Real-time wind network calculation method based on multi-parameter sensors

Abstract

This invention belongs to the field of wind network calculation and proposes a real-time wind network calculation method based on multi-parameter sensors. Building upon traditional ventilation calculation methods, this invention utilizes multi-parameter sensor data to propose a real-time wind network calculation method based on the Lagrange multiplier method and nonlinear iteration. The wind network includes wind paths and ventilation nodes; temperature, humidity, pressure, and wind speed sensors are installed at the ventilation nodes to obtain multi-parameter monitoring data for temperature, humidity, pressure, and wind speed; ultrasonic sensors are installed on the wind paths to obtain multi-parameter monitoring data for wind speed and air volume. This method achieves optimal approximation of the calculated results to the measured data while ensuring loop pressure difference balance and node air volume balance. Specific implementation steps are given in pseudocode form. Related experiments demonstrate the effectiveness of the algorithm in terms of operating efficiency and solution accuracy.

Description

Technical Field

[0001] This invention belongs to the field of wind network calculation, and particularly relates to a real-time wind network calculation method based on multi-parameter sensors. Background Technology

[0002] Mine ventilation is crucial for safe production in mines, used to increase oxygen concentration and dilute toxic gases and dust, thereby ensuring personnel safety. Ventilation design and production management require data acquisition and analysis of underground ventilation conditions, with ventilation network calculation being a vital component. Traditional ventilation network calculations rely on data such as airflow resistance, fan curves, and natural wind pressure to solve nonlinear equations. However, the complex and variable conditions of underground production lead to dynamic changes in airflow resistance, affecting calculation accuracy.

[0003] With the development of IoT technology, precision wind measurement technology and artificial intelligence, the underground ventilation status can be transmitted to the host computer in relatively real time and accurately, thus providing an important foundation for real-time calculation of the underground ventilation status.

[0004] In the project implementation, mechanical anemometers are first used to measure the wind resistance of each ventilation path throughout the mine. Then, based on the fan curves and natural wind pressure, the natural wind distribution can be calculated. Multi-parameter anemometer stations are used for real-time monitoring of the ventilation network status. Due to construction and installation cost issues, multi-parameter sensors can only be installed at a limited number of nodes and ventilation paths.

[0005] Wind network calculation is a classic problem that has been extensively studied both domestically and internationally. Among the classic methods, the loop method and the nodal pressure method are the most common. The loop method uses the airflow on the remaining tree branches as independent variables, and the airflow in other branches is a linear combination of the airflow on the remaining tree branches. Then, the pressure difference for each loop is calculated based on the airflow and air resistance, and the loop pressure differences are iteratively adjusted. The nodal pressure method uses the air pressure at each node as variables, calculates the wind conductance based on the current airflow, and then establishes a system of linear equations based on the nodal airflow balance, which is then solved iteratively.

[0006] Research on real-time calculation is relatively limited both domestically and internationally, with some studies primarily focusing on wind volume measurement corrections. However, with the development of precision wind measurement and IoT communication technologies, parameters such as wind speed, air volume, atmospheric pressure, temperature, and humidity can be transmitted to a host computer with relatively high accuracy. These technologies provide a basis for real-time and accurate estimation of wind network conditions. Summary of the Invention

[0007] The objective of this invention is to utilize multi-parameter sensing technology for downhole wind measurement, integrating temperature, humidity, pressure, and wind speed sensors at ventilation nodes; and employing ultrasonic technology in the airflow path for precise measurement of wind speed and volume. The initial natural wind distribution calculation is performed using the loop method, and specific steps of the non-matrix method are provided, thus avoiding the additional computational overhead caused by the sparsity of the adjacency matrix.

[0008] The technical solution of the present invention is as follows: a real-time wind network calculation method based on multi-parameter sensors, wherein the wind network includes wind paths and ventilation nodes; temperature sensors, humidity sensors, pressure sensors and wind speed sensors are respectively installed at the ventilation nodes to obtain multi-parameter monitoring data of temperature, humidity, pressure and wind speed; ultrasonic sensors are installed on the wind paths to obtain multi-parameter monitoring data of wind speed and air volume.

[0009] The wind network is represented by an undirected, self-loop-free connected graph G(V,E), where V = {v1, v2, ..., v...} n Let E = {e1, e2, ..., e} represent a point set. m} represents the edge set; where e i Represented as (a i b i ), a i b i ∈V, a i <b i , 1≤i≤m; R i q i h i h Ni Representing edge e respectively i The wind resistance, air volume, ventilation resistance, and natural wind pressure; h Fi Representing edge e i The air pressure of the ventilation fan is related to q. i The polynomial function; given the order pair u, v∈V, the function ξ(u, v) is defined as shown in formula (1); Let the set of edges adjacent to the ordered pair u and v be as shown in formula (2);

[0010]

[0011]

[0012] Where φ represents a null value;

[0013] The classic Scott-Hinsley method uses a matrix form for solving the problem. Since mainstream high-level programming languages ​​(C++STL, C#, Java, Python, etc.) provide standard data structures and related operations for hash and set operations, the real-time wind network solution method based on multi-parameter sensors provided in this invention is an implementation method that does not depend on matrix calculation. On the one hand, it is easy to implement; on the other hand, it avoids the extra overhead caused by sparse matrices.

[0014] Step 1: Perform natural wind distribution calculations to obtain the initial values ​​of the air volume q for each wind path. The specific steps are as follows:

[0015] 1) Let E cLet G′ represent the edge set containing the fixed air volume, and let the subgraph G′ = (V, EE) c G′ is a connected graph;

[0016] 2) Using the minimum spanning tree algorithm, generate the subtree T(V, E) of subgraph G′. t ), thus obtaining the set of remaining tree branches E′=EE t Among them, E t Represents the edge set of a subtree;

[0017] 3) For each edge e k (a k b k )∈E′, search in subtree T for a k to b k Shortest path k ;

[0018] 4) For each edge e i ∈E,S i (s, j) is a set of binary pairs, initially empty; where s takes the value 1 or -1, representing the positive or negative wind direction; j is the index of an element in E;

[0019] 5) For each shortest path Path k ,set up Let n be the sequence of nodes on the shortest path, where n is the number of nodes on the shortest path. k Let represent the shortest path length; Where b = (t + n) k -1)modn k For t = 0, 1, ..., n k -1; mod is the modulo operation;

[0020] 6) Let E f Denotes the edge set given a fixed airflow rate, for each e i ∈E f The air volume qi is initialized to a given fixed air volume; for each e i ∈E′-E f air volume q i Initialize to random values;

[0021] 7) For each shortest path Path k , The sequence of nodes on the shortest path is summed over the loop pressure difference, denoted as g. k Let d be the partial derivative of the loop pressure difference and the air volume. k ; initialized to g respectively k =0 and d k =0;

[0022] 8) For t = 0, 1, ..., n k-1, repeat the following steps in a loop:

[0023] 8.1) Let (s, i) = ξ(v) kb v kt ), where b = (t + n k -1)mod n k ;

[0024] 8.2) Order

[0025] 8.3) Let g k =g k +R i q i |q i |-h Ni -h Fi (q i );

[0026] 8.4) Order

[0027] 9) Update

[0028] 10) Iterate through steps 8) and 9) until each g k To achieve the specified accuracy or the specified maximum number of iterations;

[0029] 11) For each e i e i ∈E, the final air volume is calculated as follows:

[0030] Step 2: Construct a multi-constrained nonlinear objective function, and use the nodal wind pressure method and the Lagrange multiplier method to transform the multi-constrained nonlinear objective function into an iterative solution of an unconstrained linear system of equations (5);

[0031] Monitoring nodes are set up in the air intake circuit, return air path and ventilation nodes of the air network for installing monitoring equipment; Represents the set of all monitoring nodes. For designated monitoring nodes These represent the monitored temperature, humidity, atmospheric pressure, and wind speed at the monitoring nodes, respectively. Indicates elevation. The density of air is expressed by formula (3); The wind pressure at the monitoring node is given by formula (4); This represents the set of all monitored wind paths. For the designated monitoring air path Indicates the monitored air volume;

[0032]

[0033]

[0034] Under the constraints of wind pressure balance and air volume balance, the best estimate of the real-time monitored air volume is obtained by optimizing the objective function;

[0035] The estimated variables in the objective function include: v for each monitoring node j Corresponding wind pressure estimate p j v j ∈V; each monitored air path e i (e i ∈E) corresponds to air volume q i The objective function and constraints are shown in Equation (5). The optimization objective corresponds to the least squares estimation of wind pressure and air volume based on multi-parameter monitoring data. The first set of constraints is the ventilation resistance formula. For airways without real-time air volume monitoring, the resistance is calculated based on the measured resistance. The second set of constraints ensures the air volume balance of all monitoring nodes.

[0036]

[0037] Where (a) i b i ) represents e i The two endpoints.

[0038] The constraints of formula (5) are nonlinear, and an iterative method is used to solve them; Let q be the time t. i The iteration value is Initially The value is the result of natural wind distribution calculation;

[0039] Define wind guide As shown in formula (6), where C max The preset maximum wind resistance;

[0040]

[0041] Equation (5) is transformed into an iterative linear constraint, and the iterative linear constraint at time t is shown in Equation (7).

[0042]

[0043] Formula (7) is an equality-constrained optimization problem. Using the Lagrange multiplier method, the optimal solution is equivalently transformed into a system of linear equations for solution; where the constraints are... Total There are several equations, with a corresponding multiplier of λ. i Constraints There are |V| equations, with a corresponding multiplier of μ. j Equation (7) is transformed into Equation (8) for an unconstrained optimization problem.

[0044]

[0045] Where (a) i b i ) represents e i The two endpoints; function f is the least squares error of airflow and air pressure and the monitored values; function g e These are the Lagrange multipliers relating to the equation constraints of wind guidance, air volume, and wind pressure in the wind path; the function g v These are the Lagrange multipliers relating to the nodal airflow balance constraints; the function f r τ is a regularization term used to avoid generating ill-conditioned matrices;

[0046] Setting the partial derivatives of the objective function F with respect to each variable to zero, we obtain the system of equations (9);

[0047]

[0048] Indicates each air path e i The objective function F with respect to the multiplier λ i The partial derivative; This means that at each node v j The objective function F with respect to the multipliers μ i The partial derivative; Indicates each air path e i The objective function F with respect to air volume q i The partial derivative; This means that at each node v j The objective function F with respect to wind pressure p j The partial derivatives. Formula (9) total There are 10 variables, totaling 100 variables. A system of linear equations is used to solve the system of equations to obtain the air volume and air pressure q. i p j When τ = 0, it is the optimal solution for the optimization objective (7); achieve When t reaches the pre-specified maximum number of iterations, output q. i p j ε is the set threshold.

[0049] The algorithm flow for step 2 is summarized as in Algorithm 2.

[0050] Algorithm 2

[0051] (1) Enter the wind resistance R of each air path i The air volume q is solved using Algorithm 1.i ;

[0052] (2) The set of monitoring nodes is denoted as Calculate the monitored wind pressure according to formulas (3) and (4). The set of monitoring nodes is denoted as Record the monitored air volume

[0053] (3) Let t←0; Set the current air volume

[0054] (4) Calculate the wind direction according to formula (6)

[0055] (5) Solve the system of equations corresponding to formula (9). make

[0056] (6) Let t ← t+1;

[0057] (7) If for have If t reaches the pre-specified maximum number of iterations, the algorithm returns q. i p j .

[0058] The beneficial effects of this invention are: It collects and analyzes accurate wind, temperature, humidity, and pressure sensor data, establishes an optimal model, and achieves the best estimation of the real-time state of the wind network. Compared with traditional adjustment methods that only address air volume, this invention comprehensively considers parameters such as wind pressure and density measured and converted from multiple sensors, and obtains a more robust estimation of the wind network state through the Lagrange multiplier method and nonlinear iterative solution. Attached Figure Description

[0059] Figure 1 This is a flowchart of a real-time wind network calculation method based on multi-parameter sensors.

[0060] Figure 2 This is an example diagram of the ventilation network in the embodiment. Detailed Implementation

[0061] The flowchart of a real-time wind network calculation method based on multi-parameter sensors is as follows: Figure 1 As shown;

[0062] Natural wind distribution is based on wind volume to determine wind direction, and a balance constraint equation is established based on the wind direction. The air volume is then calculated using the Lagrange multiplier method until the iteration termination condition is met, at which point the resulting air volume is output.

[0063] Figure 2 An example of a ventilation network is given; the wind resistance and fixed air volume are shown in Table 1. Table 1 also shows the air volume under natural wind distribution, real-time monitoring, and real-time calculation.

[0064] Table 1 Fixed air volume, air resistance, and air volume values

[0065]

[0066] Table 2 Relative Wind Pressure at Nodes

[0067]

[0068] Table 2 presents the relative wind pressure under natural wind distribution, real-time monitoring, and real-time calculation. Based on the values ​​in Tables 1 and 2, comparing the differences between the air volume and relative wind pressure obtained under real-time monitoring and those obtained under real-time calculation, the wind network state estimation obtained by the method of this invention is more robust.

Claims

1. A real-time wind network calculation method based on multi-parameter sensors, characterized in that, The air network includes air paths and ventilation nodes; temperature sensors, humidity sensors, pressure sensors and wind speed sensors are installed at the ventilation nodes to obtain multi-parameter monitoring data of temperature, humidity, pressure and wind speed; ultrasonic sensors are installed on the air paths to obtain multi-parameter monitoring data of wind speed and air volume. Wind networks utilize undirected, loop-free connected graphs. It means that among them represents a point set, Represents the edge set; where Represented as , ; , , , Representing edges respectively The wind resistance, air volume, ventilation resistance, and natural wind pressure; Representing an edge The air pressure of the ventilation fan is related to polynomial functions; given order pairs Define function As shown in formula (1); Representing order pairs The set of adjacent edges is shown in formula (2); (1) (2) in, Indicates a null value; Step 1: Perform natural wind distribution calculations to obtain the air volume for each wind path. The initial values ​​are determined by the following steps: 1) Order Let the edge set containing the fixed air volume be represented. ; It is a connected graph; 2) Use the minimum spanning tree algorithm to generate a subgraph. subtree , obtained the remaining tree branches ;in, Represents the edge set of a subtree; 3) For each In the subtree Search from arrive shortest path ; 4) For each , Let be a set of binary tuples, initially empty; where, The value can be 1 or -1, indicating the positive or negative wind direction; Take as 5) For each shortest path, the index of the element; ,set up Let be the sequence of nodes on the shortest path, where Let represent the shortest path length; ,in , ; For modulo operation; 6) Let Denotes the edge set given a fixed airflow rate, for each air volume Initialize to a given fixed airflow; for each air volume Initialize to random values; 7) For each shortest path , The sequence of nodes on the shortest path is summed over the loop pressure difference, denoted as . Let the loop pressure difference and the partial derivative with respect to air volume be... ; respectively initialized to and ; 8) Repeat the following steps: 8.1) Order ;8.2) Order ; 8.3) Order ; 8.4) Order ; 9) Update ; 10) Iterate through steps 8) and 9) until each To achieve the specified accuracy or the specified maximum number of iterations; 11) For each The final air volume is calculated as follows: ; Step 2: Construct a multi-constrained nonlinear objective function, and use the nodal wind pressure method and the Lagrange multiplier method to transform the multi-constrained nonlinear objective function into an iterative solution of an unconstrained linear system of equations (9); Monitoring nodes are set up in the air intake circuit, return air path and ventilation nodes of the air network for installing monitoring equipment; , , To designate monitoring nodes; These represent the monitored temperature, humidity, atmospheric pressure, and wind speed at the monitoring nodes, respectively. Indicates elevation. The density of air is expressed by formula (3); The wind pressure at the monitoring node is given by formula (4); ; Indicates the monitored air volume; (3) (4) Under the constraints of wind pressure balance and air volume balance, the best estimate of the real-time monitored air volume is obtained by optimizing the objective function; The estimated variables in the objective function include: each monitoring node Corresponding wind pressure estimation Each Corresponding air volume , The objective function and constraints are shown in Equation (5). The optimization objective corresponds to the least squares estimation of wind pressure and air volume based on multi-parameter monitoring data. The first set of constraints is the ventilation resistance formula. For airways without real-time air volume monitoring, the resistance is calculated based on the measured resistance. The second set of constraints ensures the air volume balance of all monitoring nodes. (5) in express The two endpoints; The constraints of formula (5) are nonlinear, and an iterative method is used to solve them; , ,remember time The iteration value is Initially The calculated value is the natural wind distribution value; Define wind guide As shown in formula (6), where The preset maximum wind resistance; (6) Equation (5) is transformed into an iterative linear constraint. The iterative linear constraints at time points are shown in Equation (7); (7) Formula (7) is an equality-constrained optimization problem. Using the Lagrange multiplier method, the optimal solution is equivalently transformed into a system of linear equations for solution; where the constraints are... Total There are several equations, with corresponding multipliers of 1, 2, 3, 4, 5, 6, Constraints Total There are several equations, with corresponding multipliers of 1, 2, 3, 4, 5, 6, Equation (7) is transformed into Equation (8), an unconstrained optimization problem; (8) in express The two endpoints of the function; It is the least squares error of air volume, air pressure, and monitored values; function These are the Lagrange multipliers relating to the equation constraints of wind guidance, air volume, and wind pressure in the wind path; functions These are the Lagrange multipliers relating to the nodal airflow balance constraints; functions This is a regularization term used to avoid generating ill-conditioned matrices. Constant positive numbers; Let the objective function With the partial derivatives with respect to each variable being zero, we obtain the system of equations (9); (9) Formula (9) constitutes a total There are 10 variables, totaling 100 variables. A system of linear equations is used to solve the system of equations to obtain the air volume and air pressure. ;when When, it is the optimal solution for the optimization objective (7); for ,achieve Sometimes Output after reaching the pre-specified maximum number of iterations. ; To set a threshold.

Images