Flow control method and system for fluid engineering materials

Abstract

The present invention provides a flow control method and system for flowing engineering materials, including: collecting the flow data of the flowing engineering materials and the current construction environment parameters; inputting the flow data and the current construction environment parameters into a preset flow prediction algorithm model for operation and processing to obtain a flow trend prediction result; analyzing according to the flow trend prediction result and a preset flow control strategy rule to obtain a flow control adjustment parameter; based on the flow control adjustment parameter, adjusting the operation parameters of the conveying equipment of the flowing engineering materials to obtain an optimized flow output state. In the present invention, by combining the flow data of the engineering materials and the current construction environment parameters, the flow trend prediction result is predicted, and then the flow control adjustment parameter is generated, so as to improve the accuracy and stability of the process control. It avoids problems such as unstable flow, material waste or unqualified construction quality.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering materials, and particularly relates to a flow control method and system for flowable engineering materials. Background Art

[0002] In the application fields of flowable engineering materials, such as bridge construction, road paving, building construction and other scenarios, accurately controlling the flow of materials plays a crucial role in engineering quality and construction efficiency.

[0003] Traditional flow control methods mainly rely on empirical judgment and simple mechanical adjustment methods. For example, the valve opening is manually adjusted to roughly control the flow rate. However, this method is difficult to adapt to complex and changeable construction environments and changes in material properties.

[0004] During the construction process, environmental factors such as temperature, humidity, and pressure have a significant impact on the fluidity of flowable engineering materials. Traditional methods often cannot accurately incorporate these environmental parameters into the consideration scope of flow control. And traditional flow control lacks an effective data collection and analysis mechanism, and cannot accurately predict the flow trend, resulting in problems such as unstable flow, material waste, or unqualified construction quality during the construction process.

[0005] That is to say, at present, during the construction process, there are defects in the unstable flow control of engineering materials. Summary of the Invention

[0006] The main object of the present invention is to provide a flow control method and system for flowable engineering materials, aiming to overcome the defect of unstable flow control of engineering materials.

[0007] To achieve the above object, the present invention provides a flow control method for flowable engineering materials, including the following steps:

[0008] Collect the flow data of flowable engineering materials and the current construction environment parameters;

[0009] Input the flow data and the current construction environment parameters into a preset flow prediction algorithm model for calculation and processing to obtain a flow trend prediction result;

[0010] Analyze according to the flow trend prediction result and a preset flow control strategy rule to obtain a flow control adjustment parameter;

[0011] Based on the flow control adjustment parameter, adjust the operating parameters of the conveying equipment of flowable engineering materials to obtain an optimized flow output state.

[0012] Further, inputting the traffic data and the current construction environment parameters into a preset traffic prediction algorithm model for calculation and processing to obtain a traffic trend prediction result includes:

[0013] Input the traffic data and the current construction environment parameters into a preset traffic prediction algorithm model; wherein, the traffic prediction algorithm model includes a first sub-model based on support vector regression, a second sub-model based on random forest, and a third sub-model based on long short-term memory network;

[0014] Perform weighted fusion processing on the output results of each sub-prediction model according to a preset dynamic weight allocation strategy to obtain a traffic trend prediction result.

[0015] Further, inputting the traffic data and the current construction environment parameters into a preset traffic prediction algorithm model includes:

[0016] Perform spectrum analysis processing on the traffic data, extract the eigenvalue of its different frequency components to obtain a traffic data spectrum feature set; perform fuzzy clustering processing on the construction environment parameters, and classify the environment parameters with similar features into one category to form multiple environment parameter clustering groups;

[0017] Input the traffic data spectrum feature set and multiple environment parameter clustering groups into a preset traffic prediction algorithm model.

[0018] Further, after performing weighted fusion processing on the output results of each sub-prediction model according to a preset dynamic weight allocation strategy, it further includes:

[0019] Obtain correction rules for different construction scenarios and data anomaly situations, and perform rule matching and adjustment processing on the results of weighted fusion processing, the traffic trend prediction result.

[0020] Further, analyzing according to the traffic trend prediction result and a preset traffic control strategy rule to obtain a traffic control adjustment parameter includes:

[0021] Perform trend evaluation processing based on fuzzy logic on the traffic trend prediction result to obtain a fuzzy linguistic variable;

[0022] Perform structured representation based on knowledge graph on the preset traffic control strategy rule to obtain a rule knowledge network, and perform matching and association processing on the fuzzy linguistic variable with the nodes in the rule knowledge network to obtain a preliminary association result;

[0023] Based on the preliminary association result, use an optimization search mechanism based on genetic algorithm to search for an approximate solution of the optimal traffic control adjustment parameter combination scheme to obtain an intermediate adjustment parameter scheme;

[0024] Optimize the intermediate adjustment parameter scheme to obtain the flow control adjustment parameter.

[0025] Further, the optimizing the intermediate adjustment parameter scheme to obtain the flow control adjustment parameter includes:

[0026] Perform a fine adjustment process on the intermediate adjustment parameter scheme based on the simulated annealing algorithm;

[0027] Among them, the simulated annealing algorithm uses the intermediate adjustment parameter scheme as the initial solution, performs random perturbations in the solution space, gradually reduces the perturbation amplitude according to the preset cooling strategy, and at the same time judges whether the perturbed solution is better according to the performance evaluation index function of the flow control system. If it is better, accept the solution as the final flow control adjustment parameter;

[0028] Otherwise, accept the worse solution with a preset probability to avoid falling into local optimum, and obtain the final flow control adjustment parameter after multiple iterations.

[0029] Further, the collecting the flow rate data of the fluidity engineering material and the current construction environment parameters includes:

[0030] Use a flow sensing device based on a microfluidic chip to perform multi-point real-time monitoring on the flow rate data to obtain the flow rate data;

[0031] Collect the construction environment parameters through a distributed wireless sensor network.

[0032] The present invention also provides a flow control system for fluidity engineering materials, including:

[0033] A collection module for collecting the flow rate data of the fluidity engineering material and the current construction environment parameters;

[0034] A processing module for inputting the flow rate data and the current construction environment parameters into a preset flow prediction algorithm model for arithmetic processing to obtain a flow trend prediction result;

[0035] An analysis module for analyzing according to the flow trend prediction result and a preset flow control strategy rule to obtain a flow control adjustment parameter;

[0036] An adjustment module for adjusting the operating parameters of the conveying equipment of the fluidity engineering material based on the flow control adjustment parameter to obtain an optimized flow output state.

[0037] The present invention also provides a computer device, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0038] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0039] The flow control method and system for fluidity engineering materials provided by the present invention include: collecting the flow data of fluidity engineering materials and the current construction environment parameters; inputting the flow data and the current construction environment parameters into a preset flow prediction algorithm model for arithmetic processing to obtain a flow trend prediction result; analyzing according to the flow trend prediction result and a preset flow control strategy rule to obtain a flow control adjustment parameter; and based on the flow control adjustment parameter, adjusting the operating parameters of the conveying equipment for fluidity engineering materials to obtain an optimized flow output state. In the present invention, by combining the flow data of engineering materials and the current construction environment parameters, the flow trend prediction result is predicted, and then the flow control adjustment parameter is generated, so as to improve the accuracy and stability of process control. Problems such as unstable flow, material waste, or unqualified construction quality are avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flowchart of the flow control method for fluidity engineering materials in an embodiment of the present invention;

[0041] Figure 2 is a schematic diagram of the steps of the flow control method for fluidity engineering materials in an embodiment of the present invention;

[0042] Figure 3 is a structural block diagram of the flow control system for fluidity engineering materials in an embodiment of the present invention;

[0043] Figure 4 is a schematic structural block diagram of a computer device in an embodiment of the present invention.

[0044] The implementation, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0046] Referring to Figure 1 and Figure 2 , an embodiment of the present invention provides a flow control method for fluidity engineering materials, including the following steps:

[0047] Step S1, collecting the flow data of fluidity engineering materials and the current construction environment parameters;

[0048] Step S2: Input the flow data and the current construction environment parameters into a preset flow prediction algorithm model for arithmetic processing to obtain a flow trend prediction result;

[0049] Step S3: Analyze according to the flow trend prediction result and a preset flow control strategy rule to obtain a flow control adjustment parameter;

[0050] Step S4: Based on the flow control adjustment parameter, perform adjustment processing on the operating parameters of the conveying equipment for the fluid engineering material to obtain an optimized flow output state.

[0051] In this embodiment, as described in step S1 above, first, high-precision flow sensors are used to real-time monitor key data such as the flow velocity and flow rate of the fluid engineering material in the conveying pipeline or equipment. These sensors can quickly and accurately capture the dynamic changes in the flow rate, providing basic data for subsequent analysis and control. For example, sensors based on advanced microfluidic technology can be used, which have the characteristics of high sensitivity and fast response and can accurately measure the flow rate at a micro scale.

[0052] Then, a variety of environmental monitoring devices are used to comprehensively collect data on various environmental factors at the construction site. These factors will have a direct or indirect impact on the fluidity of the fluid engineering material. Common construction environment parameters include temperature, humidity, air pressure, etc. For example, the change in temperature causes the viscosity of the material to change, thereby affecting its flow characteristics. By collecting these parameters, the external conditions affecting the flow rate can be understood more comprehensively.

[0053] As described in step S2 above, before inputting the data into the model, it is necessary to preprocess the collected raw data to improve the data quality and the arithmetic efficiency of the model. Among them, it includes data cleaning to remove outliers and noise; data standardization to unify data with different units and magnitudes into a standard range; data encoding to convert some non-numerical data into a numerical form suitable for model processing, etc.

[0054] The above-mentioned preset flow prediction algorithm model is one of the cores of this technical solution. This model is constructed based on advanced data analysis and machine learning algorithms, and can fully explore the complex relationship between flow data and construction environment parameters. For example, a deep learning neural network model can be adopted, which has a powerful non-linear fitting ability and can automatically learn the features and patterns in the data. Through the training of a large amount of historical data, the model can learn the flow change rules under different combinations of flow data and environmental parameters, so as to accurately predict the future flow trend. During the model operation process, some special algorithms and technologies can also be applied, such as time series analysis, feature engineering, etc. Time series analysis can help the model better understand the change trend of flow data over time; feature engineering can improve the prediction accuracy of the model by extracting and constructing effective features.

[0055] As described in step S3 above, conduct in-depth analysis and interpretation of the flow trend prediction result obtained in step S2. This includes judging the change direction of the flow (such as rising, falling or remaining stable), the change speed, and the fluctuation range, etc. For example, if the prediction result shows that the flow will continue to rise in the future for a period of time, then corresponding control measures need to be considered in advance to prevent the flow from exceeding the safe or ideal range.

[0056] The preset flow control strategy rules are a series of logical rules and algorithms formulated according to factors such as engineering experience, material properties, and flow control objectives. The above rules match the flow trend prediction result with the actual control requirements to determine the appropriate control strategy and adjustment direction. For example, when the flow trend prediction result shows that the flow is about to exceed the upper limit, the rule will indicate that it is necessary to reduce the output power of the conveying equipment or adjust the opening degree of the valve.

[0057] Furthermore, according to the trend analysis result and the flow control strategy rules, through complex mathematical calculations and algorithm operations, calculate the specific flow control adjustment parameters. The above parameters include the rotation speed adjustment value of the conveying equipment, the opening degree adjustment percentage of the valve, the pressure adjustment amount of the pump, etc. Various factors need to be considered during the calculation process, such as the fluidity characteristics of the material, the performance parameters of the equipment, the changes in the construction environment, etc., to ensure the accuracy and effectiveness of the adjustment parameters.

[0058] As described in step S4 above, actually apply the flow control adjustment parameters calculated in step S3 to the conveying equipment of the fluid engineering material. This involves corresponding parameter setting and adjustment of the control system of the conveying equipment. For example, if the adjustment parameter is to increase the rotation speed of the conveying equipment, then the rotation speed needs to be increased to the specified value through the control system of the equipment.

[0059] After adjusting the operating parameters of the conveying equipment, it is necessary to continuously monitor the actual changes in the flow rate to verify whether the adjustment effect meets the expectations. If there is still a deviation between the actual flow rate and the target flow rate, it is necessary to further optimize and adjust the adjustment parameters according to the feedback information. This forms a closed-loop feedback control system that continuously optimizes and improves the flow control according to the actual situation to ensure that the flow rate always remains in the ideal output state.

[0060] In this embodiment, by combining the flow rate data and the construction environment parameters and applying the flow rate prediction algorithm model, it is possible to accurately predict the flow rate trend, discover potential flow rate problems in advance, and provide strong support for taking control measures in a timely manner. It can automatically adjust the flow rate control strategy according to different environmental conditions, improving the adaptability and stability of the system. Through precise flow rate control, material waste and construction time can be reduced, and the overall construction efficiency and quality of the project can be improved. It integrates a variety of technologies and algorithms, such as microfluidics technology, deep learning neural network, flow rate control strategy rules, etc., and has innovation and leadership in the field of flow rate control.

[0061] In one embodiment, inputting the flow rate data and the current construction environment parameters into a preset flow rate prediction algorithm model for arithmetic processing to obtain a flow rate trend prediction result includes:

[0062] Inputting the flow rate data and the current construction environment parameters into a preset flow rate prediction algorithm model; wherein, the flow rate prediction algorithm model includes a first sub-model based on support vector regression, a second sub-model based on random forest, and a third sub-model based on long short-term memory network;

[0063] Performing weighted fusion processing on the output results of each sub-prediction model according to a preset dynamic weight allocation strategy to obtain a flow rate trend prediction result.

[0064] In this embodiment, by constructing a flow rate prediction algorithm model containing multiple sub-models and adopting a unique weighted fusion method, it is possible to accurately predict the flow rate trend of the flowing engineering materials. The core idea is to make full use of the advantages of different types of sub-models and combine the dynamic weight allocation strategy to adapt to various complex data characteristics and change situations.

[0065] In this embodiment, the above-mentioned support vector regression is a machine learning method based on statistical learning theory. It finds an optimal hyperplane to minimize the sum of the distances from all sample points to this hyperplane. In traffic prediction, it takes traffic data and construction environment parameters as input features and attempts to establish a non-linear mapping relationship between these features and traffic trends. It has good adaptability and generalization ability for small sample data. In actual engineering, the data samples in some construction scenarios are limited, and support vector regression can still give relatively accurate prediction results in such cases.

[0066] The above-mentioned random forest is an ensemble learning method. It constructs multiple decision trees and combines their prediction results to obtain the final prediction result. When constructing each decision tree, the random forest randomly selects a part of the samples and features for training, thus increasing the diversity and stability of the model. It can handle high-dimensional data. For complex data types such as traffic data and construction environment parameters that contain multiple features, the random forest can effectively perform feature selection and model construction. It has high prediction accuracy and stability. Since it is an ensemble of multiple trees, it is not easy to overfit and can maintain good performance on different data subsets.

[0067] The above-mentioned long short-term memory network is a deep learning model specifically used to process time series data. It can remember long-term historical information and predict future trends based on the current input and historical information. In traffic prediction, it can make full use of the time series characteristics of traffic data and learn the changing rules of traffic over time. It has strong modeling ability for traffic data with time dependence. It can capture the long-term trends, periodic changes, and short-term fluctuation characteristics of traffic, and is very suitable for time series prediction tasks such as traffic trend prediction. It can automatically learn complex patterns and features in the data without a large amount of manual feature engineering. By learning a large amount of historical traffic data and construction environment parameters, the long short-term memory network can automatically extract important features related to traffic trends.

[0068] The above-mentioned dynamic weight allocation strategy dynamically adjusts the weight of each sub-model in the final prediction result according to the current construction state, data characteristics, and the real-time performance of each sub-model. Its purpose is to ensure that in different situations, the advantages of each sub-model can be fully utilized to improve the accuracy and stability of prediction.

[0069] In one embodiment, inputting the traffic data and the current construction environment parameters into a preset traffic prediction algorithm model includes:

[0070] Perform spectral analysis on the flow rate data, extract the eigenvalue of its different frequency components, and obtain the flow rate data spectrum feature set; perform fuzzy clustering on the construction environment parameters, classify the environment parameters with similar characteristics into one category, and form multiple environment parameter clustering groups.

[0071] Input the flow rate data spectrum feature set and multiple environment parameter clustering groups into a preset flow rate prediction algorithm model.

[0072] In this embodiment, the key lies in performing specific preprocessing operations on the flow rate data and construction environment parameters and then inputting them into a preset flow rate prediction algorithm model. The purpose is to provide a more targeted and effective data representation for the model by extracting key features and performing reasonable classification, thereby improving the accuracy and reliability of the flow rate trend prediction.

[0073] Spectral analysis is a method of analyzing a signal by converting it from the time domain to the frequency domain. For flow rate data, it contains information such as flow velocity and flow rate that change over time. Through spectral analysis, the flow rate data can be decomposed into a combination of different frequency components. For example, in fluid mechanics, fluctuations of different frequencies represent different flow phenomena. The low-frequency part is related to the large-scale flow trend, while the high-frequency part reflects local turbulence or small fluctuations.

[0074] First, divide the continuous flow rate data into multiple data segments according to a certain time window. Then, perform a fast Fourier transform (FFT) or other spectral analysis methods on each data segment to convert it from the time domain to the frequency domain. In the frequency domain, calculate the eigenvalues of various frequency components. These eigenvalues can include the amplitude, phase, energy, etc. of the frequency. For example, the frequency component with a larger amplitude represents the main fluctuation mode in the flow rate data, and the energy distribution can reflect the contribution degree of different frequency components to the overall flow rate change. Summarize and organize the eigenvalues of all data segments to form the flow rate data spectrum feature set. This feature set contains the key information of the flow rate data at different frequencies and can more comprehensively describe the dynamic characteristics of the flow rate data.

[0075] By extracting the spectral eigenvalues, the key fluctuation modes and frequency components in the flow rate data can be highlighted, reducing the interference of irrelevant information and noise. This helps the model to more accurately capture the essential change law of the flow rate. At the same time, different frequency components correspond to changes on different time scales. For example, the low-frequency component reflects the long-term flow rate trend, while the high-frequency component is related to short-term fluctuations. In this way, in the subsequent prediction model, the flow rate changes on different time scales can be analyzed and predicted according to the characteristics of different frequency components, improving the adaptability of the model to various change situations.

[0076] The above-mentioned fuzzy clustering is a clustering method based on fuzzy mathematics theory. Different from traditional hard clustering methods (which clearly divide data into different categories), fuzzy clustering allows a data point to belong to multiple clusters with a certain degree of membership. Among the construction environment parameters, there are complex associations and similarities between different parameters. For example, temperature and humidity have similar change trends to a certain extent, while pressure and flow rate affect each other in some cases. Fuzzy clustering can reasonably group them into different clusters according to the similarities between these parameters.

[0077] First, select the construction environment parameters that have an important impact on the flow rate, such as temperature, humidity, pressure, etc. Then, standardize these parameters to map their values to a unified range for subsequent clustering calculations. Adopt appropriate similarity measurement methods, such as Euclidean distance, cosine similarity, etc., to measure the similarity degree between different environment parameters. Based on these similarity measurement values, construct a similarity matrix. Use a fuzzy clustering algorithm, such as the fuzzy C-means clustering (FCM) algorithm, to process the similarity matrix. In the FCM algorithm, each data point has a membership vector indicating the degree to which it belongs to different clusters. By continuously iteratively optimizing the membership vector and the cluster centers until a certain convergence condition is met. Finally, according to the iteratively optimized membership vector, group the environment parameters with similar characteristics into one category to form multiple environment parameter clustering groups. Each clustering group represents a combination of construction environment parameters with similar characteristics.

[0078] After clustering a large number of construction environment parameters, multiple representative clustering groups are formed, greatly reducing the complexity of the data. In subsequent model inputs, instead of dealing with a large number of original environment parameters, they are input in the form of clustering groups, improving the operation efficiency and interpretability of the model. Through fuzzy clustering, the potential relationships and similarity patterns between construction environment parameters can be mined. This helps to better understand the influence mechanism of the construction environment on the flow rate and provides more valuable information for flow rate prediction. For example, if it is found that the environment parameters in a certain clustering group are closely related to the change of the flow rate, then in subsequent flow rate control, the changes of these parameters can be focused on and the flow rate control strategy can be adjusted in a timely manner according to their changes.

[0079] In one embodiment, after performing weighted fusion processing on the output results of each sub-prediction model according to a preset dynamic weight assignment strategy, it further includes:

[0080] Obtain correction rules for different construction scenarios and data anomaly situations, and perform rule matching and adjustment processing on the results of the weighted fusion processing, the flow trend prediction results.

[0081] In this embodiment, after completing the weighted fusion process of the output results of each sub-prediction model, a correction rule for different construction scenarios and data anomalies is further introduced in this embodiment to finely adjust the initially obtained fusion result, and finally a more accurate and reliable flow trend prediction result is obtained.

[0082] First, analyze the characteristics of various construction scenarios and their potential impacts on the flow trend. For example, during the foundation pouring stage of a construction project, the flow demand for fluid engineering materials is relatively large and a stable flow rate needs to be maintained to ensure the pouring quality. While during the wall plastering stage, higher precision of the flow is required and dynamic adjustment needs to be made according to different parts of the wall. Based on these scenario characteristics, corresponding correction rules are formulated. For example, in the foundation pouring scenario, if the weighted fusion result shows a sign of a decreasing flow trend, but it should be stable or increasing according to the requirements of this scenario, then a rule is needed to increase the value of the prediction result, either by adding a fixed adjustment value or increasing it by a certain proportion.

[0083] Then, identify the manifestation forms of data anomalies, such as sudden large fluctuations in flow data, values of construction environment parameters exceeding the normal range, etc. For example, when a temperature sensor fails and causes the temperature data to be abnormally high or low, this will mislead the flow prediction.

[0084] Finally, establish corresponding response rules. For large fluctuations in flow data, a threshold can be set. When the fluctuation exceeds this threshold, start the smoothing processing rule, such as using the moving average method to recalculate the recent data to eliminate the influence of abnormal fluctuations. For abnormal environmental parameters, corresponding adjustment rules are formulated according to the importance of different parameters and their influencing ways. For example, when the humidity is abnormally low and causes an increase in the fluidity of the material, the prediction result needs to be corrected accordingly to take into account the actual impact of this special situation on the flow.

[0085] Furthermore, compare and match the result after the weighted fusion process with the preset correction rules one by one. This requires a matching algorithm or logical judgment mechanism that can quickly and accurately determine which rules are applicable to the current prediction result. For example, by judging the type of the current construction scenario and whether there is an abnormal flag in the detected data, etc., to screen out the relevant correction rules.

[0086] Once the applicable correction rules are determined, the results of the weighted fusion process are adjusted according to the specific content of the rules. If it is a simple numerical adjustment rule, direct addition and subtraction operations are performed according to the specified numerical value in the rule. For example, if it is required to increase the prediction result by 10% according to a certain construction scenario rule, corresponding calculations are performed on the results after weighted fusion. For complex adjustment rules, some algorithms or models need to be called again for recalculation. For example, in the smoothing processing rule for dealing with data anomalies, relevant data needs to be statistically analyzed and calculated again to obtain the corrected flow trend prediction value.

[0087] In this embodiment, by considering various complex situations in actual construction and making targeted corrections to the preliminary prediction results, the prediction errors caused by construction scenario differences and data anomalies can be effectively reduced, greatly improving the accuracy of the flow trend prediction. The flow prediction model can better adapt to different construction environments and various emergencies, improving the flexibility and adaptability of the model, and providing a more reliable decision-making basis for the flow control of flowing engineering materials. Accurate flow trend prediction results contribute to the precise control of the flow of engineering materials, thus ensuring the quality and progress of the project construction, and reducing material waste and project risks.

[0088] In one embodiment, analyzing according to the flow trend prediction result and a preset flow control strategy rule to obtain a flow control adjustment parameter, including:

[0089] Performing a trend evaluation process based on fuzzy logic on the flow trend prediction result to obtain a fuzzy linguistic variable;

[0090] Performing a structured representation based on a knowledge graph on the preset flow control strategy rule to obtain a rule knowledge network, and performing a matching and association process between the fuzzy linguistic variable and the nodes in the rule knowledge network to obtain a preliminary association result;

[0091] Based on the preliminary association result, using an optimization search mechanism based on a genetic algorithm to search for an approximate solution of the optimal flow control adjustment parameter combination scheme to obtain an intermediate adjustment parameter scheme;

[0092] Performing an optimization process on the intermediate adjustment parameter scheme to obtain the flow control adjustment parameter.

[0093] In this embodiment, fuzzy logic is a mathematical method for dealing with fuzziness and uncertainty. In the flow trend prediction result, there are some uncertainties and fuzziness. For example, flow trends such as "slowly rising" and "rapidly falling" are fuzzy concepts rather than precise numerical values. Through fuzzy logic, these fuzzy trend descriptions can be transformed into fuzzy linguistic variables.

[0094] First, define a series of fuzzy linguistic variables, such as "low", "medium", "high", etc., to represent the degree of traffic trend. Then, according to the numerical range of the traffic trend prediction results, map them to the corresponding fuzzy linguistic variables. For example, if the traffic trend prediction result is within a certain range, it is classified as the fuzzy linguistic variable "rising at medium speed".

[0095] Then, construct the preset traffic control strategy rules into the form of a knowledge graph. A knowledge graph is a method of representing knowledge in a graphical structure, where nodes represent concepts or entities, and edges represent the relationships between them. In the traffic control strategy rules, the nodes can be various traffic control parameters, construction environment conditions, traffic trend states, etc., and the edges represent the logical relationships and influence methods between them.

[0096] Match and associate the fuzzy linguistic variables obtained in the previous step with the nodes in the knowledge graph. For example, if the fuzzy linguistic variable represents a traffic trend of "rising rapidly", then find the nodes related to "rising rapidly" in the knowledge graph, such as the node "need to increase the rotation speed of the conveying equipment", etc., and establish an association. In this way, the connection between the traffic trend and the traffic control strategy rules can be initially determined.

[0097] The genetic algorithm is an optimization algorithm that simulates the process of biological evolution. It searches for the optimal solution in the search space by simulating processes such as natural selection, crossover, and mutation. In the search for traffic control adjustment parameters, various combinations of traffic control adjustment parameters are regarded as individuals, and each individual has a corresponding fitness value, indicating its performance in meeting the traffic control objectives.

[0098] Specifically, randomly generate an initial combination of traffic control adjustment parameters as the initial population. Then, calculate the fitness value of each individual according to the fitness function (usually defined based on indicators such as the error between the traffic control objective and the actual traffic). Next, through operations such as selection, crossover, and mutation, continuously evolve the population to gradually increase the fitness value. In each generation of the evolution process, retain the individuals with higher fitness values and eliminate the individuals with lower fitness values. After multiple iterations, search for an approximate solution of the optimal traffic control adjustment parameter combination scheme, that is, the intermediate adjustment parameter scheme.

[0099] Conduct further analysis and adjustment on the intermediate adjustment parameter scheme. This includes considering some limiting conditions in the actual project, such as the maximum and minimum operating parameters of the equipment, the physical properties of the materials, etc. For example, if a certain parameter in the intermediate adjustment parameter scheme exceeds the allowable range of the equipment, it needs to be adjusted.

[0100] In this embodiment, by processing the uncertainty in the traffic trend prediction results through fuzzy logic, the actual situation can be more accurately reflected, improving the adaptability and flexibility of traffic control. Using a knowledge graph to structurally represent traffic control policy rules makes the relationships between the rules clearer, facilitating quick and accurate matching and association, and improving the efficiency and accuracy of decision-making. The application of the genetic algorithm can effectively find the optimal combination scheme of traffic control adjustment parameters in a complex search space, avoiding the problem of traditional methods falling into local optimal solutions. The final tuning process can ensure that the obtained traffic control adjustment parameters not only meet the theoretical optimization requirements but also comply with the limiting conditions and performance requirements of actual engineering, improving the reliability and stability of traffic control.

[0101] In one embodiment, the step of tuning the intermediate adjustment parameter scheme to obtain the traffic control adjustment parameters includes:

[0102] Performing a fine-tuning process on the intermediate adjustment parameter scheme based on the simulated annealing algorithm;

[0103] Among them, the simulated annealing algorithm uses the intermediate adjustment parameter scheme as the initial solution, performs random perturbations in the solution space, gradually reduces the perturbation amplitude according to a preset cooling strategy, and at the same time judges whether the perturbed solution is better according to the performance evaluation index function of the traffic control system. If it is better, the solution is accepted as the final traffic control adjustment parameter;

[0104] Otherwise, accept a worse solution with a preset probability to avoid falling into a local optimum, and finally obtain the traffic control adjustment parameters after multiple iterations.

[0105] In this embodiment, the above-mentioned simulated annealing algorithm is derived from the principle of solid annealing in physics. In the algorithm, the intermediate adjustment parameter scheme is analogized to an initial state of a solid, the solution space is analogized to the set of all states of the solid, and finding the optimal traffic control adjustment parameter is equivalent to finding the structure of the solid in the lowest energy state.

[0106] Specifically, first, use the intermediate adjustment parameter scheme as the starting point of the algorithm, which is a specific position in the solution space. This position has a certain performance, corresponding to the operation effect of the traffic control system under this parameter scheme.

[0107] Then, make a random small change to the initial solution in the solution space, just like adding a small amount of energy to the solid to change its state. This perturbation can be a small increase or decrease in one or more parameters in the intermediate adjustment parameter scheme. For example, make a small increase or decrease in the rotation speed adjustment parameter of the conveying equipment, or make a fine adjustment to the valve opening adjustment parameter.

[0108] In the simulated annealing algorithm, the cooling process is a process of gradually reducing the "temperature" of the system, where the "temperature" is a parameter that controls the search behavior of the algorithm. As the number of iterations increases, the "temperature" is gradually reduced according to a preset cooling strategy. The cooling strategy is usually designed based on the number of iterations of the algorithm or other relevant metrics. For example, an exponential cooling strategy can be adopted, that is, as the number of iterations increases, the "temperature" gradually decreases according to a certain exponential function form. The purpose of reducing the "temperature" is to gradually reduce the amplitude of random perturbations, so that the algorithm is more likely to accept better solutions in the later stage, thus realizing the transition from global search to local fine search.

[0109] For a flow control system, the performance evaluation index function can be designed according to the actual flow control objectives and the operating characteristics of the system. Common metrics include the deviation between the flow rate and the target flow rate, the stability of the flow rate, the energy consumption of the system, etc. For example, a performance evaluation index function can be the sum of the squares of the deviation between the flow rate and the target flow rate plus a penalty term related to the energy consumption of the system.

[0110] After perturbing the intermediate adjustment parameter scheme, the value of the new parameter scheme under the performance evaluation index function is calculated to determine whether the solution is better. If the value of the performance evaluation index function corresponding to the new parameter scheme is smaller, it means that it performs better in achieving the flow control objective, that is, a better solution.

[0111] When the perturbed solution is judged to be better, directly accept this solution as the new current solution, that is, update the intermediate adjustment parameter scheme to this better parameter combination. This is a natural choice that conforms to the optimization objective and helps the algorithm search in a better direction. During the search process, in order to prevent the algorithm from falling into a local optimal solution, even if the perturbed solution performs poorly, it is also accepted with a preset probability.

[0112] This probability mentioned above is usually related to the current "temperature". The higher the "temperature", the greater the probability of accepting a worse solution. In this way, in the early stage of the algorithm, the solution space can be explored more widely, increasing the possibility of finding the global optimal solution. For example, when the "temperature" is high, accept a worse solution with a probability of 30%; as the "temperature" decreases, the probability of accepting a worse solution gradually decreases, such as decreasing to less than 5% in the later stage.

[0113] In this embodiment, the simulated annealing algorithm can find a good balance between global search and local fine search during the search process. By accepting worse solutions in the early stage, it maintains the diversity of the search and avoids falling into local optima; in the later stage, it focuses more on the fine search of local areas to find more accurate optimal solutions. For different types of flow control systems and complex actual engineering environments, the simulated annealing algorithm can flexibly adapt to various optimization objectives and constraint conditions according to the performance evaluation index function. Whether in simple flow control scenarios or complex and changeable engineering practices, it can effectively find appropriate flow control adjustment parameters. The algorithm has relatively little dependence on the initial solution. Even if the intermediate adjustment parameter scheme is not very ideal, through continuous iteration and optimization, it can gradually find better solutions. This makes the entire technical solution have good robustness and stability in practical applications.

[0114] In one embodiment, collecting the flow rate data of the flowing engineering material and the current construction environment parameters includes:

[0115] Using a flow sensing device based on a microfluidic chip to perform multi-point real-time monitoring on the flow rate data to obtain the flow rate data;

[0116] Collecting the construction environment parameters through a distributed wireless sensor network.

[0117] In this embodiment, during the data collection stage, a flow sensing device based on a microfluidic chip is adopted, which can perform real-time and accurate monitoring on the flow rate data at multiple key positions, quickly and accurately obtain the dynamic change information of the flow rate. At the same time, with the help of a distributed wireless sensor network, which is widely distributed at the construction site, comprehensively collect construction environment parameters such as temperature and humidity. Each sensor node works collaboratively to converge and process the collected data, providing accurate and comprehensive basic data for subsequent flow rate prediction and control, ensuring that the flow rate control method can make accurate decisions and adjustments based on the actual situation.

[0118] Referring to Figure 3 , in another embodiment of the present invention, a flow control system for flowing engineering materials is further provided, including:

[0119] A collection module for collecting the flow rate data of the flowing engineering material and the current construction environment parameters;

[0120] A processing module for inputting the flow rate data and the current construction environment parameters into a preset flow rate prediction algorithm model for arithmetic processing to obtain a flow rate trend prediction result;

[0121] An analysis module for analyzing according to the flow rate trend prediction result and a preset flow rate control strategy rule to obtain a flow rate control adjustment parameter;

[0122] An adjustment module, configured to adjust the operating parameters of the conveying equipment for the flowable engineering material based on the flow control adjustment parameter, so as to obtain an optimized flow output state.

[0123] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to that described in the above method embodiment, and details will not be repeated here.

[0124] Refer to Figure 4 , in an embodiment of the present invention, a computer device is further provided. The computer device may be a server, and its internal structure may be as Figure 4 shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0125] Those skilled in the art can understand that Figure 4 the structure shown in

[0126] is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0127] In summary, the present invention provides a flow control method and system for flowing engineering materials in an embodiment, including: collecting flow data of flowing engineering materials and current construction environment parameters; inputting the flow data and current construction environment parameters into a preset flow prediction algorithm model for arithmetic processing to obtain a flow trend prediction result; analyzing according to the flow trend prediction result and a preset flow control strategy rule to obtain a flow control adjustment parameter; based on the flow control adjustment parameter, adjusting the operating parameters of the conveying equipment for flowing engineering materials to obtain an optimized flow output state. In the present invention, by combining the flow data of engineering materials and current construction environment parameters, the flow trend prediction result is predicted, and then the flow control adjustment parameter is generated, so as to improve the accuracy and stability of process control. The problems of unstable flow, material waste or unqualified construction quality are avoided.

[0128] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, storage, database or other medium provided by the present invention and used in the embodiments can include non-volatile and / or volatile memories. The non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. The volatile memory can include random access memory (RAM) or an external cache. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0129] It should be noted that in this article, the terms "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, device, article or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, device, article or method including that element.

[0130] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A flow control method for fluid engineering materials, characterized in that: The following steps are involved: Collect flow data of mobile engineering materials and current construction environment parameters; The flow data and the current construction environment parameters are input into a preset flow prediction algorithm model for calculation and processing to obtain a flow trend prediction result; Analyze the flow trend prediction result and the preset flow control strategy rules to obtain flow control adjustment parameters; Based on the flow control adjustment parameters, the operating parameters of the conveying equipment of the fluid engineering material are adjusted to obtain an optimized flow output state; The flow control adjustment parameters are obtained by analyzing the flow trend prediction results and the preset flow control strategy rules, including: Performing trend evaluation processing based on fuzzy logic on the flow trend prediction result to obtain fuzzy language variables; Performing a structured representation based on a knowledge graph on the preset traffic control strategy rules to obtain a rule knowledge network, matching and associating the fuzzy language variables with the nodes in the rule knowledge network to obtain a preliminary association result; Based on the preliminary correlation results, an optimization search mechanism based on a genetic algorithm is used to search for an approximate solution of the optimal combination of flow control adjustment parameters and obtain an intermediate adjustment parameter solution; The intermediate adjustment parameter scheme is optimized to obtain the flow control adjustment parameter.

2. The flow control method of fluid engineering material according to claim 1, characterized in that: The flow data and the current construction environment parameters are input into a preset flow prediction algorithm model for calculation and processing to obtain a flow trend prediction result, including: Input the flow data and current construction environment parameters into a preset flow prediction algorithm model; wherein the flow prediction algorithm model includes a first sub-model based on support vector regression, a second sub-model based on random forest, and a third sub-model based on long short-term memory network; The output results of each sub-prediction model are weighted and fused according to the preset dynamic weight allocation strategy to obtain the traffic trend prediction result.

3. The flow control method of fluid engineering material according to claim 2, characterized in that: The step of inputting the flow data and the current construction environment parameters into a preset flow prediction algorithm model includes: Performing spectrum analysis on the flow data, extracting characteristic values ​​of different frequency components, and obtaining a spectrum characteristic set of the flow data; performing fuzzy clustering on the construction environment parameters, classifying environment parameters with similar characteristics into one category, and forming multiple environment parameter clustering groups; The traffic data spectrum feature set and multiple environmental parameter clustering groups are input into a preset traffic prediction algorithm model.

4. The flow control method of fluid engineering material according to claim 2, characterized in that: After the output results of each sub-prediction model are weighted and fused according to a preset dynamic weight allocation strategy, the method further includes: Obtain correction rules for different construction scenarios and data anomalies, perform rule matching and adjustment processing on the results of weighted fusion processing, and obtain the flow trend prediction results.

5. The flow control method of fluid engineering material according to claim 1, characterized in that: The step of optimizing the intermediate adjustment parameter scheme to obtain the flow control adjustment parameter includes: Perform fine adjustment on the intermediate adjustment parameter scheme based on simulated annealing algorithm; Among them, the simulated annealing algorithm uses the intermediate adjustment parameter scheme as the initial solution, performs random perturbations in the solution space, and gradually reduces the perturbation amplitude according to the preset cooling strategy. At the same time, it judges whether the perturbed solution is better according to the performance evaluation index function of the flow control system. If it is better, the solution is accepted as the final flow control adjustment parameter; Otherwise, the worse solution is accepted with a preset probability, and the final flow control adjustment parameters are obtained after multiple iterations.

6. The flow control method of fluid engineering material according to claim 1, characterized in that: The collected flow data of mobile engineering materials and current construction environment parameters include: The flow data is obtained by using a flow sensor device based on a microfluidic chip to monitor the flow data at multiple points in real time; The construction environment parameters are collected through distributed wireless sensor networks.

7. A flow control system for fluid engineering materials, characterized in that: include: The collection module is used to collect the flow data of mobile engineering materials and the current construction environment parameters; A processing module, used for inputting the flow data and current construction environment parameters into a preset flow prediction algorithm model for calculation and processing to obtain a flow trend prediction result; An analysis module, used to analyze the flow trend prediction result and the preset flow control strategy rules to obtain flow control adjustment parameters; An adjustment module, used to adjust the operating parameters of the conveying equipment of the fluid engineering material based on the flow control adjustment parameters to obtain an optimized flow output state; The flow control adjustment parameters are obtained by analyzing the flow trend prediction results and the preset flow control strategy rules, including: Performing trend evaluation processing based on fuzzy logic on the flow trend prediction result to obtain fuzzy language variables; Performing a structured representation based on a knowledge graph on the preset traffic control strategy rules to obtain a rule knowledge network, performing matching and associating processing on the fuzzy language variables and the nodes in the rule knowledge network to obtain a preliminary association result; Based on the preliminary correlation results, an optimization search mechanism based on a genetic algorithm is used to search for an approximate solution of the optimal combination of flow control adjustment parameters and obtain an intermediate adjustment parameter solution; The intermediate adjustment parameter scheme is optimized to obtain the flow control adjustment parameter.

8. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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