A method for monitoring safety of construction environment
By introducing adaptive nonlinear search step factor and improving the long-nosed raccoon optimization algorithm, the problem that traditional PID control system is easily trapped in local optimality in building construction environments due to magnetic interference and the long-nosed raccoon algorithm is easy to fall into the local optimality, achieving more efficient and accurate temperature and humidity control, ensuring safety monitoring of the building construction environment.
Patent Information
- Application Number
- CN202510080783.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-20
- Publication Date
- 2025-04-01
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional PID control systems are susceptible to magnetic interference in building construction environments, resulting in inaccurate temperature and humidity control. The proboscoon algorithm is prone to fall into local optimality in the later stage of iteration and misses the optimal solution.
Introduce adaptive nonlinear search step factor, improve the long-nosed raccoon optimization algorithm, optimize the temperature and humidity PID control system of building construction environment, and improve control speed and accuracy.
It effectively avoids the problem of invalid search and reduced diversity that falls into the boundary area due to search distance, improves the control speed and accuracy of temperature and humidity PID control, and realizes safety monitoring of building construction environment.
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Figure CN119739029A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of PID control, and more specifically, particularly relates to a method for monitoring the safety of a construction environment. Background Art
[0002] During the construction process, many building materials are extremely sensitive to the temperature and humidity of the environment, which affects the application rate of building materials. For example, some building materials may deform, crack or degrade in an overly humid or dry environment, which will not only affect the quality of the building, but also increase the construction cost. Therefore, it is very important to monitor the temperature and humidity of the environment. In order to control costs and improve utilization, it is necessary to monitor the temperature and humidity of the environment during construction.
[0003] At present, the temperature and humidity control of the construction environment is generally carried out manually. Manual control is mainly regulated by experience, etc., and cannot achieve precise control of temperature and humidity. In order to improve the temperature and humidity monitoring of the environment, some intelligent control systems are used, and PID control is mostly adopted. However, for complex construction environments, traditional PID is easily affected by magnetic interference from the building environment.
[0004] The coati algorithm (COA) is a population-based metaheuristic algorithm in which the tuned parameters are considered to be members of the algorithm's population. The position of each coati in the search space determines the value of the parameter variable. Therefore, in COA, the final coati position represents a candidate solution to the problem. The coati algorithm (COA) is prone to falling into local optimality in the late iteration, and the search range is too large or too small, which may lead to the omission of the optimal solution. Summary of the invention
[0005] In order to solve the above technical problems, the present invention provides a method for monitoring the safety of a construction environment, introduces an adaptive nonlinear search step factor, improves the coati optimization algorithm to optimize the temperature and humidity PID control system of the construction environment, improves the control speed and accuracy of the temperature and humidity PID control, and realizes safety monitoring of the construction environment.
[0006] A method for monitoring construction environment safety comprises the following steps: S1: Build a temperature and humidity PID control system for the construction environment; S2: Convert the temperature and humidity control problem of the construction site environment into an optimizable mathematical model; S3: Introducing the search step factor to improve the position update strategy of the global exploration phase of the coati algorithm; S4: Use the improved coati algorithm to adjust the temperature and humidity PID control parameters, and apply the optimal Kp, Ki, and Kd parameters to the construction environment temperature and humidity PID control system to achieve safety monitoring of the construction environment.
[0007] Preferably, in S1, the construction environment temperature and humidity PID control system is a position PID control system, including: a real-time temperature and humidity value input unit, a target temperature and humidity value input unit, an improved coati algorithm unit, an objective function unit and a temperature and humidity control device unit, and the temperature and humidity control device unit is an actuator of the temperature and humidity controller.
[0008] Preferably, in S3, the search step factor formula is: ; In the formula, For the The search step size factor for the iteration, To search for the maximum step size factor, To search for the minimum step size factor, is the maximum number of iterations of the algorithm.
[0009] Preferably, in S2, the mathematical model can be optimized as the objective function of the improved coati algorithm for adjusting the temperature and humidity PID controller parameters, and the objective function formula is: ; In the formula, is the fitness value of the individual position of the coati in the tth iteration, e(t) is the difference between the target value of temperature and humidity in the construction environment and the real-time value of temperature and humidity in the tth iteration, t r The time to reach the target temperature and humidity for the first time. is the error weight, is the response weight.
[0010] Preferably, S4 uses the improved coati algorithm to adjust the temperature and humidity PID control parameters, and the specific steps are: S41: Initialize the parameters of the improved coati algorithm and the initial positions of the coati population and prey population; S42: The temperature and humidity PID control parameter values are mathematically converted into a set in the form of a space vector, and encoded as the position of the coati; S43: Simulate the behavior of coatis, establish the mathematical model of the algorithm's global exploration and local development phase position update strategy, and update the PID parameter set solution; S44: Global exploration phase, for the population Each individual updates the population position through formula 1 and formula 2, and then Each individual updates the population position through formula 3 and formula 2; S45: Local development stage, using formula 4 and formula 5 to simulate the escape behavior of coatis and update the population position; S46: Current iteration number Self-increment, determine whether the current number of iterations reaches the maximum number of iterations T. If so, exit the loop, output the global optimal parameter solution, and assign it to the parameters of the construction environment temperature and humidity PID control; otherwise, return to execute S41.
[0011] Preferably, formula 1 is: , where is the new position of the i-th raccoon in the j-dimension, is the current position of the i-th raccoon in the j-dimension, is the current position of the prey in the j dimension, is the total size of coatis, is the algorithm dimension, For the The search step factor of the iteration, r is a random number between 0 and 1, The random value of two numbers {1,2}.
[0012] Preferably, Formula 2 is: , where is the current optimal position of the i-th raccoon, is the fitness value of the i-th raccoon’s latest position, is the fitness value of the optimal position of the i-th raccoon, is the latest position of the i-th raccoon.
[0013] Preferably, Formula 3 is: , where is a random real number in the interval [0,1], is a random value of two numbers {1,2}, is the fitness value of the current prey position, is the latest position of the i-th raccoon, For the The search step size factor for the iteration, is the fitness value of the optimal position of the i-th raccoon, is the current position of the prey in the j dimension, is the current position of the i-th raccoon in the j dimension.
[0014] Preferably, formula 4 is: , where is the new position of the i-th raccoon in the j dimension during the development phase, is the lower limit of the current algorithm position, is the upper limit of the current algorithm position, r is a random number between 0 and 1, is the current position of the i-th raccoon in the j dimension.
[0015] Preferably, Formula 5 is: , where is the current optimal position of the i-th raccoon, is the latest position of the i-th raccoon The fitness value of is the new position of the i-th raccoon in the j dimension during the development phase, is the fitness value of the optimal position of the i-th raccoon.
[0016] Compared with the prior art, the present invention has the following beneficial effects: In the present invention, an adaptive nonlinear search step factor is introduced to effectively avoid the problems of invalid search and reduced diversity falling into the boundary area due to the search distance, improve the coati optimization algorithm to optimize the temperature and humidity PID control system of the construction environment, improve the control speed and accuracy of the temperature and humidity PID control, and realize the safe monitoring of the construction environment. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] Figure 1 It is a schematic diagram of the overall process of the present invention; Figure 2 It is a flow chart of setting temperature PID parameters of the improved coati algorithm of the present invention; Figure 3 This is the result diagram of the improved coati algorithm for setting the temperature PID parameters of the present invention; Figure 4 It is a comparison chart of the fitness values before and after the improvement of the coati algorithm of the present invention. DETAILED DESCRIPTION
[0018] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.
[0019] See also Figure 1-Figure 4 The present invention provides a construction environment safety monitoring method, which optimizes the construction environment temperature and humidity PID control system by improving the coati algorithm to control the temperature and humidity of the construction site. Figure 1 As shown, the specific steps are: S1. Build a temperature and humidity PID control system for the construction environment.
[0020] S2. Convert the temperature and humidity control problem of the construction site environment into an optimizable mathematical model.
[0021] S3. Introduce the search step factor to improve the position update strategy of the global exploration phase of the coati algorithm.
[0022] S4. Use the improved coati algorithm to adjust the temperature and humidity PID control parameters, and apply the optimal Kp, Ki, and Kd parameters to the construction environment temperature and humidity PID control system to achieve safety monitoring of the construction environment.
[0023] In S1, the construction environment temperature and humidity PID control system is a position PID control system, including: a real-time temperature and humidity value input unit, a target temperature and humidity value input unit, an improved coati algorithm unit, an objective function unit and a temperature and humidity control device unit, and the temperature and humidity control device unit is the actuator of the temperature and humidity controller.
[0024] The construction site environment temperature and humidity control problem is converted into an optimizable mathematical model, which is used as the objective function of the improved coati algorithm to adjust the temperature and humidity PID controller parameters. The objective function formula is: , where is the fitness value of the individual position of the coati in the tth iteration, e(t) is the difference between the target value of temperature and humidity in the construction environment and the real-time value of temperature and humidity in the tth iteration, t r The time to reach the target temperature and humidity for the first time. is the error weight, is the response weight.
[0025] The objective function is designed by considering the minimum difference between the target value of temperature and humidity in the construction environment and the real-time value of temperature and humidity and the minimum time to reach the target temperature and humidity for the first time.
[0026] In S3, the search step factor is performed, and the mathematical model formula is: , where For the The search step size factor for the iteration, To search for the maximum step size factor, To search for the minimum step size factor, is the maximum number of iterations of the algorithm.
[0027] The search step factor is adaptive and nonlinear. In the iterative optimization process, the standard coati algorithm has problems of too large or too small search range. Too large search range leads to reduced population diversity, while too small search range affects the optimization speed of the algorithm. Through improvement, the above problems can be effectively avoided and the ability of the algorithm to escape from the local optimum can be improved.
[0028] The improved coati algorithm is used to adjust the temperature and humidity PID control parameters, such as Figure 2 As shown, the specific steps are: S41, initializing the parameters of the improved coati algorithm and the initial positions of the coati population and the prey population, that is, the initial values of the temperature and humidity PID controller parameters; S42, mathematically transforming the temperature and humidity PID control parameter values into a set in the form of a space vector, and encoding them as the position of the coati; S43, simulate the behavior of coatis, establish the mathematical model of the position update strategy in the global exploration and local development phases of the algorithm, and update the PID parameter set solution; S44, global exploration phase, the population Each individual updates the population position through formula 1 and formula 2, and then Each individual updates the population position through formula 3 and formula 4; S45, local development stage, simulating the escape behavior of coatis through formula 4 and formula 5 to update the population position; S46, current number of iterations Self-increment, determine whether the current number of iterations reaches the maximum number of iterations T. If so, exit the loop, output the global optimal parameter solution, and assign it to the parameters of the construction environment temperature and humidity PID control; otherwise, return to execute S41.
[0029] Among them, formula 1 is: , where is the new position of the i-th raccoon in the j-dimension, is the current position of the i-th raccoon in the j-dimension, is the current position of the prey in the j dimension, is the total size of coatis, is the algorithm dimension, For the The search step factor of the iteration, r is a random number between 0 and 1, The random value of two numbers {1,2}; Formula 2 is: , where is the current optimal position of the i-th raccoon, is the fitness value of the i-th raccoon’s latest position, is the fitness value of the optimal position of the i-th raccoon, is the latest position of the i-th raccoon; Formula three is: , where is a random real number in the interval [0,1], is a random value of two numbers {1,2}, is the fitness value of the current prey position, is the latest position of the i-th raccoon, For the The search step size factor for the iteration, is the fitness value of the optimal position of the i-th raccoon, is the current position of the prey in the j dimension, is the current position of the i-th raccoon in the j dimension; Formula 4 is: , where is the new position of the i-th raccoon in the j dimension during the development phase, is the lower limit of the current algorithm position, is the upper limit of the current algorithm position, r is a random number between 0 and 1, is the current position of the i-th raccoon in the j dimension; Formula 5 is: , where is the current optimal position of the i-th raccoon, is the latest position of the i-th raccoon The fitness value of is the new position of the i-th raccoon in the j dimension during the development phase, is the fitness value of the optimal position of the i-th raccoon.
[0030] In S41, the improved coati algorithm is initialized, including the algorithm population size N, the problem dimension D, the total number of algorithm iterations T and the current number of iterations t, as well as the current algorithm search upper bound ub and search lower bound lb. The position initialization formula of the coati population individuals and the prey population individuals is: , where is the position of the ith individual, ubj is the algorithm search upper bound, lbj is the algorithm search lower bound, and r is a random number between 0 and 1; In S42, the temperature and humidity PID control parameter values are mathematically converted into a set in the form of space vectors and encoded as the position of the coati, and the encoding is real number encoding.
[0031] In the specific implementation, we first tested it in Matlab and Simulink, set the improved coati population size N=100, the prey population size n=50, the maximum iteration T=30, the problem dimension D=3, the algorithm optimization upper bound ub=100, the algorithm optimization upper bound lb=0, and the maximum value of the search step factor 2, the minimum value 0.1, and used the improved coati algorithm to adjust the temperature PID control system parameters. The adjustment effect is as follows Figure 3 shown.
[0032] Objective Function Calculate the fitness value of the coati position. The fitness value determines the performance of the temperature PID control system parameters. The smaller the fitness value, the better the temperature PID control system parameters. Figure 4 As shown in the figure, the fitness value comparison chart of the coati algorithm before and after improvement shows that compared with the fitness value of the coati optimization algorithm (COA) before improvement, the fitness value of the improved coati optimization algorithm (SCOA) has a smaller fitness value at the beginning. As the iterative optimization proceeds, the fitness value of the improved coati optimization algorithm (SCOA) is the smallest, indicating that the improved coati optimization algorithm (SCOA) is better at setting the temperature PID control system parameters.
[0033] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.
Claims
1. A construction environment safety monitoring method, characterized in that: The following steps are involved: S1: Build a temperature and humidity PID control system for the construction environment; S2: Convert the temperature and humidity control problem of the construction site environment into an optimizable mathematical model; S3: Introducing the search step factor to improve the position update strategy of the global exploration phase of the coati algorithm; S4: Use the improved coati algorithm to adjust the temperature and humidity PID control parameters, and apply the optimal Kp, Ki, and Kd parameters to the construction environment temperature and humidity PID control system to achieve safety monitoring of the construction environment.
2. A construction environment safety monitoring method as claimed in claim 1, characterized in that: In S1, the construction environment temperature and humidity PID control system is a position PID control system, including: a real-time temperature and humidity value input unit, a target temperature and humidity value input unit, an improved coati algorithm unit, an objective function unit and a temperature and humidity control device unit, and the temperature and humidity control device unit is the actuator of the temperature and humidity controller.
3. A construction environment safety monitoring method as claimed in claim 1, characterized in that: In S3, the search step factor formula is: ； In the formula, For the The search step size factor for the iteration, To search for the maximum value of the step size factor, To search for the minimum step size factor, is the maximum number of iterations of the algorithm.
4. A construction environment safety monitoring method as claimed in claim 1, characterized in that: In S2, the mathematical model can be optimized as the objective function of the improved coati algorithm to adjust the temperature and humidity PID controller parameters. The objective function formula is: ； In the formula, is the fitness value of the individual position of the coati in the tth iteration, e(t) is the difference between the target value of temperature and humidity in the construction environment and the real-time value of temperature and humidity in the tth iteration, t r The time to reach the target temperature and humidity for the first time. is the error weight, is the response weight.
5. A construction environment safety monitoring method as claimed in claim 1, characterized in that: S4 uses the improved coati algorithm to adjust the temperature and humidity PID control parameters. The specific steps are as follows: S41: Initialize the parameters of the improved coati algorithm and the initial positions of the coati population and prey population; S42: The temperature and humidity PID control parameter values are mathematically converted into a set in the form of a space vector, and encoded as the position of the coati; S43: Simulate the behavior of coatis, establish the mathematical model of the algorithm's global exploration and local development phase position update strategy, and update the PID parameter set solution; S44: Global exploration phase, for the population Each individual updates the population position through formula 1 and formula 2, and then Each individual updates the population position through formula 3 and formula 2; S45: Local development stage, using formula 4 and formula 5 to simulate the escape behavior of coatis and update the population position; S46: Current iteration number Self-increment, determine whether the current number of iterations reaches the maximum number of iterations T, if so, exit the loop, output the global optimal parameter solution, and assign it to the parameters of the construction environment temperature and humidity PID control; otherwise, return to execute S41.
6. A construction environment safety monitoring method as claimed in claim 5, characterized in that: Formula 1 is: , where is the new position of the i-th raccoon in the j-dimension, is the current position of the i-th raccoon in the j-dimension, is the current position of the prey in the j dimension, is the total size of coatis, is the algorithm dimension, For the The search step factor of the iteration, r is a random number between 0 and 1, The random value of two numbers {1,2}.
7. A construction environment safety monitoring method as claimed in claim 5, characterized in that: Formula 2 is: , where is the current optimal position of the i-th raccoon, is the fitness value of the i-th raccoon’s latest position, is the fitness value of the optimal position of the i-th raccoon, is the latest position of the i-th raccoon.
8. A construction environment safety monitoring method as claimed in claim 5, characterized in that: Formula three is: , where is a random real number in the interval [0,1], is a random value of two numbers {1,2}, is the fitness value of the current prey position, is the latest position of the i-th raccoon, For the The search step size factor for the iteration, is the fitness value of the optimal position of the i-th raccoon, is the current position of the prey in the j dimension, is the current position of the i-th raccoon in the j dimension.
9. A construction environment safety monitoring method as claimed in claim 5, characterized in that: Formula 4 is: , where is the new position of the i-th raccoon in the j dimension during the development phase, is the lower limit of the current algorithm position, is the upper limit of the current algorithm position, r is a random number between 0 and 1, is the current position of the i-th raccoon in the j dimension.
10. A construction environment safety monitoring method as claimed in claim 5, characterized in that: Formula 5 is: , where is the current optimal position of the i-th raccoon, is the latest position of the i-th raccoon The fitness value of is the new position of the i-th raccoon in the j dimension during the development phase, is the fitness value of the optimal position of the i-th raccoon.
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