A construction engineering construction quality whole-process digital control method and system

Abstract

The application belongs to the technical field of general control or regulation system, and particularly relates to a building engineering construction quality whole-process digital control method and system, which comprises the following steps: obtaining an environmental wind speed reference sequence, a radiation intensity reference sequence, and an internal temperature reference sequence and a surface temperature reference sequence of concrete of a current control period; determining a surface temperature fluctuation degree; determining an environmental coupling degree, an environmental activity index, and a particle trajectory trust weight; obtaining an optimized particle velocity update expression; determining a global optimal particle, outputting a control instruction to adjust a cooling water flow, and realizing internal temperature control of the concrete. By introducing the particle trajectory trust weight based on multi-dimensional data feature analysis and optimizing the particle velocity update expression, the application overcomes the limitation of the fixed weight distribution strategy of the traditional PSO algorithm in updating the particle velocity, and improves the accuracy of the internal temperature control of the concrete and the durability of the engineering entity.

Description

Technical Field

[0001] This invention relates to the field of general control or regulation systems. More specifically, this invention relates to a digital control method and system for the entire process of construction quality in building engineering. Background Technology

[0002] With the digital transformation and intelligent upgrading of the construction industry, the construction quality control of large-volume concrete structures has become a core pain point in project management. In projects such as bridge anchorages, dam base slabs, high-rise building foundation slabs, and large equipment foundations, the volume of concrete poured is enormous. Due to the release of a large amount of heat from the cement hydration reaction, and the poor thermal conductivity of concrete itself, internal heat accumulates and is difficult to dissipate, easily forming a temperature difference between the inside and outside. If the temperature difference is too large, the resulting temperature stress will cause surface cracks or through cracks in the concrete structure, seriously affecting the structural safety and durability of the project.

[0003] Currently, the industry commonly uses a combination of buried cooling water pipes and automated temperature control for cooling and curing. Traditional control systems often employ PID control algorithms, adjusting the flow rate or velocity of cooling water based on data collected by temperature sensors. However, the construction site environment is extremely complex, with various noise influences, making it difficult for traditional fixed-parameter PID control to achieve ideal control results. A new fuzzy PID control algorithm based on particle swarm optimization (PSO-FuzzyPID) utilizes the global optimization capability of the particle swarm algorithm to automatically adjust the proportional, integral, and derivative coefficients of the FuzzyPID controller online. This algorithm boasts advantages such as fast convergence speed and high accuracy, enabling precise control of the internal temperature of concrete.

[0004] Existing PSO-FuzzyPID algorithms typically employ fixed weight allocation strategies for the inertial, self-awareness, and social awareness components when updating particle velocities. However, in actual open-air construction sites, the environment is extremely harsh and variable. Temperature sensors laid on the concrete surface are highly susceptible to interference from external environmental factors. For example, sudden gusts of wind can carry away surface heat, causing a sharp drop in readings, while strong sunlight after cloud cover can cause a sharp rise in readings. These noise data caused by the environment, rather than by internal heat accumulation in the concrete, can cause the PSO algorithm to drive the particle swarm to gather in the wrong parameter space. This leads to significant fluctuations in the optimized FuzzyPID parameters, ultimately causing the electric regulating valve to open or close frequently and drastically. This not only fails to eliminate the actual temperature rise but also creates cold stress inside the concrete due to sudden changes in cooling water flow, inducing cracks.

[0005] Among existing related technologies, Chinese patent document CN109976147B, entitled "A Temperature Control Method for Large-Volume Concrete Based on Intelligent Learning," discloses an automatic temperature control method involving the control of hydration heat in large-volume concrete. In the data processing and control stage, after receiving raw data from temperature sensors arranged on the large-volume concrete, the control module analyzes the actual temperature characteristics using conventional data preprocessing and data mining algorithms. The analyzed output is then used as the input for PID control to control water flow and heat exchange. This patent application uses standard data for anomaly handling and feedback adjustment of concrete temperature data. The drive and PID closed-loop feedback mechanism typically uses fixed adjustment logic based on historical data and current setting deviations as its control commands. Its shortcomings are: it does not take into account the complex environment of large-volume concrete pouring sites in actual open-air environments, where sudden gusts of wind and cloud cover can cause strong non-stationary, non-thermal high-frequency fluctuations in surface temperature sensors. When faced with complex weather conditions, the fixed control logic is prone to misinterpreting real hydration heat temperature changes as noise filtering and failing to respond in time, or misinterpreting strong ambient wind pressure and light noise as real signals, thus inducing the PID controller to output incorrect valve adjustment commands. Its adaptive capability is insufficient. Summary of the Invention

[0006] To address the technical problem that the existing PSO-FuzzyPID algorithm uses a fixed weight allocation strategy for particle velocity updates, which leads to distortion of the optimized FuzzyPID parameters and affects the temperature control effect inside concrete, this invention provides solutions in the following aspects.

[0007] In a first aspect, the present invention provides a digital control method for the entire construction quality process of building engineering, comprising: acquiring an environmental wind speed reference sequence, a radiation intensity reference sequence, and internal and surface temperature reference sequences of concrete for the current control cycle; determining the surface temperature fluctuation degree for the current control cycle based on the variance within the surface temperature reference sequence and the maximum first-order difference between adjacent times; determining the environmental coupling degree for the current control cycle based on the covariance between the surface temperature reference sequence and the environmental wind speed and radiation intensity reference sequences, respectively; determining the environmental activity index for the current control cycle based on the standard deviations of the environmental wind speed and radiation intensity reference sequences, respectively; determining the particle trajectory trust weight for the current control cycle based on the surface temperature fluctuation degree, environmental coupling degree, and environmental activity index; obtaining an optimized particle velocity update expression for the current control cycle based on the particle trajectory trust weight; determining the globally optimal particle for the current control cycle based on the optimized particle velocity update expression; and outputting control commands to adjust the cooling water flow rate using the FuzzyPID controller parameters corresponding to the globally optimal particle, thereby achieving internal temperature control of concrete.

[0008] This invention highlights the fluctuation characteristics of temperature changes affected by noise by calculating the degree of surface temperature fluctuation and utilizing the variance and the maximum value of the first-order difference within the surface temperature reference sequence. It analyzes the intensity of environmental noise interference on surface temperature by calculating environmental coupling degree and environmental activity index, thus achieving environmental noise identification. By combining the degree of surface temperature fluctuation, environmental coupling degree, and environmental activity index to calculate particle trajectory trust weights, the particle velocity update expression is optimized, enhancing the anti-interference capability of the PSO algorithm in complex backgrounds. Finally, by generating globally optimal particles based on the optimized particle velocity update expression and outputting control commands to adjust cooling water flow, the accuracy and robustness of internal temperature control for concrete in building engineering are improved.

[0009] Preferably, obtaining the environmental wind speed reference sequence, radiation intensity reference sequence, and concrete internal temperature reference sequence and surface temperature reference sequence for the current control cycle includes: selecting environmental wind speed data, radiation intensity data, and concrete internal temperature data and surface temperature data corresponding to several sampling times before the start of the current control cycle, to obtain the environmental wind speed reference sequence, radiation intensity reference sequence, and concrete internal temperature reference sequence and surface temperature reference sequence for the current control cycle.

[0010] Preferably, the degree of surface temperature fluctuation satisfies the expression:

[0011] In the formula, The degree of surface temperature fluctuation in the current control cycle. The variance of the surface temperature reference sequence in the current control cycle. This is the first-order difference sequence corresponding to the surface temperature reference sequence for the current control cycle. This is the function for finding the maximum value.

[0012] This invention achieves the assessment of surface temperature fluctuation by constructing a positive correlation function relationship based on the product of the variance term and the first-order difference maximum term. The variance term reflects the overall dispersion of the surface temperature reference sequence, while the first-order difference maximum term amplifies the peak response of the numerical changes within the surface temperature reference sequence. This allows the control period, which is more susceptible to noise, to calculate a larger value of surface temperature fluctuation, thus providing a reliable basis for the calculation of particle trajectory trust weights.

[0013] Preferably, the method for obtaining the environmental coupling degree is as follows: the covariance between the surface temperature reference sequence and the environmental wind speed reference sequence in the current control cycle is taken as the first value, the covariance between the surface temperature reference sequence and the radiation intensity reference sequence in the current control cycle is taken as the second value, the product of the first value and the second value is normalized to the maximum and minimum values, and the result is taken as the environmental coupling degree.

[0014] This invention constructs a positive correlation between the surface temperature reference sequence and the covariance products of the environmental wind speed reference sequence and the radiation intensity reference sequence, respectively, to assess the degree of environmental coupling. This reflects the degree of influence of the external environment on the surface temperature, enabling the calculation of a larger environmental coupling degree in highly coupled environmental regions. This effectively analyzes environmental interference and provides a basis for calculating the environmental coupling characteristics of particle trajectory trust weights.

[0015] Preferably, the method for obtaining the environmental activity index is as follows: calculate the product between the standard deviation of the environmental wind speed reference sequence of the current control cycle and the standard deviation of the radiation intensity reference sequence of the current control cycle, perform maximum and minimum value normalization on the product, and use the result as the environmental activity index.

[0016] Preferably, the method for obtaining the particle trajectory trust weight is as follows: Calculate the product of the environmental coupling degree, the environmental activity index, and the surface temperature fluctuation level to obtain a third value; perform inverse maximum-minimum normalization on the third value, so that the larger the third value, the smaller the normalization result, and use the obtained normalization result as the particle trajectory trust weight. The method involves calculating the product of the environmental coupling degree, the environmental activity index, and the surface temperature fluctuation level to obtain a third value, performing maximum-minimum normalization on the inverse of the third value, and using the result as the particle trajectory trust weight.

[0017] This invention achieves the evaluation of particle trajectory trust weight by integrating the analysis of surface temperature fluctuation degree, environmental coupling degree, and environmental activity index. The surface temperature fluctuation degree enhances the attenuation weight of the control cycle that is greatly affected by noise, and the environmental coupling degree and environmental activity amplify the attenuation contribution of environmental noise interference. This results in the calculation of a smaller particle trajectory trust weight for the control cycle that is greatly affected by noise, thereby distinguishing the true surface temperature change trend from the surface temperature change trend affected by environmental noise.

[0018] Preferably, the optimized particle velocity update expression satisfies the expression: In the formula, and For the first Second and third During the nth iteration The particle in the first Speed ​​in each dimension For inertial weights, The particle trajectory trust weight for the current control cycle. This is the reciprocal of the particle trajectory trust weight for the current control cycle. and Individual learning factors and social learning factors, and It is a random number. For the first During the nth iteration The particle in the first The optimal solution for each dimension. For the first During the nth iteration The global optimal solution corresponding to each dimension. For the first During the nth iteration The particle in the first Position in each dimension.

[0019] This invention achieves adaptive particle velocity updates by dynamically adjusting the particle velocity update expression using particle trajectory trust weights as multiplicative factors, thus ensuring the accuracy of the globally optimal particle.

[0020] Preferably, determining the globally optimal particle for the current control cycle includes: calculating the optimized particle velocity using the optimized particle velocity update expression, substituting the optimized particle velocity into the particle position update expression to obtain the positions of all particles corresponding to all iterations within the current control cycle, traversing the fitness values ​​of all particles corresponding to all iterations, and identifying the particle with the smallest fitness value as the globally optimal particle for the current control cycle.

[0021] Preferably, the output control command adjusts the cooling water flow rate to achieve concrete internal temperature control, including: calculating the deviation and deviation change rate between the maximum value in the internal temperature reference sequence of the current control cycle and the target control temperature; using the FuzzyPID controller parameters corresponding to the globally optimal particle to perform fuzzy inference on the deviation and deviation change rate to determine the output value of the FuzzyPID controller; converting the output value into an opening command for an electric regulating valve; and adjusting the cooling water flow rate and controlling the concrete internal temperature by controlling the opening of the electric regulating valve.

[0022] Secondly, the present invention provides a digital control system for the entire process of construction quality in building engineering, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned digital control method for the entire process of construction quality in building engineering is realized.

[0023] By adopting the above technical solution, a computer program is generated from the above-mentioned digital control method for the entire construction quality process of building engineering, and stored in the memory so that it can be loaded and executed by the processor. In this way, a terminal device can be made based on the memory and the processor for convenient use.

[0024] The beneficial effects of this invention are as follows: This invention solves the technical problem of global optimal solution distortion caused by the fixed weight allocation strategy in the traditional PSO algorithm during particle velocity update by introducing a particle velocity update expression optimization mechanism based on multidimensional data feature analysis.

[0025] This invention establishes the intrinsic mapping relationship between the degree of surface temperature fluctuation and surface temperature change by analyzing the variance and first-order difference of the surface temperature reference sequence. On this basis, it further integrates the environmental coupling degree and the environmental activity index, and tightly couples the indicators reflecting surface temperature changes with the indicators reflecting external environmental disturbances, thereby realizing the identification of environmental noise background.

[0026] This invention calculates the particle trajectory trust weights that match the actual surface temperature changes at the corresponding moment for each control cycle. By optimizing the particle velocity update expression, it achieves accurate matching between particle velocity and actual surface temperature change characteristics. For control cycles heavily affected by environmental noise, it automatically filters out noise-induced errors; for control cycles lightly affected by environmental noise or unaffected by environmental noise, it maintains stable parameter optimization. Ultimately, this invention improves the accuracy and anti-interference capability of internal temperature control in building engineering concrete, enabling timely and precise adjustment of cooling water flow, providing a solid technical guarantee for construction quality assurance and improved engineering durability. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a digital control method for the entire construction quality process of a building project according to the present invention; Figure 2 A comparison chart showing the internal temperature control effects when using the traditional particle velocity update expression in existing technologies and when using the optimized particle velocity update expression in this invention; Figure 3 The diagram shows a comparison of the valve opening of an electric regulating valve when using the traditional particle velocity update expression in the existing technology and when using the optimized particle velocity update expression in this invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0030] This invention discloses a method for digital control of the entire construction quality process in building engineering, referring to... Figure 1 This includes steps S001-S005, specifically: S001: Obtain the ambient wind speed reference sequence, radiation intensity reference sequence, and the internal temperature reference sequence and surface temperature reference sequence of concrete.

[0031] Specifically, a miniature automatic weather station is erected at a high point on the construction site to collect environmental wind speed and radiation intensity data. Temperature sensors are pre-embedded inside the concrete pouring body to collect internal temperature data, and temperature sensors are deployed on the concrete surface to collect surface temperature data. All collected data are aligned using Lagrange interpolation. Before the start of the current control cycle, several sampling times corresponding to environmental wind speed, radiation intensity, and the internal and surface temperatures of the concrete are selected to obtain the environmental wind speed reference sequence, radiation intensity reference sequence, and the internal and surface temperature reference sequences for the current control cycle. In this embodiment, the environmental wind speed data, radiation intensity data, and internal and surface temperature data of the concrete are... The sampling frequency for radiation intensity data, as well as the internal and surface temperature data of concrete, is 1Hz. The selection method for the environmental wind speed reference sequence, radiation intensity reference sequence, and internal and surface temperature reference sequences of concrete for the current control cycle is to select 300 sampling times before the start of the current control cycle. In other embodiments, the implementer can set the sampling frequency for environmental wind speed data, radiation intensity data, and internal and surface temperature data of concrete, as well as the selection method for the environmental wind speed reference sequence, radiation intensity reference sequence, and internal and surface temperature reference sequences of concrete for the current control cycle, according to the actual implementation situation.

[0032] S002: Determine the degree of surface temperature fluctuation.

[0033] It should be noted that existing PSO-FuzzyPID algorithms, when adaptively adjusting concrete temperature control parameters, directly calculate fitness based on the surface temperature reference sequence of the control cycle. This makes it difficult to identify and eliminate transient high-frequency noise caused by environmental factors such as gusts of wind or cloud cover at the construction site. This leads the algorithm to mistakenly identify non-temperature fluctuations as system control deviations, causing the particle swarm to aggregate in the incorrect parameter space, thus reducing the stability and accuracy of temperature control. Due to the significant thermal inertia of concrete structures, its actual surface temperature changes exhibit a continuous and smooth monotonic trend over a short period. Therefore, this invention determines the degree of surface temperature fluctuation to characterize the non-stationarity of the surface temperature reference sequence fluctuation in the current control cycle.

[0034] Specifically, the degree of surface temperature fluctuation satisfies the expression: ; In the formula, The degree of surface temperature fluctuation in the current control cycle. The variance of the surface temperature reference sequence in the current control cycle. This is the first-order difference sequence corresponding to the surface temperature reference sequence for the current control cycle. This is the function for finding the maximum value.

[0035] In the formula, The larger the value, the greater the dispersion of all data points in the surface temperature reference sequence of the current control cycle relative to the mean, indicating that the overall oscillation amplitude in the surface temperature reference sequence of the current control cycle is more violent, and therefore the greater the surface temperature fluctuation of the current control cycle. It reflects the maximum value of the rate of change of values ​​among all adjacent data points in the surface temperature reference sequence of the current control cycle. The larger this value is, the greater the possibility of a large numerical jump in the surface temperature reference sequence of the current control cycle. It also indicates that the overall oscillation amplitude in the surface temperature reference sequence of the current control cycle is more drastic and therefore the surface temperature fluctuation is greater in the current control cycle.

[0036] S003: Determine the environmental coupling degree, environmental activity index, and particle trajectory trust weight.

[0037] It should be noted that temperature fluctuations on the concrete surface can originate from both actual temperature rises caused by internal hydration heat accumulation and environmental noise caused by sudden changes in external wind or sunlight. Therefore, simply analyzing the numerical fluctuations of the surface temperature reference sequence in the current control cycle is insufficient to determine the true cause of the temperature fluctuations on the concrete surface, leading to a significant reduction in the robustness and safety of internal concrete temperature control. Based on the principle of thermodynamic coupling, the actual temperature rise of the concrete itself is usually uncorrelated with changes in ambient wind speed and light intensity, while noise generated by external environmental influences shows a significant correlation with these factors. Therefore, this invention combines the covariance between the surface temperature reference sequence and the ambient wind speed and radiation intensity reference sequences, as well as the environmental activity index, to determine the particle trajectory trust weight. This weight is used to characterize the true signal fidelity of the surface temperature reference sequence in the current control cycle, thereby decoupling the real data from environmental noise.

[0038] Specifically, the method for obtaining the environmental coupling degree is as follows: the covariance between the surface temperature reference sequence and the environmental wind speed reference sequence in the current control cycle is taken as the first value, and the covariance between the surface temperature reference sequence and the radiation intensity reference sequence in the current control cycle is taken as the second value. The product of the first value and the second value is normalized by the maximum and minimum values ​​to obtain the environmental coupling degree.

[0039] It should be noted that a larger first value indicates a stronger correlation between the surface temperature reference sequence and the ambient wind speed reference sequence in the current control cycle, thus indicating a greater environmental coupling degree in the current control cycle. Similarly, a larger second value indicates a stronger correlation between the surface temperature reference sequence and the radiation intensity reference sequence in the current control cycle, also indicating a greater environmental coupling degree in the current control cycle.

[0040] Specifically, the method for obtaining the environmental activity index is as follows: calculate the product between the standard deviation of the environmental wind speed reference sequence for the current control cycle and the standard deviation of the radiation intensity reference sequence for the current control cycle, and perform maximum and minimum value normalization on the product to obtain the environmental activity index.

[0041] It should be noted that the larger the standard deviation of the environmental wind speed reference sequence for the current control cycle, the more discrete the numerical distribution within the sequence, indicating a greater likelihood of drastic fluctuations in wind speed at the corresponding moment in the current control cycle, and thus a higher environmental activity index for that cycle. Similarly, the larger the standard deviation of the radiation intensity reference sequence for the current control cycle, the more discrete the numerical distribution, indicating a greater likelihood of significant fluctuations in radiation intensity at the corresponding moment in the current control cycle, and thus a higher environmental activity index for that cycle.

[0042] Specifically, the method for obtaining the particle trajectory trust weight is as follows: Calculate the product of the environmental coupling degree, the environmental activity index, and the surface temperature fluctuation level to obtain a third value; perform inverse maximum-minimum normalization on the third value, so that the larger the third value, the smaller the normalization result, thus obtaining the particle trajectory trust weight. (The process is repeated twice in the original text.)

[0043] It should be noted that a greater degree of surface temperature fluctuation indicates a more severe overall oscillation within the surface temperature reference sequence of the current control cycle, suggesting a higher likelihood of noise influence on the surface temperature reference sequence, and consequently, a smaller particle trajectory trust weight for the current control cycle. A greater environmental coupling indicates a stronger correlation between the surface temperature reference sequence and the environmental wind speed and radiation intensity reference sequences of the current control cycle, suggesting a higher likelihood of external environmental noise affecting the data fluctuations within the surface temperature reference sequence, and consequently, a smaller particle trajectory trust weight for the current control cycle. A higher environmental activity index indicates a greater likelihood of severe fluctuations in the external environment at the corresponding moment of the current control cycle, thus a greater degree of noise influence on the surface temperature reference sequence, and consequently, a smaller particle trajectory trust weight for the current control cycle.

[0044] S004: Optimize the particle velocity update expression.

[0045] It should be noted that after obtaining the particle trajectory trust weights for the current control cycle, this invention will dynamically adjust the speed update mechanism of the PSO algorithm based on the particle trajectory trust weights. Traditional PSO algorithms typically use a fixed weight allocation strategy for the inertial part, self-cognition part, and social cognition part when updating particle speed. They lack the ability to adaptively adjust the optimization strategy based on the data quality of the surface temperature reference sequence. This invention utilizes particle trajectory trust weights to better balance the inertial behavior and social learning behavior of particles, making the algorithm more robust in noisy environments.

[0046] Specifically, the optimized particle velocity update expression satisfies the following expression: ; In the formula, and For the first Second and third During the nth iteration The particle in the first Speed ​​in each dimension For inertial weights, The particle trajectory trust weight for the current control cycle. This is the reciprocal of the particle trajectory trust weight for the current control cycle. and Individual learning factors and social learning factors, and For the range of values ​​within random numbers, For the current control cycle up to the [number]th During the nth iteration The particle in the first The individual historical optimal solution in each dimension. For the current control cycle up to the [number]th In the nth iteration, all particles are at the... The global historical optimal solution in each dimension For the current control cycle, the first During the nth iteration The particle in the first Position in each dimension For the inertial part, For the part about self-awareness, For the social cognition part, in this embodiment, the inertia weight is set to 0.5. In other embodiments, implementers can set it according to the actual implementation situation. For example, when the concrete curing environment is complex and the noise interference is strong, and the global search capability of the algorithm is required to avoid getting trapped in local optima, the preset inertia weight of the algorithm can be appropriately increased to enhance the particle's exploration capability. When the response speed and convergence accuracy of the internal temperature control of concrete are required to be high, the preset inertia weight of the algorithm can be appropriately decreased to improve the algorithm's development capability and convergence efficiency. In this embodiment, the individual learning factor is set to 2. In other embodiments, implementers can set it according to the actual implementation situation. For example, when the reference value of the surface temperature reference sequence is high and more particle learning is required, the individual learning factor can be set to 2. When focusing on optimization based on its own historical experience to maintain population diversity, the individual learning factor can be appropriately increased. When it is necessary to reduce the dependence of particles on their own historical trajectories and focus on following the trend of the group, the individual learning factor can be appropriately decreased to adjust the search behavior of the algorithm. In this embodiment, the social learning factor is set to 2. In other embodiments, implementers can set it according to the actual implementation situation. For example, when it is necessary to speed up the convergence of the particle swarm to the global optimum and improve the rapid response capability to temperature deviation, the social learning factor can be appropriately increased to enhance information sharing among particles. When it is necessary to prevent premature convergence to a false local optimum caused by noise, the social learning factor can be appropriately decreased to reduce the blind following of particles to the current group optimum.

[0047] In the formula, In this item, The larger the value, the more the surface temperature reference sequence is affected by noise, indicating that the first... The particle in the first When updating the speed in the next iteration, it tends to retain the first iteration's speed. The speed at the nth iteration, i.e., the speed at the th iteration Individual particles tend to retain their original motion tendency and ignore current transient disturbances, therefore The larger the weight in the optimized particle velocity update expression, the better. In this item, The larger the value, the more the surface temperature reference sequence is affected by noise. This indicates that when the reliability of the surface temperature reference sequence is low, the particles rely more on their own historically verified optimal positions, i.e., the positions up to the [number]th [position] within the current control cycle. During the nth iteration The particle in the first The individual historical optimal solution in each dimension, therefore The larger the weight in the optimized particle velocity update expression, the better. In this item, The smaller the value, the more the surface temperature reference sequence is affected by noise, indicating that up to the [number]th ... In the next iteration, all particles... The greater the likelihood that the global historical optimal solution of the i-th dimension is affected by noise, the more likely it is that the i-th dimension is affected by noise. The particle is directed towards the cutoff point. In the next iteration, all particles... The shorter the convergence step size of the global historical optimal solution in each dimension, the less likely the particle swarm will be misled by false global historical optimal solutions caused by noise and get trapped in local optima. The smaller the weight in the optimized particle velocity update expression.

[0048] S005: Achieve internal temperature control of concrete.

[0049] Specifically, achieving internal temperature control in concrete includes: The optimized particle velocity update expression is used to calculate the optimized velocities of all particles in all iterations within the current control cycle. Substituting the optimized particle velocities into the particle position update expression yields the optimized positions of all particles in all iterations within the current control cycle. Each particle position is a three-dimensional array containing the proportional, integral, and derivative coefficients of the FuzzyPID controller. Based on the optimized positions of all particles in all iterations within the current control cycle, the fitness values ​​of all particles in all iterations are obtained. The particle with the smallest fitness value is the globally optimal particle for the current control cycle. It should be noted that in this embodiment, the maximum number of iterations of the PSO algorithm is set to 50. In other embodiments, implementers can set the maximum number of iterations according to the actual implementation situation. For example, when the control accuracy of the internal temperature of concrete is high, the maximum number of iterations can be appropriately increased to improve the optimization accuracy. When the real-time control requirement of the internal temperature of concrete is high, the maximum number of iterations can be appropriately reduced to reduce the calculation delay. In this embodiment, when calculating the fitness value of each particle, the time multiplied by the absolute error integral index is used as the fitness evaluation function.

[0050] The deviation and rate of change of the deviation between the maximum value in the internal temperature reference sequence of the current control cycle and the target control temperature are calculated. In this embodiment, the target control temperature is set to 25℃. In other embodiments, the implementer can set it according to the actual implementation situation. The deviation and rate of change of the deviation are fuzzy inferred using the parameters of the FuzzyPID controller corresponding to the globally optimal particle to determine the output value of the FuzzyPID controller, that is, the opening degree of the electric regulating valve. The output value is converted into the opening command of the electric regulating valve. The cooling water flow rate and the internal temperature of the concrete are controlled by controlling the opening degree of the electric regulating valve.

[0051] like Figure 2 As shown in the figure, this diagram illustrates the change in the internal temperature of concrete over a control cycle under environmental disturbances. Existing technologies exhibit slow response in the initial control phase and show significant oscillations and overshoot after approaching the target temperature, making it difficult to stabilize near the set value. In contrast, the curve of this invention rapidly and smoothly approaches the target temperature and remains highly stable after reaching the set value, with almost no overshoot or large fluctuations. This demonstrates that by introducing particle trajectory trust weights, this invention effectively suppresses environmental noise interference, achieving precise control of the internal temperature of concrete, significantly outperforming existing technologies. Figure 3 As shown in the figure, this figure compares the changes in the opening degree of the electric regulating valve under the same operating conditions as the control cycle of the prior art and the present invention. The valve opening degree curve under the control of the prior art fluctuates frequently and violently, showing obvious jitter characteristics, which means that the electric regulating valve is frequently making large adjustments, which can easily lead to mechanical wear and shorten the equipment life. In contrast, the valve opening degree curve under the control of the present invention has a small change amplitude and low frequency. This shows that the control strategy of the present invention can effectively filter out environmental noise, avoid unnecessary malfunctions of the electric regulating valve, and significantly reduce the mechanical wear of the valve while ensuring temperature control accuracy and extending the service life of the equipment.

[0052] This invention also discloses a digital control system for the entire construction quality process of a building project, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a digital control method for the entire construction quality process of a building project according to the present invention is implemented.

[0053] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

Claims

1. A method for digital control of the entire construction quality process in building engineering, characterized in that, include: Obtain the environmental wind speed reference sequence, radiation intensity reference sequence, and internal temperature and surface temperature reference sequence of concrete for the current control cycle; The degree of surface temperature fluctuation in the current control cycle is determined based on the variance within the surface temperature reference sequence of the current control cycle and the maximum first-order difference between adjacent time points. The environmental coupling degree of the current control cycle is determined based on the covariance between the surface temperature reference sequence and the environmental wind speed reference sequence and the radiation intensity reference sequence, respectively. The environmental activity index of the current control cycle is determined based on the standard deviation of the environmental wind speed reference sequence and the radiation intensity reference sequence, respectively. The particle trajectory trust weight of the current control cycle is determined based on the surface temperature fluctuation degree, the environmental coupling degree, and the environmental activity index. Based on the particle trajectory trust weight, the optimized particle velocity update expression for the current control cycle is obtained; Based on the optimized particle velocity update expression, determine the globally optimal particle for the current control cycle; By utilizing the parameters of the FuzzyPID controller corresponding to the globally optimal particle, control commands are output to adjust the cooling water flow rate, thereby achieving internal temperature control of the concrete.

2. The method for digital control of the entire construction quality process of a building project according to claim 1, characterized in that, The process of obtaining the environmental wind speed reference sequence, radiation intensity reference sequence, and concrete internal temperature reference sequence and surface temperature reference sequence for the current control cycle includes: selecting environmental wind speed data, radiation intensity data, and concrete internal temperature data and surface temperature data corresponding to several sampling times before the start of the current control cycle, and obtaining the environmental wind speed reference sequence, radiation intensity reference sequence, and concrete internal temperature reference sequence and surface temperature reference sequence for the current control cycle.

3. The method for digital control of the entire construction quality process of building engineering according to claim 1, characterized in that, The degree of surface temperature fluctuation satisfies the expression: ； In the formula, The degree of surface temperature fluctuation in the current control cycle. The variance of the surface temperature reference sequence in the current control cycle. This is the first-order difference sequence corresponding to the surface temperature reference sequence for the current control cycle. This is the function for finding the maximum value.

4. The method for digital control of the entire construction quality process of a building project according to claim 1, characterized in that, The method for obtaining the environmental coupling degree is as follows: the covariance between the surface temperature reference sequence and the environmental wind speed reference sequence in the current control cycle is taken as the first value, the covariance between the surface temperature reference sequence and the radiation intensity reference sequence in the current control cycle is taken as the second value, the product of the first value and the second value is normalized by the maximum and minimum values, and the result is taken as the environmental coupling degree.

5. The method for digital control of the entire construction quality process of a building project according to claim 1, characterized in that, The method for obtaining the environmental activity index is as follows: calculate the product between the standard deviation of the environmental wind speed reference sequence of the current control cycle and the standard deviation of the radiation intensity reference sequence of the current control cycle, perform maximum and minimum value normalization on the product, and use the result as the environmental activity index.

6. The method for digital control of the entire construction quality process of a building project according to claim 1, characterized in that, The method for obtaining the particle trajectory trust weight is as follows: calculate the product between the environmental coupling degree, the environmental activity index and the surface temperature fluctuation degree to obtain a third value; perform reverse maximum-minimum normalization on the third value so that the larger the third value is, the smaller the normalization result is, and use the obtained normalization result as the particle trajectory trust weight.

7. The method for digital control of the entire construction quality process of building engineering according to claim 1, characterized in that, The optimized particle velocity update expression satisfies the following expression: ； In the formula, and For the first Second and third During the nth iteration The particle in the first Speed ​​in each dimension For inertial weights, The particle trajectory trust weight for the current control cycle. This is the reciprocal of the particle trajectory trust weight for the current control cycle. and Individual learning factors and social learning factors, and It is a random number. For the first During the nth iteration The particle in the first The optimal solution for each dimension. For the first During the nth iteration The global optimal solution corresponding to each dimension. For the first During the nth iteration The particle in the first Position in each dimension.

8. The method for digital control of the entire construction quality process of building engineering according to claim 1, characterized in that, The process of determining the globally optimal particle for the current control cycle includes: calculating the optimized particle velocity using the optimized particle velocity update expression; substituting the optimized particle velocity into the particle position update expression to obtain the positions of all particles corresponding to all iterations within the current control cycle; traversing the fitness values ​​of all particles corresponding to all iterations; and identifying the particle with the smallest fitness value as the globally optimal particle for the current control cycle.

9. The method for digital control of the entire construction quality process of a building project according to claim 1, characterized in that, The output control command adjusts the cooling water flow rate to achieve internal temperature control of the concrete, including: calculating the deviation and rate of change of the maximum value in the internal temperature reference sequence of the current control cycle from the target control temperature; using the FuzzyPID controller parameters corresponding to the globally optimal particle to perform fuzzy inference on the deviation and rate of change of the deviation to determine the output value of the FuzzyPID controller; converting the output value into an opening command for an electric regulating valve; and adjusting the cooling water flow rate and controlling the internal temperature of the concrete by controlling the opening of the electric regulating valve.

10. A digital control system for the entire construction quality process of building engineering, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a digital control method for the entire process of construction quality of building engineering is implemented according to any one of claims 1-9.

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