A method and system for tracing the source of dust pollution in port yards

Abstract

The present invention relates to the technical field of pollution source tracing, and in particular to a method and system for tracing the source of dust pollution in a port yard, the method comprising the following steps: collecting the true dust concentration and dust diffusion influencing factors at different monitoring points in the port yard, and constructing a dust diffusion evaluation model; when the location of the pollution source is not determined, based on the true dust concentration, the dust diffusion influencing factors and the dust diffusion evaluation model, using an improved sparrow search algorithm to obtain the location and source strength of the pollution source; obtaining a matrix estimation formula for defining an inverse Gaussian function according to a least squares estimation method, and then defining an inverse Gaussian function according to the dust diffusion evaluation model; when the location of the pollution source is determined, according to the true dust concentration and the dust diffusion influencing factors, the source strength of the pollution source is obtained based on the inverse Gaussian function. The present invention does not need to install a large number of monitoring equipment that is costly and complex to install and maintain, and can intelligently, accurately and efficiently trace the location and source strength of the pollution source in the port yard.

Description

Technical Field

[0001] The present invention relates to the technical field of pollution source tracing, and in particular to a method and system for tracing the source of dust pollution in a port yard. Background Art

[0002] Dust pollution has always been one of the key issues of urban air pollution in my country. As one of the main components of total suspended matter pollution in coastal and riverside cities, port operation dust has a direct threat to the atmospheric environment due to its rapid diffusion and migration. Port operation dust pollution refers to the phenomenon that solid particles generated in port areas and caused by various activities are suspended in the air, resulting in deterioration of air quality. This type of pollution is common in port cities around the world, and its severity is particularly significant in some large international port areas. It not only causes a large amount of solid particles to enter the atmosphere, endangering air quality and human health, but also deposits in water bodies, causing water pollution and having a negative impact on the surrounding ecosystem. Therefore, it is of key significance in the fields of environmental protection, meteorology and engineering to accurately predict the atmospheric diffusion process of port dust pollution and accurately trace the pollution emission sources.

[0003] Dust diffusion in the atmosphere is a complex physical phenomenon that, in many cases, involves multiple spatial scales, multiple physics, and complex geometric structures. Traditional methods for determining source strength often rely on direct measurement equipment or empirical estimates, but these methods have limitations, such as the high cost and complex installation and maintenance of direct measurement equipment, and the poor accuracy of empirical estimates. Therefore, it is necessary to explore a more accurate and efficient method for tracing the source of port yard dust pollution. Summary of the invention

[0004] In view of the defects in the prior art, the present invention provides a method and system for tracing the source of dust pollution in a port yard.

[0005] In order to achieve the above-mentioned purpose, in the first aspect, the present invention provides a method for tracing the source of dust pollution in a port yard, the method comprising the following steps: collecting the true dust concentration and dust diffusion influencing factors at different monitoring points in the port yard, and constructing a dust diffusion evaluation model; when the location of the pollution source is uncertain, based on the true dust concentration, the dust diffusion influencing factors and the dust diffusion evaluation model, the location and source strength of the pollution source are obtained using an improved sparrow search algorithm; according to the least squares estimation method, a matrix estimation formula for defining an inverse Gaussian function is obtained, and then the inverse Gaussian function is defined according to the dust diffusion evaluation model; when the location of the pollution source is determined, according to the true dust concentration and the dust diffusion influencing factors, the source strength of the pollution source is obtained based on the inverse Gaussian function. The present invention does not need to install a large number of monitoring equipment that is costly and complex to install and maintain, and can intelligently, accurately and efficiently trace the location and source strength of the pollution source in the port yard.

[0006] Optionally, the dust diffusion influencing factors include wind speed, rainfall intensity, ground roughness coefficient and atmospheric stability level;

[0007] Collecting the actual dust concentration and dust diffusion influencing factors at different monitoring points in the port yard and building a dust diffusion evaluation model includes the following steps:

[0008] A plurality of monitoring points are set up in the port yard, and the actual dust concentration and various dust diffusion influencing factors at different monitoring points are collected;

[0009] Considering the influence of dust settling and ground roughness on dust diffusion, the dust diffusion evaluation model is constructed based on the Gaussian plume diffusion formula.

[0010] Furthermore, by collecting the actual dust concentration, factors affecting dust diffusion, and constructing a dust diffusion evaluation model, the present invention can be used to set the fitness function of the sparrow search algorithm when the location of the pollution source is uncertain; it can also be used to define the inverse Gaussian function and calculate the monitoring point coefficient when the location of the pollution source is determined, thereby achieving accurate tracing of the pollution source location and source strength.

[0011] Optionally, the dust diffusion evaluation model satisfies the following relationship:

[0012]

[0013] in, is the dust concentration assessment value at the monitoring point, is the coordinate position of the monitoring point in the spatial rectangular coordinate system, A, B and C are all reference symbols set for the convenience of writing, and is the adjustable coefficient, is the rainfall intensity, Q is the source strength of the pollution source, H is the height of the pollution source, D is the ground roughness coefficient, is the wind speed, is the horizontal diffusion parameter, is the vertical diffusion parameter.

[0014] Furthermore, the dust diffusion evaluation model constructed by the present invention further considers the influence of dust settlement and ground roughness on dust diffusion on the basis of the Gaussian plume diffusion formula, so that the model can more accurately reflect the actual diffusion of dust in a complex environment, thereby improving the accuracy of the tracing results of the pollution source location and source strength.

[0015] Optionally, when the location of the pollution source is uncertain, based on the actual dust concentration, the dust diffusion influencing factors and the dust diffusion evaluation model, using an improved sparrow search algorithm to obtain the location and source strength of the pollution source includes the following steps:

[0016] Improving the sparrow search algorithm to obtain the improved sparrow search algorithm;

[0017] Using the actual dust concentration, the dust diffusion influencing factors and the dust diffusion evaluation model to set the fitness function of the improved sparrow search algorithm;

[0018] When the location of the pollution source is uncertain, a sparrow population is generated based on the pollution source information of dust, and the location and source strength of the pollution source are obtained using the improved sparrow search algorithm.

[0019] Furthermore, the present invention improves the sparrow search algorithm to enable it to jump out of the local optimum, and uses the actual dust concentration, dust diffusion influencing factors and dust diffusion evaluation model to set the fitness function, thereby improving the accuracy of the tracing results of the pollution source location and source strength when the pollution source location is uncertain.

[0020] Optionally, the improving the sparrow search algorithm to obtain the improved sparrow search algorithm comprises the following steps:

[0021] Use Tent chaos map to initialize the sparrow population;

[0022] After updating the positions of the discoverer, follower, and sentinel, the fitness variance of the sparrow population is compared with the variance threshold to determine whether the algorithm is trapped in a local optimum;

[0023] If the algorithm falls into a local optimum, the jump model is used to change the position of the discoverer and re-update the positions of the discoverer, follower, and sentinel. The jump model satisfies the following relationship:

[0024]

[0025] in, is the updated position of the jth discoverer, is the position of the jth discoverer, and t is a pseudo-random number uniformly distributed in [0,1].

[0026] Furthermore, the present invention improves the sparrow search algorithm so that it can jump out of the local optimum when trapped in the local optimum, thereby improving the accuracy of the algorithm optimization result.

[0027] Optionally, the fitness function satisfies the following relationship:

[0028]

[0029] in, is the fitness value, is the dust concentration assessment value of the dust at the i-th monitoring point, is the coordinate position of the i-th monitoring point in the spatial rectangular coordinate system, is the actual dust concentration at the i-th monitoring point, and m is the number of the monitoring points.

[0030] Optionally, obtaining a matrix estimation formula for defining an inverse Gaussian function according to a least squares estimation method, and then defining the inverse Gaussian function according to the dust diffusion evaluation model comprises the following steps:

[0031] Obtaining a matrix estimation formula for defining the inverse Gaussian function according to a least squares estimation method;

[0032] The dust diffusion evaluation model is described as a linear function of dust concentration with respect to source strength of pollution source;

[0033] The inverse Gaussian function is defined according to the matrix estimation formula and the linear function.

[0034] Furthermore, the present invention defines an inverse Gaussian function based on a matrix estimation formula and a dust diffusion evaluation model, so as to facilitate accurate inference of the source strength of the pollution source when subsequently determining the location of the pollution source.

[0035] Optionally, the matrix estimation formula and the inverse Gaussian function are shown in the following formulae:

[0036]

[0037]

[0038] in, is the coefficient matrix, is the independent variable matrix, is the dependent variable matrix, is the source intensity matrix of the pollution source, B is the coefficient matrix of the monitoring points, C is the atmospheric diffusion concentration matrix, and T represents transposition.

[0039] Optionally, when determining the location of the pollution source, obtaining the source strength of the pollution source based on the inverse Gaussian function according to the actual dust concentration and the dust diffusion influencing factors includes the following steps:

[0040] Determine the atmospheric diffusion concentration matrix using the actual dust concentration at different monitoring points;

[0041] Determining the horizontal diffusion coefficient and the vertical diffusion coefficient based on the location of the pollution source, the location of the monitoring point and the atmospheric stability level;

[0042] The monitoring point coefficients are calculated using the horizontal diffusion coefficient, vertical diffusion coefficient, wind speed, rainfall intensity, ground roughness coefficient and the pollution source location at each of the monitoring points, and then the monitoring point coefficient matrix is ​​obtained. The monitoring point coefficients satisfy the following relationship:

[0043]

[0044] Wherein, b is the monitoring point coefficient, is the dust concentration assessment value at the monitoring point, is the coordinate position of the monitoring point in the spatial rectangular coordinate system, Q is the source strength of the pollution source;

[0045] The obtained atmospheric diffusion concentration matrix and the monitoring point coefficient matrix are brought into the inverse Gaussian function to calculate the source strength matrix, thereby determining the source strength of the pollution source.

[0046] Furthermore, when determining the atmospheric diffusion concentration matrix and the monitoring point coefficient matrix, the present invention can use the inverse Gaussian function to infer the source strength of the pollution source, thereby accurately tracing the source strength of the pollution source when determining the location of the pollution source.

[0047] In the second aspect, the present invention provides a port yard dust pollution tracing system, the port yard dust pollution tracing system includes: data acquisition equipment, data output equipment, processor and storage, the storage includes a computer readable storage medium, the computer readable storage medium stores a computer program, the computer program includes program instructions, the program instructions when executed by the processor, the processor implements the port yard dust pollution tracing method provided by the present invention. The system can stably run the method provided by the present invention, thereby improving the efficiency of port yard dust pollution tracing, and also can improve the practicality of the method of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 A schematic diagram of a process for tracing the source of dust pollution in a port yard according to an embodiment of the present invention;

[0050] Figure 2 The present invention is a schematic diagram of the framework of a port yard dust pollution tracing system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0052] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0053] It should be noted in advance that, in an optional embodiment, except for independent explanations, the same symbols or letters appearing in all formulas have the same meanings and values.

[0054] In an alternative embodiment, see Figure 1 This embodiment provides a method for tracing the source of dust pollution in a port yard, and the method comprises the following steps:

[0055] S1. Collect the actual dust concentration and dust diffusion influencing factors at different monitoring points in the port yard, and build a dust diffusion evaluation model.

[0056] Step S1 collects the actual dust concentration, dust diffusion influencing factors and constructs a dust diffusion evaluation model. It can be used to set the fitness function of the sparrow search algorithm when the pollution source location is uncertain, and can also be used to define the inverse Gaussian function and calculate the monitoring point coefficient when the pollution source location is determined, thereby accurately tracing the pollution source location and source strength. Step S1 specifically includes the following steps:

[0057] S11. Setting a plurality of monitoring points in the port yard, and collecting the actual dust concentration and various dust diffusion influencing factors at different monitoring points.

[0058] Specifically, in this embodiment, 7 monitoring points are randomly set in the port yard, and a dust concentration detector is used to collect the actual dust concentration of different dusts at different monitoring points. In addition, the dust diffusion influencing factors at each monitoring point need to be collected.

[0059] More specifically, the factors affecting dust diffusion include wind speed, rainfall intensity, ground roughness coefficient and atmospheric stability level. The factors affecting dust diffusion can be obtained through existing technical means, and the specific collection method will not be described in detail here.

[0060] S12. Considering the influence of dust settling and ground roughness on dust diffusion, the dust diffusion evaluation model is constructed based on the Gaussian plume diffusion formula.

[0061] Specifically, in this embodiment, rainfall will change the air humidity and cause dust to settle to the ground, while the ground roughness can reflect the interaction between the surface and the atmosphere and affect the diffusion of dust in the atmosphere. Therefore, this embodiment further considers the influence of dust deposition and ground roughness on dust diffusion on the basis of the Gaussian plume diffusion formula to construct a dust diffusion evaluation model, so that the model can more accurately reflect the actual diffusion of dust in a complex environment, improve the accuracy of dust concentration calculated using the dust diffusion evaluation model at different monitoring points, and thus improve the accuracy of subsequent tracing of pollution source locations and source strengths.

[0062] More specifically, the dust diffusion evaluation model satisfies the following relationship:

[0063]

[0064] in, is the dust concentration assessment value at the monitoring point, is the coordinate position of the monitoring point in the spatial rectangular coordinate system. A, B and C are all reference symbols set for the convenience of writing. and is the adjustable coefficient, and Take 0.00001 and 0.55 respectively, is the rainfall intensity, Q is the source strength of the pollution source, H is the height of the pollution source, D is the ground roughness coefficient, is the wind speed, is the horizontal diffusion parameter, is the vertical diffusion parameter.

[0065] S2. When the location of the pollution source is uncertain, based on the actual dust concentration, the dust diffusion influencing factors and the dust diffusion evaluation model, an improved sparrow search algorithm is used to obtain the location and source strength of the pollution source.

[0066] Wherein, step S2 specifically includes the following steps:

[0067] S21. Improve the sparrow search algorithm to obtain the improved sparrow search algorithm.

[0068] Step S21 improves the sparrow search algorithm so that it can jump out of the local optimum when it is trapped in the local optimum, and uses the actual dust concentration, dust diffusion influencing factors and dust diffusion evaluation model to set the fitness function, thereby improving the accuracy of the tracing results of the pollution source location and source strength when the pollution source location is uncertain. Step S21 specifically includes the following steps:

[0069] S211. Use Tent chaotic mapping to initialize the sparrow population.

[0070] Specifically, in this embodiment, the initial parameters of the sparrow search algorithm are set, and then the sparrow population is initialized using the Tent chaotic map.

[0071] More specifically, the initial parameters of the sparrow search algorithm may refer to step S23.

[0072] S212. After updating the positions of the discoverer, follower and sentinel, the fitness variance of the sparrow population is compared with the variance threshold to determine whether the algorithm falls into a local optimum.

[0073] Specifically, in this embodiment, after executing step S211, population division and fitness evaluation can be performed, and then the positions of the discoverer, follower and sentinel are updated. After the positions of the discoverer, follower and sentinel are updated, the fitness variance of the sparrow population is compared with the variance threshold to determine whether the algorithm is trapped in a local optimum. When the fitness variance of the sparrow population is greater than the variance threshold, it is determined that the algorithm is trapped in a local optimum.

[0074] Furthermore, the variance threshold is specifically 0.04, and the update formulas of the positions of the discoverer, the follower and the alerter can refer to the prior art. In other optional embodiments, the variance threshold can also be set to other values ​​according to actual needs.

[0075] S213. If the algorithm falls into a local optimum, the escape model is used to change the position of the discoverer and re-update the positions of the discoverer, follower, and sentinel.

[0076] Specifically, in this embodiment, if the algorithm is trapped in a local optimum, the escape model is used to update the finder position and return to step S212, and steps S212 and S213 are continuously executed until the algorithm escapes from the local optimum. The escape model is shown in the following formula:

[0077]

[0078] in, is the updated position of the jth discoverer, is the position of the jth finder, and t is a pseudo-random number that follows a uniform distribution in [0,1]. This embodiment updates the finder position by escaping the model so that the algorithm jumps out of the local optimum, thereby improving the accuracy of the algorithm optimization result.

[0079] Furthermore, when the algorithm jumps out of the local optimum, it updates the historical optimal position and fitness value of the population individuals, and outputs the best sparrow individual when the maximum number of iterations is reached.

[0080] S22: using the actual dust concentration, the dust diffusion influencing factors and the dust diffusion evaluation model to set the fitness function of the improved sparrow search algorithm.

[0081] Specifically, in this embodiment, the fitness function satisfies the following relationship:

[0082]

[0083] in, is the fitness value, is the dust concentration assessment value of the dust at the i-th monitoring point, is the coordinate position of the i-th monitoring point in the spatial rectangular coordinate system, is the actual dust concentration at the i-th monitoring point, and m is the number of monitoring points.

[0084] More specifically, the actual dust concentration at each monitoring point and the factors affecting dust diffusion are brought into the dust diffusion evaluation model to calculate the fitness value when the algorithm is running.

[0085] S23. When the location of the pollution source is uncertain, a sparrow population is generated based on the dust pollution source information, and the location and source strength of the pollution source are obtained using the improved sparrow search algorithm.

[0086] Specifically, in this embodiment, when the location of the pollution source is uncertain, a sparrow population is generated based on the pollution source information of dust, and the pollution source information of dust includes the location of the pollution source and the source strength of the pollution source, that is, each individual sparrow represents a possible location and source strength of the pollution source, and then the location and source strength of the pollution source can be obtained using an improved sparrow search algorithm. Among them, the location of the pollution source is represented by the height of the pollution source and the coordinate position of the pollution source on the water surface.

[0087] More specifically, when using the improved sparrow search algorithm to obtain the location and source strength of the pollution source, the sparrow population is set to 30, the maximum number of iterations is set to 100, and the safety threshold is , the lower and upper bounds of the search space are set to 0.1 and 100, respectively.

[0088] Furthermore, when the location of the pollution source is uncertain, the improved sparrow search algorithm is used to obtain the location and source strength of a single pollution source, which not only improves the accuracy of tracing the location and source strength of the pollution source, but also improves the intelligence level of tracing the location and source strength of the pollution source.

[0089] S3. Obtain a matrix estimation formula for defining an inverse Gaussian function according to a least squares estimation method, and further define the inverse Gaussian function according to the dust diffusion evaluation model.

[0090] Step S3 defines an inverse Gaussian function based on the matrix estimation formula and the dust diffusion evaluation model, so as to accurately infer the source strength of the pollution source when determining the location of the pollution source. Step S3 specifically includes the following steps:

[0091] S31. Obtain a matrix estimation formula for defining the inverse Gaussian function according to a least squares estimation method.

[0092] Specifically, in this embodiment, taking polynomial as an example, the essence of curve fitting is to construct a Vandermonde matrix through a sample set, and transform a univariate n-order polynomial nonlinear regression problem into an n-variable linear regression problem. Assume that there is a set of data points ,in , , and there is a relationship between p and q:

[0093]

[0094] in, is the k-order polynomial model parameter to be determined, which can be obtained by minimizing the residual sum of squares To find the optimal parameter analytical solution, that is:

[0095]

[0096] In order to achieve the above goals, the parameters should be Calculate the partial derivatives respectively, that is:

[0097]

[0098] Simplifying the above formula, we get:

[0099]

[0100] Convert the above formula into matrix form, and we get:

[0101]

[0102] make , , , then the above formula can be simplified to:

[0103]

[0104] Finally, the least squares matrix estimation formula is obtained as follows:

[0105]

[0106] in, is the coefficient matrix, is the independent variable matrix, is the dependent variable matrix, and T represents the transpose.

[0107] S32. Describe the dust diffusion evaluation model as a linear function of dust concentration with respect to source strength of the pollution source.

[0108] Specifically, in this embodiment, according to the dust diffusion evaluation model given in step S12, the dust diffusion evaluation model is described as a linear function of dust concentration with respect to the source strength of the pollution source, that is:

[0109]

[0110] Among them, c is the dust concentration assessment value and b is the monitoring point coefficient.

[0111] S33. Define the inverse Gaussian function according to the matrix estimation formula and the linear function.

[0112] Specifically, in this embodiment, according to the matrix estimation formula, the inverse Gaussian function is defined as:

[0113]

[0114] in, is the source intensity matrix of the pollution source, B is the coefficient matrix of the monitoring points, C is the atmospheric diffusion concentration matrix, and T represents transposition.

[0115] S4. When determining the location of the pollution source, the source strength of the pollution source is obtained based on the inverse Gaussian function according to the actual dust concentration and the dust diffusion influencing factors.

[0116] Wherein, step S4 specifically includes the following steps:

[0117] S41. Determine the atmospheric diffusion concentration matrix using the actual dust concentrations at different monitoring points.

[0118] Specifically, in this embodiment, the atmospheric diffusion concentration matrix is:

[0119]

[0120] S42. Determine the horizontal diffusion coefficient and the vertical diffusion coefficient according to the location of the pollution source, the location of the monitoring point and the atmospheric stability level.

[0121] Specifically, in this embodiment, this step is a prior art method and will not be described in detail here.

[0122] S43, using the horizontal diffusion coefficient, vertical diffusion coefficient, wind speed, rainfall intensity, ground roughness coefficient and the pollution source position at each of the monitoring points to calculate the monitoring point coefficient, and then obtain a monitoring point coefficient matrix.

[0123] Specifically, in this embodiment, the monitoring point coefficient satisfies the following relationship:

[0124]

[0125] Wherein, b is the monitoring point coefficient. The horizontal diffusion coefficient, vertical diffusion coefficient, wind speed, rainfall intensity, and ground roughness coefficient at each monitoring point are brought into this relationship, and the monitoring point coefficient at each monitoring point can be calculated by combining the pollution source location and the monitoring point location.

[0126] Furthermore, after calculating the monitoring point coefficients of each monitoring point, a monitoring point coefficient matrix can be obtained. The monitoring point coefficient matrix is ​​in the same form as the independent variable matrix in step S31, that is:

[0127]

[0128] In the monitoring point coefficient matrix, the value of k is the same as the number of pollution sources.

[0129] S44, bringing the obtained atmospheric diffusion concentration matrix and the monitoring point coefficient matrix into the inverse Gaussian function to calculate the source strength matrix, and then determine the source strength of the pollution source.

[0130] Specifically, in this embodiment, the obtained atmospheric diffusion concentration matrix and monitoring point coefficient matrix are substituted into the inverse Gaussian function to calculate the corresponding source strength matrix, and then the source strength of each pollution source is quickly obtained when the location of the pollution source is known.

[0131] Furthermore, as shown in Table 1, for the four pollution sources in a port yard, the actual source strengths measured by the dust concentration detector were 95g / s, 80g / s, 105g / s and 142g / s respectively. Seven monitoring points were set up in the port yard, and the final source strength tracing results were 94.998g / s, 79.997g / s, 105.004g / s and 142.007g / s respectively. The relative errors with the actual source strengths of the four pollution sources were less than 0.1%, with high accuracy. The estimated total dust emission of the four pollution sources was 422.0005g / s, which was very close to the actual total emission of 422g / s. It can be seen that when determining the atmospheric diffusion concentration matrix and the monitoring point coefficient matrix, the use of the inverse Gaussian function can infer the source strengths of multiple pollution sources, and the tracing results are accurate and reliable.

[0132] Table 1 Source strength tracing results and accuracy assessment of pollution sources in a port yard

[0133] It should be noted that, in some cases, the actions described in the specification can be performed in a different order and still achieve the desired results. In this embodiment, the order of steps given is only to make the embodiment appear clearer and easier to explain, rather than to limit it.

[0134] In an optional embodiment, in order to improve the efficiency of port yard dust pollution tracing and improve the practicality of this method, please refer to Figure 2 This embodiment also provides a port yard dust pollution tracing system, which includes: a data acquisition device 1, a data output device 2, a processor 3 and a storage 4, wherein the storage 4 includes a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, wherein the computer program includes program instructions, and when the program instructions are executed by the processor 3, the processor 3 implements the contents described in steps S1 to S4.

[0135] Specifically, in this embodiment, the data acquisition device 1 includes a dust concentration detector and an instrument for collecting various dust diffusion influencing factors. The data acquisition device 1 also includes a digital display screen, and relevant personnel can manually input the collected actual dust concentration and dust diffusion influencing factors on the digital display screen; the data output device 2 also includes a digital display screen for displaying the traceability results.

[0136] In summary, this method constructs a dust diffusion evaluation model. When the location of the pollution source is unknown, this method improves the sparrow search algorithm to trace the location and source strength of a single dust pollution source in the port yard; when the location of the pollution source is known, this method obtains the matrix estimation formula for defining the inverse Gaussian function according to the least squares estimation method, and then defines the inverse Gaussian function according to the dust diffusion evaluation model, and uses the inverse Gaussian function to achieve accurate reverse calculation of the source strength of the port yard dust pollution source. Therefore, this method does not need to install a large number of expensive and complex monitoring equipment, and can intelligently, accurately and efficiently trace the location and source strength of the port yard pollution source, which has high practical value and environmental significance. In addition, the present invention also provides a system compatible with this method, which can stably run this method, thereby improving the efficiency of tracing the source of port yard dust pollution, and also can improve the practicality of this method.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for tracing the source of dust pollution in a port yard, characterized in that: The steps include: Collect the actual dust concentration and dust diffusion influencing factors at different monitoring points in the port yard, and build a dust diffusion evaluation model; The dust diffusion evaluation model satisfies the following relationship: ， in, is the dust concentration assessment value at the monitoring point, is the coordinate position of the monitoring point in the spatial rectangular coordinate system, A, B and C are all reference symbols set for the convenience of writing, and is the adjustable coefficient, is the rainfall intensity, Q is the source strength of the pollution source, H is the height of the pollution source, D is the ground roughness coefficient, is the wind speed, is the horizontal diffusion parameter, is the vertical diffusion parameter; When the location of the pollution source is uncertain, the location and source strength of the pollution source are obtained using an improved sparrow search algorithm based on the actual dust concentration, the dust diffusion influencing factors and the dust diffusion evaluation model; Obtaining a matrix estimation formula for defining an inverse Gaussian function according to a least squares estimation method; The dust diffusion evaluation model is described as a linear function of dust concentration with respect to source strength of pollution source; Defining the inverse Gaussian function according to the matrix estimation formula and the linear function; The matrix estimation formula and the inverse Gaussian function are shown in the following formulas: ， ， in, is the coefficient matrix, is the independent variable matrix, is the dependent variable matrix, is the source intensity matrix of the pollution source, B is the coefficient matrix of the monitoring points, C is the atmospheric diffusion concentration matrix, and T represents transposition; When determining the location of the pollution source, the source strength of the pollution source is obtained based on the inverse Gaussian function according to the actual dust concentration and the dust diffusion influencing factors.

2. A method for tracing the source of dust pollution in a port yard according to claim 1, characterized in that: The dust diffusion influencing factors include wind speed, rainfall intensity, ground roughness coefficient and atmospheric stability level; Collecting the actual dust concentration and dust diffusion influencing factors at different monitoring points in the port yard and building a dust diffusion evaluation model includes the following steps: A plurality of monitoring points are set up in the port yard, and the actual dust concentration and various dust diffusion influencing factors at different monitoring points are collected; Considering the influence of dust settling and ground roughness on dust diffusion, the dust diffusion evaluation model is constructed based on the Gaussian plume diffusion formula.

3. A method for tracing the source of dust pollution in a port yard according to claim 1, characterized in that: When the location of the pollution source is uncertain, based on the actual dust concentration, the dust diffusion influencing factors and the dust diffusion evaluation model, using the improved sparrow search algorithm to obtain the location and source strength of the pollution source includes the following steps: Improving the sparrow search algorithm to obtain the improved sparrow search algorithm; Using the actual dust concentration, the dust diffusion influencing factors and the dust diffusion evaluation model to set the fitness function of the improved sparrow search algorithm; When the location of the pollution source is uncertain, a sparrow population is generated based on the pollution source information of dust, and the location and source strength of the pollution source are obtained using the improved sparrow search algorithm.

4. A method for tracing the source of dust pollution in a port yard according to claim 3, characterized in that: The step of improving the sparrow search algorithm to obtain the improved sparrow search algorithm comprises the following steps: Use Tent chaos map to initialize the sparrow population; After updating the positions of the discoverer, follower, and sentinel, the fitness variance of the sparrow population is compared with the variance threshold to determine whether the algorithm is trapped in a local optimum; If the algorithm falls into a local optimum, the jump model is used to change the position of the discoverer and re-update the positions of the discoverer, follower, and sentinel. The jump model satisfies the following relationship: ， in, is the updated position of the jth discoverer, is the position of the jth discoverer, and t is a pseudo-random number uniformly distributed in [0,1].

5. A method for tracing the source of dust pollution in a port yard according to claim 3, characterized in that: The fitness function satisfies the following relationship: ， in, is the fitness value, is the dust concentration assessment value of the dust at the i-th monitoring point, is the coordinate position of the i-th monitoring point in the spatial rectangular coordinate system, is the actual dust concentration at the i-th monitoring point, and m is the number of the monitoring points.

6. A method for tracing the source of dust pollution in a port yard according to claim 1, characterized in that: When determining the location of the pollution source, obtaining the source strength of the pollution source based on the inverse Gaussian function according to the actual dust concentration and the dust diffusion influencing factors includes the following steps: Determine the atmospheric diffusion concentration matrix using the actual dust concentration at different monitoring points; Determining the horizontal diffusion coefficient and the vertical diffusion coefficient based on the location of the pollution source, the location of the monitoring point and the atmospheric stability level; The monitoring point coefficients are calculated using the horizontal diffusion coefficient, vertical diffusion coefficient, wind speed, rainfall intensity, ground roughness coefficient and the pollution source location at each of the monitoring points, and then the monitoring point coefficient matrix is ​​obtained. The monitoring point coefficients satisfy the following relationship: ， Wherein, b is the monitoring point coefficient, is the dust concentration assessment value at the monitoring point, is the coordinate position of the monitoring point in the spatial rectangular coordinate system, Q is the source strength of the pollution source; The obtained atmospheric diffusion concentration matrix and the monitoring point coefficient matrix are brought into the inverse Gaussian function to calculate the source strength matrix, thereby determining the source strength of the pollution source.

7. A port yard dust pollution tracing system, characterized in that: The port yard dust pollution tracing system comprises: a data acquisition device, a data output device, a processor and a storage device, wherein the storage device comprises a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and wherein the computer program comprises program instructions, and when the program instructions are executed by the processor, the processor implements the port yard dust pollution tracing method as described in any one of claims 1 to 6.

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