[0005] In a first aspect, this application provides a method for optimizing carbon emissions from dry-mix mortar, comprising: Collect particle size distribution data, crushing value and replacement rate of recycled aggregate in dry mortar to form a set of performance indicators of recycled aggregate, and establish a mapping relationship between the set of performance indicators of recycled aggregate and carbon emissions. During the mixing process of dry mortar, multiple sensing units arranged inside the mixing chamber are used to acquire the acoustic signals generated by the mixing and collision of recycled aggregates. The acoustic signal is converted into a collision feature spectrum that reflects the intensity of particle interaction in dry mortar, and the time-varying characteristics of the collision feature spectrum are extracted to identify the mixing uniformity state that characterizes the degree of mixing uniformity of recycled aggregate. A time-series analysis model is used to continuously learn the mixing uniformity state in order to dynamically predict the critical triggering state of the sudden change in the performance of dry powder mortar caused by recycled aggregate. The mapping relationship is coordinated with the critical triggering state to form an optimized balance parameter that simultaneously satisfies the carbon emission constraints. Based on the optimized balance parameter, dynamic control instructions for adjusting the proportion of recycled aggregate are generated in real time.

[0006] Optionally, a time-series analysis model is used to continuously learn the mixing uniformity state in order to dynamically predict the critical triggering state of sudden changes in the performance of dry-mixed mortar caused by recycled aggregate, including: A data stream is obtained to represent the sequence of changes in the uniformity of the mixture, the data stream containing a temporal alternation of uniformity state codes and disordered state codes; A time series analysis model is used to continuously learn the mixing uniformity state in order to count the duration of consecutive mixing uniformity state codes and calculate the interval between adjacent mixing disorder state codes. The duration of the duration and the interval duration are used as the state stability feature and the state transition interval feature, respectively, and the shortening rate of the state transition interval feature and the decay rate of the state stability feature are calculated. The shortening rate value and the decay rate value are combined to generate a state transition characteristic value. When the state transition characteristic value exceeds a preset range, it is marked as an abnormal point in the mixed state of the recycled aggregate. The occurrence frequency and time density of multiple mixed state anomalies are accumulated. When the occurrence frequency and time density simultaneously exceed a preset threshold, it is determined that the critical triggering state of the dry powder mortar performance change caused by recycled aggregate has been reached.

[0007] Optionally, the acoustic signal is converted into a collision feature spectrum reflecting the intensity of particle interaction in the dry mortar, and the time-varying characteristics of the collision feature spectrum are extracted to identify the mixing uniformity state characterizing the degree of mixing uniformity of recycled aggregate, including: A frequency decomposition operation is performed on the acoustic signal to obtain a collision feature spectrum characterizing the intensity of particle interaction in dry mortar, and the energy value of the collision feature spectrum is obtained. The collision feature spectrum of a continuous time series is divided into fixed time windows, and the absolute difference in energy values ​​of the collision feature spectrum between adjacent time windows is calculated as the fluctuation intensity parameter. When the fluctuation intensity parameter is lower than the preset stability threshold, the recycled aggregate in the time window is determined to be in a high uniformity state and a mixed uniformity status code is generated. When the fluctuation intensity parameter is higher than the preset disorder threshold, it is determined to be in a low uniformity state and a mixed disorder status code is generated. The mixing uniformity state code is connected with the mixing disorder state code to form a sequence of changes in the mixing uniformity state that characterizes the degree of mixing uniformity of recycled aggregate.

[0008] Optionally, the mapping relationship is coordinated with the critical triggering state to form optimized balance parameters that simultaneously satisfy carbon emission constraints, and dynamic control commands for adjusting the proportion of recycled aggregate are generated in real time based on the optimized balance parameters, including: Extract the reference value of carbon emissions corresponding to the current recycled aggregate replacement rate from the mapping relationship, and calculate the allowable adjustment range of carbon emissions based on the determination result of the critical trigger state. Under the constraint that the carbon emissions are within the floating range, the carbon emission baseline value of the substitution rate is calculated with the determination result of the critical triggering state as the control priority. The difference between the carbon emission benchmark value and the current actual substitution rate is calculated to obtain an optimized balance parameter for the substitution rate that can both suppress sudden changes in the performance of dry powder mortar and meet carbon emission constraints. The substitution rate optimization balance parameters are converted into the number of speed control pulses of the conveyor belt to generate a dynamic control command containing the number of speed control pulses, which is then sent to the batching actuator of the recycled aggregate.

[0009] Optionally, during the mixing process of dry mortar, multiple sensing units arranged inside the mixing chamber acquire the acoustic signals generated by the collision of aggregates during mixing, including: Multiple vibration sensing units are fixedly installed on the inner wall of the mixing chamber to acquire the motion trajectory of the mixing blades. The surface of the vibration sensing head of the vibration sensing unit maintains a preset distance from the motion trajectory and faces the mixing area of ​​the recycled aggregate. During the process of starting the mixing equipment to drive the movement of recycled aggregate, the piezoelectric sensing diaphragm of the vibration sensing unit captures the mechanical vibration waves generated by the collision of particles in the recycled aggregate and converts the mechanical vibration waves into voltage fluctuation signals. The voltage fluctuation signal is transmitted to the signal acquisition terminal via a shielded cable, and the time-domain waveform of the voltage fluctuation signal is recorded in the signal acquisition terminal at a preset sampling frequency. Configure channel numbers for the vibration sensing unit and bind the channel numbers to time-domain waveforms to form structured acoustic signal data blocks; The structured acoustic signal data blocks are integrated to form the acoustic signal generated by the collision of aggregates.

[0010] Optionally, during the process of starting the mixing equipment to drive the movement of the recycled aggregate, the piezoelectric sensing diaphragm of the vibration sensing unit captures the mechanical vibration waves generated by the collision of particles in the recycled aggregate, and converts the mechanical vibration waves into voltage fluctuation signals, including: When the mixing equipment is started and the mixing blades are driven to move the recycled aggregate, the particles of the recycled aggregate collide to generate mechanical vibration waves. The mechanical vibration wave is transmitted to the surface of the piezoelectric sensing diaphragm of the vibration sensing unit, so that the piezoelectric sensing diaphragm is subjected to periodic deformation stress. The crystalline material inside the piezoelectric sensing diaphragm responds to the periodic deformation stress to generate piezoelectric charges, and the piezoelectric charges are converted into continuous current fluctuations through a conversion circuit; The continuous current fluctuations are converted into voltage fluctuation signals through an impedance matching circuit.

[0011] Optionally, data on particle size distribution, crushing value, and replacement rate of recycled aggregate in dry mortar are collected to form a set of performance indicators for recycled aggregate, and a mapping relationship between the set of performance indicators and carbon emissions is established, including: The mass weight of each particle size after the sieving test of recycled aggregate in dry mortar is used as the particle size distribution data. At the same time, the total weight of the crushed material after the standard crushing test is weighed as the crushing value, and the proportion of recycled aggregate in the total aggregate is recorded as the replacement rate. The particle size distribution data, crushing value, and replacement rate are combined into a data record group, and a set of performance indicators for recycled aggregates is constructed based on the data record group. Retrieve the energy consumption ledger corresponding to the recycled aggregate production line, and extract the total readings of the electricity metering instruments and the fuel consumption from the energy consumption ledger; According to the preset carbon emission coefficient specification, the total reading of the power meter is converted into the first carbon emission amount, and the fuel consumption is converted into the second carbon emission amount according to the preset fuel type corresponding coefficient. The first carbon emission amount and the second carbon emission amount are added together to form the carbon emission amount of the recycled aggregate. A mapping relationship between the set of performance indicators of recycled aggregates and carbon emissions is established in the database storage unit, so that the particle size distribution data, crushing value and replacement rate of the set of performance indicators of recycled aggregates can be returned as the corresponding carbon emissions through query operations.

[0012] Secondly, this application provides a dry mortar carbon emission optimization system, comprising: A module is established to collect particle size distribution data, crushing value and replacement rate of recycled aggregate in dry mortar, so as to form a set of performance indicators of recycled aggregate, and to establish a mapping relationship between the set of performance indicators of recycled aggregate and carbon emissions. The acquisition module is used to acquire the acoustic wave signal generated by the mixing and collision of recycled aggregates through multiple sensing units arranged inside the mixing chamber during the dry mortar mixing process. The identification module is used to convert the acoustic signal into a collision feature spectrum that reflects the intensity of particle interaction in dry mortar, and to extract the time-varying characteristics of the collision feature spectrum to identify the mixing uniformity state that characterizes the degree of mixing uniformity of recycled aggregate. The prediction module is used to continuously learn the mixing uniformity state using a time-series analysis model in order to dynamically predict the critical triggering state of the sudden change in the performance of dry mortar caused by recycled aggregate. The generation module is used to coordinate and analyze the mapping relationship with the critical triggering state to form an optimized balance parameter that simultaneously satisfies the carbon emission constraints, and to generate dynamic control instructions in real time based on the optimized balance parameter to adjust the proportion of recycled aggregate.

[0013] Thirdly, embodiments of this application provide a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a dry mortar carbon emission optimization method as described in the first aspect above.

[0014] Fourthly, embodiments of this application provide a computer storage medium storing a computer program, which, when executed by a computer, implements a method for optimizing carbon emissions from dry mortar as described in the first aspect.

[0015] This application's technical solution achieves intelligent control of recycled aggregates in dry-mix mortar through multi-source data fusion and dynamic optimization technology. Specifically, a material evaluation system under environmental constraints is established based on the mapping relationship between performance indicators and carbon emissions; acoustic signal spectrum analysis accurately captures the uniformity characteristics of the recycled aggregate mixing process; and a time-series prediction model effectively identifies critical states of performance mutations. This method overcomes the limitations of traditional static proportioning, achieving real-time optimization of the recycled aggregate addition ratio through dynamic control commands. While ensuring the stability of dry-mix mortar performance, it maximizes resource utilization and significantly improves the green preparation level and intelligent control capabilities of building materials.

[0016] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of a method for optimizing carbon emissions from dry mortar provided in this application is shown; Figure 2 A scenario diagram is shown for a method to optimize carbon emissions from dry mortar provided in this application; Figure 3 A schematic diagram of the structure of a dry mortar carbon emission optimization system provided in this application is shown; Figure 4 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0020] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0021] Researchers have discovered that the low-carbon transformation of building materials faces severe challenges: while static database-based mix optimization systems can calculate theoretical carbon reduction potential, their lack of real-time sensing and dynamic control leads to project implementation failures. The root cause is that low-carbon materials such as recycled aggregates are prone to mortar performance risks due to performance fluctuations (such as sudden changes in crushing values ​​and abnormal mixing uniformity). Existing solutions rely on fixed carbon footprint data, failing to capture the microscopic evolution of aggregates during actual incorporation, resulting in a dynamic imbalance between carbon emission optimization and performance assurance goals.

[0022] To address this contradiction, this invention proposes a method for optimizing carbon emissions in dry mortar. Its innovation lies in breaking through the constraints of static optimization models by interpreting the acoustic characteristics of particle collisions and establishing a dynamic decision-making chain for abrupt risk. Specifically: Performance indicators such as particle size distribution and crushing value of recycled aggregates are collected to construct a quantitative mapping relationship between them and carbon emissions; an acoustic sensor array is embedded in the mixing chamber to capture aggregate collision sound waves in real time and interpret them as time-varying spectral characteristics characterizing mixing uniformity; the evolution law of the mixing state is continuously learned through a time-series model to dynamically predict the critical trigger threshold for performance degradation; finally, the carbon emission mapping relationship and critical state parameters are integrated to generate an adaptive control command for the proportion of recycled aggregate added. This method overturns traditional optimization logic: time-varying acoustic spectrum analysis achieves millisecond-level perception of particle-level mixing uniformity for the first time, filling the monitoring blind spot of microscopic processes; the critical state prediction mechanism transforms the risk of performance abrupt changes into a dynamic decision boundary, achieving zero compromise on material performance within the carbon emission constraint framework; the closed-loop control flow establishes a four-dimensional correlation between "aggregate performance - mixing state - carbon emission mapping - critical risk," forming an instantaneous response optimization capability for the production process.

[0023] 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 some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Figure 1 A flowchart of a method for optimizing carbon emissions from dry mortar is provided as an embodiment of this application, such as... Figure 1 As shown, the method includes: 101. Collect particle size distribution data, crushing value and replacement rate of recycled aggregate in dry mortar to form a set of performance indicators of recycled aggregate, and establish a mapping relationship between the set of performance indicators of recycled aggregate and carbon emissions.

[0025] Optionally, step 101 may specifically include the following steps: 1011. The mass weight of each particle size after the sieving test of recycled aggregate in dry mortar is used as the particle size distribution data. At the same time, the total weight of the crushed material after the standard crushing test is used as the crushing value. The proportion of the recycled aggregate in the total aggregate is recorded as the replacement rate.

[0026] 1012. The particle size distribution data, crushing value and replacement rate are combined into a data record group, and a set of performance indicators of recycled aggregate is formed based on the data record group.

[0027] 1013. Retrieve the energy consumption ledger corresponding to the recycled aggregate production line, and extract the total readings of the electricity metering instruments and the fuel consumption from the energy consumption ledger.

[0028] 1014. The total reading of the power meter is converted into a first carbon emission amount according to the preset carbon emission coefficient specification, and the fuel consumption is converted into a second carbon emission amount according to the preset fuel type corresponding coefficient. The first carbon emission amount and the second carbon emission amount are superimposed to form the carbon emission amount of the recycled aggregate.

[0029] 1015. Establish a mapping relationship between the set of performance indicators of recycled aggregates and carbon emissions in the database storage unit, so that the particle size distribution data, crushing value and substitution rate of the set of performance indicators of recycled aggregates can be returned as the corresponding carbon emissions through query operations.

[0030] In the above scheme, recycled aggregate refers to aggregate from recycled construction waste. Particle size distribution data refers to the mass distribution of particles of different sizes. Crushing value is a quantitative indicator of the aggregate's resistance to crushing. Replacement rate refers to the proportion of recycled aggregate in the total aggregate. The set of recycled aggregate performance indicators refers to a combination of data reflecting the characteristics of recycled aggregate. Carbon emissions refer to the carbon dioxide equivalent generated during the production process. Screening test refers to the test that separates particles of different sizes through a sieve. Standard crushing test refers to the standard test for determining the compressive strength of aggregate. Data record set refers to test data organized in a fixed format. Energy consumption ledger refers to an account recording energy usage. Total reading of electricity metering instruments refers to the cumulative value of electricity consumption. Fuel consumption refers to the total amount of fuel used. Carbon emission coefficient specification refers to the coefficient standard for converting energy into carbon emissions. First carbon emission refers to the carbon emissions corresponding to electricity consumption. Second carbon emission refers to the carbon emissions corresponding to fuel consumption. Database storage unit refers to the database module that stores data. Query operation refers to the operation of retrieving data from the database.

[0031] In this embodiment, the system first executes a physical property testing process for recycled aggregates: The recycled aggregates in the dry mortar are graded and sieved using a standard sieve testing device. After separating the aggregate particles according to their size range, a precision electronic balance is used to weigh the particles in each size range, and the resulting values ​​are recorded as particle size distribution data. Simultaneously, according to the specification for "Recycled Coarse Aggregates for Concrete," the recycled aggregate samples are placed in a compression testing machine for a standard crushing test. After the test, the crushed material is collected, and the total weight of the crushed material is weighed again using an electronic balance and converted into a crushing value (the percentage of crushed material mass to the original sample mass). Furthermore, the proportion of recycled aggregates in the mixture is monitored in real time using an aggregate proportioning analyzer, and this value is recorded as the replacement rate. This step quantifies the physical properties of the aggregates into calculable engineering parameters.

[0032] Subsequently, the system integrates the test data to form a structured set: the three types of data output in step 1011—particle size distribution data (mass percentage of each particle size interval), crushing value (mechanical strength index), and replacement rate (proportion of recycled aggregate usage)—are combined into standardized data record groups by timestamp and sample number using a data binding engine. This record group contains a unique identifier for the aggregate sample and the correlation between the three types of parameters. The system imports the data record groups from multiple samples into the performance index integration module, arranging them in matrix form as a set of recycled aggregate performance indicators. The row vectors represent different samples, and the column vectors correspond to the three types of parameters: particle size distribution, crushing value, and replacement rate.

[0033] Next, the system collects energy consumption data from the recycled aggregate production line: It retrieves the energy consumption ledger of the recycled aggregate production line in real time via an industrial IoT data interface; this ledger is automatically recorded by the production line's energy management system. The system extracts two types of core data from the ledger: first, the total readings of electricity meters during a specific production cycle (unit: kilowatt-hours); and second, the fuel consumption of production line equipment (such as crushers and conveyor belts) (unit: liters). The data extraction scope covers the entire process of aggregate crushing, screening, and washing, ensuring a strict correspondence with the aggregate sample production period in step 1011.

[0034] Then, the system converts energy data into carbon emissions: based on preset carbon emission coefficient standards (such as the "Standard for Calculating Carbon Emissions from the Utilization and Disposal of Construction Waste"), the total reading of the electricity meter is multiplied by the regional power grid average carbon emission factor through an electricity carbon emission converter to generate the first carbon emission. Simultaneously, based on the fuel type (diesel or gasoline), a preset coefficient corresponding to the fuel type is selected, and the fuel carbon emission converter converts the fuel consumption into a second carbon emission. Finally, the carbon emission superposition module adds the two types of carbon emissions together to output the total carbon emission value for this batch of recycled aggregate.

[0035] Finally, the system establishes a mapping relationship between performance indicators and carbon emissions: a relational data table is created in the database storage unit, using the set of recycled aggregate performance indicators from step 1012 as the index key (including particle size distribution data, crushing value, and replacement rate), and the carbon emissions from step 1014 as the mapping value. A structured query language engine enables bidirectional association: inputting a specific combination of aggregate performance parameters returns the carbon emissions; conversely, inputting a target carbon emission threshold returns the range of aggregate performance that meets the criteria. This mapping relationship supports dynamic updates, automatically expanding the mapping table entries when new production line data is added.

[0036] In practical applications, when using recycled aggregate from construction waste to optimize carbon emissions in dry mortar, the system performs carbon emission optimization modeling for the recycled aggregate generated from crushed construction waste. First, a sieving test is conducted on the recycled aggregate, and the mass of particles of each size is recorded as particle size distribution data. Simultaneously, a standard crushing test is performed, and the total weight of the crushed material is measured as the crushing value. The proportion of recycled aggregate in the total aggregate is then calculated as the replacement rate.

[0037] Subsequently, the aforementioned particle size distribution data, crushing values, and replacement rates are integrated into a data record set, and a set of performance indicators for recycled aggregates is constructed based on the data record sets of all test batches. The system retrieves the energy consumption ledger corresponding to the recycled aggregate production line, extracting the total electricity meter readings (e.g., total electricity consumption of the production line) and fuel consumption (e.g., diesel consumption of transport vehicles) within a specific production cycle. According to the preset carbon emission coefficient specifications, the total electricity readings are converted into the first carbon emission; the fuel consumption is converted into the second carbon emission according to the carbon emission coefficient corresponding to the fuel type (e.g., diesel combustion emission factor), and the two are added together to generate the carbon emission of that batch of recycled aggregates.

[0038] Finally, a mapping relationship between the set of performance indicators of recycled aggregates and carbon emissions is established in the database storage unit. For example, when inputting the particle size distribution data (high proportion of fine particles), crushing value (low crushing rate), and substitution rate (high proportion of natural aggregates), the corresponding carbon emissions can be directly returned through a query operation. This mapping relationship is used to dynamically optimize mortar formulations: if the query shows that the carbon emissions of a certain set of performance indicators of recycled aggregates are lower than a preset threshold, its substitution rate is automatically increased to achieve the carbon emission reduction target; otherwise, the aggregate pretreatment process is optimized to improve the stability of the crushing value.

[0039] The overall solution in step 101 above achieves a precise correlation analysis between recycled aggregate performance and carbon emissions. Through a systematic data acquisition process, key performance indicators such as particle size distribution, crushing value, and replacement rate are integrated into a structured dataset, and an innovative mapping relationship with carbon emissions is established. This technology overcomes the limitations of traditional material performance evaluation and environmental impact assessment, achieving a cross-dimensional correlation from aggregate physical properties to environmental benefits. Through precise energy consumption data conversion and the application of carbon emission coefficients, a scientifically reliable carbon footprint calculation model is constructed, providing a quantitative basis for subsequent green material optimization. This analytical method, which combines material performance with environmental impact, provides a completely new evaluation dimension for the development of sustainable building materials.

[0040] 102. During the mixing process of dry mortar, multiple sensing units installed inside the mixing chamber are used to acquire the acoustic signals generated by the mixing and collision of recycled aggregates.

[0041] Optionally, step 102 may specifically include the following steps: 1021. Multiple vibration sensing units are fixedly installed on the inner wall of the mixing chamber to obtain the motion trajectory of the mixing blades. The surface of the vibration sensing head of the vibration sensing unit maintains a preset distance from the motion trajectory and faces the mixing area of ​​the recycled aggregate.

[0042] 1022. During the process of starting the mixing equipment to drive the movement of recycled aggregate, the piezoelectric sensing diaphragm of the vibration sensing unit captures the mechanical vibration waves generated by the collision of particles in the recycled aggregate and converts the mechanical vibration waves into voltage fluctuation signals.

[0043] Step 1022 may specifically include the following process: during the process of starting the stirring equipment and driving the stirring blades to move the recycled aggregate, the particles of the recycled aggregate collide to generate mechanical vibration waves; the mechanical vibration waves are transmitted to the surface of the piezoelectric sensing diaphragm of the vibration sensing unit, so that the piezoelectric sensing diaphragm is subjected to periodic deformation stress; the crystal material inside the piezoelectric sensing diaphragm responds to the periodic deformation stress to generate piezoelectric charge, and the piezoelectric charge is converted into continuous current fluctuation through a conversion circuit; the continuous current fluctuation is converted into a voltage fluctuation signal through an impedance matching circuit.

[0044] 1023. The voltage fluctuation signal is transmitted to the signal acquisition terminal via a shielded cable, and the time-domain waveform of the voltage fluctuation signal is recorded in the signal acquisition terminal at a preset sampling frequency.

[0045] 1024. Configure channel numbers for the vibration sensing unit and bind the channel numbers to time-domain waveforms to form structured acoustic signal data blocks.

[0046] 1025. Integrate the structured acoustic signal data blocks to form an acoustic signal generated by aggregate mixing and collision.

[0047] In the above scheme, the mixing chamber refers to the internal space of the dry mortar mixing equipment. The sensing unit refers to the sensor assembly used to detect physical quantities. The acoustic signal refers to acoustic data generated by vibration. The vibration sensing unit refers to the sensor that detects mechanical vibration. The motion trajectory of the mixing blades refers to the path of the mixing blades. The vibration pickup head refers to the sensing part of the vibration sensor. The preset distance refers to the set minimum safe distance. The mixing zone refers to the space where aggregates and cementitious materials are mixed. Mechanical vibration wave refers to vibration fluctuations generated by collisions. The voltage fluctuation signal refers to a signal in which voltage changes over time. The shielded cable refers to a cable with electromagnetic shielding function. The signal acquisition terminal refers to the equipment that collects and processes signals. The preset sampling frequency refers to the fixed frequency for signal acquisition. The time-domain waveform refers to the waveform diagram of the signal in the time domain. The channel number refers to the number that identifies the sensor channel. The structured acoustic signal data block refers to acoustic data organized in a specific format. The piezoelectric sensing diaphragm refers to a sensing element based on the piezoelectric effect. The periodic deformation stress refers to periodically changing mechanical stress. The piezoelectric charge refers to the charge generated by the piezoelectric material. The conversion circuit refers to the circuit that converts charge into an electrical signal. Continuous current fluctuation refers to the continuous change of current. Impedance matching circuit refers to an electronic circuit that matches impedance.

[0048] In this embodiment, firstly, multiple vibration sensing units are fixedly installed on the inner wall of the mixing chamber. Based on the mechanical structure design of the mixing equipment, the bases of the vibration sensing units are bolted to preset positions on the inner wall of the chamber. Simultaneously, a motion trajectory tracker acquires the rotation path and coverage area of ​​the mixing blades in real time, adjusting the angle and height of the pickup head of each vibration sensing unit to ensure that its sensing surface maintains a constant preset distance from the movement trajectory of the mixing blades and is directly aligned with the center of the recycled aggregate mixing area. This process utilizes a laser rangefinder to calibrate distance parameters, forming a monitoring network covering the mixing hot zone, providing a hardware foundation for capturing aggregate collision signals.

[0049] Subsequently, the system activates the mixing equipment to drive the movement of the recycled aggregate: when the mixing motor drives the mixing blades to push the recycled aggregate particles to collide with each other, high-frequency mechanical vibration waves are generated. The piezoelectric sensing diaphragm of the vibration sensing unit directly contacts and transmits the vibration energy to the inner wall of the cavity, and its surface deforms due to periodic pressure changes. The piezoelectric crystal material inside the diaphragm responds to the deformation stress and converts mechanical energy into piezoelectric charge through the piezoelectric effect. The charge is amplified by the charge amplifier in the conversion circuit to generate continuous current fluctuations, and then the current-voltage relationship is adjusted by the impedance matching circuit, finally outputting a standardized voltage fluctuation signal. This process realizes the complete conversion of aggregate collision kinetic energy into an electrical signal.

[0050] Next, the system transmits voltage fluctuation signals via shielded cables: a double-layered metal braided shielded cable connects all vibration sensing units to the signal acquisition terminal, with the outer layer of the cable grounded to suppress electromagnetic interference. The signal acquisition terminal has a built-in analog-to-digital converter that performs equally spaced digital sampling of the input analog signal at a preset sampling frequency, generating a time-domain waveform containing timestamps, voltage amplitude, and phase information. This waveform is stored in a buffer in time-series format, providing the raw data stream for subsequent structured processing.

[0051] The system then assigns channel numbers to the vibration sensing units: a unique channel number is assigned to each vibration sensing unit based on its physical installation location, with the numbering rules corresponding to the cavity space partitions. A data binding engine associates the channel numbers with the time-domain waveforms acquired by the corresponding sensors, forming spatially labeled structured acoustic signal data blocks. Each data block contains a triplet of channel number, time series, and sampling frequency, ensuring that the signal source location can be traced in subsequent analysis.

[0052] Finally, the system integrates the structured acoustic signal data blocks: all the structured acoustic signal data blocks output in step 1024 are aligned according to time windows, and the waveform data of each channel are superimposed through a multi-channel signal synthesizer. The synthesizer uses a time synchronization calibration algorithm to eliminate the micro-hour delay difference in the transmission of signals from different sensors, and finally fuses them to generate a multi-dimensional sound field dataset that fully describes the acoustic signals generated by aggregate mixing and collision, with dimensions including time, spatial coordinates, and sound pressure intensity.

[0053] In practical applications, in the carbon emission optimization of dry mortar production lines using recycled aggregate from construction waste, during the dry mortar mixing process, in order to optimize the amount of recycled aggregate and reduce carbon emissions, the system has multiple vibration sensing units fixedly installed axially and radially on the inner wall of the mixing chamber. At the same time, the motion trajectory of the mixing blades is obtained through a motion trajectory algorithm to ensure that the surface of the vibration sensor head of each vibration sensing unit maintains a preset distance from the motion trajectory and is precisely oriented towards the mixing area of ​​the recycled aggregate.

[0054] Next, during the process of starting the mixing equipment to drive the movement of recycled aggregate, the collision between recycled aggregate particles generates mechanical vibration waves. These waves are transmitted to the surface of the piezoelectric induction diaphragm of the vibration sensing unit, causing the diaphragm to bear periodic deformation stress. The crystal material inside the piezoelectric induction diaphragm responds to the periodic deformation stress and generates piezoelectric charges. The charges are converted into continuous current fluctuations by the conversion circuit, and then converted into voltage fluctuation signals by the impedance matching circuit.

[0055] Then, the voltage fluctuation signal is transmitted to the signal acquisition terminal via a shielded cable, where the time-domain waveform of the voltage fluctuation signal is recorded at a preset sampling frequency. The system assigns a channel number to each vibration sensing unit and binds the channel number to the time-domain waveform (e.g., the sensor numbered C3 is bound to the peak waveform of the sound wave it acquires), forming a structured sound wave signal data block.

[0056] Finally, all structured acoustic signal data blocks are integrated to form an acoustic signal generated by aggregate mixing and collision. This acoustic signal can provide real-time feedback on the mixing uniformity of recycled and natural aggregates through spectrum analysis. If the high-frequency components of the acoustic signal suddenly increase, it indicates that the crushing rate of recycled aggregates is too high, and the stirring speed needs to be reduced to avoid material waste caused by excessive aggregate crushing; conversely, it increases the replacement rate of recycled aggregates, reduces the carbon contained in the mining of natural aggregates, and achieves dynamic carbon emission reduction control in dry mortar production.

[0057] The overall scheme in step 102 above enables real-time monitoring and signal acquisition of aggregate collision behavior during the mixing process. Through an innovative arrangement of vibration sensing units, mechanical vibration waves during the recycled aggregate mixing process are accurately captured. This technology uses the piezoelectric induction principle to convert microscopic collision behavior into measurable electrical signals, achieving a digital characterization of the mixing state. The innovative signal acquisition and processing workflow ensures the integrity and accuracy of the acoustic signals, providing a high-quality data foundation for subsequent analysis. This non-contact online monitoring method overcomes the subjectivity and lag of traditional manual observation, achieving an objective quantitative evaluation of the mixing process and providing reliable data support for process optimization.

[0058] 103. The acoustic signal is converted into a collision feature spectrum that reflects the intensity of particle interaction in dry mortar, and the time-varying characteristics of the collision feature spectrum are extracted to identify the mixing uniformity state that characterizes the degree of mixing uniformity of recycled aggregate.

[0059] Optionally, step 103 may specifically include the following steps: 1031. Perform frequency decomposition on the acoustic signal to obtain a collision feature spectrum characterizing the intensity of particle interaction in dry mortar, and obtain the energy value of the collision feature spectrum.

[0060] 1032. Divide the collision feature spectrum of a continuous time series into fixed time windows, and calculate the absolute difference in energy values ​​of the collision feature spectrum between adjacent time windows as the fluctuation intensity parameter.

[0061] 1033. When the fluctuation intensity parameter is lower than the preset stability threshold, the recycled aggregate in the time window is determined to be in a high uniformity state and a mixed uniformity status code is generated. When the fluctuation intensity parameter is higher than the preset disorder threshold, it is determined to be in a low uniformity state and a mixed disorder status code is generated.

[0062] 1034. Connect the mixing uniformity state code with the mixing disorder state code to form a change sequence of mixing uniformity state characterizing the degree of mixing uniformity of recycled aggregate.

[0063] In the above scheme, the collision characteristic spectrum refers to the spectral distribution reflecting the collision characteristics of particles. Mixing uniformity state refers to the degree of uniformity of aggregate distribution in the mortar. Frequency decomposition operation refers to the process of decomposing a signal into different frequency components. Energy value refers to the energy magnitude of each frequency component in the spectrum. Time window refers to the time period used to segment the time series. Fluctuation intensity parameter refers to the quantitative index of energy change. Preset stability threshold refers to the critical value for judging a uniform state. Preset disorder threshold refers to the critical value for judging a disordered state. High uniformity state refers to a state where aggregate distribution is uniform. Mixing uniformity state code refers to the encoding that identifies a uniform state. Low uniformity state refers to a state where aggregate distribution is uneven. Mixing disorder state code refers to the encoding that identifies a disordered state. Change sequence refers to the sequence of states changing over time.

[0064] In this embodiment, the system first performs frequency decomposition on the acquired acoustic signal: the time-domain acoustic signal is converted into a frequency-domain signal using a Fast Fourier Transform algorithm, generating a collision characteristic spectrum characterizing the intensity of particle interaction in dry mortar. The horizontal axis of this spectrum represents the frequency value, and the vertical axis represents the energy amplitude. The system calculates the area of ​​this spectrum within the effective frequency range using a spectrum energy integrator and defines it as the energy value. This process quantifies the mechanical vibration characteristics of aggregate collisions within the mixing chamber into analyzable spectral energy parameters.

[0065] Subsequently, the system segments the collision characteristic spectrum of the continuous time series according to fixed time windows: based on a preset mixing process cycle (e.g., the mixing process cycle can be 30 seconds per window), the spectrum data is divided into segments of equal length. The energy values ​​of adjacent time windows are extracted by the fluctuation intensity calculation module, and the absolute difference between the two values ​​is calculated to generate a fluctuation intensity parameter reflecting the degree of energy abrupt change. The larger the value of this parameter, the more drastic the change in aggregate collision energy within adjacent time periods, indicating significant fluctuations in mixing uniformity.

[0066] Next, the system determines the mixing state based on the comparison between the fluctuation intensity parameter and a preset threshold: when the fluctuation intensity parameter is lower than the preset stability threshold (indicating gentle energy changes), it is determined that the recycled aggregate distribution is balanced and the collision energy is stable within this time window, generating a mixing uniformity state code (such as binary code "1") representing high uniformity. When the parameter is higher than the preset disorder threshold (indicating violent energy fluctuations), it is determined to be a low uniformity state caused by aggregate agglomeration or segregation, generating a mixing disorder state code (such as binary code "0") representing an abnormality. This process uses a state coding engine to convert physical parameters into state symbols.

[0067] Finally, the system concatenates the time-generated status codes into a sequence of mixing uniformity state changes: the mixing uniformity status codes or mixing disorder status codes corresponding to each time window are chained together by a sequence synthesizer to form a complete sequence of mixing uniformity state changes that describes the evolution of uniformity throughout the entire mixing cycle. This sequence, indexed by time, can be directly mapped to mixing process parameters (such as rotation speed and duration), guiding the optimization of the mixing process.

[0068] In practical applications, for monitoring the mixing uniformity of recycled coal gangue sand used in dry mortar production, the system monitors the mixing uniformity in real time using acoustic signals (structured data blocks output in step 1025) collected by the vibration sensing unit inside the mixing chamber, targeting low-carbon formulations where recycled coal gangue sand replaces natural sand. First, a frequency decomposition operation (such as a fast Fourier transform) is performed on the acoustic signal to generate a collision characteristic spectrum characterizing the intensity of particle interactions in the dry mortar, and the energy value of this spectrum in the core frequency band is extracted.

[0069] Subsequently, the collision feature spectrum of the continuous time series is segmented by a fixed time window, and the absolute difference of the energy values ​​of the collision feature spectrum between adjacent time windows is calculated as the fluctuation intensity parameter. When the fluctuation intensity parameter is lower than the preset stability threshold, the system determines that the recycled aggregate in that time window is in a high uniformity state and generates a mixed uniformity status code (such as code "H"); when the fluctuation intensity parameter is higher than the preset disorder threshold, it is determined to be in a low uniformity state and generates a mixed disorder status code (such as code "L").

[0070] Finally, the uniformity status code and the disorder status code are connected sequentially over time to form a sequence of changes in the uniformity of the recycled aggregate mixture (e.g., the sequence "HHLH" indicates that the mixing process has experienced a brief period of disorder before returning to uniformity). This sequence is fed back to the mixing equipment in real time through the production line control system: if low uniformity status codes appear continuously, the mixing speed is automatically reduced to decrease the breakage of coal gangue particles; if the sequence remains high uniformity status codes, the recycled sand replacement rate is increased to reduce the carbon contained in natural sand mining, thus achieving dynamic carbon emission reduction control in dry mortar production.

[0071] The overall scheme in step 103 above achieves intelligent evaluation of the mixing uniformity of recycled aggregates. Advanced signal processing technology transforms the raw acoustic signal into a collision characteristic spectrum with clear physical meaning, and an innovative uniformity evaluation method based on time-varying characteristics is proposed. This technology achieves real-time quality monitoring of the mixing process through dynamic calculation and state determination of fluctuation intensity parameters. The designed mixing state coding system accurately reflects the changing trend of mixing quality, providing an intuitive basis for process adjustment. This technical approach of transforming physical signals into process evaluation indicators significantly improves the quality control level of the mixing process and ensures the product consistency of dry mortar.

[0072] 104. A time-series analysis model is used to continuously learn the mixing uniformity state in order to dynamically predict the critical triggering state of the sudden change in the performance of dry powder mortar caused by recycled aggregate.

[0073] Optionally, step 104 may specifically include the following steps: 1041. Obtain a data stream of the change sequence of the mixing uniformity state, wherein the data stream contains a time-series alternating record of the mixing uniformity state code and the mixing disorder state code.

[0074] 1042. The mixing uniformity state is continuously learned using a time series analysis model to statistically analyze the duration of continuous mixing uniformity state codes and calculate the interval between adjacent mixing disorder state codes.

[0075] 1043. The duration of the continuous period and the duration of the interval are used as the state stability characteristic and the state transition interval characteristic, and the shortening rate of the state transition interval characteristic and the decay rate of the state stability characteristic are calculated.

[0076] 1044. The shortening rate value and the decay rate value are combined to generate a state transition characteristic quantity. When the state transition characteristic quantity exceeds a preset amplitude, it is marked as an abnormal point in the mixed state of the recycled aggregate.

[0077] 1045. Accumulate the occurrence frequency and time density of multiple mixed state anomalies. When the occurrence frequency and time density simultaneously exceed a preset threshold, it is determined that the critical triggering state of the dry powder mortar performance change caused by recycled aggregate has been reached.

[0078] In the above scheme, the time series analysis model refers to the mathematical model for analyzing time series data. The critical trigger state refers to the key state that leads to a sudden change in performance. Data stream refers to a continuously transmitted data sequence. Time-series alternating recording refers to state data recorded alternately in chronological order. Duration period length refers to the duration of a uniform state. Interval period length refers to the time interval between disordered states. State stability characteristic quantity refers to a quantitative indicator reflecting state stability. State transition interval characteristic quantity refers to a quantitative indicator of the interval between state changes. Shortening rate value refers to the rate at which the interval shortens. Decay rate value refers to the rate at which the stable time decreases. State transition characteristic quantity refers to a quantitative indicator reflecting the characteristics of state changes. Preset amplitude refers to the critical change quantity for judging anomalies. Mixed state anomaly points refer to state points with abnormal mixing uniformity. Occurrence frequency refers to the number of times an anomaly point appears. Time density refers to the number of anomaly points per unit time. Preset threshold refers to the critical value for judging critical states. Performance mutation refers to a sudden change in material properties.

[0079] In this embodiment, the system first acquires a data stream of the mixing homogeneity state change sequence: It continuously receives the mixing homogeneity state change sequence generated in step 1034 through a real-time monitoring interface. This data stream consists of alternating timestamp-ordered mixing homogeneity state codes (e.g., binary "1") representing a stable mixing state and mixing disorder state codes (e.g., binary "0") representing segregation anomalies. The system uses a data stream buffer to extract segments at fixed time windows (e.g., every 30 seconds) to ensure the integrity and continuity of the time-series records, providing raw input for subsequent analysis.

[0080] Subsequently, the system employs a time-series analysis model to continuously learn the mixing uniformity state: the data stream from step 1041 is input into the sliding window analysis module, the occurrence frequency of consecutive mixing uniformity state codes is counted and multiplied by the time resolution to generate the duration of a stable mixing period; simultaneously, the time interval between two adjacent mixing disorder state codes is detected to generate the interval duration reflecting the frequency of abnormal fluctuations. This process dynamically updates the two types of parameters through a state transition tracker, capturing the stability change trend of the stirring process in real time.

[0081] Next, the system calculates the dynamic rate of change of the state parameters: based on the duration sequence output in step 1042, the slope of its decrease over time is calculated using a linear regression fitter, defined as the decay rate value characterizing the rate of decay of the steady state; simultaneously, for the interval duration sequence, the amount of shortening per unit time is calculated, generating a shortening rate value characterizing the acceleration of the anomalous frequency. The system correlates the two types of rate parameters through a rate coupling engine to form a joint feature quantity describing the intensity of state transitions.

[0082] Then, the system identifies anomalies in the mixing state: the shortening rate value and the decay rate value from step 1043 are weighted and superimposed to generate a state transition characteristic quantity that comprehensively reflects the degree of stability degradation. When this characteristic quantity exceeds a preset amplitude (such as a rate threshold), the anomaly marker marks the anomaly in the mixing state at the corresponding timestamp position and records the numerical amplitude of the anomaly. This process avoids misjudgments caused by differences in the stirring process by comparing thresholds rather than absolute values.

[0083] Finally, the system determines the critical trigger state for performance mutation: within a preset observation period (such as a single mixing cycle), the number of occurrences of abnormal points in the mixing state is accumulated through an anomaly counter; at the same time, the density value of the occurrence of abnormal points per unit time is calculated. When the number of occurrences and the time density both exceed the preset threshold, the critical state trigger determines that the current mixing process has reached the critical trigger state for the performance mutation of dry powder mortar caused by recycled aggregate, and triggers a real-time warning signal.

[0084] In practical applications, in the performance mutation early warning system for industrial steel slag recycled aggregate applied to dry mortar production lines, the system acquires in real-time a sequence data stream of changes in the mixing uniformity state output from the mixing process (e.g., the sequence "HHLHLL"). This data stream consists of alternating temporal records of mixing uniformity state codes ("H") and mixing disorder state codes ("L"). The mixing uniformity state is continuously learned through a temporal analysis model (e.g., an online learning network based on LSTM): the model first calculates the duration of consecutive mixing uniformity state codes (e.g., three consecutive "H"s lasting 30 seconds), and simultaneously calculates the interval duration of adjacent mixing disorder state codes (e.g., 15 seconds between two "L"s).

[0085] Subsequently, the duration of the duration is defined as the state stability feature, and the interval duration is defined as the state transition interval feature. The model further calculates the shortening rate of the state transition interval feature (e.g., the rate at which the interval shortens from 15 seconds to 5 seconds) and the decay rate of the state stability feature (e.g., the rate at which the duration of "H" decays from 30 seconds to 10 seconds). The shortening rate and decay rate are combined to generate the state transition feature. When this feature exceeds a preset range (e.g., the rate of change exceeds a threshold), the system marks it as an abnormal point in the mixed state of the recycled aggregate.

[0086] The system continuously accumulates the occurrence frequency and time density of multiple abnormal mixing states. When both the occurrence frequency and time density exceed a preset threshold, the system determines that a critical trigger state has been reached where recycled aggregate causes a sudden change in the performance of dry mortar. This determination result is linked to the production line's carbon control system: triggering adjustments to the steel slag aggregate pretreatment process (such as increasing the addition of surface modifiers) to stabilize the mixing state, avoiding rework due to insufficient mortar strength caused by aggregate segregation, and reducing energy consumption and implicit carbon emissions in repeated production processes.

[0087] The overall solution in step 104 above achieves intelligent early warning of sudden changes in the performance of dry-mix mortar. Through the continuous learning capability of the time-series analysis model, it innovatively captures the evolution law of the mixing uniformity state. The proposed method for calculating state transition characteristics can accurately identify abnormal signs in the mixing process, and the critical trigger state determination mechanism achieves early warning of performance risks. The designed anomaly point cumulative analysis algorithm comprehensively considers the frequency and density of anomalies, significantly improving the accuracy and reliability of the early warning. This data-driven predictive maintenance method effectively avoids quality accidents caused by sudden changes in material properties, improving the stability and controllability of the production process.

[0088] 105. Perform a coordinated analysis between the mapping relationship and the critical triggering state to form an optimized balance parameter that simultaneously satisfies the carbon emission constraints, and generate dynamic control instructions in real time based on the optimized balance parameter to adjust the proportion of recycled aggregate.

[0089] Optionally, step 105 may specifically include the following steps: 1051. Extract the reference value of carbon emissions corresponding to the current recycled aggregate replacement rate from the mapping relationship, and calculate the allowable adjustment range of carbon emissions based on the determination result of the critical trigger state.

[0090] 1052. Under the constraint that the carbon emissions are within the floating range, the carbon emission benchmark value of the substitution rate is calculated with the determination result of the critical triggering state as the control priority.

[0091] 1053. The difference between the carbon emission benchmark value and the current actual substitution rate is calculated to obtain the substitution rate optimization balance parameter that can both suppress the sudden change in the performance of dry powder mortar and meet the carbon emission constraints.

[0092] 1054. The substitution rate optimization balance parameter is converted into the number of speed control pulses of the conveyor belt to generate a dynamic control command containing the number of speed control pulses and send it to the batching actuator of the recycled aggregate.

[0093] In the above scheme, optimized balance parameters refer to the optimal parameters that balance performance and carbon emissions. Carbon emission constraints refer to the conditions that limit carbon emissions. Dynamic control commands refer to control signals that are adjusted in real time. Carbon emission reference values ​​refer to the carbon emissions corresponding to a specific substitution rate. Adjustment fluctuation range refers to the range within which carbon emissions can vary. Control priority refers to the order of priority of control strategies. Carbon emission benchmark values ​​refer to the optimized carbon emission target values. Difference calculation refers to calculating the difference between two values. Conveyor belt speed control pulse count refers to the pulse signals that control the speed of the conveyor belt. Batching actuator refers to the actuator that controls the aggregate proportion. Recycled aggregate addition ratio refers to the proportion of recycled aggregate in the total aggregate. Suppressing sudden performance changes in dry mortar refers to measures to prevent sudden performance degradation. Carbon emission constraints refer to the limiting conditions for carbon emissions.

[0094] In this embodiment, the system first extracts the carbon emission reference value corresponding to the current recycled aggregate replacement rate from the mapping relationship: It accesses the mapping relationship established in step 1015 through the database query engine, inputs the recycled aggregate replacement rate parameter value used in the current production line, and returns the historical carbon emission reference value corresponding to that replacement rate. Simultaneously, based on the critical triggering state result determined in step 1045 (such as "imminent mutation" or "stable"), the allowable fluctuation range of carbon emissions is determined through the floating range calculation module: if the critical triggering state indicates a high risk of performance mutation, the floating range is narrowed to prioritize quality stability; if the risk is low, the range is widened to increase carbon emission reduction potential. This process quantifies the quality risk level into a flexible boundary for carbon emissions.

[0095] Subsequently, the system calculates the carbon emission baseline value for the substitution rate under the constraint of the carbon emission floating range: the judgment result of the critical triggering state is used as the control priority. When it is judged as high risk, the control priority is automatically set to "prioritize suppressing performance mutations". At this time, the carbon emission baseline value is taken as the upper limit of the floating range to ensure that the increase in substitution rate will not trigger quality mutations. When it is judged as low risk, the priority is switched to "prioritize carbon emission reduction", and the baseline value is taken as the lower limit. The baseline value optimizer dynamically selects the range boundary value and outputs the carbon emission baseline value that meets the current quality risk monitoring requirements, providing the core constraint target for substitution rate optimization.

[0096] Next, the system generates substitution rate optimization balance parameters through difference calculation: the carbon emission baseline value output in step 1052 and the real-time carbon emission monitoring value corresponding to the actual substitution rate on the current production line (e.g., 51 kg CO2 / ton) are input into the difference calculator to calculate the difference (e.g., baseline value 48 kg CO2 vs. actual value 51 kg CO2 → difference -3 kg CO2). Based on the preset "carbon emission-substitution rate conversion coefficient" (e.g., the carbon emission-substitution rate conversion coefficient can be 2% substitution rate adjustment for every 1 kg CO2 difference), the difference value is converted into substitution rate optimization balance parameters. This parameter directly indicates that the substitution rate needs to be reduced by 6% to simultaneously meet carbon emission constraints and avoid the risk of sudden performance changes, achieving dual-objective synergistic optimization.

[0097] Finally, the system converts the replacement rate optimization balance parameters into dynamic control commands: the replacement rate optimization balance parameters are mapped to the number of speed control pulses of the conveyor belt through a speed conversion model. The conversion logic is based on the linear relationship between aggregate flow rate and speed. The generated dynamic control commands, containing specific pulse counts, are sent in real time to the batching actuator of the recycled aggregate via an industrial communication protocol, driving the conveyor belt motor to adjust its speed and achieving precise second-level control of the recycled aggregate addition ratio.

[0098] In practical applications, in the dynamic carbon control system for coal gangue recycled sand applied to dry mortar production lines, the system first extracts the reference value of carbon emissions corresponding to the current recycled aggregate replacement rate (such as the historical average carbon emissions corresponding to a certain replacement rate) from the mapping relationship, and calculates the allowable adjustment range of carbon emissions based on the judgment result of the critical triggering state (such as the performance mutation risk level).

[0099] Subsequently, under the constraint that carbon emissions are within a fluctuating range, the carbon emission baseline value of the substitution rate is calculated based on the determination of the critical triggering state as the control priority (such as avoiding a sudden change in mixing uniformity as the primary objective).

[0100] Next, the difference between the carbon emission baseline value and the current actual substitution rate is calculated to generate a substitution rate optimization balance parameter that can both suppress sudden changes in the performance of dry powder mortar and meet carbon emission constraints.

[0101] Ultimately, the system converts the replacement rate optimization balance parameters into the number of conveyor belt speed control pulses (e.g., increasing the number of speed control pulses to increase the amount of recycled sand fed), generating a dynamic control command containing the number of speed control pulses and sending it to the batching actuator of the recycled aggregate. This command adjusts the conveyor belt feeding ratio in real time: if the optimization balance parameters require an increase in the replacement rate, the amount of recycled coal gangue sand fed is increased to reduce the carbon hidden in natural sand mining; conversely, the replacement rate is reduced and the mixing parameter optimization module is activated to ensure stable mortar workability and avoid increased waste and repeated production energy consumption caused by sudden performance changes.

[0102] The overall solution described in step 105 achieves intelligent optimization and control of recycled aggregate usage. Through an innovative coordination analysis algorithm, carbon emission constraints and process stability requirements are organically combined to generate scientifically sound and reasonable optimal balance parameters. This technology overcomes the limitations of traditional single-objective optimization, achieving a dual guarantee of environmental benefits and material performance. The designed dynamic control command generation mechanism can adjust the recycled aggregate addition ratio in real time, ensuring continuous optimization of the production process. This closed-loop control system tightly connects monitoring, analysis, and execution, forming a complete intelligent control chain, providing reliable technical support for the industrial application of green building materials.

[0103] The following are specific examples for steps 101 to 105, such as Figure 2 As shown: In the dynamic carbon control system for the application of recycled aggregates from waste concrete in dry mortar production lines, the system first collects data on the particle size distribution of recycled aggregates (by weighing the mass of each particle size through sieving tests), crushing value (by weighing the total weight of crushed material after standard crushing tests), and replacement rate (the proportion of recycled aggregates in the total aggregate volume), forming a set of performance indicators for recycled aggregates. Simultaneously, the system retrieves the energy consumption ledger of the recycled aggregate production line, converts the electricity readings into the first carbon emission amount according to the preset carbon emission coefficient specifications, converts the fuel consumption into the second carbon emission amount according to the fuel type coefficient, and generates the carbon emission amount by superimposing them. The system then establishes a mapping relationship between the set of performance indicators for recycled aggregates and the carbon emission amount in the database storage unit.

[0104] In the mixing process, multiple vibration sensing units are fixedly installed on the inner wall of the mixing chamber, with their pickup heads maintaining a preset distance from the movement trajectory of the mixing blades and facing the mixing area of ​​the recycled aggregate. When the mixing equipment is started and the recycled aggregate is driven to move, the mechanical vibration waves generated by the collision of particles are transmitted to the surface of the piezoelectric induction diaphragm. Due to the periodic deformation stress of the diaphragm, the internal crystal material generates piezoelectric charges, which are converted into continuous current fluctuations by the conversion circuit, and then output as voltage fluctuation signals through the impedance matching circuit. The voltage fluctuation signals are transmitted to the signal acquisition terminal through the shielded cable, and the time-domain waveform is recorded according to the preset sampling frequency. Each vibration sensing unit is assigned a channel number, and the channel number is bound to the time-domain waveform to form a structured acoustic signal data block. Finally, the data blocks are integrated to form an acoustic signal.

[0105] Subsequently, frequency decomposition is performed on the acoustic signal to generate a collision feature spectrum and extract its energy value. The collision feature spectrum of the continuous time series is divided into fixed time windows, and the absolute difference of the energy values ​​of adjacent time windows is calculated as the fluctuation intensity parameter. When the fluctuation intensity parameter is lower than the preset stability threshold, a mixed uniform state code (such as "H") is generated; when it is higher than the preset disorder threshold, a mixed disorder state code (such as "L") is generated. The state codes are then connected to form a change sequence of the mixed uniformity state. The system uses a time series analysis model to continuously learn the mixed uniformity state: based on the change sequence data stream, the duration of the continuous mixed uniform state code and the interval of the adjacent mixed disorder state code are statistically analyzed and used as the state stability feature and state transition interval feature, respectively. The decay rate and shortening rate of the two are calculated and combined to generate the state transition feature. When the value exceeds the preset amplitude, it is marked as a mixed state anomaly point. The occurrence frequency and time density of the anomaly points are accumulated. When both exceed the preset threshold, the critical trigger state is determined to be reached.

[0106] Finally, a reference value for carbon emissions corresponding to the current replacement rate is extracted from the mapping relationship, and the carbon emission adjustment fluctuation range is calculated based on the critical trigger state determination result. Under the constraint of the fluctuation range, the carbon emission benchmark value of the replacement rate is calculated with the critical trigger state as the control priority. The difference between the benchmark value and the current replacement rate is calculated to generate the replacement rate optimization balance parameter. This parameter is converted into the number of conveyor belt speed control pulses, forming a dynamic control command sent to the batching actuator. When the command requires an increase in the replacement rate, the proportion of recycled aggregate is increased to reduce the carbon hidden in the mining of natural aggregate; conversely, the replacement rate is reduced and the mixing parameters are optimized in conjunction to avoid the increase in waste and repeated production energy consumption caused by uneven mixing, thus achieving a dynamic balance between carbon emission reduction and process stability in dry mortar.

[0107] Figure 3 This application provides a schematic diagram of the structure of a dry mortar carbon emission optimization system, as shown in the embodiment. Figure 3 As shown, the system includes: Module 31 is established to collect particle size distribution data, crushing value and replacement rate of recycled aggregate in dry mortar, so as to form a set of performance indicators of recycled aggregate and establish a mapping relationship between the set of performance indicators of recycled aggregate and carbon emissions. The acquisition module 32 is used to acquire the acoustic wave signal generated by the mixing and collision of recycled aggregates through multiple sensing units arranged inside the mixing chamber during the mixing process of dry mortar. The identification module 33 is used to convert the acoustic signal into a collision feature spectrum that reflects the intensity of particle interaction in dry mortar, and to extract the time-varying characteristics of the collision feature spectrum to identify the mixing uniformity state that characterizes the degree of mixing uniformity of recycled aggregate. Prediction module 34 is used to continuously learn the mixing uniformity state using a time series analysis model in order to dynamically predict the critical triggering state of the sudden change in dry powder mortar performance caused by recycled aggregate. The generation module 35 is used to coordinate and analyze the mapping relationship with the critical triggering state to form an optimized balance parameter that simultaneously satisfies the carbon emission constraints, and to generate dynamic control instructions in real time based on the optimized balance parameter to adjust the proportion of recycled aggregate.

[0108] Figure 3 The aforementioned dry mortar carbon emission optimization system can perform... Figure 1 The implementation principle and technical effects of the dry mortar carbon emission optimization method described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the dry mortar carbon emission optimization system described in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0109] In one possible design, Figure 3The dry mortar carbon emission optimization system of the embodiment shown can be implemented as a computing device, such as... Figure 4 As shown, the computing device may include a storage component 41 and a processing component 42; The storage component 41 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 42.

[0110] The processing component 42 is used for the above Figure 1 The embodiment describes a method for optimizing carbon emissions from dry mortar.

[0111] The processing component 42 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0112] Storage component 41 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0113] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0114] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0115] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0116] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0117] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1The embodiment shown is a method for optimizing carbon emissions from dry powder mortar.

[0118] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0119] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0120] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method of dry powder mortar carbon emission optimization, characterized by, include: Collect particle size distribution data, crushing value and replacement rate of recycled aggregate in dry mortar to form a set of performance indicators of recycled aggregate, and establish a mapping relationship between the set of performance indicators of recycled aggregate and carbon emissions. During the mixing process of dry mortar, multiple sensing units arranged inside the mixing chamber are used to acquire the acoustic signals generated by the mixing and collision of recycled aggregates. The acoustic signal is converted into a collision feature spectrum that reflects the intensity of particle interaction in dry mortar, and the time-varying characteristics of the collision feature spectrum are extracted to identify the mixing uniformity state that characterizes the degree of mixing uniformity of recycled aggregate. A time-series analysis model is used to continuously learn the mixing uniformity state in order to dynamically predict the critical triggering state of the sudden change in the performance of dry powder mortar caused by recycled aggregate. The mapping relationship and the critical triggering state are coordinated and analyzed to form an optimized balance parameter that simultaneously satisfies the carbon emission constraints. Based on the optimized balance parameter, dynamic control instructions for adjusting the proportion of recycled aggregate are generated in real time. The acoustic signal is converted into a collision feature spectrum reflecting the intensity of particle interaction in dry mortar, and the time-varying characteristics of the collision feature spectrum are extracted to identify the mixing uniformity state characterizing the degree of mixing uniformity of recycled aggregate, including: A frequency decomposition operation is performed on the acoustic signal to obtain a collision feature spectrum characterizing the intensity of particle interaction in dry mortar, and the energy value of the collision feature spectrum is obtained. The collision feature spectrum of a continuous time series is divided into fixed time windows, and the absolute difference in energy values ​​of the collision feature spectrum between adjacent time windows is calculated as the fluctuation intensity parameter. When the fluctuation intensity parameter is lower than the preset stability threshold, the recycled aggregate in the time window is determined to be in a high uniformity state and a mixed uniformity status code is generated. When the fluctuation intensity parameter is higher than the preset disorder threshold, it is determined to be in a low uniformity state and a mixed disorder status code is generated. The mixing uniformity state code is connected with the mixing disorder state code to form a change sequence of mixing uniformity state characterizing the degree of mixing uniformity of recycled aggregate. A time-series analysis model is used to continuously learn the mixing uniformity state in order to dynamically predict the critical triggering state of sudden changes in dry powder mortar performance caused by recycled aggregate, including: A data stream is obtained to represent the sequence of changes in the uniformity of the mixture, the data stream containing a temporal alternation of uniformity state codes and disordered state codes; A time series analysis model is used to continuously learn the mixing uniformity state in order to count the duration of consecutive mixing uniformity state codes and calculate the interval between adjacent mixing disorder state codes. The duration of the duration and the interval duration are used as the state stability feature and the state transition interval feature, respectively, and the shortening rate of the state transition interval feature and the decay rate of the state stability feature are calculated. The shortening rate value and the decay rate value are combined to generate a state transition characteristic value. When the state transition characteristic value exceeds a preset range, it is marked as an abnormal point in the mixed state of the recycled aggregate. The cumulative occurrence frequency and time density of multiple mixed state anomalies are determined. When the occurrence frequency and time density simultaneously exceed a preset threshold, it is determined that the critical triggering state of the sudden change in the performance of dry powder mortar caused by recycled aggregate is reached. The mapping relationship is coordinated with the critical triggering state to form optimized balance parameters that simultaneously satisfy carbon emission constraints. Based on the optimized balance parameters, dynamic control commands for adjusting the proportion of recycled aggregate are generated in real time, including: Extract the reference value of carbon emissions corresponding to the current recycled aggregate replacement rate from the mapping relationship, and calculate the allowable adjustment range of carbon emissions based on the determination result of the critical trigger state. Under the constraint that the carbon emissions are within the floating range, the carbon emission baseline value of the substitution rate is calculated with the determination result of the critical triggering state as the control priority. The difference between the carbon emission benchmark value and the current actual substitution rate is calculated to obtain an optimized balance parameter for the substitution rate that can both suppress sudden changes in the performance of dry powder mortar and meet carbon emission constraints. The substitution rate optimization balance parameters are converted into the number of speed control pulses of the conveyor belt to generate a dynamic control command containing the number of speed control pulses, which is then sent to the batching actuator of the recycled aggregate.

2. The method of claim 1, wherein, During the mixing process of dry mortar, multiple sensing units deployed inside the mixing chamber acquire the acoustic signals generated by the mixing and collision of recycled aggregates, including: Multiple vibration sensing units are fixedly installed on the inner wall of the mixing chamber to acquire the motion trajectory of the mixing blades. The surface of the vibration sensing head of the vibration sensing unit maintains a preset distance from the motion trajectory and faces the mixing area of ​​the recycled aggregate. During the process of starting the mixing equipment to drive the movement of recycled aggregate, the piezoelectric sensing diaphragm of the vibration sensing unit captures the mechanical vibration waves generated by the collision of particles in the recycled aggregate and converts the mechanical vibration waves into voltage fluctuation signals. The voltage fluctuation signal is transmitted to the signal acquisition terminal via a shielded cable, and the time-domain waveform of the voltage fluctuation signal is recorded in the signal acquisition terminal at a preset sampling frequency. Configure channel numbers for the vibration sensing unit and bind the channel numbers to time-domain waveforms to form structured acoustic signal data blocks; The structured acoustic signal data blocks are integrated to form an acoustic signal generated by the mixing and collision of recycled aggregates.

3. The method of claim 2, wherein, During the process of starting the mixing equipment to drive the movement of recycled aggregate, the piezoelectric induction diaphragm of the vibration sensing unit captures the mechanical vibration waves generated by the collision of particles in the recycled aggregate, and converts the mechanical vibration waves into voltage fluctuation signals, including: When the mixing equipment is started and the mixing blades are driven to move the recycled aggregate, the particles of the recycled aggregate collide to generate mechanical vibration waves. The mechanical vibration wave is transmitted to the surface of the piezoelectric sensing diaphragm of the vibration sensing unit, so that the piezoelectric sensing diaphragm is subjected to periodic deformation stress. The crystalline material inside the piezoelectric sensing diaphragm responds to the periodic deformation stress to generate piezoelectric charges, and the piezoelectric charges are converted into continuous current fluctuations through a conversion circuit; The continuous current fluctuations are converted into voltage fluctuation signals through an impedance matching circuit.

4. The method according to claim 1, characterized in that, Data on particle size distribution, crushing value, and replacement rate of recycled aggregates in dry mortar are collected to form a set of performance indicators for recycled aggregates. A mapping relationship between this set of performance indicators and carbon emissions is then established, including: The mass weight of each particle size after the sieving test of recycled aggregate in dry mortar is used as the particle size distribution data. At the same time, the total weight of the crushed material after the standard crushing test is weighed as the crushing value, and the proportion of recycled aggregate in the total aggregate is recorded as the replacement rate. The particle size distribution data, crushing value, and replacement rate are combined into a data record group, and a set of performance indicators for recycled aggregates is constructed based on the data record group. Retrieve the energy consumption ledger corresponding to the recycled aggregate production line, and extract the total readings of the electricity metering instruments and the fuel consumption from the energy consumption ledger; According to the preset carbon emission coefficient specification, the total reading of the power meter is converted into the first carbon emission amount, and the fuel consumption is converted into the second carbon emission amount according to the preset fuel type corresponding coefficient. The first carbon emission amount and the second carbon emission amount are added together to form the carbon emission amount of the recycled aggregate. A mapping relationship between the set of performance indicators of recycled aggregates and carbon emissions is established in the database storage unit, so that the particle size distribution data, crushing value and replacement rate of the set of performance indicators of recycled aggregates can be returned as the corresponding carbon emissions through query operations.

5. A dry mortar carbon emission optimization system, used to implement the method according to any one of claims 1-4, characterized in that, include: A module is established to collect particle size distribution data, crushing value and replacement rate of recycled aggregate in dry mortar, so as to form a set of performance indicators of recycled aggregate, and to establish a mapping relationship between the set of performance indicators of recycled aggregate and carbon emissions. The acquisition module is used to acquire the acoustic wave signal generated by the mixing and collision of recycled aggregates through multiple sensing units arranged inside the mixing chamber during the dry mortar mixing process. The identification module is used to convert the acoustic signal into a collision feature spectrum that reflects the intensity of particle interaction in dry mortar, and to extract the time-varying characteristics of the collision feature spectrum to identify the mixing uniformity state that characterizes the degree of mixing uniformity of recycled aggregate. The prediction module is used to continuously learn the mixing uniformity state using a time-series analysis model in order to dynamically predict the critical triggering state of the sudden change in the performance of dry mortar caused by recycled aggregate. The generation module is used to coordinate and analyze the mapping relationship with the critical triggering state to form an optimized balance parameter that simultaneously satisfies the carbon emission constraints, and to generate dynamic control instructions in real time based on the optimized balance parameter to adjust the proportion of recycled aggregate.

6. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a method for optimizing carbon emissions of dry mortar as described in any one of claims 1 to 4.

7. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a method for optimizing carbon emissions from dry mortar as described in any one of claims 1 to 4.

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