Concrete pipeline production energy consumption real-time monitoring method and system based on Internet of Things

Abstract

The invention provides a concrete pipeline production energy consumption real-time monitoring method and system based on the Internet of Things, and relates to the technical field of industrial Internet of Things energy consumption management, and the method comprises the steps: collecting the material, equipment and environment data of concrete pipeline production based on the Internet of Things, building a reference model, and obtaining a theoretical optimal energy consumption curve. And then bearing friction infrasonic waves, concrete crack energy spectrums and steam condensation film thickness distribution are collected in the links of stirring, vibrating and curing correspondingly. And when the actual energy consumption deviates from the theoretical curve, reconstructing an energy path of the curing thermodynamic field according to the thickness of the condensation film, positioning an abnormal process and starting compensation. Integrating the compensated infrasonic wave, energy spectrum and film thickness data to form a comprehensive model, and detecting abrupt change of the Betti number; and if continuous exceeding of unit energy consumption occurs at the same time, tracing the root of the process-level problem and generating an energy consumption abnormity diagnosis report, so that accurate monitoring, dynamic compensation and intelligent diagnosis of the energy consumption of the whole process of concrete pipeline production can be realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial internet of things energy consumption management, and in particular to a concrete pipe production energy consumption real-time monitoring method and system based on the internet of things. BACKGROUND

[0002] In the whole process of mixing, vibrating and curing of concrete pipe production, the problem of high energy consumption is prominent, especially in the aspects of motor driving, mechanical vibration and steam heating. The current industry urgently needs to realize the collaborative monitoring of energy consumption in the whole process, dynamically optimize the energy efficiency chain by real-time collection of material proportioning, equipment operation parameters and environmental data, accurately locate the root cause of abnormal energy consumption, and trigger adaptive compensation. Finally, it needs to rely on the theoretical optimal model to realize dynamic parameter adjustment, reduce the comprehensive energy consumption per unit product, and respond to the intelligent manufacturing upgrading demand under the "double carbon" target.

[0003] The existing representative scheme adopts an industrial internet of things energy efficiency monitoring platform such as the Internet of Things (IoT) system, collects current, power and temperature data through an intelligent gateway connected to PLC devices, and uploads them to the cloud to generate visual dashboards. The system can monitor the energy consumption status of the device, implement threshold alarm and remote start-stop control, and preliminarily replace manual inspection, thereby shortening the fault response cycle.

[0004] However, this scheme has serious limitations: first, the monitoring dimension is limited to electrical parameters, and does not cover key process data such as friction infrasound waves, vibrating crack energy spectrum, and curing condensation film thickness, resulting in energy consumption analysis being disconnected from actual production conditions; second, it lacks a baseline energy consumption model and theoretical optimal curve support, making it impossible to dynamically identify deviations and even more impossible to reconstruct energy flow or initiate compensation; third, fault diagnosis relies on device-level single-point alarms and does not integrate multi-process data to build a propagation chain. When the number of mutations and energy consumption exceed the threshold, it is impossible to associate the causal relationship between vibrating cracks and steam consumption, and it is also difficult to automatically trace the process-level root cause, and manual intervention is still required for troubleshooting. SUMMARY

[0005] The present application aims to provide a concrete pipe production energy consumption real-time monitoring method and system based on the internet of things to solve the problems of energy consumption anomaly tracing difficulty and dynamic compensation lag caused by data fragmentation and model absence in the prior art.

[0006] To solve the above technical problems, in a first aspect, the present application provides a concrete pipe production energy consumption real-time monitoring method based on the internet of things, comprising: establishing a baseline energy consumption model according to the collected material proportioning data, equipment operation data and environmental data in the concrete pipe production process; A theoretical optimal energy consumption curve is derived from the benchmark energy consumption model, and the theoretical optimal energy consumption curve includes a critical value of electromagnetic conversion efficiency, a boundary range of mechanical impact consumption, and a coefficient of phase change thermal resistance; According to the theoretical optimal energy consumption curve, the friction infrasound characteristics of the stirring motor bearing are collected in the stirring link, the crack energy release spectrum inside the concrete is monitored in the vibrating link, and the thickness distribution of the steam condensation film is measured in real time in the curing link; According to the thickness distribution of the steam condensation film, an energy flow path of the thermodynamic field in the curing link is generated, when the actual energy consumption curve deviates from the theoretical optimal energy consumption curve, the energy flow path of the curing thermodynamic field is reconstructed, and the abnormal process and the starting compensation operation are found out; The friction infrasound characteristics, the crack energy release spectrum, and the thickness distribution of the steam condensation film after the compensation operation are integrated into a comprehensive data model, and the mutation of the Betti number is detected according to the comprehensive data model, when the Betti number mutation and the continuous over-standard unit energy consumption occur at the same time, the energy consumption problem root of the process level is traced, and an energy consumption abnormality diagnosis report is generated according to the tracing result.

[0007] Optionally, the friction infrasound characteristics, the crack energy release spectrum, and the thickness distribution of the steam condensation film after the compensation operation are integrated into a comprehensive data model, and the mutation of the Betti number is detected according to the comprehensive data model, when the Betti number mutation and the continuous over-standard unit energy consumption occur at the same time, the energy consumption problem root of the process level is traced, and an energy consumption abnormality diagnosis report is generated according to the tracing result, including: The friction infrasound characteristics, the crack energy release spectrum, and the thickness distribution of the steam condensation film after the compensation operation are input into a data fusion mechanism to construct a comprehensive data model; The Betti number mutation detection operation is performed through the comprehensive data model, and the Betti number mutation value of the connection relationship and the number of cavities in the model topology structure is calculated; When the Betti number mutation value and the continuous over-standard unit energy consumption occur at the same time, the energy consumption problem root tracing operation is performed in the stirring link, the vibrating link, and the curing link, and an energy consumption abnormality diagnosis report is generated according to the tracing result.

[0008] Optionally, the energy consumption problem root tracing operation is performed in the stirring link, the vibrating link, and the curing link, and an energy consumption abnormality diagnosis report is generated according to the tracing result, including: In the stirring link, the difference between the actual conversion efficiency of electric energy and the critical value of electromagnetic conversion efficiency in the theoretical optimal energy consumption curve is calculated as the electric energy conversion loss value according to the frequency distribution characteristics of the friction infrasound characteristics; In the vibrating link, the increment of the mechanical energy dissipation exceeding the boundary range is calculated as the mechanical energy dissipation increment according to the strength-time spectrum of the crack energy release spectrum and the boundary range of the mechanical impact consumption in the theoretical optimal energy consumption curve; In the curing link, the deviation value of the actual heat transfer resistance and the ideal coefficient is calculated as the heat loss exceeding value according to the thickness distribution of the steam condensation film and the coefficient of the phase change thermal resistance in the theoretical optimal energy consumption curve; The generated energy consumption anomaly diagnosis report includes an energy consumption anomaly diagnosis report labeled with an abnormal process position and an exceeding degree based on the quantitative results of the electric energy conversion loss value, the mechanical energy dissipation increment and the heat loss exceeding value.

[0009] Optionally, the energy flow path of the thermodynamic field in the curing link is generated according to the thickness distribution of the steam condensation film, when the actual energy consumption curve deviates from the theoretical optimal energy consumption curve, the energy flow path of the curing thermodynamic field is reconstructed, and the abnormal process and the starting compensation operation are found out, including: Based on the thickness distribution of the steam condensation film, the real-time transfer direction sequence and the energy intensity level of the heat transferred from the steam source to the concrete are calculated to generate an initial energy flow path; When the actual energy consumption curve deviates from the theoretical optimal energy consumption curve is monitored, the real-time transfer direction sequence and the energy intensity level are recalculated in combination with the thickness distribution of the current steam condensation film, the initial energy flow path is reconstructed, and a reconstructed energy flow path is generated; The difference between the initial energy flow path and the reconstructed energy flow path is compared to identify the abnormal process causing the deviation, and the abnormal process is a steam supply control process or a temperature maintenance process; The compensation operation is started for the abnormal process to automatically adjust the steam valve opening degree or the heater power parameter to correct the energy flow path.

[0010] Optionally, the real-time transfer direction sequence and the energy intensity level of the heat transferred from the steam source to the concrete are calculated based on the thickness distribution of the steam condensation film to generate an initial energy flow path, including: The thickness measurement values of the space coordinate points are extracted based on the thickness distribution of the steam condensation film, and the area where the thickness change between adjacent coordinate points exceeds the normal fluctuation range is identified as a thermal resistance mutation area; A basic transfer path is generated in the thickness continuously decreasing direction from the steam source, a bypass path is generated in the thermal resistance mutation area, and the basic transfer path and the bypass path are integrated to form a real-time direction sequence of heat transfer; According to the thickness measurement value, a thickness characteristic region is divided, a small thickness region is marked as a high-intensity energy level, a moderate thickness region is marked as a medium-intensity energy level, and a large thickness region is marked as a low-intensity energy level, and the energy intensity level is marked in segments along a real-time direction sequence; The real-time transmission direction sequence and the energy intensity level are mapped into a weighted directed graph structure, and a continuous transmission path from a steam source to a concrete surface is generated based on the weighted directed graph structure as an initial energy flow path.

[0011] Optionally, according to the theoretical optimal energy consumption curve, the friction infrasound characteristics of the stirring motor bearing are collected at the stirring link, the crack release energy spectrum inside the concrete is monitored at the vibrating link, and the thickness distribution of the steam condensation film is measured in real time at the curing link, including: Based on the electromagnetic efficiency critical value in the theoretical optimal energy consumption curve, the bearing friction sensitive period of the stirring link is located, and the infrasound signal of the stirring motor bearing is collected using an infrasound sensor in the bearing friction sensitive period, and the frequency distribution characteristics of the infrasound signal are extracted as the friction infrasound characteristics; Based on the energy loss boundary range of mechanical impact consumption in the theoretical optimal energy consumption curve, the crack high-occurrence interval of the vibrating link is located, and the mechanical wave signal released by the crack is captured using a concrete internal vibration sensor in the crack high-occurrence interval, and the distribution atlas of the signal intensity change with time of the mechanical wave signal is analyzed as the crack release energy spectrum; Based on the heat transfer resistance coefficient in the theoretical optimal energy consumption curve, the significant area of the steam film thickness change in the curing link is located, the thickness measurement value of each spatial point on the surface of the steam condensation film is obtained using an infrared thickness scanner in the significant area, and the thickness distribution of the thickness change with the spatial position is generated based on the thickness measurement value.

[0012] Optionally, the theoretical optimal energy consumption curve is derived through the reference energy consumption model, and the theoretical optimal energy consumption curve includes a critical value of electromagnetic conversion efficiency, a boundary range of mechanical impact consumption, and a coefficient of phase change thermal resistance, including: An optimal path calculation operation is performed through the reference energy consumption model, and under the optimal combination conditions of material proportioning data, equipment operation data, and environmental data, a continuous path with the minimum energy consumption in the whole production process is derived; In the continuous path, a parameter determination operation is performed through the reference energy consumption model, and the critical value of electromagnetic efficiency of electric energy to mechanical energy conversion, the energy loss boundary range of mechanical impact consumption, and the thermal resistance coefficient of material phase change process are determined; The electromagnetic efficiency critical value, the energy loss boundary range, and the thermal resistance coefficient are integrated through the reference energy consumption model to generate a theoretical optimal energy consumption curve.

[0013] In a second aspect, the application provides a real-time monitoring system for energy consumption in concrete pipe production based on Internet of Things, comprising: a collecting module for establishing a benchmark energy consumption model according to collected material proportioning data, equipment operation data and environmental data in the production process of the concrete pipe; a processing module for deriving a theoretical optimal energy consumption curve from the benchmark energy consumption model, the theoretical optimal energy consumption curve including a critical value of electromagnetic conversion efficiency, a boundary range of mechanical impact consumption and a coefficient of phase change thermal resistance; an executing module for collecting friction infrasound characteristics of a stirring motor bearing in the stirring link, monitoring crack energy release spectrum inside the concrete in the vibrating link and measuring thickness distribution of a steam condensation film in the curing link according to the theoretical optimal energy consumption curve; a compensating module for generating an energy flow path of a thermodynamic field in the curing link according to the thickness distribution of the steam condensation film, reconstructing the energy flow path of the curing thermodynamic field and finding out abnormal processes and starting compensation operation when an actual energy consumption curve deviates from the theoretical optimal energy consumption curve; a generating module for integrating the friction infrasound characteristics, the crack energy release spectrum and the thickness distribution of the steam condensation film processed by the compensation operation into a comprehensive data model, detecting mutation of the Bem number according to the comprehensive data model, tracing the root cause of energy consumption problems of the process level when the Bem number mutation and continuous over-standard unit energy consumption occur simultaneously, and generating an energy consumption anomaly diagnosis report according to the tracing result.

[0014] In a third aspect, the application provides an electronic device, comprising: a memory for storing a computer program; a processor for executing the computer program to implement the steps of the real-time monitoring method for energy consumption in concrete pipe production based on Internet of Things as described in the first aspect.

[0015] In a fourth aspect, the application provides a computer readable storage medium having a computer program stored therein, the computer program being executable by a processor to implement the steps of the real-time monitoring method for energy consumption in concrete pipe production based on Internet of Things as described in the first aspect.

[0016] The concrete pipe production energy consumption real-time monitoring method based on the Internet of Things provided in the application generates a theoretical optimal energy consumption curve containing an electromagnetic efficiency critical value, a mechanical impact boundary and a phase change thermal resistance coefficient by establishing a benchmark energy consumption model integrating material proportioning, equipment operation and environmental data, guiding accurate collection of friction infrasound waves in the mixing link, crack energy spectrum in the vibrating link and steam condensation film thickness in the curing link; when actual energy consumption deviates from the theoretical curve, the curing thermodynamics field energy path is dynamically reconstructed according to the condensation film thickness, abnormal procedures are located and compensation is triggered; finally, a comprehensive model is constructed by integrating the compensated multi-source data, cross-procedure fault tracing is realized through correlation analysis of the catastrophe number mutation and energy consumption exceeding the standard, a diagnostic report is automatically generated, and closed-loop optimization and abnormal root self-healing of the concrete pipe production whole-process energy consumption are achieved.

[0017] Further, the friction infrasound wave characteristics, crack energy release spectrum and steam condensation film thickness input data are fused into a data fusion mechanism to construct a comprehensive data model, and catastrophe number mutation detection is performed through the model; when the catastrophe number mutation value and unit energy consumption continuously exceed the standard at the same time, the energy consumption problem root of the mixing, vibrating and curing links is traced across procedures, and a diagnostic report is automatically generated. The scheme breaks through the limitation of traditional single-point alarm by fusing multi-procedure compensation data and catastrophe number topology analysis, accurately captures cross-link abnormal correlation; the root tracing is triggered by the double criteria of "catastrophe number mutation + continuous energy consumption exceeding the standard", the dynamic decoupling and self-diagnosis of the fault propagation path are realized, and the accuracy and response efficiency of energy consumption abnormal positioning are significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0019] Figure 1 A flowchart of a concrete pipe production energy consumption real-time monitoring method based on the Internet of Things provided by the embodiments of the present application; Figure 2 A specific flowchart of a concrete pipe production energy consumption real-time monitoring method based on the Internet of Things provided by the embodiments of the present application; Figure 3 A specific implementation scenario diagram of a concrete pipe production energy consumption real-time monitoring method based on the Internet of Things provided by the embodiments of the present application; Figure 4 A specific implementation diagram of a concrete pipe production energy consumption real-time monitoring method based on the Internet of Things provided by the embodiments of the present application; Figure 5A structural schematic diagram of a concrete pipe production energy consumption real-time monitoring system based on an Internet of Things is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0020] In the field of energy consumption monitoring of the whole process (mixing-vibrating-curing) of concrete pipe production, although the existing industrial Internet of Things energy efficiency platform realizes basic data collection and alarm, it has three fundamental defects: first, the monitoring dimension is limited to electrical parameters such as current and temperature, completely ignoring key process data such as friction infrasound waves, crack energy spectrum, and condensation film thickness, resulting in that the energy consumption analysis is divorced from the actual production state; second, it lacks a benchmark energy consumption model and a theoretical optimal curve support, and only relies on simple threshold alarm, which cannot dynamically identify energy efficiency deviations such as electromagnetic conversion efficiency attenuation, mechanical impact overrun, or phase change thermal resistance anomaly; third, fault diagnosis is in a single process fragmentation state, which cannot associate the causal relationship between the sudden change of the Beihao number and the energy consumption exceeding the standard across processes, resulting in that the abnormal root cause tracing relies on manual experience, with high misjudgment rate and response lag.

[0021] To solve the above problems, the present application provides a concrete pipe production energy consumption real-time monitoring method based on an Internet of Things, the core of which is: constructing a benchmark energy consumption model through material proportioning, equipment operation, and environmental data, generating a theoretical optimal curve containing electromagnetic efficiency critical value, mechanical impact boundary, and phase change thermal resistance coefficient; synchronously collecting mixing infrasound waves, vibrating crack energy spectrum, and curing condensation film thickness according to the curve, and reconstructing the thermodynamic field energy path to trigger compensation when the energy consumption deviates; finally, constructing a comprehensive model by fusing the compensated data, and realizing cross-process fault tracing with the dual criteria of "sudden change of Beihao number + continuous energy consumption exceeding the standard". This method completely overcomes the limitations of the background technology: the combination of multi-dimensional process data collection and benchmark model enables energy consumption monitoring to deepen from surface statistics to energy efficiency mechanism level; the dynamic energy path reconstruction mechanism realizes real-time self-healing of deviations; the introduction of Beihao number topology analysis and dual criteria makes the implicit cross-process fault chain explicit for the first time, automatically outputs accurate diagnosis reports, and promotes the energy consumption management of concrete production to transition from "passive alarm" to "active optimization-self-proven tracing".

[0022] In order to enable personnel in the technical field to better understand the present application scheme, the present application will be further described in detail below in combination with the drawings and specific embodiments. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of the present application.

[0023] The core of the present application is to provide a concrete pipe production energy consumption real-time monitoring method based on an Internet of Things, and a flowchart of one specific embodiment thereof is as shown in Figure 1 The method comprises: S101, according to the collected material proportioning data, equipment operation data and environmental data in the concrete pipe production process, a benchmark energy consumption model is established; In the above scheme, the material proportioning data refers to the proportion combination of each raw material component in the concrete pipe production process, including the mixing ratio parameters of cement, sand, water and other materials, which is used to quantify the influence of raw material characteristics on energy consumption; the equipment operation data refers to the real-time working state parameters of core equipment such as mixers and vibrators, including motor power, speed and vibration frequency information, which is used to reflect the mechanical energy conversion efficiency; the environmental data refers to the external condition parameters such as temperature and humidity in the production site, including the thermodynamic environment fluctuation information in the curing kiln, which is used to evaluate the interaction between environment and energy consumption; the benchmark energy consumption model refers to a dynamic calculation framework constructed based on the above three types of data, which generates a theoretical energy consumption reference standard adaptable to different production conditions by analyzing the energy consumption rules in the historical optimal production records, and provides a scientific benchmark for real-time energy consumption monitoring.

[0024] In the embodiment of the application, first, through the sensor network installed in the mixer, the vibrating device and the curing kiln, three types of original data in the concrete pipe production, i.e. material proportioning data, equipment operation data and environmental data, are continuously collected, such as a production record showing that the cement proportion is 38%, the mixer power is 21kW and the environmental temperature is 58℃. Second, the original data is automatically cleaned to eliminate invalid records caused by equipment abnormalities, and the effective data is retained and stored according to the process. Then, the K-means clustering algorithm is used to group the cleaned data according to similar characteristics: input material proportioning coefficient, equipment power fluctuation range and environmental temperature and humidity parameters, and output typical production mode grouping, such as identifying the "high cement proportion-low temperature mode" group, which contains all data with cement proportion >40% and temperature <50℃. Then, a benchmark energy consumption model is established for each data group: through multiple linear regression analysis, the formula is constructed, where represents the benchmark energy consumption value, M is the material influence coefficient, D is the equipment influence coefficient, E is the environmental influence coefficient, is a weight parameter, and C is a constant term. For example, inputting the historical data of the "high cement-low temperature" group with cement 42% and temperature 48℃, the benchmark energy consumption model calculates that the reasonable energy consumption should be 920kWh. Finally, the benchmark energy consumption model is verified with new production data: if the model outputs 905kWh when inputting cement proportion 41% and temperature 49℃, and the actual energy consumption is 915kWh with an error within 5%, then it is confirmed that the benchmark energy consumption model can be used for subsequent monitoring.

[0025] In practical applications, in the implementation of a certain concrete pipe production plant, first, the material proportioning data is collected through the weight sensor installed in the mixing system, such as collecting the C30 grade concrete formula: the proportion of cement, sand, stone and water is 1:1.8:3.2:0.45, at the same time, the equipment operation data is recorded by the motor monitoring module, such as recording that the mixer power fluctuates between 19.5-23.5kW, and the temperature and humidity sensors are deployed in the curing workshop to obtain environmental data, such as temperature interval 58-63℃, humidity 70-75%; secondly, the data platform automatically cleans the abnormal records, and eliminates the data points of a certain power drop to 5kW caused by power grid fluctuation, and stores the effective data of 30 consecutive days according to the process; then, two typical production modes are identified by using clustering algorithm, which are "high sand stone ratio-high temperature mode" (sand stone ratio>80% and temperature>60℃) and "low cement ratio-high humidity mode" (cement ratio<15% and humidity>78%); subsequently, a benchmark energy consumption model is established for "high sand stone ratio-high temperature mode": input historical data: sand stone ratio 82%, temperature 62℃, calculate benchmark energy consumption by formula , wherein M=82 is the sand stone influence value, D=21.5 is the average power value, and E=5 is the temperature coefficient, and the theoretical value 218kWh is obtained; finally, when verifying the model, input new data: sand stone ratio 83%, temperature 61℃, output energy consumption value 221kWh, which has an error of less than 5% compared with the actual value 228kWh, confirming that the benchmark model can be used for subsequent real-time monitoring.

[0026] The overall scheme of S101 above, by fusing material proportioning, equipment state and environmental data, constructs a dynamic benchmark energy consumption model, upgrades the traditional static energy consumption monitoring to adaptive analysis: the model automatically identifies the coupling law of raw material characteristics, equipment performance and environmental changes, and generates a reasonable energy consumption interval in real time, providing a scientific reference benchmark for subsequent accurate monitoring, and completely solving the misjudgment problem caused by manual preset threshold.

[0027] S102, obtaining a theoretical optimal energy consumption curve through the benchmark energy consumption model, the theoretical optimal energy consumption curve comprising a critical value of electromagnetic conversion efficiency, a boundary range of mechanical impact consumption, and a coefficient of phase change thermal resistance; Optionally, step S102 can specifically include the following steps: S1021, performing an optimal path calculation operation through the benchmark energy consumption model, under the optimal combination conditions of material proportioning data, equipment operation data and environmental data, deriving a continuous path with the minimum energy consumption in the whole production process; S1022, performing a parameter determination operation through the benchmark energy consumption model in the continuous path, determining the critical value of electromagnetic efficiency of electric energy to mechanical energy conversion, the boundary range of energy loss of mechanical impact consumption, and the thermal resistance coefficient of material phase change process; S1023, integrating the electromagnetic efficiency threshold, energy loss boundary range and thermal resistance coefficient through the benchmark energy consumption model to generate a theoretical optimal energy consumption curve.

[0028] In the above scheme, the theoretical optimal energy consumption curve refers to an energy consumption dynamic reference trajectory generated based on the benchmark energy consumption model, which contains three types of core parameters: electromagnetic conversion efficiency threshold, mechanical impact consumption boundary range and phase change thermal resistance coefficient. By integrating the variation of parameters on the time axis, a dynamic judgment benchmark is provided for real-time monitoring of the whole process energy consumption. The electromagnetic conversion efficiency threshold refers to the minimum efficiency threshold allowed when converting electrical energy into mechanical energy, which contains the ratio relationship between input power and output mechanical power of the motor, and can be used to judge the abnormal loss of electrical energy in the mixing link. The mechanical impact consumption boundary range refers to the reasonable fluctuation interval of energy loss when the vibrating equipment impacts the concrete, which contains the correlation characteristics of impact energy and material stiffness, and can be used to identify the mechanical energy waste in the vibrating link. The phase change thermal resistance coefficient refers to the standard reference value of heat transfer resistance in steam curing, which contains the conversion relationship between condensation film thickness and thermal conductivity, and can be used to evaluate the heat transfer efficiency in the curing link.

[0029] In the embodiments of the present application, first, the historical optimal data combination is screened in the benchmark energy consumption model through the dynamic programming algorithm in step S1021. The energy consumption nodes of the "mixing-vibrating-curing" whole process under these combinations are calculated, and the Dijkstra path search algorithm is used to connect the nodes of each process to generate a continuous path with the minimum energy consumption from raw material input to finished product output. For example, 5 optimal combinations are selected from 50 historical data, and through path calculation, it is found that when connecting "mixing power 21kW (node A) → vibrating frequency 115Hz (node B) → curing temperature 62℃ (node C)", the total energy consumption is only 765kWh, which is lower than the average value of 820kWh of other paths, and A→B→C is determined as the continuous path with the minimum energy consumption.

[0030] Then, based on the continuous path with the minimum energy consumption, the key parameters are quantified in step S1022: in the mixing link, the electromagnetic efficiency threshold is calculated through the electromagnetic conversion formula, and the calculation formula is as follows: , wherein is the input electric power, is the output mechanical power, for example, when the input 22kW electric power is output 18.5kW mechanical power, ; in the vibrating link, the mechanical energy loss boundary range is calculated according to the law of conservation of energy, and the calculation formula is as follows: , wherein k is the concrete stiffness coefficient, A is the amplitude, and f is the frequency, for example, when k=1200, A=0.005, and f=100, , the boundary range is set to 1.2-1.8J; in the maintenance link, the thermal resistance coefficient of the material phase change process is calculated according to the Fourier heat conduction law, and the calculation formula is as follows: , wherein is the condensation film thickness, is the thermal conductivity, for example , the thermal resistance coefficient .

[0031] Finally, the electromagnetic efficiency critical value, mechanical energy loss boundary range, and thermal resistance coefficient determined in step S1022 are arranged according to the process time axis through step S1023. The three spline interpolation algorithms are connected to each parameter key point: the electromagnetic efficiency is 84% at the beginning of stirring, the mechanical impact boundary is 1.8J at the peak of vibration, and the thermal resistance is 0.15m²K / W at the end of maintenance, to generate a smooth curve. For example, in the 0-10 minute stirring section, the curve decreases from 84% to 82%; in the 10-20 minute vibration section, the impact energy consumption increases from 1.2J to 1.8J; in the 20-60 minute maintenance section, the thermal resistance is stable at 0.15m²K / W.

[0032] In practical application, in a certain concrete pipeline factory, first, the historical optimal 10 data combinations are screened out through the benchmark model, such as cement ratio 37.2%±0.5%, stirring power 20.3kW±0.8, and maintenance temperature 60.5℃±1. The path optimization method is used to calculate that when the process connection of medium-speed stirring (power 21kW)→low-frequency vibration (impact energy 1.4J)→constant temperature maintenance (62℃ for 45 minutes) is adopted, the whole process energy consumption is reduced to the minimum value 802kWh, forming the optimal path "S21→V15→C62". Secondly, the key parameters are determined on the path: the average value of the optimal data group in the stirring link is 85.1%, and the electromagnetic efficiency critical value is set to 84.5%; the optimal interval of concrete impact energy loss in the vibration link is 1.3-1.6J, and the mechanical consumption boundary is set to 1.2-1.7J; in the maintenance link, the thermal resistance coefficient is calculated according to the condensation film thickness 0.11mm±0.02 and the thermal conductivity 0.85W / mK, which is 0.13m²K / W. Finally, the parameters are integrated according to the process time axis: the electromagnetic efficiency is 84.5% at the beginning of the 0-12 minute stirring section, the impact energy consumption increases from 1.2J to 1.7J in the 12-22 minute vibration section, and the thermal resistance is kept at 0.13m²K / W in the 22-65 minute maintenance section, and a continuous theoretical optimal energy consumption curve is generated through the curve smoothing algorithm.

[0033] The whole scheme of S102 above converts the reference energy consumption model into a dynamic monitoring standard. By calculating the optimal energy consumption path of the whole process and quantifying the electromagnetic efficiency critical value, mechanical impact boundary and thermal resistance coefficient, a theoretical optimal curve changing with time is generated to provide a scientific judgment benchmark for real-time energy consumption comparison, so that subsequent monitoring can accurately capture deviations such as electrical energy conversion decay, mechanical impact overrun and thermal transfer abnormalities.

[0034] S103, according to the theoretical optimal energy consumption curve, collecting the friction infrasound characteristics of the stirring motor bearing at the stirring link, monitoring the crack release energy spectrum inside the concrete at the vibrating link, and measuring the thickness distribution of the steam condensation film in real time at the curing link; Optionally, step S103 can specifically include the following steps: S1031, positioning the bearing friction sensitive period of the stirring link based on the electromagnetic efficiency critical value in the theoretical optimal energy consumption curve, collecting the infrasound signal of the stirring motor bearing using an infrasound sensor within the bearing friction sensitive period, and extracting the frequency distribution characteristics of the infrasound signal as the friction infrasound characteristics; S1032, positioning the crack high-risk interval of the vibrating link based on the energy loss boundary range consumed by mechanical impact in the theoretical optimal energy consumption curve, capturing the mechanical wave signal released by cracks using a concrete internal vibration sensor within the crack high-risk interval, and analyzing the distribution map of the signal intensity of the mechanical wave signal changing with time as the crack release energy spectrum; S1033, positioning the significant area of the steam film thickness change in the curing link based on the thermal resistance coefficient of heat transfer in the theoretical optimal energy consumption curve, obtaining the thickness measurement value of each spatial point on the surface of the steam condensation film using an infrared thickness scanner within the significant area, and generating the thickness distribution of the thickness changing with the spatial position based on the thickness measurement value.

[0035] In the above scheme, the friction infrasound characteristics refer to the low-frequency sound signal characteristics generated by abnormal wear of the stirring motor bearing, including the sound frequency distribution form, which is used to judge the electrical energy loss caused by bearing friction; the crack release energy spectrum refers to the mechanical vibration energy distribution map released when the concrete cracks, including the waveform characteristics of the vibration intensity changing with time, which is used to evaluate the mechanical energy waste at the vibrating link; the steam condensation film thickness distribution refers to the spatial variation data of the steam water film thickness attached to the surface of the concrete in the curing link, including the thickness measurement values of different position points, which is used to analyze the thermal energy transfer efficiency.

[0036] In this embodiment, firstly, step S1031 calculates the bearing friction-sensitive period where electromagnetic efficiency is most prone to fluctuation based on the critical value of electromagnetic efficiency in the theoretically optimal energy consumption curve: by analyzing the time distribution of electromagnetic efficiency below the critical value in historical data, the sensitive period after stirring begins is determined. During the sensitive period, an infrasound sensor is tightly attached to the bearing housing of the stirring motor, and raw acoustic signals are collected at a sampling rate of 1000 times per second. The time-domain acoustic signal is converted into frequency-domain energy distribution data using a fast Fourier transform algorithm to obtain the full-band energy spectrum. The energy proportion of the characteristic frequency band strongly correlated with bearing friction is extracted, where the range of the characteristic frequency band is determined by the inherent fault frequency of the bearing model. The calculation formula for the energy proportion of the characteristic frequency band is as follows: ,in and The start and end frequencies of the characteristic frequency band. This represents the energy value corresponding to frequency f. This represents the upper limit of the full frequency band supported by the sensor, and the calculation result of the energy ratio of the characteristic frequency band is used as the characteristic of frictional infrasound.

[0037] Next, in step S1032, based on the mechanical impact consumption boundary in the theoretically optimal curve, the high-incidence zone of cracks most prone to exceeding the impact energy limit is identified: the working time of the vibrating equipment near the upper limit of the boundary is statistically analyzed to determine the high-incidence zone after the start of vibration. Piezoelectric vibration sensors are pre-embedded inside the concrete, capturing mechanical wave signals at a sampling rate of 500 times per second in the high-incidence crack zone. A three-dimensional energy spectrum is generated using a short-time Fourier transform algorithm: the signal is divided into time windows of 0.1 seconds, and the intensity value of each frequency component within each window is calculated to form a time-frequency-intensity distribution map. The pulse duration and peak intensity are analyzed, and this distribution is used as the crack release energy spectrum.

[0038] Finally, based on the thermal resistance coefficient of the phase change material in the theoretically optimal curve in step S1033, the thickness region where the thermal resistance is prone to exceed the standard is located: according to the thermal resistance formula... Back-calculation of condensation film thickness The critical value was determined, and the central area of ​​the curing kiln was identified as a significant region based on infrared thermal imaging. An infrared thickness scanner was used to perform a gridded scan of this significant region: 100 measuring points were divided into 20cm × 20cm sections, and the thickness value was measured at each point. Next, a spatial thickness distribution was generated using an inverse distance weighted interpolation algorithm: based on the measured points, the thickness values ​​of the surrounding unmeasured points were calculated using the following formula: ,in For the thickness of the point to be determined, The actual thickness at the measured point. The distance between two points is used as the basis for the thickness distribution cloud map. All point data are integrated to generate a thickness spatial distribution cloud map, showing how the thickness varies with spatial location.

[0039] In practical application, in a certain concrete pipe factory, first, the mixing link data collection is performed: according to the critical value 84.5% of the electromagnetic efficiency in the theoretical optimal energy consumption curve, the sensitive period of bearing friction after 8 to 12 minutes after mixing is determined by analyzing historical data; in this period, the infrasound sensor is adsorbed on the motor bearing shell, and the sound wave signal is collected at a sampling rate of 1000 times per second, and after fast Fourier transform, the feature frequency band of 120-150 Hz (determined according to the bearing model) is extracted, and the energy proportion of 58% is calculated as the friction infrasound characteristics. Then, the vibrating link monitoring is performed: according to the mechanical impact boundary 1.2-1.7 J in the theoretical curve, the crack high incidence interval of 4 to 6 seconds after vibrating is locked; the vibration sensor is embedded in the concrete, the mechanical wave signal is captured at a sampling rate of 500 times per second, the time-frequency intensity spectrum is generated by short-time Fourier transform, and the pulse characteristics of 6.5 J intensity and 0.3 seconds duration at 5.2 seconds are identified as the crack release energy spectrum. Finally, the curing link measurement is performed: based on the thermal resistance coefficient 0.13 m²K / W in the theoretical curve, the critical value of the condensation film thickness is 0.104 mm, and the infrared thermal image is combined to determine that the middle 1 m x 1 m of the curing kiln is the significant area; using the infrared thickness scanner, 36 measurement points are scanned in a 20 cm x 20 cm grid, the thickness is measured to be 0.11-0.19 mm, the thickness of the unmeasured points is calculated by inverse distance weighted interpolation, and the thickness distribution with the change of spatial position is formed. The thickness distribution shows that the average thickness of the northwest corner is 0.15 mm and the southeast corner is 0.12 mm.

[0040] The overall scheme of S103 above, under the guidance of the theoretical optimal energy consumption curve, directionally collects the motor bearing friction sound wave characteristics in the mixing link, the concrete crack energy release spectrum in the vibrating link, and the steam condensation film thickness distribution in the curing link, breaks through the limitation of traditional monitoring of current and temperature, realizes multi-dimensional energy consumption monitoring from equipment appearance to material interior, and provides direct evidence reflecting mechanical wear, structural damage and heat transfer failure for subsequent energy consumption deviation diagnosis.

[0041] S104, according to the thickness distribution of the steam condensation film, an energy flow path of the thermodynamic field in the curing link is generated, when the actual energy consumption curve deviates from the theoretical optimal energy consumption curve, the energy flow path of the curing thermodynamic field is reconstructed, and the abnormal process is found out and the compensation operation is started; Optionally, step S104 can specifically include the following steps: S1041, based on the thickness distribution of the steam condensation film, the real-time transmission direction sequence and energy intensity level of heat from the steam source to the concrete are calculated to generate an initial energy flow path; In the step S1041, the thickness measurement value of the spatial coordinate point can be extracted based on the thickness distribution of the steam condensation film, and the area where the thickness change between adjacent coordinate points exceeds the normal fluctuation range is identified as the thermal resistance mutation area; a basic transfer path is generated in the direction of continuously decreasing thickness from the steam source, a bypass path is generated in the thermal resistance mutation area, and the basic transfer path and the bypass path are integrated to form a real-time direction sequence of heat transfer; the thickness characteristic area is divided according to the thickness measurement value, the area with smaller thickness is marked as a high-intensity energy level, the area with moderate thickness is marked as a medium-intensity energy level, and the area with larger thickness is marked as a low-intensity energy level, and the energy intensity level is marked section by section along the real-time direction sequence; the real-time transfer direction sequence and the energy intensity level are mapped into a weighted directed graph structure, and a continuous transfer path from the steam source to the concrete surface is generated based on the weighted directed graph structure as an initial energy flow path.

[0042] In the step S1042, when the actual energy consumption curve deviates from the theoretical optimal energy consumption curve, the real-time transfer direction sequence and the energy intensity level are recalculated in combination with the thickness distribution of the current steam condensation film, the initial energy flow path is reconstructed, and a reconstructed energy flow path is generated. In the step S1043, the difference between the initial energy flow path and the reconstructed energy flow path is compared, and the abnormal process causing the deviation is identified, which is the steam supply control process or the temperature maintenance process. In the step S1044, a compensation operation is started for the abnormal process, and the steam valve opening degree or the heater power parameter is automatically adjusted to correct the energy flow path.

[0043] In the above scheme, the energy flow path refers to the dynamic trajectory of heat transfer from the steam source to the concrete surface, which contains the transfer direction sequence and the energy intensity level information, and is used to visualize the flow efficiency of heat energy in the maintenance link. The thermal resistance mutation area refers to the area where the thickness of the steam condensation film changes abnormally, and the thickness difference exceeds the normal fluctuation range, which hinders heat transfer. The weighted directed graph structure is a path model that uses arrows to represent the transfer direction and numbers to mark the energy intensity, which can accurately describe the relationship between the heat transfer path and the efficiency. The compensation operation refers to the behavior of automatically adjusting the steam valve opening degree or the heater power, which is used to correct the heat transfer deviation.

[0044] In the embodiments of the present application, first, based on the real-time collected steam condensate film thickness distribution data, the thickness measurement values of all spatial coordinate points are extracted, the thickness difference values of adjacent points are calculated, and the area with a difference value exceeding a preset threshold is identified as a thermal resistance mutation area. The basic transfer path is generated from the steam source position as the starting point along the direction of continuous thickness reduction. The bypass path is generated in the thermal resistance mutation area, and the basic path and the bypass path are integrated into a real-time transfer direction sequence. Then, the thickness value is divided into energy intensity levels: thickness ≤ 0.10 mm is high intensity (level 3), 0.11-0.15 mm is medium intensity (level 2), and > 0.15 mm is low intensity (level 1), and the levels are labeled along the sequence. Finally, the direction sequence and the energy level are mapped into a weighted directed graph: the node represents the position, the arrow represents the transfer direction, and the weight number represents the energy level, forming an initial energy flow path. For example, the thickness data scanned by a certain curing kiln: the steam source (0, 0) has a thickness of 0.20 mm corresponding to level 1, point A (1, 1) has a thickness of 0.12 mm corresponding to level 2, point B (2, 2) has a thickness of 0.18 mm, and point C (3, 3) has a thickness of 0.09 mm corresponding to level 3. The basic path (0, 0)→(1, 1)→(3, 3) is generated, and point B is bypassed to point D (2, 1) with a thickness of 0.11 mm due to the mutation. The weighted directed graph is: (0, 0)-1→(1, 1)-2→(2, 1)-2→(3, 3)-3.

[0045] Secondly, when the actual curing energy consumption exceeds the theoretical optimal energy consumption curve is monitored, the path is recalculated combined with the latest condensate film thickness data: first, update the thermal resistance mutation area. Secondly, the transfer direction is re-planned: taking the steam source as the starting point, the basic path is generated along the new thickness gradient, and the new bypass path is generated in the mutation area. Then, the energy intensity level is updated, and finally the reconstructed energy flow path is generated. For example, the initial energy flow path is (0, 0)-1→(1, 1)-2→(2, 1)-2→(3, 3)-3, when the energy consumption is detected to be excessive, the thickness of the original mutation area point B increases from 0.18 mm to 0.23 mm, the difference value with the adjacent point expands to 0.10 mm, a new point E (1, 2) with a thickness of 0.25 mm is added, and the reconstructed path avoids B and E, and changes the route to point F (0.14 mm) and point G (0.12 mm). The reconstructed energy flow path is generated: (0, 0)-1→F-2→G-2→(3, 3)-3.

[0046] Next, step S1043 compares the initial energy flow path with the reconstructed energy flow path: if the weight of the starting segment of the path decreases, such as initial weight 3 → reconstructed weight 1, it indicates insufficient energy output from the steam source, and is judged as an abnormality in the steam supply control process; if the weight of the ending segment of the path decreases, such as initial weight 3 → reconstructed weight 1, it indicates that heat cannot be effectively transferred to the concrete, and is judged as an abnormality in the temperature maintenance process; if both occur simultaneously, it is judged as a mixed abnormality. For example, in the curing process of a concrete pipe plant, the weight sequence of the initial energy flow path is: steam source to first node weight 3, second node weight 2, ending point weight 3; when the actual energy consumption exceeds the standard, the path is reconstructed, and the new sequence shows that the weight of the steam source to first node decreases to 1, and the weight of the node before the ending point decreases from 3 to 1; the system automatically judges that the sudden drop in the weight of the first node indicates insufficient steam supply pressure, and the decrease in the weight of the ending point reflects the failure of the insulation layer, and simultaneously outputs the diagnostic conclusion of "mixed abnormality of steam supply control process and temperature maintenance process".

[0047] Finally, compensation is triggered in step S1044 based on the type of anomaly: if the anomaly is in steam supply, the steam valve opening is automatically increased to enhance the steam flow and boost the initial energy; if the anomaly is in temperature maintenance, the heater power is automatically increased to compensate for heat loss at the end; for mixed anomalies, dual compensation is performed simultaneously. After compensation, energy consumption data is re-acquired until it matches the theoretical curve. Continuing the example above, for the aforementioned mixed anomaly, the system automatically triggers dual compensation: first, the steam valve opening is increased from the initial 50% to 54% to enhance the steam source output pressure; simultaneously, the power of the curing kiln heater is increased from 32kW to 35kW to compensate for heat loss at the end. After compensation, the path weights are re-detected; the weight from the steam source to the first node is restored to 2.5, and the weight at the end point is increased back to 2.8, with the actual energy consumption curve returning to the theoretical optimal range.

[0048] In practical applications, in a certain large concrete pipe maintenance system, a significant thermal resistance mutation is detected at the northwest corner of the maintenance kiln during initial path construction: point P (0, 2) with a thickness of 0.11 mm and adjacent point Q (0, 3) with a thickness of 0.20 mm, with a difference of 0.09 mm exceeding the threshold of 0.05 mm, generating a detour path "steam source → R (0.12 mm) → S (0.09 mm) → concrete surface", with R point medium intensity 2 and S point high intensity 3, forming a weighted graph source-1 → R-2 → S-3. When the actual energy consumption exceeds the standard for 3 consecutive periods, new thickness scanning shows that the S point thickens to 0.17 mm and a new mutation point T (1, 3) with a thickness of 0.22 mm is added, and the path is reconstructed as "source → R → U (0.10 mm) → concrete surface", with the new weight sequence source-1 → R-2 → U-2 → terminal-1 (terminal weight 3 → 1). The system determines that the sudden drop in terminal weight is an abnormality in the temperature maintenance process, and the kiln body edge insulation layer is damaged, automatically triggering compensation: increasing the edge heater power from 28 kW to 31 kW, and adding a temporary insulation layer in the damaged area. After compensation, the terminal weight of the path is restored to 2.8, and the energy consumption curve returns to the theoretical range.

[0049] The overall scheme of S104 generates a heat transfer path through the thickness distribution of the condensation film, automatically reconstructs the energy flow trajectory and locates the process level fault when the maintenance energy consumption exceeds the standard, triggers directional compensation of the steam valve opening or the heater power, realizes closed-loop self-correction of energy consumption deviation, and breaks through the hysteresis of traditional manual adjustment, so that the thermodynamic field of the maintenance link always tends to be in the optimal state.

[0050] S105, integrate the friction infrasound characteristics, crack energy release spectrum and thickness distribution of the steam condensation film after the compensation operation into a comprehensive data model, and detect the mutation of the Betti number according to the comprehensive data model. When the Betti number mutation and continuous over-standard unit energy consumption occur at the same time, trace back to the root cause of the energy consumption problem, and generate an energy consumption abnormality diagnosis report according to the tracing result.

[0051] Optionally, step S105 can specifically include the following steps: S1051, input the friction infrasound characteristics, crack energy release spectrum and thickness distribution of the steam condensation film after the compensation operation into a data fusion mechanism, and construct a comprehensive data model; S1052, execute the Betti number mutation detection operation through the comprehensive data model, and calculate the Betti number mutation value of the connection relationship and the number of cavities in the model topology structure; S1053, when the Betti number mutation value and the continuous over-standard unit energy consumption occur at the same time, execute the energy consumption problem root cause tracing operation in the mixing link, the vibrating link and the maintenance link, and generate an energy consumption abnormality diagnosis report according to the tracing result.

[0052] The step S1053 can specifically include the following processes: in the stirring link, the difference between the actual conversion efficiency of electric energy and the critical value of the electromagnetic conversion efficiency in the theoretical optimal energy consumption curve is calculated as the electric energy conversion loss value according to the frequency distribution characteristics of the friction infrasonic wave features; in the vibrating link, the increment of the mechanical energy dissipation that exceeds the boundary range of the mechanical impact consumption in the theoretical optimal energy consumption curve is calculated as the mechanical energy dissipation increment according to the intensity-time spectrum of the crack energy release spectrum; in the curing link, the deviation value between the actual heat transfer resistance and the ideal coefficient is calculated as the heat loss over-standard value according to the thickness distribution of the steam condensation film and the coefficient of the phase change thermal resistance in the theoretical optimal energy consumption curve; and the generated energy consumption anomaly diagnosis report includes the energy consumption anomaly diagnosis report that labels the abnormal process position and the over-standard degree based on the quantitative results of the electric energy conversion loss value, the mechanical energy dissipation increment and the heat loss over-standard value.

[0053] In the above scheme, the comprehensive data model refers to a unified analysis framework that fuses the compensated friction infrasonic wave features, the crack energy spectrum and the condensation film thickness distribution, and is used to describe the energy consumption state of the whole process; the Bayesian network mutation value refers to the abnormal change amount of the "number of holes" or "connection relationship" in the model topological structure, and reflects the breaking of the propagation chain of cross-process faults, for example, an increase in the number of holes indicates that the energy consumption is isolated; the electric energy conversion loss value is the deviation of the actual electromagnetic efficiency of the stirring link from the theoretical critical value, and quantifies the electric energy waste caused by the motor bearing wear; the mechanical energy dissipation increment is the additional loss of the vibration impact energy that exceeds the theoretical boundary, and reflects the damage degree of the concrete structure; and the heat loss over-standard value is the deviation of the curing thermal resistance from the theoretical coefficient, and measures the decrease amount of the steam heat transfer efficiency.

[0054] In the embodiment of the present application, as shown in the figure, Figure 2 firstly, the friction infrasonic wave features, the crack energy release spectrum and the thickness distribution of the steam condensation film after the compensation operation are aligned according to the production process time axis through step S1051. Secondly, a fusion model is established through a graph neural network (GNN): taking the stirring, vibrating and curing three processes as nodes, storing the corresponding data features in the node attributes, storing the infrasonic wave proportion value in the stirring node, storing the pulse intensity and duration in the vibrating node, storing the thickness range in the curing node, and taking the energy consumption transmission between processes as edges, for example, the weight of the stirring-vibrating edge is the process conversion energy consumption value 85 kWh. Finally, the topological graph structure is generated as a comprehensive data model, for example: node 1 (stirring) attribute=[58%, time T1], node 2 (vibrating) attribute=[6.5J / 0.3s, T2], node 3 (curing) attribute=[0.06mm, T3], edge 1-2 weight=85, edge 2-3 weight=120.

[0055] Next, in step S1052, the integrated data model is converted into a three-dimensional topological complex structure, where process nodes are mapped to vertices (0-dimensional simplex), energy transfer relationships between processes are mapped to edges (1-dimensional simplex), and data feature associations are mapped to faces (2-dimensional simplex). Then, the topological invariants of this complex are calculated, i.e., the 0-dimensional Betty number β0 represents the number of connected regions, and the 1-dimensional Betty number β1 represents the number of voids; then, the formula is used... Calculate the mutation value of the Betty number, where The mutation value of Betty number. The k-videbetti number represents the baseline model. The k-Viberti number represents the current model; when k=0 The number of connected components in a topology, such as a fully connected model. When k=1 Indicates the number of holes, such as a model without holes. For example, benchmark models , When the maintenance node becomes disconnected, it causes Furthermore, the formation of voids between vibration and curing leads to... At that time, the mutation value was obtained. This value quantifies the degree of breakage in a cross-process failure chain.

[0056] Finally, in step S1053, when the Betty number mutation value Δβ≥1 and the unit energy consumption continuously exceeds the theoretical optimal energy consumption curve threshold, a three-stage traceability is initiated: in the stirring stage, the actual electromagnetic efficiency is calculated based on the characteristics of frictional infrasound, and the electrical energy conversion loss value is obtained by comparing it with the theoretical critical value. ,in This represents the critical value of the electromagnetic efficiency for the theoretically optimal curve. The actual electromagnetic efficiency is estimated from the characteristics of frictional infrasound. During the vibration compaction process, the actual impact energy is calculated based on the crack energy spectrum, and the increase in mechanical energy dissipation is obtained by comparing it with the theoretical boundary. ,in This represents the upper limit of the mechanical impact consumption boundary. The actual impact energy is extracted from the peak value of the crack energy spectrum; during the curing process, the actual thermal resistance is calculated based on the thickness of the condensate film, and compared with the theoretical coefficient to obtain the value of excessive heat loss. ,in The theoretical thermal resistance coefficient, The actual thermal resistance is calculated from the thickness of the condensation film. Finally, a diagnostic report is generated based on three quantitative values. For example, it indicates that the heat loss during the curing process exceeds the standard by 0.03 m²K / W as the main cause, and the loss during the stirring process is 5.2% as the secondary cause. The report also outputs a maintenance suggestion to "replace the sealing strip of the curing kiln".

[0057] In practical applications, in a certain concrete pipe production line, first, the compensated process data is integrated into a comprehensive model: the subsonic wave characteristic value of the mixing link (63% energy at 150-180 Hz), the crack energy spectrum of the vibrating link (7.1 J pulse lasting 0.35 seconds), and the condensation film thickness distribution of the curing link (northwest-southeast difference 0.11 mm) are constructed into a topological structure through a graph neural network, the node attributes store the data of each process, and the edge weight marks the energy consumption of the process conversion, mixing → vibrating 92 kWh, vibrating → curing 138 kWh. Second, it is detected that the topological mutation: the reference model has β0=1, β1=0, the current model has β0=2 due to the disconnection of the curing node, and the vibrating-curing edge breaks to form a cavity, making β1=1, and the beta number mutation value Δβ=|2-1|+|1-0|=2. At the same time, the unit energy consumption is continuously over-standard by 14%, triggering a three-link traceability: the actual electromagnetic efficiency of the mixing link is 78.5%, which is lower than the critical value of 85.2%, and the power loss value is 6.7%; the actual impact of the vibrating link is 1.98 J, which exceeds the upper limit of the boundary 1.75 J, and the mechanical dissipation increment is 0.23 J; the actual thermal resistance of the curing link is 0.18 m²K / W, which is higher than the theoretical value 0.15 m²K / W, and the heat loss over-standard value is 0.03 m²K / W. Finally, a diagnosis report is generated, marking the curing link as the main abnormal source, with an over-standard value of 0.03 m²K / W, and it is recommended to replace the curing kiln sealing strip and check the mixer bearing in priority.

[0058] The overall scheme of S105 above, by fusing the compensated multi-process process data to construct a comprehensive model, using the beta number topological mutation detection to capture the cross-process fault chain breaking characteristics, when the mutation and energy consumption over-standard occur simultaneously, accurately quantifying the loss value of the mixing, vibrating, and curing links, automatically generating a diagnosis report marking the main abnormal source, realizing the closed-loop management of energy consumption problems from implicit association identification to process-level responsibility traceability. The following is a complete example for steps 101-105, as shown in Figure 3 When implemented in a certain concrete pipe factory, first, 30 consecutive days of production data are collected through a sensor network: material proportioning data such as C40 concrete formula, cement: sand: stone: water = 1:1.2:2.5:0.38, equipment operation data such as mixer power fluctuation range 20-24 kW, vibrator frequency 115 Hz±3, and environmental data such as curing kiln temperature and humidity, with temperature 62℃±2 and humidity 65%-70%. The data platform automatically removes abnormal records and identifies the typical production mode "high cement-low humidity mode" corresponding to cement proportion >38% and humidity <68% using clustering algorithm. A reference energy consumption model is established for this mode, inputting historical data: cement proportion 39.5%, humidity 66%, calculating the reference energy consumption value 912 kWh, and inputting new data in the verification stage: cement proportion 40.1%, humidity 67%, outputting energy consumption value 928 kWh, with an error of less than 1% compared with the actual value 935 kWh, confirming the effectiveness of the reference energy consumption model.

[0059] Secondly, the optimal data set is screened out based on the benchmark model, which is cement ratio of 37.8% ± 0.3%, mixing power of 21.5 kW ± 0.5, and curing temperature of 61℃ ± 1. The optimal path of the whole process is calculated as follows: medium-speed mixing (22 kW) → medium-frequency vibration (impact energy 1.55 J) → gradient temperature curing (60℃ → 65℃). The key parameters are determined on the path: the critical value of electromagnetic efficiency in the mixing link is 85.6%, the mechanical impact boundary in the vibration link is 1.4-1.8 J, and the thermal resistance coefficient in the curing link is 0.142 m²K / W. The parameters are integrated according to the process time axis: the electromagnetic efficiency in the mixing section decreases from 85.6% to 83.5% in 0-12 minutes, the impact energy in the vibration section increases linearly from 1.4 J to 1.8 J in 12-22 minutes, and the thermal resistance in the curing section is stable at 0.142 m²K / W in 22-70 minutes. A smooth theoretical optimal energy consumption curve is generated by cubic spline interpolation, as shown in FIG. 2. Figure 4

[0060] Then, the process data is collected according to the theoretical optimal energy consumption curve: the bearing friction characteristic frequency band of 135-165 Hz is detected in the sensitive period of electromagnetic efficiency in the mixing link from 10th to 14th minute, and the energy ratio is 67% (normal should be <60%); a 6.8 J intensity pulse lasting for 0.25 seconds is captured in the upper limit period of impact boundary in the vibration link from 5th to 7th second (normal should be <6 J / <0.15 seconds); the average thickness of 0.16 mm in the northwest corner corresponds to the thermal resistance of 0.201 m²K / W, and the average thickness of 0.13 mm in the southeast corner corresponds to the thermal resistance of 0.163 m²K / W in the significant thermal resistance area in the middle of the curing kiln, and the difference between the northwest corner and the southeast corner is 0.03 mm, which exceeds the threshold.

[0061] ​Then, the initial energy flow path is dynamically generated based on the condensation film thickness distribution: the spatial grid point thickness data is obtained by scanning, point P1 is 0.12mm thick, P2 is 0.18mm thick, and the thickness difference between P2 and the adjacent point is identified as 0.06mm, which is greater than the threshold value of 0.05mm, as a thermal resistance mutation zone; the basic path "source→Q1(0.15mm)→Q2(0.10mm)" is planned along the thickness decreasing direction from the steam source (0.20mm) as the starting point, the bypass path is generated through Q3(0.13mm) in the mutation zone, and the direction sequence "source→Q1→Q3→Q2" is integrated; the intensity of Q1 is marked (level 2), the intensity of Q3 is marked (level 2), and the high intensity of Q2 is marked (level 3), forming the weighted directed graph source-1→Q1-2→Q3-2→Q2-3 as the initial energy flow path. When the real-time monitoring shows that the maintenance energy consumption exceeds the standard by 18%, the path is reconstructed combined with the new thickness data: the thickness of the original mutation zone P2 increases to 0.23mm, a new area R with a thickness of 0.25mm is added, the path is re-planned to avoid P2 and R, and the route is changed to S1(0.14mm) and S2(0.12mm), and the energy level is updated to the new weighted graph source-1→S1-2→S2-2→Q2-1; by comparing the path differences, it is found that the weight of the steam source to the first node is 2→1, and the weight of the end node is 3→1, it is determined that the steam supply pressure is insufficient and the kiln body heat preservation is ineffective; the double compensation mechanism is triggered: the steam valve opening degree is automatically increased from 50% to 54% to improve the steam flow, and at the same time the heater power is adjusted from 35kW to 37.8kW to compensate for the heat loss, and after compensation the end weight is restored to 2.7 and the energy consumption deviation is reduced to 4.2%.

[0062] Finally, the multi-source data after compensation is fused: the secondary wave characteristics of the mixing link (energy ratio of 135-165Hz is 67%), the crack energy spectrum of the vibrating link (6.8J pulse lasts for 0.25 seconds), and the thickness distribution of the maintenance link (0.16mm in the northwest corner / thermal resistance 0.201m²K / W) are used to construct a topological model through a graph neural network, the node attributes are marked with process characteristics, and the edge weight records the energy consumption value of the process connection, and the mixing→vibrating 94kWh→vibrating→maintenance 142kWh. The topological structure is detected to mutate, the number of connected domains of the reference model is , the number of holes , the current model forms an independent connected domain due to the separation of the maintenance node , and the energy flow from vibrating to maintenance is broken and a hole is formed , the Betti number mutation value is calculated ; at the same time, the unit energy consumption exceeds the standard by 15% for 3 consecutive hours, triggering the three-link quantitative traceability: the actual electromagnetic efficiency of the mixing link is 77.5%, which is lower than the critical value of 85.6%, and the electric energy loss value ; the actual impact energy of the vibrating link is 6.8J, which is higher than the upper limit of 6.0J, and the mechanical dissipation increment ; the actual thermal resistance of the maintenance link is 0.201m²K / W, which is higher than the theoretical value of 0.142m²K / W, and the heat loss exceeds the standard value The diagnostic report is generated, indicating that the core abnormal source is the excessive heat resistance of 41.5% in the maintenance link, and the secondary source is the power loss of 9.5% in the stirring link, and it is suggested to immediately overhaul the kiln body seal and replace the stirring machine bearing.

[0063] Figure 5 A structural schematic diagram of a specific embodiment of a concrete pipe production energy consumption real-time monitoring system based on the Internet of Things provided by the embodiment of the present application, with reference to Figure 5 The system can include: The acquisition module 51 is configured to establish a benchmark energy consumption model according to the collected material proportioning data, equipment operation data and environmental data in the concrete pipe production process; The processing module 52 is configured to derive a theoretical optimal energy consumption curve through the benchmark energy consumption model, and the theoretical optimal energy consumption curve includes a critical value of electromagnetic conversion efficiency, a boundary range of mechanical impact consumption and a coefficient of phase change thermal resistance; The execution module 53 is configured to collect the friction infrasound characteristics of the stirring motor bearing in the stirring link, monitor the crack energy spectrum inside the concrete in the vibrating link, and measure the thickness distribution of the steam condensation film in the maintenance link according to the theoretical optimal energy consumption curve; The compensation module 54 is configured to generate an energy flow path of the thermodynamic field in the maintenance link according to the thickness distribution of the steam condensation film, and when the actual energy consumption curve deviates from the theoretical optimal energy consumption curve, reconstruct the energy flow path of the maintenance thermodynamic field and find out the abnormal process and start the compensation operation; The generation module 55 is configured to integrate the friction infrasound characteristics, the crack energy release spectrum and the thickness distribution of the steam condensation film processed by the compensation operation into a comprehensive data model, and detect the mutation of the Bem number according to the comprehensive data model, and when the Bem number mutation and the continuous over-standard unit energy consumption occur at the same time, trace back to the root cause of the energy consumption problem of the process level, and generate an energy consumption abnormality diagnosis report according to the tracing result.

[0064] The concrete pipe production energy consumption real-time monitoring system based on the Internet of Things of the embodiment of the present application is used to realize the aforementioned concrete pipe production energy consumption real-time monitoring method based on the Internet of Things, and therefore the specific embodiments in the concrete pipe production energy consumption real-time monitoring system based on the Internet of Things can be seen in the embodiment part of the concrete pipe production energy consumption real-time monitoring method based on the Internet of Things in the foregoing, and the specific embodiments can be referred to the description of the corresponding part of the embodiment, which will not be repeated here.

[0065] The present application also provides an electronic device comprising a memory for storing a computer program and a processor for executing the computer program to implement the steps of the aforementioned concrete pipe production energy consumption real-time monitoring method based on the Internet of Things.

[0066] The application further provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the method for monitoring energy consumption of concrete pipe production in real time based on Internet of Things.

[0067] In an example embodiment, the computer readable storage medium can include, but is not limited to, a U disk, a read-only memory, a random access memory, a mobile hard disk, a magnetic disk or an optical disk, and various media capable of storing a computer program.

[0068] The embodiment of the application further provides a computer program product, wherein the computer program product comprises a computer program, and the computer program is executed by a processor to implement the steps in the method for monitoring energy consumption of concrete pipe production in real time based on Internet of Things.

[0069] The skilled person can further realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized by electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in general terms in the above description. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the application.

[0070] The above describes in detail the method and system for monitoring energy consumption of concrete pipe production in real time based on Internet of Things. The principles and implementation modes of the application are described by using specific examples. The above description of the examples is only used to help understand the method and core idea of the application. It should be pointed out that the ordinary skilled person in the art can make some improvements and modifications to the application without departing from the principles of the application. These improvements and modifications also fall within the protection scope of the application.

Claims

1. A method for real-time monitoring of energy consumption in concrete pipe production based on the Internet of Things, characterized in that, include: A benchmark energy consumption model is established based on the material proportioning data, equipment operation data, and environmental data collected during the concrete pipe production process. The theoretical optimal energy consumption curve is obtained through the benchmark energy consumption model. The theoretical optimal energy consumption curve includes the critical value of electromagnetic conversion efficiency, the boundary range of mechanical shock consumption, and the coefficient of phase change thermal resistance. Based on the theoretical optimal energy consumption curve, the frictional infrasound characteristics of the mixing motor bearing are collected during the mixing process, the energy spectrum of crack release inside the concrete is monitored during the vibration process, and the thickness distribution of the steam condensation film is measured in real time during the curing process. Based on the thickness distribution of the steam condensation film, an energy flow path of the thermodynamic field in the curing process is generated. When the actual energy consumption curve deviates from the theoretical optimal energy consumption curve, the energy flow path of the curing thermodynamic field is reconstructed, and the abnormal process is identified and compensation operation is initiated. The characteristics of frictional infrasound waves, crack energy release spectrum, and thickness distribution of steam condensation film after compensation are integrated into a comprehensive data model. Based on the comprehensive data model, the mutation of the Betty number is detected. When the mutation of the Betty number and the continuous exceedance of unit energy consumption occur at the same time, the root cause of the energy consumption problem at the process level is traced, and an energy consumption anomaly diagnosis report is generated based on the tracing results.

2. The method according to claim 1, characterized in that, The process integrates the characteristics of frictional infrasound waves, crack energy release spectrum, and thickness distribution of steam condensation film after compensation processing into a comprehensive data model. Based on this model, abrupt changes in the Betty number are detected. When both abrupt changes in the Betty number and continuous exceedances of unit energy consumption occur simultaneously, the root cause of the energy consumption problem at the process level is traced. Based on the tracing results, an energy consumption anomaly diagnostic report is generated, including: The characteristics of frictional infrasound waves after compensation, the crack energy release spectrum, and the thickness distribution of the vapor condensation film are input into the data fusion mechanism to construct a comprehensive data model. The Betty number mutation detection operation is performed through the comprehensive data model to calculate the Betty number mutation value of the number of holes and the connection relationship in the model topology. When the mutation value of the Betty number and the unit energy consumption continuously exceed the threshold of the theoretical optimal energy consumption curve occur simultaneously, the root cause tracing operation of energy consumption problem is performed in the mixing, vibration and curing stages, and an energy consumption anomaly diagnosis report is generated based on the tracing results.

3. The method according to claim 2, characterized in that, The process of tracing the root causes of energy consumption problems during the mixing, vibration, and curing stages, and generating an energy consumption anomaly diagnostic report based on the tracing results, includes: In the stirring process, the difference between the actual energy conversion efficiency and the critical value of the electromagnetic conversion efficiency in the theoretical optimal energy consumption curve is calculated based on the frequency distribution characteristics of the frictional infrasound wave and the critical value of the electromagnetic conversion efficiency in the stirring process. This difference is used as the energy conversion loss value. In the vibration stage, the increment of the actual mechanical energy dissipation exceeding the boundary range in the intensity-time spectrum of the crack energy release spectrum and the theoretical optimal energy consumption curve is calculated as the mechanical energy dissipation increment. In the maintenance process, the deviation between the actual heat transfer resistance and the ideal coefficient is calculated based on the distribution of the steam condensation film thickness and the coefficient of the phase change thermal resistance in the theoretical optimal energy consumption curve, which is taken as the value of excessive heat loss. The generated energy consumption anomaly diagnostic report includes an output of the energy consumption anomaly diagnostic report, which marks the location of the abnormal process and the degree of exceedance, based on the quantitative results of the electrical energy conversion loss value, the increase in mechanical energy dissipation, and the excessive value of heat loss.

4. The method according to claim 1, characterized in that, The process involves generating an energy flow path for the thermodynamic field during the curing process based on the thickness distribution of the steam condensation film. When the actual energy consumption curve deviates from the theoretically optimal energy consumption curve, the energy flow path of the curing thermodynamic field is reconstructed, and the abnormal process is identified and compensation operations are initiated, including: Based on the thickness distribution of the steam condensation film, the real-time heat transfer direction sequence and energy intensity level from the steam source to the concrete are calculated to generate an initial energy flow path. When a deviation is detected between the actual energy consumption curve and the theoretical optimal energy consumption curve, the real-time transmission direction sequence and energy intensity level are recalculated based on the current thickness distribution of the steam condensation film, and the initial energy flow path is reconstructed to generate a reconstructed energy flow path. By comparing the initial energy flow path with the reconstructed energy flow path, the abnormal process that caused the deviation is identified. The abnormal process is either the steam supply control process or the temperature maintenance process. In response to the abnormal process, a compensation operation is initiated, automatically adjusting the steam valve opening or heater power parameters to correct the energy flow path.

5. The method according to claim 4, characterized in that, Based on the thickness distribution of the steam condensation film, the real-time heat transfer direction sequence and energy intensity level from the steam source to the concrete are calculated to generate an initial energy flow path, including: Based on the thickness distribution of the vapor condensation film, the thickness measurement values ​​of spatial coordinate points are extracted, and the regions where the thickness variation between adjacent coordinate points exceeds the normal fluctuation range are identified as thermal resistance abrupt change regions. A basic heat transfer path is generated starting from the steam source and moving along the direction of continuous thickness reduction. A bypass path is generated in the thermal resistance abrupt change region. The basic heat transfer path and the bypass path are then integrated to form a real-time direction sequence of heat transfer. Based on the thickness measurement values, the thickness feature regions are divided, and regions with smaller thicknesses are marked as high-intensity energy levels, regions with moderate thicknesses as medium-intensity energy levels, and regions with larger thicknesses as low-intensity energy levels. The energy intensity levels are marked segment by segment along the real-time direction sequence. The real-time transmission direction sequence and energy intensity level are mapped to a weighted directed graph structure, and a continuous transmission path from the steam source to the concrete surface is generated based on the weighted directed graph structure as the initial energy flow path.

6. The method according to claim 1, characterized in that, Based on the theoretically optimal energy consumption curve, the following methods are employed: collecting frictional infrasound characteristics of the mixing motor bearings during the mixing stage; monitoring the energy spectrum released from cracks inside the concrete during the vibration stage; and measuring the thickness distribution of the vapor condensation film in real time during the curing stage. Based on the electromagnetic efficiency critical value in the theoretical optimal energy consumption curve, the bearing friction sensitive period of the stirring link is located. During the bearing friction sensitive period, an infrasound sensor is used to collect the infrasound signal of the stirring motor bearing, and the frequency distribution characteristics of the infrasound signal are extracted as the friction infrasound characteristics. Based on the energy loss boundary range of mechanical impact consumption in the theoretical optimal energy consumption curve, the high-incidence zone of cracks in the vibration stage is located. Within the high-incidence zone of cracks, the mechanical wave signal released by the cracks is captured by the internal vibration sensor of the concrete. The distribution spectrum of the signal intensity of the mechanical wave signal over time is analyzed as the energy spectrum of crack release. Based on the heat transfer resistance coefficient in the theoretically optimal energy consumption curve, a significant area of ​​vapor film thickness variation in the maintenance process is located. Within this significant area, an infrared thickness scanner is used to obtain thickness measurements at various spatial points on the surface of the vapor condensation film. Based on these thickness measurements, a thickness distribution pattern as the thickness varies with spatial location is generated.

7. The method according to claim 1, characterized in that, The theoretically optimal energy consumption curve is derived from the benchmark energy consumption model. This curve includes the critical value of electromagnetic conversion efficiency, the boundary range of mechanical shock consumption, and the coefficient of phase change thermal resistance. By performing the optimal path calculation operation through the benchmark energy consumption model, under the optimal combination of material ratio data, equipment operation data and environmental data, the continuous path with the minimum energy consumption in the entire production process is derived. In the continuous path, the parameter determination operation is performed through the benchmark energy consumption model to determine the critical value of electromagnetic efficiency for the conversion of electrical energy to mechanical energy, the energy loss boundary range consumed by mechanical impact, and the thermal resistance coefficient of the material phase transformation process; By integrating the electromagnetic efficiency critical value, energy loss boundary range, and thermal resistance coefficient into the benchmark energy consumption model, a theoretically optimal energy consumption curve is generated.

8. A real-time monitoring system for energy consumption in concrete pipeline production based on the Internet of Things, characterized in that, include: The data acquisition module is used to establish a baseline energy consumption model based on the material ratio data, equipment operation data, and environmental data collected during the concrete pipe production process. The processing module is used to derive the theoretical optimal energy consumption curve through the benchmark energy consumption model. The theoretical optimal energy consumption curve includes the critical value of electromagnetic conversion efficiency, the boundary range of mechanical shock consumption, and the coefficient of phase change thermal resistance. The execution module is used to collect the frictional infrasound characteristics of the mixing motor bearing in the mixing stage, monitor the energy spectrum of cracks released inside the concrete in the vibration stage, and measure the thickness distribution of the steam condensation film in real time in the curing stage, based on the theoretical optimal energy consumption curve. The compensation module is used to generate the energy flow path of the thermodynamic field in the curing process based on the thickness distribution of the steam condensation film. When the actual energy consumption curve deviates from the theoretical optimal energy consumption curve, the energy flow path of the curing thermodynamic field is reconstructed, and the abnormal process is identified and the compensation operation is initiated. The generation module is used to integrate the characteristics of frictional infrasound waves, crack energy release spectrum, and thickness distribution of steam condensation film after compensation operation into a comprehensive data model. Then, based on the comprehensive data model, it detects the mutation of the Betty number. When the mutation of the Betty number and the continuous exceedance of unit energy consumption occur at the same time, the root cause of the energy consumption problem at the process level is traced, and an energy consumption anomaly diagnosis report is generated based on the tracing results.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the IoT-based real-time monitoring method for energy consumption in concrete pipe production as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the implementation of the Internet of Things-based real-time monitoring method for energy consumption in concrete pipe production as described in any one of claims 1 to 7.

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