An RFID traceability management system for site materials

Abstract

This application relates to the field of material traceability management technology, specifically to an RFID traceability management system for construction site materials. The system includes: real-time monitoring of the operating status signal of a mixer motor to generate an operation time window; utilizing the metal shielding effect of the mixing drum, combined with the operation time window, to verify the identity of materials actually put into the mixing drum; extracting the waveform characteristics of the motor stator current during mixing; and combining the deviation of the waveform characteristics from a standard reference, as well as the identified dominant abnormal factors, to execute a hierarchical control strategy. This application aims to provide a site management method that can adapt to metal interference environments and provide low-cost verification of material consumption and mixing quality throughout the entire process, thereby improving the timeliness and accuracy of traceability management and ensuring the construction quality of the project.

Description

Technical Field

[0001] This application relates to the field of material traceability management technology, specifically to an RFID traceability management system for construction site materials. Background Technology

[0002] In the concealed construction phases of water conservancy and hydropower projects, tunnels, and underground engineering, the quality of cement grouting and concrete pouring directly determines the structural safety and durability of the building. However, construction sites are usually filled with large metal facilities such as steel cages, trolleys, and metal mixing drums, which generate strong multipath reflection interference for radio frequency identification (RFID) signals.

[0003] While existing material management systems widely employ RFID technology for material tracking, in environments with metallic reflections, readers often misread nearby stockpiles of spare materials or passing transport vehicles, making it impossible for the system to accurately determine whether materials have actually been added to the mixing tank. This misreading phenomenon makes it difficult to match material consumption data with actual project progress, resulting in discrepancies between records and actual inventory.

[0004] Furthermore, existing management systems lack physical monitoring methods for the mixing process. To meet deadlines, construction workers may engage in irregular practices such as scanning codes without adding materials or adding too much water and too little material. Such behavior leads to an uncontrolled water-cement ratio (the ratio of water to cementitious materials) in the slurry, severely impacting the strength of the project structure. Current monitoring methods largely rely on expensive online viscometers or flow meters, which are not only costly but also easily damaged in harsh construction environments and difficult to maintain. Summary of the Invention

[0005] To address the aforementioned technical problems, the purpose of this application is to provide an RFID traceability management system for construction site materials. The specific technical solution adopted is as follows: This application proposes an RFID traceability management system for construction site materials, the system comprising: The global timing and signal acquisition module is used to monitor the operating status signals of the mixer motor in real time and generate the operation time window; The material input logic determination module is used to utilize the metal shielding effect of the mixing tank and, in conjunction with the operation time window, to confirm the identity of the material actually input into the mixing tank by monitoring the flipping of the RFID tag status from continuous reading to signal disappearance. The current feature extraction and trend analysis module is used to extract the waveform features of the motor stator current during the mixing process. The waveform features include the aggregate impact peak value, the resistance attenuation coefficient, and the steady-state current average value, which are used to verify the dry material input amount, water-binder ratio and mixing efficiency, and the final slurry consistency, respectively. The anomaly verification and closed-loop control module is used to combine the characteristic deviation of the waveform features from the standard reference, as well as the identified dominant anomaly factors, to execute a hierarchical control strategy.

[0006] Preferably, in the global timing and signal acquisition module, the operating status signal is a binary discrete variable, including a low level 0 and a high level 1, where 0 represents that the motor is in a stopped state and 1 represents that the motor is in a running state.

[0007] Preferably, in the global timing and signal acquisition module, the method for generating the operation time window is as follows: The start time of this batch of mixing operation is recorded by detecting when the operating status signal changes from low level to high level, and the stop time of this batch of mixing operation is recorded by detecting when the operating status signal changes from high level to low level. The start time and stop time are used as the life cycle boundaries of this batch of mixing operations. Based on the start time, the preset time interval for material preparation is defined backward, and the preset confirmation time interval for signal disappearance verification is defined backward.

[0008] Preferably, in the material input logic determination module: During the material preparation time interval, add electronic tags that have been read more times than a preset effective reading threshold to the candidate material list; Within the signal disappearance verification interval, electronic tags with zero read counts and already added to the candidate material list are determined to be materials that have been added to the mixing tank. Aggregate all electronic tags corresponding to materials that have been added to the mixing tank to generate an actual set of added materials, and parse the material specification and model identifier of the electronic tags in the set as the index key of the current verification reference parameter.

[0009] Preferably, in the current feature extraction and trend analysis module, the material resistance current is obtained by first subtracting the pre-calibrated idling current constant from the motor stator current, and the waveform features are extracted after shielding the start-up surge period; The idling current constant is obtained by pre-calibrating the average current value collected after the mixer has been running stably with the motor started in an empty state; the shielding of the start-up surge period is achieved by setting the start-up shielding time.

[0010] Preferably, the extraction of the waveform features includes: The peak impact value of the aggregate is extracted at the initial stage of mixing to verify the amount of dry material input; the peak impact value of the aggregate is the maximum material resistance current at the initial stage of mixing. During the mid-stirring process, the resistance decay coefficient was extracted based on the exponential decay model to verify the water-cement ratio and mixing efficiency. The mean steady-state current is extracted at the end of the mixing period to verify the final slurry consistency; the mean steady-state current is the average of the resistance currents of all materials at the end of the mixing period.

[0011] Preferably, the method for extracting the resistance attenuation coefficient includes: selecting a time window during the middle of stirring, using the mean steady-state current at the end of stirring as the asymptotic constant of the exponential attenuation model, performing a logarithmic transformation on the discrete current data to linearize the exponential relationship, and using the least squares regression method to solve for the slope of the straight line and taking the opposite number to obtain the resistance attenuation coefficient.

[0012] Preferably, the method for implementing the hierarchical control strategy is as follows: When the deviation of the feature is less than or equal to the preset allowable threshold, the operation is deemed compliant, an enable command is sent to the grouting pump to allow grout output, and the current feature observation vector is stored in the construction log as a grout rheological fingerprint. When the deviation of the feature is greater than the preset allowable threshold and the dominant abnormal factor is the abnormal impact peak of aggregate, it is determined that the dry material input is insufficient, and a material replenishment prompt is sent to the control panel. The alarm is lifted after the material replenishment and retest are qualified. When the deviation of the characteristic exceeds the preset allowable threshold and the dominant abnormal factor is the resistance attenuation coefficient or the average steady-state current, it is determined that the water-cement ratio is seriously unbalanced. A forced shutdown command is immediately sent to the grouting pump, the delivery pipeline is cut off, and an audible and visual alarm is triggered.

[0013] Preferably, the method for calculating the feature deviation is as follows: The peak impact value of the aggregate, the drag attenuation coefficient, and the mean steady-state current are combined into a current characteristic observation vector; The Mahalanobis distance algorithm is used to calculate the characteristic deviation between the current characteristic observation vector of the current batch mixing operation and the current characteristic observation vector of the standard reference.

[0014] Preferably, the method for identifying the dominant abnormal factor is as follows: Calculate the absolute value of the standardized deviation of each feature component in the waveform relative to the reference mean; The characteristic component corresponding to the largest absolute value of the standardized deviation was selected as the dominant abnormal factor causing the abnormality of this batch of mixing operations. The characteristic components include aggregate impact peak value, drag attenuation coefficient, and steady-state current average value.

[0015] This application has at least the following beneficial effects: First, by introducing motor operating condition signals as logic gating and utilizing the metal shielding effect of the mixing tank as a physical filter, this invention effectively filters out environmental reflection interference from surrounding spare materials and passing vehicles. This method, without increasing hardware costs such as weight sensors, confirms the spatial allocation of material consumption and solves the problem of discrepancies between records and actual materials caused by cross-reading of RFID signals at the construction site. Second, by segmentally analyzing the waveform characteristics of the motor stator current, especially extracting the resistance attenuation rate, this invention establishes a mapping relationship between the rheological properties of the slurry and the trend of motor load changes. Compared with conventional current threshold or total energy consumption monitoring, this technique can quantitatively characterize the process of slurry transformation from a solid-liquid mixture to a homogeneous fluid, thereby identifying physical property anomalies caused by minute changes in the water-cement ratio, achieving low-cost, full-process monitoring of mixing quality. Finally, based on a differentiated control strategy of characteristic components, this invention can distinguish between two different types of anomalies: insufficient dry material and imbalanced proportions, triggering material replenishment prompts or forced shutdown commands respectively. This avoids material waste caused by blind discarding and also prevents substandard slurry from being injected into the formation, ensuring the construction quality of concealed works. In summary, this application aims to provide an on-site management method that can adapt to metal interference environments and provide low-cost verification of material consumption and mixing quality throughout the entire process, thereby improving the timeliness and accuracy of traceability management and ensuring the construction quality of the project. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages 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 only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of an RFID traceability management system for construction site materials provided in one embodiment of this application. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an RFID traceability management system for construction site materials proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0020] The following description, in conjunction with the accompanying drawings, details the specific solution of the RFID traceability management system for construction site materials provided in this application.

[0021] Please see Figure 1 The document illustrates a flowchart of an RFID traceability management system for construction site materials according to an embodiment of this application. The system includes: a global timing and signal acquisition module, a material input logic determination module, a current feature extraction and trend analysis module, and an anomaly verification and closed-loop control module. The specific implementation details of each module are as follows: M1: Global Timing and Signal Acquisition Module This module serves as the timing reference source for the entire system, responsible for real-time monitoring of the mixer motor's operating status and generating global time anchor points required for subsequent logical judgments. This module receives level signals from the field-programmable logic controller (PLC) or motor controller and outputs standardized operating time windows.

[0022] P101: Motor Status Monitoring and Time Anchor Point Locking In order to accurately align continuous radio frequency scan data with specific batches of mixing operations, the system first needs to acquire a specific trigger signal.

[0023] The system acquires the operating status signal of the mixer motor in real time at a high frequency (e.g., 10Hz) via an industrial bus (such as Modbus TCP / RTU) or a hardwired I / O interface. The signal is a binary discrete variable: • When the motor is stopped, ; • When the motor is in rotary stirring operation, .

[0024] System continuous monitoring When the level edge changes, the following logic is executed to lock the time anchor point: 1. Start-up time lock: When the system detects that the signal changes from low level (0) to high level (1), the system records the current system timestamp and marks it as the start-up time of this batch of mixing operations. This moment marks the moment the motor is powered on and begins to rotate.

[0025] 2. Stop Time Locking: When the system detects that the signal changes from high level (1) to low level (0), it records the current system timestamp and marks it as the stop time of this batch of mixing operations. This moment marks the point at which the motor stops rotating after the power is cut off.

[0026] These two moments ( This constitutes the lifecycle boundary of this batch of mixing operations, and all subsequent data processing modules are based on this boundary. Index the time axis relative to the zero point.

[0027] P102: Time Sequencing of the Entire Job Cycle Startup time based on P101 lock The system traces backward and extends forward on the timeline, defining two time windows with specific physical meanings to capture the ready-to-use state and the state of materials entering the container, respectively.

[0028] 1. Define the material preparation time range The system is set to a preset duration before the motor starts. (This embodiment is set to 60 seconds, a value determined based on the on-site material feeding cycle time.) Defined interval During this period, workers move the materials to be used to the vicinity of the mixer hopper or feeding port, and the RFID tags are within the effective coverage range of the reader.

[0029] 2. Define the signal disappearance verification interval. The system is set to a preset confirmation time after the motor starts. (This embodiment is set to 15 seconds, covering the feeding and initial mixing process). Defined interval During this period, the material is fed into the metal mixing tank and tumbles with the blades.

[0030] M2: Material Input Logic Determination Module This module aims to solve the problem of environmental reflection interference (MultipathEffect) caused by the dense metal facilities at the construction site, and to distinguish between the spare materials piled up next to it and the materials actually put into the mixing tank.

[0031] This module utilizes the characteristic that the mixing tank is usually a thick-walled metal container, which has a significant electromagnetic shielding effect on internal radio frequency signals when the mixing tank is closed and stirred (i.e., the Faraday cage effect).

[0032] P201: Candidate Tag Capture in the Material Preparation Area To ensure the accuracy of the decision-making logic, the antenna layout of the RF reader first needs to be spatially defined. In this embodiment, the directional antenna of the RF reader is configured to cover the feed inlet area of ​​the mixer, which is the only physical channel for materials to enter the mixing tank. The main lobe of the antenna's radiation pattern is aligned directly above the feed inlet to ensure that materials must pass through this area before being fed in.

[0033] During the material preparation time range Within the system, the following filtering logic is executed to determine all candidate tags that may participate in this task: 1. The system traverses the reader in All raw tag reading records captured during the period.

[0034] 2. For each uniquely identified electronic tag Count the number of times it is read within this interval. .

[0035] 3. If Greater than the preset valid read threshold If the label is found to be present in the waiting area (e.g., 3 times), the material corresponding to that label will be added to the candidate material list. The output of this step It includes all possible objects, including materials that are about to be added, as well as spare materials that are simply piled up nearby but not involved in this mixing.

[0036] P202: Signal state reversal detection in the verification interval In order to The system isolates ambient background noise (i.e., materials not yet introduced) and uses the signal disappearance interval to verify the process. Differential verification is performed on the signal characteristics within the system. System traversal. Each electronic tag in Search for its in Read records within the specified range and perform the following logical checks: 1. Interference Detection (Not Implemented): If in During this period, the reader can still read The signal (i.e., the number of reads) This indicates that the tag is not shielded by the metal mixing drum and is still within the line-of-sight range of the reader antenna (e.g., parked next to the hopper or a passing vehicle). The system flags it as environmental reflection interference and removes it from the list.

[0037] 2. Investment Confirmation (Already Invested): If in During this period, the reader was completely unable to read anything. The signal (i.e., the number of reads) ), and the label is in Previously existed in the candidate material list If the condition of P201 is met, it is determined that the electronic tag has undergone a signal state reversal, that is, the material has entered the metal mixing tank through the feeding port.

[0038] P203: Identification and Baseline Index of Actual Input Materials The system aggregates the electronic tags corresponding to all materials that meet the confirmation conditions for being added to the mixing tank, generating a set of actual input materials for this batch of mixing operations. Subsequently, the system analyzes... Business attributes in: 1. Read the coded information stored inside each electronic tag to extract the material's specifications and model identifier (material type). (For example, PO 42.5 cement or C30 grouting material, and establish a standard operating procedure reference library based on all material types.) ).

[0039] 2. As an index key, the corresponding current verification reference parameter is retrieved from the local pre-configured database (which will be used in the M3 module).

[0040] Data flow description: This module outputs... and It will be passed to the subsequent M3 module to guide the extraction and verification of current characteristics; at the same time, the information of the materials put in will be recorded in the digital log of this operation.

[0041] M3: Current Feature Extraction and Trend Analysis Module This module aims to solve the technical problem that traditional RFID traceability can only verify the arrival of materials on site, but cannot verify that the materials have been correctly consumed. This module uses the stator current of the motor as a direct mapping of the stirring resistance, and constructs a multi-dimensional physical verification logic by extracting the current waveform characteristics during the stirring process in segments.

[0042] P301: Current data preprocessing (surge and idling removal) To obtain clean current data that is only related to material resistance and to eliminate interference from motor mechanical friction and nonlinear current during startup, the system performs the following preprocessing steps: 1. Data Acquisition: The system continuously acquires the motor stator current sequence at a fixed sampling frequency (e.g., 50Hz) through a high-frequency current transformer or frequency converter interface. The time range for data collection covers the period from the start time of this batch of mixing operations. until the stopping time The entire process.

[0043] 2. No-load current deduction: Since the bearing friction, wind resistance and no-load excitation current of the motor itself are fixed mechanical losses, they must be removed from the total current.

[0044] In this embodiment, the system calls the idle current constant pre-stored in the controller. This constant was pre-obtained through the following calibration process: The motor was started with the mixer empty, and after it stabilized (e.g., after 30 seconds of operation), the average current value over a period of time was collected as... .

[0045] The system performs subtraction on all collected real-time current data to generate a material resistance current sequence. : in The function is used to ensure that the calculation result is non-negative. After this processing, It only represents the physical resistance component generated by the materials (dry materials, slurry) in the mixing tank.

[0046] 3. Startup Surge Shielding: During startup (within approximately 0.5 seconds of energization), the motor generates a startup surge current that is 5-7 times the rated current. This current is an electrical characteristic and is unrelated to material resistance. Directly incorporating it into calculations will severely interfere with subsequent peak value extraction.

[0047] The system is set to have a startup blocking time. (For example, set to 1.0 second). The system automatically ignores time intervals during feature extraction. All current data within, only processed Subsequent stable operation data.

[0048] P302: First-stage feature extraction (aggregate impact peak value) This step extracts the current characteristics at the initial stage of mixing to verify the amount of dry materials (such as cement and aggregates) added.

[0049] Physical mechanism explanation: In the initial stage after the agitator starts, the added dry material has not yet been fully coated and lubricated by water. The dry friction between particles and the impact between particles and the barrel wall will cause the motor resistance to reach its maximum value for the entire cycle. The magnitude of this peak value is positively correlated with the total amount of dry material added.

[0050] Specific implementation steps: 1. Determine the search range: The system determines the search range based on the total duration of this batch of mixing operations. Define the time window for the first stage (initial stirring). In this embodiment, the time window is selected from the end of the shielding time to 15% of the total duration, i.e., the interval. .

[0051] 2. Peak Extraction: The system iterates through all peak values ​​within the specified interval. Data points were selected, and the maximum value was assigned as the aggregate impact peak value. .

[0052] 3. Data flow: Extracted data This will be passed as the first component of the feature vector to the subsequent M4 module for verification. If... A value significantly lower than the standard benchmark usually means that the dry materials input is insufficient (e.g., one bag of cement was added less).

[0053] P303: Second-stage feature extraction (resistance decay trend) This step extracts the current drop rate during the middle of the stirring process to verify the water-to-gel ratio (the ratio of water to solids) and mixing efficiency.

[0054] Physical mechanism explanation: As moisture gradually penetrates into the interior of the dry particles, the lubricity of the slurry increases rapidly, and the stirring resistance decreases exponentially. The rate of decrease (i.e., the attenuation coefficient) directly reflects the water-cement ratio: the more water, the smaller the friction between particles, the faster the resistance decreases, and the larger the attenuation coefficient.

[0055] Specific implementation steps: 1. Determine the fitting interval: The system determines the fitting interval based on the total task duration. The time window for mid-stage stabilization (mid-stirring) is selected; in this embodiment, it is set to... This range avoids the initial severe shock and the final steady-state fluctuations.

[0056] 2. Obtain the steady-state reference (constant) (Fixed): To improve the stability of the fitting algorithm, the system first calculates the average current value in the third stage (end of stirring). (See page 304 for details), and use it as the asymptote constant in the exponential decay model. That is, setting. .

[0057] 3. Logarithmic Transformation and Linearization: The original exponential model is The system will collect discrete current data. Perform the transformation, let At this point, the originally non-linear exponential relationship transforms into a linear relationship: It is also worth noting that when At that time, that is To prevent numerical overflow, a very small positive number is set (protection bias). replace .

[0058] 4. Least Squares Regression Solution: The system uses the Ordinary Least Squares (OLS) method to solve the transformed data points. Perform linear regression and solve for the slope of the line. The regression formula is: In the formula, This represents the arithmetic mean of all discrete sampling times within the fitting interval. This represents all dependent variables after logarithmic transformation within the fitting interval. The arithmetic mean of the values ​​is used to obtain the final drag attenuation coefficient. That is, the negative number of the regression slope, i.e. .

[0059] 5. Data flow: Calculated... This will be passed as the second component of the feature vector to the subsequent M4 module for verification. If A value significantly higher than the standard benchmark usually indicates a high water-to-cement ratio (adding too much water causes the resistance to drop too quickly).

[0060] P304: Third-stage feature extraction (steady-state rheological mean) This step extracts the steady-state current at the end of the stirring process to verify the final slurry consistency (yield stress).

[0061] Physical mechanism explanation: At the end of the mixing process, the slurry has reached a homogeneous state, and the motor load at this point mainly depends on the final yield stress of the fluid. For slurries with the same proportions, the higher the consistency, the greater the steady-state resistance; the lower the consistency (dilution), the smaller the steady-state resistance.

[0062] Specific implementation steps: 1. Determine the statistical interval: The system selects a stable time window before the end of stirring (the final stage of stirring). In this embodiment, it is set to... .

[0063] 2. Calculate the arithmetic mean: The system calculates the arithmetic mean of all values ​​within this interval. The data points are summed and then divided by the total number of sampling points. The steady-state current average value is obtained. .

[0064] 3. Data flow: Calculated... It is not only passed to the M4 module as the third component of the feature vector, but is also passed back to step P303 as a constant term. This process, through its application, creates a closed-loop optimization within the algorithm. If... A value significantly lower than the standard benchmark usually means that the final slurry is too thin (possibly due to excessive water or insufficient dry material).

[0065] M4: Anomaly Detection and Closed-Loop Control Module This module aims to establish a logical closed loop between physical characteristics and on-site construction actions. By comprehensively evaluating the deviation of this batch of mixing operations from the standard process and identifying the dominant factors causing the anomalies (such as whether it is a lack of materials or too much water added), the system can implement differentiated hierarchical control strategies for different types of anomalies.

[0066] P401: Calculation of Multidimensional Feature Deviation To comprehensively evaluate the stirring quality, the system combines the three feature components extracted in the previous steps into a current feature observation vector. Subsequently, the system uses the Mahalanobis distance algorithm, which reflects the correlation between variables, to calculate the feature deviation between the current feature observation vector of the current batch mixing operation and the current feature observation vector of the standard reference.

[0067] Specific implementation steps: 1. Loading baseline parameters: The system loads the material type confirmed by the M2 module. From the established standard operating procedure reference library Retrieve the corresponding reference parameters: o Feature mean vector : That is, the current characteristic observation vector of the standard reference, where each characteristic component is the reference mean of the corresponding characteristic component, representing the ideal characteristic center of this type of material under standard process.

[0068] o Feature covariance matrix : Represents the natural fluctuation range between features and their correlation (for example, adding too much water usually leads to rapid decay and low steady state; this negative correlation is recorded by the matrix).

[0069] 2. Deviation Calculation: The system calculates the characteristic deviation of the current batch mixing operation according to the following formula. : oAmong them, The difference vector represents the gap between the current characteristic observation vector of the current batch mixing operation and the current characteristic observation vector of the standard reference.

[0070] o The inverse of the covariance matrix (with a small regularization term) (To prevent irreversibility), it is used to standardize and weight the fluctuation amplitudes of different characteristics.

[0071] o Calculation results It is a scalar value; the larger the value, the more serious the deviation of the current batch of mixing operations from the standard process.

[0072] P402: Anomaly Attribution Analysis When the feature deviation When the preset allowable threshold is exceeded, the system needs to further analyze which step went wrong. This step uses the standardized deviation comparison method to identify the dominant abnormal factor.

[0073] Specific implementation steps: 1. Calculate the standardized deviation of the characteristic components: for the current characteristic observation vector Each feature component in ( Corresponding to The system calculates its value relative to this characteristic component. The baseline mean The absolute value of the standardized deviation : in, For this characteristic component Standard deviation of the benchmark value This formula eliminates dimensional differences, making characteristics with different physical meanings comparable. It should be noted that due to sensor noise and environmental interference, It's almost impossible for the value to be 0, but considering what might happen... If the value is 0, then a very small positive number is introduced into the denominator. This processing method ensures that the algorithm can maintain numerical stability even when the feature fluctuations are minimal or during the initialization phase, and can respond to small physical deviations.

[0074] 2. Identify the dominant anomaly factors: System comparison , , The magnitude of the value is used to select the characteristic component with the largest value as the dominant abnormal factor causing the abnormality in this batch of mixing operations. For example, if If the value is the largest, it is determined to be an abnormal aggregate impact peak, i.e., an abnormal dry material input; if If the value is the maximum, it is determined to be the resistance attenuation coefficient, which means the mixing rate is abnormal (water-binder ratio problem).

[0075] P403: Implementation of Hierarchical Control Strategy The system according to Based on the magnitude of the anomaly and the attribution results of the dominant anomaly factor of P402, the following three-level control logic is executed through the PLC interface: 1. Level 1 Judgment (Compliance Release): If ( The system determines compliance with the pre-set allowable threshold. It sends an enable command to the grouting pump, allowing grout output. Simultaneously, it analyzes the current characteristic observation vector. The slurry rheological fingerprint is stored in the construction log.

[0076] 2. Secondary Judgment (Material Shortage Warning and Remediation): If And the dominant abnormal factor is the peak impact of aggregate ( An abnormal reading (usually too low) indicates insufficient dry material input. At this point, the slurry properties have not yet undergone irreversible changes, and the system does not lock the mixer but instead sends a material replenishment prompt to the control panel. The alarm can be deactivated after the worker replenishes the material and the test confirms it is within acceptable limits.

[0077] 3. Three-level judgment (mixture imbalance and blockage): If And the dominant anomaly factor is the drag attenuation coefficient ( or mean steady-state current ( An abnormality was detected, indicating a severe imbalance in the water-cement ratio. The system immediately sent a forced shutdown command to the grouting pump, cut off the delivery pipeline, and triggered an audible and visual alarm.

[0078] Furthermore, to prevent the slurry from solidifying and damaging the pipelines inside the equipment due to system misjudgment or prolonged shutdown, the system initiates a countdown alarm (5 minutes in this embodiment) simultaneously with triggering the forced shutdown command. If no manual confirmation signal (via a physical key or password-based reset command) is received before the countdown ends, the system will automatically open the cleaning water valve and execute an emergency cleaning procedure to discharge and discard substandard slurry and equipment residue, ensuring equipment safety.

[0079] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0080] It should be noted that, unless otherwise specified and limited, terms such as “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such article or device. Without further limitations, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items.

[0081] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not invented in this application.

[0082] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. An RFID traceability management system for construction site materials, characterized by, The system comprises: A global timing and signal acquisition module for monitoring the running state signal of the mixer motor in real time and generating a job time window; A material input logic judgment module for using the metal shielding effect of the mixing drum, combining the job time window, and through monitoring the RFID tag state from the flip of continuous reading to signal disappearance to confirm the actual input material identity into the mixing drum; A current feature extraction and trend analysis module for extracting the waveform features of the motor stator current during mixing, including the aggregate impact peak, resistance decay coefficient, and steady-state current mean, which are used to verify the dry material input amount, water-binder ratio and mixing efficiency, and final slurry consistency; An abnormality checking and closed-loop control module for combining the feature deviation of the waveform features and standard benchmark, and the identified dominant abnormal factors to execute a hierarchical control strategy.

2. The RFID traceability management system for construction site materials as claimed in claim 1, wherein, In the global timing and signal acquisition module, the running state signal is a binary discrete variable, including low level 0 and high level 1, 0 representing the motor in a stop state and 1 representing the motor in a running state.

3. A system for RFID traceability management of construction site materials as claimed in claim 2 wherein, In the global timing and signal acquisition module, the generation method of the job time window is: The time when the running state signal jumps from low level to high level is recorded as the start time of the current batch mixing job, and the time when the running state signal jumps from high level to low level is recorded as the stop time of the current batch mixing job; The start time and stop time are taken as the life cycle boundaries of the current batch mixing job, and a material preparation time interval is defined based on the start time by backtracking a preset preset time, and a signal disappearance verification interval is defined by extending a preset confirmation time.

4. The RFID traceability management system for construction site materials as claimed in claim 1, wherein, In the material input logic judgment module: In the material preparation time interval, electronic tags with a reading frequency greater than a preset effective reading threshold are added to a candidate material list; In the signal disappearance verification interval, electronic tags with a reading frequency of zero and added to the candidate material list are judged as the corresponding materials having been input into the mixing drum; All electronic tags corresponding to the materials having been input into the mixing drum are aggregated to generate an actual input material set, and the material specification model identification of the electronic tags in the set is analyzed as an index key of the current verification benchmark parameter.

5. The RFID traceability management system for construction site materials as claimed in claim 1 wherein, In the current feature extraction and trend analysis module, the material resistance current is obtained by first deducting the pre-labeled idle current constant from the motor stator current, and the waveform features are extracted after shielding the start-up surge period; The idle current constant is obtained by pre-labeling the average current value collected after starting the motor in the empty drum state of the mixer and waiting for stable operation; the shielding of the start-up surge period is achieved by setting a start-up shielding time.

6. A system for RFID traceability management of construction site materials as claimed in claim 5 wherein, The extraction of the waveform features includes: The aggregate impact peak is extracted at the initial stage of mixing to verify the dry material input amount; the aggregate impact peak is the maximum material resistance current at the initial stage of mixing; The resistance decay coefficient is extracted based on the exponential decay model at the middle stage of mixing to verify the water-binder ratio and mixing efficiency; The steady-state current mean is extracted at the end stage of mixing to verify the final slurry consistency; the steady-state current mean is the mean of all material resistance currents at the end stage of mixing.

7. A RFID traceability management system for construction site materials as claimed in claim 6 wherein, The extraction method of the resistance attenuation coefficient comprises: selecting a time window in the middle of stirring, taking the average of the steady-state current in the late stage of stirring as the asymptote constant of the exponential decay model, performing logarithmic transformation on the discrete current data to linearize the exponential relationship, and solving the slope of the straight line by using the least square method regression to obtain the resistance attenuation coefficient.

8. The RFID traceability management system for construction site materials as claimed in claim 1 wherein, The execution method of the hierarchical control strategy is: When the characteristic deviation degree is less than or equal to a preset allowable threshold, it is determined that the operation is compliant, an opening enable instruction is sent to the grouting pump, slurry output is allowed, and the current characteristic observation vector is stored as a slurry flow fingerprint in a construction log; When the characteristic deviation degree is greater than the preset allowable threshold and the dominant abnormal factor is the aggregate impact peak abnormality, it is determined that the dry material is insufficient, a material supplement prompt is sent to the operation table, and the alarm is removed after the retest is qualified; When the characteristic deviation degree is greater than the preset allowable threshold and the dominant abnormal factor is the resistance attenuation coefficient or the steady-state current average abnormality, it is determined that the water-binder ratio is seriously out of adjustment, a forced shutdown instruction is immediately sent to the grouting pump, the conveying pipeline is cut off, and an audible and light alarm is triggered.

9. A RFID traceability management system for construction site materials as claimed in claim 8 wherein, The calculation method of the characteristic deviation degree comprises: The aggregate impact peak value, the resistance attenuation coefficient and the steady-state current average value are combined into a current characteristic observation vector; The Mahalanobis distance algorithm is used to calculate the characteristic deviation degree between the current batch of stirring operation current characteristic observation vector and the standard reference current characteristic observation vector.

10. The RFID traceability management system for construction site materials as claimed in claim 8, wherein, The identification method of the dominant abnormal factor comprises: The standardized deviation absolute value of each characteristic component in the waveform feature relative to the reference average value is calculated; The characteristic component corresponding to the largest numerical value of the standardized deviation absolute value is selected as the dominant abnormal factor causing the abnormality of the current batch of stirring operation; The characteristic component comprises the aggregate impact peak value, the resistance attenuation coefficient and the steady-state current average value.

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