Method and system for dynamically evaluating blockage risk of concrete conveying pipeline

Abstract

The invention provides a method and a system for dynamically evaluating the blockage risk of a concrete conveying pipeline. According to the method, firstly, geometric feature information of the concrete conveying pipeline is obtained, meanwhile, in the concrete conveying process, pressure data and flow velocity data in the concrete conveying pipeline are collected in real time, then, based on the geometric feature information, dynamic compensation processing is conducted on the pressure data, and compensated pressure data are generated; and then combining the compensated pressure data and flow velocity data to calculate a blockage risk evaluation value in the current concrete conveying state, and finally outputting a blockage risk evaluation report according to the blockage risk evaluation value. According to the technical scheme provided by the invention, the structure interference false alarm is effectively eliminated, the accurate positioning and graded response of the blockage risk position are realized, and the blockage treatment efficiency and reliability are improved.

Description

Technical Field

[0001] The present application relates to the technical field of electronic digital data processing, and in particular to a method and system for dynamically assessing the risk of blockage in a concrete delivery pipeline. Background Art

[0002] In concrete pumping construction scenarios such as super high-rise buildings and large-scale water conservancy projects, blockage accidents in concrete delivery pipelines are frequent and sudden due to their long distance, multi-bend structure and complex concrete rheological properties.

[0003] The current mainstream solution adopts a real-time monitoring system based on dual-parameter thresholds of pressure and flow rate. Specifically, real-time data is collected through pressure sensors and flow meters installed at key nodes of the pipeline. When the pressure value is detected to continuously exceed the preset static threshold and is accompanied by a decrease in flow rate, a blockage alarm signal is triggered. The system relies on wireless transmission technology to realize multi-node data aggregation and generate pressure and flow rate trend curves on the central control platform to assist manual judgment.

[0004] Although the monitoring system can capture some signs of blockage, it does not consider the dynamic impact of structural parameters such as the pipe bending angle and length on fluid resistance, resulting in distorted interpretation of pressure data, especially frequent false alarms in areas with dense pipe bends; at the same time, the fixed threshold cannot adapt to the changes in operating conditions in different pumping stages (such as startup, constant pressure, and end), and the misjudgment rate is high; more importantly, its alarm mechanism only marks abnormalities in the time dimension and cannot associate the spatial position of the pipeline to generate disposal guidelines, making it difficult for operation and maintenance personnel to quickly locate the blocked section and formulate a clearing strategy. Summary of the Invention

[0005] The present application provides a method and system for dynamically assessing the risk of blockage in a concrete delivery pipeline, which is used to solve the problems in the prior art of high false alarm rate caused by the decoupling of pressure data from the physical structure of the pipeline, false triggering caused by the inability of fixed thresholds to respond to changes in pumping conditions, and low efficiency in locating blockage points caused by the spatiotemporal separation of alarm signals.

[0006] In a first aspect, the present application provides a method for dynamically assessing the risk of blockage in a concrete delivery pipeline, comprising: Acquiring geometric characteristic information of the concrete delivery pipeline, wherein the geometric characteristic information includes a bending angle and a length; During the concrete delivery process, real-time collection of pressure data and flow rate data in the concrete delivery pipeline; Based on the geometric feature information, dynamically compensating the pressure data to generate compensated pressure data; Calculating a blockage risk assessment value under a current concrete delivery state by combining the compensated pressure data and the flow rate data; A congestion risk assessment report is output based on the congestion risk evaluation value.

[0007] Optionally, based on the geometric feature information, dynamically compensating the pressure data to generate compensated pressure data includes: Calculating a bending angle compensation value based on the bending angle; Based on the length, proportionally adjusting the bending angle compensation value to generate a comprehensive compensation parameter; Applying the comprehensive compensation parameters to the pressure data to generate compensated pressure data.

[0008] Optionally, based on the length, the bending angle compensation value is proportionally adjusted to generate a comprehensive compensation parameter, including: Obtain predefined pipeline resistance benchmark parameters; Calculating the influence factor of the bending angle on the unit length according to the mapping relationship between the bending angle compensation value and the pipeline resistance reference parameter; The influence factor of the bending angle on the unit length is combined with the length, and a correction value of the pipeline comprehensive resistance is generated by continuous superposition calculation; The pipeline comprehensive resistance correction value is used as a comprehensive compensation parameter.

[0009] Optionally, applying the comprehensive compensation parameter to the pressure data to generate compensated pressure data includes: Establish a dynamic balance model including a pressure balance unit and a resistance correction unit; inputting the pressure data into the pressure balancing unit; Inputting the comprehensive compensation parameter into the resistance correction unit to generate a dynamic correction component; performing a cyclic superposition operation on the pressure data and the dynamic correction component in the pressure balance unit; When the cyclic superposition result meets the preset fluid dynamic balance condition, the compensated pressure data is output.

[0010] Optionally, combining the compensated pressure data and the flow rate data to calculate a blockage risk assessment value under the current concrete delivery state includes: extracting abnormal pressure period characteristic values ​​from the compensated pressure data; extracting a flow velocity decay period characteristic value from the flow velocity data; Detecting an overlapping area on a time axis between the characteristic value of the abnormal pressure period and the characteristic value of the decay period; generating a blockage characteristic quantification value based on the duration and degree of overlap of the overlapping region; The blockage characteristic quantification value is converted into a blockage risk assessment value.

[0011] Optionally, detecting an overlapping area on a time axis between the characteristic value of the abnormal pressure period and the characteristic value of the decay period includes: Defining a main pressure action area of ​​the characteristic value of the abnormal pressure period, wherein the main pressure action area covers the pressure rising stage and the peak duration stage; Defining an effective attenuation region of the characteristic value of the flow velocity attenuation period, wherein the effective attenuation region extends from the flow velocity inflection point to the flow velocity recovery threshold point; Capturing the temporal overlap state of the pressure main action area and the attenuation effective area through a sliding time window; When the start time of the pressure main action area is ahead of the attenuation effective area and the delay is less than a first set threshold, it is recorded as a valid overlap event; Aggregate the duration and occurrence intervals of all valid overlapping events to generate the overlapping area.

[0012] Optionally, a congestion risk assessment report is output according to the congestion risk evaluation value, including: Constructing a pumping pipeline risk distribution map, wherein the pumping pipeline risk distribution map is associated with geometric feature information of the concrete delivery pipeline; Based on the numerical value of the congestion risk assessment value, the emergency response events, the buffer processing events and the monitoring and tracking events are divided; Mapping the emergency response event to a first priority area of ​​the pumping pipeline risk distribution map, mapping the buffer processing event to a second priority area of ​​the pumping pipeline risk distribution map, and mapping the monitoring and tracking event to a third priority area of ​​the pumping pipeline risk distribution map; Generate multi-level handling instructions corresponding to the first priority area, the second priority area, and the third priority area through a risk level conversion device; The pumping pipeline risk distribution map and the multi-level disposal instructions are synthesized by using a report generating device to generate a blockage risk assessment report with position marks.

[0013] In a second aspect, the present application provides a dynamic assessment system for the risk of blockage in a concrete delivery pipeline, comprising: An acquisition module, configured to acquire geometric feature information of the concrete delivery pipe, wherein the geometric feature information includes a bending angle and a length; The acquisition module is used to collect the pressure data and flow rate data in the concrete delivery pipeline in real time during the concrete delivery process; a processing module, configured to perform dynamic compensation processing on the pressure data based on the geometric feature information to generate compensated pressure data; a calculation module, configured to calculate a blockage risk assessment value under a current concrete delivery state by combining the compensated pressure data and the flow rate data; The output module is used to output a congestion risk assessment report according to the congestion risk evaluation value.

[0014] In a third aspect, the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a dynamic assessment method for the risk of blockage in a concrete delivery pipeline as described in the first aspect above.

[0015] In a fourth aspect, the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a method for dynamically assessing the risk of blockage in a concrete delivery pipeline as described in the first aspect.

[0016] This application establishes a multi-dimensional perception framework for concrete delivery status by synchronously acquiring pipeline geometric feature information and real-time pumping parameters, thus achieving a transition from passive response to active prediction in blockage risk assessment. Geometric feature information is used as a structural constraint and is collaboratively processed with dynamically collected pressure and flow rate data to form a physical basis for pressure compensation and risk calculation, fundamentally overcoming the defects of traditional methods in decoupling structural parameters from flow status. The final output risk assessment report runs through the entire process of pumping process monitoring, risk warning and disposal decision-making, significantly improving the safety management and control capabilities of high-risk construction scenarios.

[0017] Furthermore, an innovative mechanism for generating compensation values ​​based on pipe bend angles and incorporating pipe length ratio adjustments has been developed to construct a dynamic pressure correction model tailored to the rheological properties of concrete. This approach addresses the differences between sudden changes in local resistance in bends and accumulated resistance along straight sections, converting geometric features into comprehensive compensation parameters that are applied to the raw pressure data. This effectively eliminates pressure signal distortion caused by the pipe's physical structure, ensuring that the compensated pressure data truly reflects the actual obstruction state of the concrete flow, laying an accurate data foundation for subsequent risk assessments.

[0018] Furthermore, by extracting characteristic values ​​from periods of abnormal pressure and velocity decay and detecting overlapping regions, a spatiotemporally coupled blockage event recognition model was constructed. This method overcomes the limitations of single-parameter thresholds and exploits the synchronous nature of pressure surges and velocity decay to capture the precursory features of pipe blockage. Quantified blockage feature values ​​generated based on overlapping region characteristics accurately characterize the intensity and development trend of blockage events, ultimately converting them into scientifically reliable risk assessment values. This improves the robustness of risk assessment under complex operating conditions and avoids misjudgments caused by intermittent fluctuations.

[0019] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1 A flowchart of a method for dynamically assessing the risk of blockage in a concrete delivery pipeline provided by the present application is shown; Figure 2 The present invention provides a structural diagram of a dynamic assessment system for blockage risk of concrete delivery pipelines; Figure 3 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0022] In order to enable people skilled in the art to better understand the solution of this application, the technical solution of this application will be clearly and completely described below in conjunction with the drawings in this application.

[0023] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0024] The following will be combined with the accompanying drawings to clearly and completely describe the technical solutions in this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of this application.

[0025] Figure 1 A flowchart of a method for dynamic assessment of blockage risk of concrete delivery pipeline is provided for this application. Figure 1 As shown, the method includes: Step 101: Acquire geometric feature information of a concrete delivery pipeline, wherein the geometric feature information includes a bending angle and a length.

[0026] In this step, geometric feature information refers to a set of key parameters describing the physical structure of the concrete pipeline, primarily used to quantify fluid flow resistance and the impact of spatial position. The bend angle specifically represents the degree of curvature of the pipe bend (measured in degrees), and its value directly correlates to the change in local resistance as the fluid passes through the bend. The length, measured in meters, represents the physical extension of the pipeline, representing the overall or segmented scale and used to assess the cumulative effect of resistance along the entire route. These parameters work together to provide the foundation for subsequent dynamic compensation and risk calculations.

[0027] In this embodiment, the system first calls a pre-built pipeline configuration database interface and uses a database query algorithm (such as SQL retrieval technology) to extract stored bend angle data. This data comes from digital drawings in the design phase or laser scanning point cloud processing during installation. Next, the same interface or an independent measurement module (such as an ultrasonic ranging sensor or a segmented accumulation algorithm) is used to synchronously obtain length values. For segmented pipelines, the length calculation adopts an accumulation strategy to ensure global consistency. These parameters are then combined into a structured dataset (JSON or XML format) and transmitted to downstream modules via an internal communication bus. The entire process uses data verification logic (such as outlier filtering rules) to ensure information accuracy, ultimately generating a geometric feature information package that can be used immediately.

[0028] For example, in a high-rise building project pumping scenario, a pre-configured delivery pipeline system consisting of a 120-meter straight pipe section and three 90-degree bends is used. During system initialization, the pipeline information management software is started, and the design documents are queried through the database API to extract the bending angle data of the bends (each 90 degrees). The segment lengths (each 40 meters) are measured using ultrasonic sensors and then summed. Based on these values, a geometric feature information data packet is generated as the input benchmark for subsequent dynamic compensation processing.

[0029] Step 102 : During the concrete delivery process, real-time pressure data and flow rate data in the concrete delivery pipeline are collected.

[0030] In this step, pressure data refers to the pressure of the fluid inside the pipeline (typically measured in megapascals) acquired by a pressure sensor, representing changes in the concrete's flow resistance. Velocity data refers to the speed of concrete moving within the pipeline (typically measured in meters per second) measured by a flow meter, reflecting the flow continuity. Both types of data are synchronized using timestamps, providing real-time input for core dynamic assessments.

[0031] In this embodiment, it is achieved through a multi-node sensor network and real-time processing flow: first, piezoelectric pressure sensors and ultrasonic flow meters are deployed at key locations in the pipeline (such as bends and straight pipe interfaces), and the original physical signals are converted into digital electrical signals through the sensor microprocessor; second, the LoRa wireless transmission protocol is used to aggregate the data packets of each node (including sensor ID, timestamp, and value) to the edge computing gateway; then, the gateway runs a data preprocessing algorithm (including median filtering denoising and abnormal pulse suppression) to clean the original signal and align the timestamps based on a unified clock source; finally, the cleaned data is time-series sliced ​​through a sliding window mechanism, and a standardized pressure-flow rate data stream is output for downstream modules to call.

[0032] Continuing with the aforementioned high-rise building project, the system deployed a set of pressure sensors (P1-P3) and ultrasonic flow meters (V1-V3) after each of the three 90-degree bends. After concrete pumping began, the P2 sensor detected a pressure fluctuation of 1.5 MPa, while the V2 sensor simultaneously recorded a drop in flow velocity to 0.8 m / s. The edge gateway received these three sets of data via LoRa, applied a median filter to eliminate mechanical vibration noise, and aligned the timing with 10ms accuracy. The resulting data set was standardized, with the "Segment_2" location tag, providing the basic input source for dynamic compensation in step 103.

[0033] Step 103: Based on the geometric feature information, dynamically compensate the pressure data to generate compensated pressure data.

[0034] In this step, dynamic compensation processing specifically refers to the correction calculation performed on real-time pressure data using the geometric characteristics of the pipeline (bending angle and length). The purpose is to eliminate the inherent resistance interference caused by the pipeline structure, so that the pressure value only reflects the actual state of concrete flow; the compensated pressure data is the correction result output by this correction calculation. Its physical meaning is the net pressure value after excluding the influence of the pipeline structure resistance, which is used to accurately identify blockage anomalies.

[0035] In this embodiment, the system is implemented collaboratively through multi-level computing modules: first, a bending angle compensation algorithm (such as an angular resistance mapping table based on experimental calibration data) is called to generate a bending angle compensation value based on the input bending angle; then, a proportional adjustment algorithm is executed to load a preset pipeline resistance benchmark database, and an interpolation function is used to calculate the influence factor of the bending angle on the unit length. This influence factor is then combined with the input length through cumulative integral operations to generate a comprehensive pipeline resistance correction value; then, a dynamic balance model is constructed, and the real-time pressure data stream is input into a pressure balance unit. At the same time, the comprehensive resistance correction value is input into the resistance correction unit to generate a dynamic correction component sequence; in the pressure balance unit, a cyclic iterative superposition (such as Newton-Raphson numerical approximation) is performed on the original pressure data and the dynamic correction component. The cycle is terminated when the pressure gradient change meets the convergence condition of the fluid continuity equation, and the compensated pressure data sequence is output to complete the structural resistance distortion correction.

[0036] Based on the geometric features obtained in the aforementioned high-rise building project (three 90° bends, a single 40-meter section) and the 1.5 MPa pressure data collected at the P2 node, the system first calculated the 90° bend compensation value as 0.2 units through the angular resistance mapping table; it retrieved the pipeline benchmark database and used cubic spline interpolation to calculate the influence factor of 0.5, and then accumulated the 40-meter length to generate a comprehensive correction value of 0.8 through cumulative integration; the dynamic equilibrium model iterated the original pressure of 1.5 MPa and the correction component sequence for five rounds. When the pressure fluctuation stabilized within the ±0.05 MPa threshold, it outputted a compensated pressure of 1.2 MPa. This result eliminated the influence of the inherent resistance of the bend section and provided accurate input for the subsequent blockage risk assessment in step 104.

[0037] Step 104 : Calculate a blockage risk assessment value under the current concrete delivery state by combining the compensated pressure data and the flow rate data.

[0038] In this step, the blockage risk assessment value specifically refers to a comprehensive evaluation index generated by quantitatively analyzing the temporal correlation characteristics of the compensated pressure data and flow rate data. Its physical meaning is to characterize the probability and severity of blockage in the current pipeline (usually output in the form of a percentage or risk level), which is used to drive the subsequent graded response mechanism.

[0039] In this embodiment, this is achieved through a multi-level data analysis process: first, a time series feature extraction algorithm (such as local extreme value detection based on a sliding window) is used to identify the characteristic values ​​of the abnormal pressure period from the compensated pressure data, and the derivative analysis method is used to extract the characteristic values ​​of the velocity decay period from the velocity data. Secondly, a time axis alignment engine is constructed, and the temporal overlap of the two characteristic values ​​is calculated using the dynamic time warping (DTW) algorithm. When the "start time of the main pressure action area precedes the start time of the decay effective area and the delay is lower than the set threshold" is met, it is recorded as a valid overlapping event. Then, the duration and overlapping area ratio of all valid overlapping events are aggregated to generate a quantitative value of the blockage feature. Finally, a normalization mapping function (such as Sigmoid transformation) is used to convert this quantitative value into a blockage risk assessment value in the range of 0-100 to complete the real-time risk assessment.

[0040] For example, continuing with the high-rise building project case, based on the compensated pressure data of 1.2 MPa and its abnormal fluctuation period (t=10s-30s) generated in step 103, combined with the attenuation period (t=11s-30s) extracted from the velocity data, the timeline alignment engine detects that the start time difference between the two is 1 second (<2 seconds threshold), and determines a valid overlapping event; calculates the overlapping duration of 19 seconds and the area proportion of 95%, and generates a blockage risk assessment value of 70% through Sigmoid function mapping, indicating that the pipe section has a moderate blockage risk.

[0041] Step 105: Output a congestion risk assessment report based on the congestion risk evaluation value.

[0042] In this step, the blockage risk assessment report refers to a structured output document that integrates risk levels, pipeline locations, and disposal instructions. Its core function is to convert the blockage risk assessment value into an executable disposal plan with spatial coordinate identification, and ultimately guide on-site precise dredging operations.

[0043] In this embodiment, the risk space mapping engine is used to implement the following: first, the pipeline geometric feature information is parsed, and a spatial coordinate system conversion algorithm is used to generate a pumping pipeline risk distribution map (such as mapping the bending angle to the spatial curvature); secondly, the risk grading rule library is called, and when the blockage risk assessment value exceeds the upper threshold, it is marked as an emergency response event, if it is in the middle range, it is marked as a buffer processing event, and if it is below the lower limit, it is marked as a monitoring and tracking event; then, the topological association algorithm is used to map the three-level events to the first, second, and third priority areas of the risk distribution map (such as binding the bend number with a coordinate matching algorithm); then the risk level conversion device is triggered, and the pre-stored multi-level disposal instruction library (including vibration dredging parameters, countercurrent flushing scheme, etc.) is indexed according to the priority area; finally, the report generation device performs spatial data fusion, and synthesizes the vector layer of the risk distribution map and the disposal instruction text into a PDF format report, and annotates the physical coordinates of the risk pipe section through location tags (such as Segment_ID latitude and longitude codes).

[0044] Continuing with the aforementioned high-rise building project case, the system defines the coordinates of the No. 3 90° elbow (x102, y205) as the second-priority area in the risk distribution map based on the 70% blockage risk assessment value of the Segment_2 pipe segment. The risk level conversion device calls the "buffer treatment plan library" to generate a disposal instruction to "activate the low-frequency vibration device, amplitude parameter 3mm." The final report generation device outputs a PDF report with geo-tagged data. The first page shows the location of the No. 3 elbow highlighted (coordinates x102, y205), and the second page contains a vibration parameter operation guide to guide operation and maintenance personnel in accurately performing dredging operations.

[0045] In order to solve the problem of pressure distortion caused by the lack of differential compensation for the local resistance of the bend area and the resistance along the straight pipe section in the prior art, as an practicable embodiment, according to step 103, the pressure data is dynamically compensated based on the geometric feature information to generate compensated pressure data, including: Step 201: Calculate a bending angle compensation value based on the bending angle.

[0046] In this step, the bending angle compensation value refers to the quantitative parameter of the local resistance of the bend calculated by the fluid mechanics model (unit: virtual resistance unit), and its physical meaning is the correction amount required to eliminate the additional pressure loss caused by the bending angle.

[0047] In this embodiment, it is implemented through an angular resistance mapping calculation engine, which first calls a preset angular resistance coefficient database (containing resistance reference values ​​corresponding to different bending angles), and then uses a nonlinear interpolation algorithm (such as cubic spline interpolation) to map the input bending angle to a corresponding bending angle compensation value.

[0048] Step 202: Based on the length, proportional adjustment is performed on the bending angle compensation value to generate a comprehensive compensation parameter.

[0049] In this step, the comprehensive compensation parameter is a global correction factor (dimensionless coefficient) that integrates the length scale effect and is used to expand the local bending resistance into the equivalent resistance compensation value of the entire pipe section.

[0050] In this embodiment, the pipeline resistance baseline parameter (the standard value of the resistance per unit length of a straight pipe section) is first loaded, and the unit length influence coefficient is obtained by querying the resistance influence factor mapping table based on the bending angle compensation value. Then, based on the length data, the unit influence coefficient is extended along the pipeline length through continuous superposition calculation (such as piecewise integration or accumulator algorithm) to generate a pipeline comprehensive resistance correction value as a comprehensive compensation parameter.

[0051] Step 203: Apply the comprehensive compensation parameters to the pressure data to generate compensated pressure data.

[0052] In this step, the compensated pressure data is the purified pressure value (unit: MPa) after eliminating the distortion of pipeline structure resistance through the dynamic balance model, which directly reflects the actual flow state.

[0053] In this embodiment, a closed-loop model including a pressure balancing unit (storing pressure data) and a resistance correction unit (receiving comprehensive compensation parameters) is first constructed; then, the comprehensive compensation parameters are converted into a dynamic correction component sequence (such as differential component pulses) using the resistance correction unit; then, an iterative superposition algorithm (such as Newton's iteration method) is used in the pressure balancing unit to cyclically superimpose components on the original pressure data until the fluid dynamic balance condition is met (such as the pressure gradient change rate < the set convergence threshold); finally, stable compensated pressure data is output.

[0054] In order to solve the problem that the resistance compensation mechanism in the prior art does not consider the nonlinear effect of the bending angle on the resistance per unit length, as another embodiment, according to step 202, the bending angle compensation value is proportionally adjusted based on the length to generate a comprehensive compensation parameter, including: Step 301: Obtain predefined pipeline resistance reference parameters.

[0055] In this step, the pipeline resistance benchmark parameter refers to the fluid resistance constant per unit length of a standard straight pipeline calibrated through experiments (unit: resistance unit / meter). Its physical meaning is the inherent resistance value per meter generated when the fluid flows in an ideal straight pipe section. It is used to quantify the impact benchmark of other structural resistances.

[0056] In this embodiment, the system starts the resistance parameter loading module, accesses the "resistance benchmark library" stored in the SQLite database, and uses an index query algorithm (such as B-tree index retrieval) to extract benchmark values ​​that match the current concrete rheological properties.

[0057] Step 302: Calculate the influence factor of the bending angle on the unit length according to the mapping relationship between the bending angle compensation value and the pipeline resistance reference parameter.

[0058] In this step, the impact factor refers to the enhancement coefficient (dimensionless) of the bending angle compensation value on the resistance per unit pipe length. The quantitative formula is: impact factor = (bending angle compensation value / pipeline resistance baseline parameter) × nonlinear correction coefficient, where the nonlinear correction coefficient is generated by a preset bending resistance mapping curve.

[0059] In this embodiment, the bending angle compensation value calculated in step 201 and the pipeline resistance reference parameter obtained in step 301 are first received; a precompiled mapping function library (including a piecewise spline function set) is called to calculate the initial coefficient with the reference parameter as the denominator and the bending angle compensation value as the numerator; the initial coefficient is then nonlinearly adjusted using a correction coefficient table (storing curvature correction weights for different bending angles), and finally the unit length influence factor is output.

[0060] Step 303: Combine the influence factor of the bending angle on the unit length with the length, and generate a pipeline comprehensive resistance correction value through continuous superposition calculation.

[0061] In this step, continuous superposition calculation refers to the mathematical process of accumulating unit influencing factors along the length of the pipeline; the pipeline comprehensive resistance correction value is the total correction amount output by this calculation (unit: resistance unit), and its physical meaning is the cumulative correction value of the resistance caused by bending in the entire pipe section.

[0062] In this embodiment, the influence factor (unit-length resistance enhancement coefficient) generated in step 302 is combined with the input pipeline length data and a segmented accumulation algorithm (e.g., a discrete integrator module) is used to perform a continuous superposition operation. Specifically, after loading the influence factor value, the pipeline length is discretized into microsegments (e.g., virtual segment points). The influence factor is then applied to each microsegment to calculate a local resistance correction. The local correction values ​​are then continuously summed using an accumulator. When the integration covers the entire length, the overall pipeline resistance correction value is output.

[0063] Step 304: Use the pipeline comprehensive resistance correction value as a comprehensive compensation parameter.

[0064] In this step, the comprehensive compensation parameter refers to the resistance compensation factor (unit: resistance unit) directly converted from the pipeline comprehensive resistance correction value, which is used to correct the original pressure data in the subsequent dynamic compensation model.

[0065] In this embodiment, the pipeline comprehensive resistance correction value generated in step 303 is received and a direct assignment operation is performed (without additional calculation), that is, it is output to the downstream module as a comprehensive compensation parameter.

[0066] In order to solve the problem of overcompensation caused by ignoring the dynamic balance characteristics of the fluid during the pressure compensation process in the prior art, as another embodiment, according to step 203, applying the comprehensive compensation parameter to the pressure data to generate compensated pressure data includes: Step 401: Establish a dynamic balance model including a pressure balance unit and a resistance correction unit.

[0067] In this step, the dynamic balance model refers to a digital simulation system constructed based on the Navier-Stokes equations of fluid mechanics, which is used to simulate the dynamic relationship between pressure data and structural resistance; the pressure balance unit is the computing core, responsible for storing and iteratively processing pressure data; the resistance correction unit is a correction module, dedicated to converting comprehensive compensation parameters into physical compensation quantities. The two form a closed-loop feedback mechanism through the data channel.

[0068] In this embodiment, a fluid dynamics simulation library (such as the OpenFOAM kernel) is first called to initialize the model structure based on the pipeline geometric characteristic parameters. The pressure balance unit is configured as a real-time pressure data container (using double-precision floating-point array storage), and the resistance correction unit is designed as a compensation converter (with a built-in transfer function algorithm). Secondly, a data exchange channel (such as a shared memory bus) is established between the two units, and the iterative control logic is configured (setting the gradient convergence threshold). The core process is to allocate unit memory after loading the model kernel, then establish a two-way communication protocol, and finally complete the model initialization.

[0069] Step 402: Input the pressure data into the pressure balance unit.

[0070] In this embodiment, the system reads the pressure data stream (serialized data packet with timestamp) collected in real time in step 102, then verifies the data integrity (CRC check), then unpacks the time series data, sorts it by timestamp, and finally writes it to the specified memory address; when the last data packet is written successfully, the pending status flag of the pressure balance unit is triggered.

[0071] Step 403: Input the comprehensive compensation parameters into the resistance correction unit to generate a dynamic correction component.

[0072] In this step, the dynamic correction component refers to the instantaneous pressure correction sequence (unit: MPa) generated by the resistance correction unit. Its technical essence is the differential expression of the comprehensive compensation parameters in the fluid dynamics model, which is used to gradually eliminate the interference of structural resistance on the original pressure data.

[0073] In this embodiment, the resistance correction unit receives a comprehensive compensation parameter (scalar value), calls an internal preset differential equation solver (such as a Runge-Kutta method module), and constructs a time-varying correction function based on the law of conservation of fluid momentum; and generates a dynamic correction component sequence through discrete time sampling (for example, outputting component values ​​at millisecond time steps).

[0074] Step 404 : Perform a cyclic superposition operation on the pressure data and the dynamic correction component in the pressure balance unit.

[0075] In this step, the cyclic superposition operation refers to the iterative subtraction process of the pressure data aligned by timestamp and the dynamic correction component. Its mathematical essence is to perform incremental compensation on the original pressure value sequence.

[0076] In this embodiment, the pressure balancing unit loads the original pressure data sequence (with millisecond timestamps) and performs clock synchronization matching with the dynamic correction component sequence (such as hash timestamp mapping); then starts the loop counter and performs floating-point subtraction operations at each time point (the original pressure value minus the correction component value at the corresponding moment); each cycle updates the compensated pressure value in the storage area and uses the result as input for the next round.

[0077] Step 405: When the cyclic superposition result satisfies the preset fluid dynamic balance condition, the compensated pressure data is output.

[0078] In this step, the fluid dynamic equilibrium condition refers to the convergence criterion (based on the pressure gradient change rate) for determining whether the compensation is complete. When the pressure gradient change value of n consecutive iterations is less than the convergence threshold, it is determined that the system has entered a stable state and meets the equilibrium condition.

[0079] In this embodiment, after each loop superposition, the gradient change of the current compensated pressure sequence is calculated (the absolute difference between the results of two previous and subsequent iterations). When the gradient change is less than a convergence threshold (e.g., 0.01 MPa / ms) for a preset number of consecutive times (e.g., 3 times), a termination signal is triggered. Finally, the current storage area data is frozen and the compensated pressure data is output.

[0080] In order to solve the problem that the single parameter threshold method in the prior art cannot capture the precursory chain reaction of pipe blockage, as another embodiment, according to step 104, the compensated pressure data and the flow rate data are combined to calculate the blockage risk assessment value under the current concrete delivery state, including: Step 501: extracting abnormal pressure period characteristic values ​​from the compensated pressure data.

[0081] In this step, the abnormal pressure period characteristic value refers to the set of discontinuous fluctuation characteristics caused by blockage in the compensated pressure data, including the pressure rise rate (unit: MPa / s) and the pressure fluctuation amplitude (unit: MPa), which is used to quantify the spatiotemporal characteristics of the pressure mutation. Its technical essence is to express the true blockage signal after eliminating the interference of the pipeline structure.

[0082] In this embodiment, wavelet denoising (using the Db4 wavelet basis function) is first performed on the compensated pressure data to eliminate high-frequency environmental noise. Secondly, an adaptive sliding window (the window width is dynamically adjusted according to the rheological properties of concrete) is used to scan the data stream. If the pressure rise rate detected in the window exceeds the rate threshold and the fluctuation amplitude continuously exceeds the amplitude tolerance, it is marked as an abnormal period. Finally, a period feature value structure (including the start timestamp, duration, maximum rise rate, and peak-to-peak value) is output.

[0083] Step 502: extracting a flow velocity decay period characteristic value from the flow velocity data.

[0084] In this step, the velocity decay period characteristic value refers to the set of characteristic quantities of the continuous decrease in concrete flow velocity, including the velocity inflection point (timestamp), the decay slope (unit: m / s²), and the recovery threshold point (the moment when the flow velocity returns to the preset ratio). Its physical meaning is to characterize the dynamic process of flow obstruction.

[0085] In this embodiment, a first-order derivative operation is performed on the flow rate data to generate an acceleration sequence, and a zero-crossing detection algorithm is used to locate the flow rate inflection point (the derivative changes from positive to negative). From the inflection point, the flow rate is traced backward. When the absolute value of the flow rate decline slope is continuously greater than the attenuation threshold and continues to the recovery threshold point (the point where the flow rate reaches 85% of the initial value), it is marked as a decay period. Finally, the characteristic value structure (inflection point time, attenuation slope, recovery time) is output.

[0086] Step 503: Detect an overlapping area on the time axis between the characteristic value of the abnormal pressure period and the characteristic value of the attenuation period.

[0087] In this step, the overlapping area refers to the overlapping interval between the main pressure action area and the attenuation effective area on the time axis. Its technical essence is to confirm the physical authenticity of the blockage event through time correlation to avoid false alarms.

[0088] In this embodiment, the pressure main action area (the starting point to the peak end point of the pressure rise) extracted in step 501 and the attenuation effective area (the flow velocity inflection point to the recovery point) extracted in step 502 are called; the two intervals are scanned by sliding the time window with a step size of 1 second. When it is detected that the starting time of the pressure main action area is ahead of the starting time of the attenuation effective area and the time difference is ≤ the set threshold (such as 2 seconds), the effective overlapping event is marked; the overlapping duration and the overlapping area ratio (overlapping duration / pressure main action area duration) are calculated; and finally the overlapping area object (start time, end time, duration, area ratio) is output.

[0089] Step 504: Generate a blockage characteristic quantization value based on the duration and overlap degree of the overlapping area.

[0090] In this step, the quantified value of the blocking feature is the intermediate value of the overlapping spatiotemporal features converted into a scalar risk indicator (range 0-1). The calculation formula is: quantified value = duration weight × overlap duration + overlap weight × area ratio, and the weight coefficient is calibrated by the experiment.

[0091] In this embodiment, a preset weight parameter library is loaded (e.g., a duration weight of 0.6 and a proportion weight of 0.4); the duration and area proportion values ​​of the overlapping area object are called; a weighted calculation is performed: quantized value = (0.6 × duration / maximum benchmark duration) + (0.4 × area proportion); the maximum benchmark duration is dynamically adjusted based on the pipeline length (e.g., a base of 0.5 seconds per meter); and finally, the quantized value of the blockage feature is output.

[0092] Step 505: Convert the blockage characteristic quantization value into a blockage risk assessment value.

[0093] In this embodiment, the quantized value of the congestion feature is read; a Sigmoid function processor is called to calculate a conversion value: S(x)=1 / (1+e^(-(10x-5))); and the result is multiplied by 100 to generate a congestion risk assessment value.

[0094] In order to solve the problem of misidentification of overlapping events caused by interference between instantaneous pressure fluctuations and flow rate noise in the prior art, as another embodiment, according to step 503, detecting the overlapping area of ​​the characteristic value of the abnormal pressure period and the characteristic value of the decay period on the time axis includes: Step 601: define a main pressure action area of ​​the characteristic value of the abnormal pressure period, wherein the main pressure action area covers the pressure rising stage and the peak duration stage.

[0095] In this step, the main pressure zone refers to the core period of the abnormal pressure event. Its physical meaning is the core duration of pressure distortion caused by the blockage, covering the pressure rise phase (the period when the pressure derivative is > 0 and continues to increase) and the peak duration phase (the period when the pressure remains at a plateau at a high threshold of ≥ 80% of the peak value). This definition excludes periods of interference from pressure drops and fluctuation noise.

[0096] In this embodiment, based on the compensated pressure data, the third-order derivative is first used to locate the starting point of the pressure rise (the position where the first-order derivative > the rising threshold appears for the first time); then the pressure peak point is locked through local extreme value detection; and the pressure is scanned backward from the peak point until the pressure value falls below 80% of the peak value for the first time, which is marked as the end point.

[0097] Step 602: defining an effective attenuation region of the characteristic value of the flow velocity attenuation period, wherein the effective attenuation region lasts from the flow velocity inflection point to the flow velocity recovery threshold point.

[0098] In this step, the effective attenuation zone refers to the period of substantial velocity decline, extending from the velocity inflection point (the turning point where the second-order derivative turns from positive to negative) to the velocity recovery threshold (the point where the velocity returns to 85% of the pre-attenuation baseline value). This definition avoids interference from normal velocity fluctuations and temporary decreases.

[0099] In this embodiment, the second-order derivative sequence of the flow rate is calculated to detect the inflection point when the second-order derivative is less than 0 for the first time. The flow rate is tracked backward from the inflection point, and the recovery point is marked when the flow rate continues to be lower than the baseline value before the inflection point and rises to 85% of the baseline value for the first time.

[0100] Step 603: Capture the temporal overlap state of the pressure main action area and the attenuation effective area through a sliding time window.

[0101] In this step, the sliding time window refers to a dynamic detection window that moves along the time axis (the window width is configurable). Its core function is to identify the spatiotemporal correlation between two characteristic intervals in continuous scanning. The temporal overlap state specifically refers to the partial or complete overlap of the main pressure action area and the attenuation effective area in the time dimension (that is, there is a common time segment between the two intervals). This state must satisfy the fluid dynamics causal relationship that pressure fluctuations lead flow velocity attenuation.

[0102] In this embodiment, this step is implemented in a complete process through a dynamic window scanning algorithm: the system first sets an initial sliding window based on the start time of the pressure main action area (the right boundary of the window is the start time plus the preset maximum delay tolerance), and loads the time interval of the pressure main action area and the time interval of the attenuation effective area; during the window displacement process (the step size is configurable), when it is detected that the starting point of the pressure main action area is located in the current window, and the starting point of the attenuation effective area is also present in the window, and the timing relationship of the starting point of the pressure main action area leading the starting point of the attenuation effective area is satisfied, the overlapping duration of the two intervals is calculated in real time (the minimum value of the end points of the two intervals is subtracted from the maximum value of the starting point); each window position that meets the conditions is recorded as a valid event. After the traversal is completed, the overlapping duration and time difference of all events are aggregated into a timing coincidence state matrix output, forming a quantitative proof of the spatiotemporal correlation of the congestion event.

[0103] Step 604: When the start time of the pressure main action area is ahead of the attenuation effective area and the delay is less than a first set threshold, it is recorded as a valid overlap event.

[0104] In this step, a valid overlap event is defined as a coincidence state recording unit that satisfies strict fluid dynamics causality (pressure fluctuation precedes flow rate decay) and eliminates sensor errors. Its structure includes the time difference (the duration of the pressure onset leading the flow rate decay) and the core parameters of the overlap segment (starting point, duration, etc.). The first set threshold is the upper limit of the error tolerance time difference set to eliminate sensor sampling delay or transmission jitter (usually the fluid transfer time + system error margin).

[0105] In this embodiment, the system first calls the starting timestamp of the pressure main action area and the starting timestamp of the attenuation effective area, and then calculates the time difference between the starting timestamp of the pressure main action area and the starting timestamp of the attenuation effective area; when the time difference is greater than 0 (to ensure that the pressure fluctuation is ahead) and the time difference is less than the first set threshold (excluding non-causal delays), the event recorder is triggered to generate a valid overlapping event object (pressure start time, flow rate start time, time difference, overlapping period start and end points); the overlapping state that does not meet the conditions is filtered out and discarded to ensure that only physically real blockage signals are retained in the event library.

[0106] Step 605: Aggregate the durations and occurrence intervals of all valid overlapping events to generate overlapping areas.

[0107] In this embodiment, the system first traverses the valid overlapping event queue and extracts the time difference and duration parameters of each event; then calculates the dynamic weight based on the time difference (formula: weight = 1 / (1+Δt)), and performs a weighted average calculation of the duration (∑(weight×duration) / ∑weight); at the same time, each event is sorted by timestamp, and the minimum value of the start time interval between adjacent events is obtained; finally, the start time of the first event, the end time of the last event, the weighted duration and the minimum interval value are integrated and output as a structured overlapping area.

[0108] In order to solve the problem in the prior art of delayed emergency response caused by the disconnection between warning signals and spatial disposal, as another embodiment, according to step 105, a congestion risk assessment report is output based on the congestion risk evaluation value, including: Step 701: construct a pumping pipeline risk distribution map, wherein the pumping pipeline risk distribution map is associated with geometric feature information of the concrete delivery pipeline.

[0109] In this step, the pumping pipeline risk distribution map is a visual digital model based on the pipeline's spatial topology. Its core approach is to map pipeline geometric features (bend angles, length) into geometric structural elements in a coordinate system (bends are mapped to curvature radii, and straight pipes are mapped to length vectors), and to mark the spatial location of risk assessment results. This map precisely links physical pipeline segments with risk values ​​through coordinate binding, resolving the limitation of existing technologies that prevented the localization of blocked sections.

[0110] In this embodiment, the system calls the geometric feature information of the concrete conveying pipeline, uses a coordinate transformation algorithm (such as affine transformation) to convert the bending angle into a three-dimensional space curvature lattice (90° bends are mapped to π / 2 radians), and converts the length data into a linear vector; constructs a topological network diagram based on the pipeline connection relationship (nodes are bends / interfaces, and edges are straight pipe sections); injects the blockage risk assessment value into the corresponding pipe section topology node, and generates a dynamic and interactive risk distribution map through rendering using a color gradient algorithm (red = high risk, yellow = medium risk, blue = low risk).

[0111] Step 702: Classify emergency response events, buffer processing events, and monitoring and tracking events based on the numerical value of the congestion risk evaluation value.

[0112] In this step, emergency response events represent extreme risk conditions that require immediate interruption of pumping and disposal, buffering events refer to critical risk conditions that allow short-term operation but require intervention, and monitoring and tracking events represent low-risk conditions that only require continuous observation. The three types of events are strictly divided through a dynamic threshold mechanism, and their threshold boundaries are dynamically calibrated according to the rheological properties of concrete.

[0113] In this embodiment, this step is implemented through a threshold segmentation engine that is adaptive to rheological properties. The system first calls the concrete property database (including grade and aggregate particle size parameters), matches the preset threshold rule library, and activates the corresponding threshold group. The blockage risk assessment value is then input into the three-level judgment module. When the risk value exceeds the high-risk threshold, it is marked as an emergency response event (triggering an immediate shutdown protocol); when it is between the medium threshold and the high-risk threshold, it is marked as a buffer processing event (activating the buffer period intervention process); and when it is below the medium threshold, it is marked as a monitoring and tracking event (enabling low-priority monitoring).

[0114] Step 703: Map the emergency response event to the first priority area of ​​the pumping pipeline risk distribution map, map the buffer processing event to the second priority area of ​​the pumping pipeline risk distribution map, and map the monitoring and tracking event to the third priority area of ​​the pumping pipeline risk distribution map.

[0115] In this step, the first priority area refers to the coordinates of the physical pipe sections marked as the highest risk level in the risk distribution map (highlighted in red). These areas are used to locate blockages requiring immediate action. The second priority area is the coordinates of the medium-risk pipe sections (yellow warning), which identify blockages where a buffered response is permitted. The third priority area is the coordinates of the low-risk pipe sections (marked in blue), which require only continuous monitoring. These three areas are precisely tied to specific pipeline locations through spatial coding, forming a closed-loop mapping from event level to spatial coordinates.

[0116] In this embodiment, the system first parses the three types of event objects divided in step 702 and extracts their associated pipeline location identifiers (such as bend numbers or GPS coordinates); calls the topological structure of the pumping pipeline risk distribution map generated in step 701, and uses a coordinate matching algorithm (such as a KD tree spatial index) to bind emergency response events to the first priority area (writing into the red alarm coordinate set), maps buffer processing events to the second priority area (yellow warning coordinate set), and associates monitoring and tracking events to the third priority area (blue monitoring coordinate set); finally, updates the visual rendering engine of the risk distribution map so that the three-color areas are dynamically superimposed on the pipeline geometric model, realizing real-time visual association between risk events and physical locations.

[0117] Step 704: Generate multi-level handling instructions corresponding to the first priority area, the second priority area, and the third priority area through the risk level conversion device.

[0118] In this step, the risk level conversion device refers to an intelligent decision-making module with a built-in dredging strategy rule library, whose core function is to map priority areas into specific operation instructions; the multi-level disposal instructions are a set of dredging operation parameters customized according to the risk level.

[0119] In this embodiment, a risk level conversion device is used to load a preset multi-level disposal instruction library (including three-level policy templates for emergency response, buffer processing, and monitoring and tracking), and the corresponding template is called according to the input area type; for the first priority area, the high-pressure backwash template is matched and the position coordinates are injected to generate specific parameters (such as performing 20MPa backwash at the coordinates (x305, y418)); for the second priority area, the low-frequency vibration template is activated to generate vibration parameters (such as 2Hz±0.5 amplitude); for the third priority area, the flow rate monitoring template is called to set the inspection frequency (such as 5 seconds / time sampling); and finally, the multi-level disposal instructions with location binding are output.

[0120] Step 705: Using a report generating device, the pumping pipeline risk distribution map and the multi-level handling instructions are synthesized to generate a blockage risk assessment report with position marks.

[0121] In this step, the report generation device refers to an automated system that integrates spatial data fusion and document rendering engine.

[0122] In this embodiment, the system first calls the pumping pipeline risk distribution map (including spatial vector data of three-color priority areas) and a multi-level disposal instruction set; uses a coordinate alignment algorithm to accurately match the position parameters in the instructions with the spatial elements of the risk distribution map (such as the KD tree index associating coordinates with instructions); then converts the risk distribution map into an interactive vector layer through a document rendering engine, and embeds operation labels in the corresponding coordinate positions of the multi-level disposal instructions (such as marking the backwash parameter next to the red area); finally, a report entity is generated through a PDF / HTML synthesizer, in which the position mark uses a QR code or hyperlink to bind the coordinate navigation function to form a blockage risk assessment report that can directly hit the blockage point.

[0123] Figure 2 A structural diagram of a dynamic assessment system for concrete pipeline blockage risk is provided for this application. Figure 2 As shown, the system includes: An acquisition module 21 is configured to acquire geometric feature information of the concrete delivery pipe, wherein the geometric feature information includes a bending angle and a length; The acquisition module 22 is used to collect the pressure data and flow rate data in the concrete delivery pipeline in real time during the concrete delivery process; a processing module 23 for performing dynamic compensation processing on the pressure data based on the geometric feature information to generate compensated pressure data; A calculation module 24 is configured to calculate a blockage risk assessment value under a current concrete delivery state by combining the compensated pressure data and the flow rate data; The output module 25 is configured to output a congestion risk assessment report according to the congestion risk assessment value.

[0124] Figure 2 The concrete delivery pipeline blockage risk dynamic assessment system can be executed Figure 1 The implementation principles and technical effects of the method for dynamically assessing the risk of blockage in a concrete delivery pipeline described in the illustrated embodiment are not further elaborated. The specific manner in which the various modules and units in the system for dynamically assessing the risk of blockage in a concrete delivery pipeline described in the aforementioned embodiment perform their operations has been described in detail in the embodiments of the method and will not be further elaborated here.

[0125] In one possible design, Figure 2 The concrete delivery pipeline blockage risk dynamic assessment system of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0126] The processing component 32 is used for the above Figure 1 The embodiment provides a method for dynamically assessing the risk of blockage in a concrete delivery pipeline.

[0127] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also 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 method.

[0128] The storage component 31 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 memory 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 memory, flash memory, magnetic disk, or optical disk.

[0129] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0130] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0131] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0132] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0133] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a method for dynamically assessing the risk of blockage in a concrete delivery pipeline.

[0134] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0135] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0136] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion 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, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for dynamic assessment of blockage risk of concrete delivery pipeline, characterized in that: include: Acquiring geometric characteristic information of the concrete delivery pipe, wherein the geometric characteristic information includes a bending angle and a length; During the concrete delivery process, real-time collection of pressure data and flow rate data in the concrete delivery pipeline; Based on the geometric feature information, dynamically compensating the pressure data to generate compensated pressure data; Calculating a blockage risk assessment value under a current concrete delivery state by combining the compensated pressure data and the flow rate data; A congestion risk assessment report is output based on the congestion risk evaluation value.

2. The method according to claim 1, characterized in that Based on the geometric feature information, dynamically compensating the pressure data to generate compensated pressure data includes: Calculating a bending angle compensation value based on the bending angle; Based on the length, proportionally adjusting the bending angle compensation value to generate a comprehensive compensation parameter; Applying the comprehensive compensation parameters to the pressure data to generate compensated pressure data.

3. The method according to claim 2, characterized in that Based on the length, the bending angle compensation value is proportionally adjusted to generate a comprehensive compensation parameter, including: Obtain predefined pipeline resistance benchmark parameters; Calculating the influence factor of the bending angle on the unit length according to the mapping relationship between the bending angle compensation value and the pipeline resistance reference parameter; The influence factor of the bending angle on the unit length is combined with the length, and a correction value of the pipeline comprehensive resistance is generated by continuous superposition calculation; The pipeline comprehensive resistance correction value is used as a comprehensive compensation parameter.

4. The method according to claim 2, characterized in that Applying the comprehensive compensation parameter to the pressure data to generate compensated pressure data includes: Establish a dynamic balance model including a pressure balance unit and a resistance correction unit; inputting the pressure data into the pressure balancing unit; Inputting the comprehensive compensation parameter into the resistance correction unit to generate a dynamic correction component; performing a cyclic superposition operation on the pressure data and the dynamic correction component in the pressure balance unit; When the cyclic superposition result meets the preset fluid dynamic balance condition, the compensated pressure data is output.

5. The method according to claim 1, wherein Calculating a blockage risk assessment value under the current concrete delivery state by combining the compensated pressure data and the flow rate data includes: extracting abnormal pressure period characteristic values ​​from the compensated pressure data; extracting a flow velocity decay period characteristic value from the flow velocity data; Detecting an overlapping area on a time axis between the characteristic value of the abnormal pressure period and the characteristic value of the decay period; generating a blockage characteristic quantification value based on the duration and degree of overlap of the overlapping region; The blockage characteristic quantification value is converted into a blockage risk assessment value.

6. The method according to claim 5, characterized in that Detecting an overlapping area between the characteristic value of the abnormal pressure period and the characteristic value of the decay period on a time axis includes: Defining a main pressure action area of ​​the characteristic value of the abnormal pressure period, wherein the main pressure action area covers the pressure rising stage and the peak duration stage; Defining an effective attenuation region of the characteristic value of the flow velocity attenuation period, wherein the effective attenuation region extends from the flow velocity inflection point to the flow velocity recovery threshold point; Capturing the temporal overlap state of the pressure main action area and the attenuation effective area through a sliding time window; When the start time of the pressure main action area is ahead of the attenuation effective area and the delay is less than a first set threshold, it is recorded as a valid overlap event; Aggregate the duration and occurrence intervals of all valid overlapping events to generate the overlapping area.

7. The method according to claim 1, characterized in that Output a congestion risk assessment report based on the congestion risk evaluation value, including: Constructing a pumping pipeline risk distribution map, wherein the pumping pipeline risk distribution map is associated with geometric feature information of the concrete delivery pipeline; Based on the numerical value of the congestion risk assessment value, emergency response events, buffer processing events, and monitoring and tracking events are divided; Mapping the emergency response event to a first priority area of ​​the pumping pipeline risk distribution map, mapping the buffer processing event to a second priority area of ​​the pumping pipeline risk distribution map, and mapping the monitoring and tracking event to a third priority area of ​​the pumping pipeline risk distribution map; Generate multi-level handling instructions corresponding to the first priority area, the second priority area, and the third priority area through a risk level conversion device; The pumping pipeline risk distribution map and the multi-level disposal instructions are synthesized by using a report generating device to generate a blockage risk assessment report with position marks.

8. A concrete delivery pipeline blockage risk dynamic assessment system, characterized in that: include: An acquisition module, configured to acquire geometric feature information of the concrete delivery pipe, wherein the geometric feature information includes a bending angle and a length; The acquisition module is used to collect the pressure data and flow rate data in the concrete delivery pipeline in real time during the concrete delivery process; a processing module, configured to perform dynamic compensation processing on the pressure data based on the geometric feature information to generate compensated pressure data; a calculation module, configured to calculate a blockage risk assessment value under a current concrete delivery state by combining the compensated pressure data and the flow rate data; The output module is used to output a congestion risk assessment report according to the congestion risk evaluation value.

9. A computing device, characterized in that The method comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a dynamic assessment method for blockage risk of a concrete delivery pipeline as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, a dynamic assessment method for blockage risk of a concrete delivery pipeline according to any one of claims 1 to 7 is implemented.

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