Dust concentration prediction method and device, electronic equipment and medium

Abstract

The invention relates to the technical field of sand and dust prediction, and discloses a sand and dust concentration prediction method, which comprises the following steps: S1, acquiring geographical coordinate information of a sand and dust prediction point and confirming each factor influencing the sand and dust concentration; s2, constructing a relation equation between each factor and the sand and dust concentration and a weight coefficient of the relation equation between each factor and the sand and dust concentration according to each factor influencing the sand and dust concentration; s3, performing fitting on the equation of each factor and the sand and dust concentration and the weight coefficient of the relation equation of each factor and the sand and dust concentration to construct a sand and dust concentration model of the sand and dust prediction point; and S4, acquiring parameters of a relation equation between each factor and the sand and dust concentration, and substituting the parameters into the sand and dust concentration model of the sand and dust prediction point to obtain the sand and dust concentration of the sand and dust prediction point.

Description

Technical Field

[0001] The present invention relates to the technical field of sand and dust prediction, and in particular to a method, device, electronic equipment and medium for predicting sand and dust concentration. Background Art

[0002] "Sand and desert" is a general term for deserts, Gobi and wasteland areas. These areas have vast landforms and rich resources such as wind and solar energy, becoming the "new battlefield" for the development of new energy. However, facing the special challenges of the sand and desert environment to equipment, such as wind and sand corrosion, wear and tear, etc., they are still the main factors restricting the development of new energy. Accurately obtaining sand and dust concentration data can take corresponding protective measures to ensure the normal operation of equipment.

[0003] The acquisition of dust concentration data mainly relies on sedimentation cylinders, dust sensors, and satellite remote sensing technologies. Sedimentation cylinders require manual sampling and analysis, and the data is intuitive and reliable, but it is time-consuming and labor-intensive, and the sampling site is limited. The data obtained is the accumulated value of a certain period of time. For changeable weather, the small amount of data is not representative, and the large amount of data is time-consuming and labor-intensive. Dust sensors can achieve real-time monitoring of dust concentration and solve the purpose of manual difficulties. They can also obtain the distribution concentration of dust particle size, but the dust environment has obvious damage and clogging effects on precision equipment. After a period of time, the reliability of the data is questioned, and the installation of some environmental sensors is restricted. The method of obtaining dust concentration by arranging equipment is not only inefficient, but also the accuracy of the data cannot be guaranteed. Summary of the invention

[0004] The technical problem to be solved by the present invention is to solve the problem that the existing method for obtaining dust concentration is difficult to obtain the dust concentration and the obtained dust concentration is inaccurate.

[0005] In order to solve the above technical problems, the present invention provides a method for predicting dust concentration, the method comprising:

[0006] S1, obtain the geographical coordinate information of the dust prediction point and confirm the various factors affecting the dust concentration;

[0007] S2, constructing a relationship equation between each factor and the dust concentration and a weight coefficient of the relationship equation between each factor and the dust concentration according to each factor affecting the dust concentration;

[0008] S3, fitting the equations of the various factors and the dust concentration and the weight coefficients of the relationship equations of the various factors and the dust concentration to construct a dust concentration model for the dust prediction point;

[0009] S4, obtaining the parameters of the relationship equation between various factors and dust concentration and bringing them into the dust concentration model of the dust prediction point to obtain the dust concentration of the dust prediction point.

[0010] Furthermore, the various factors affecting the dust concentration specifically include the concentration of the dust source, the temperature, humidity, wind speed, wind direction and visibility at the dust forecast point.

[0011] Furthermore, the dust concentration model is specifically:

[0012]

[0013] Among them, F(C) is the dust concentration, k is the weight coefficient, f(x) is the relationship equation between each factor and the dust concentration, and x is the parameter of the relationship equation between each factor and the dust concentration.

[0014] Furthermore, the dust concentration equation of the dust source includes:

[0015]

[0016] f(ss) is the dust concentration of the dust source, ai is the weight coefficient of the i-th dust source, i is the i-th dust source affecting the dust environment. Xi is the parameter when the dust volume of the i-th dust source reaches the prediction point;

[0017] Among them, the fitting equation of f(ss) is:

[0018] X=SV d

[0019]

[0020] The dry settling rate is derived based on the combined effects of buoyancy, gravity, air resistance, etc. on particles.

[0021]

[0022] Where S is the atmospheric sand content, i.e. the dust concentration in the sand source area, V is the dry deposition rate, and v is the w is the average wind speed,

[0023] The dust propagation distance formula is as follows:

[0024]

[0025] D(t) represents the dust propagation distance in km, and D0 represents the straight-line distance from the dust source to the prediction point.

[0026] Furthermore, the weight coefficient is determined by a hierarchy analysis method.

[0027] Furthermore, in addition to the relationship equation between the concentration factor of the dust source and the dust concentration, the relationship equations between other factors and the dust concentration are obtained based on historical data through linear or nonlinear fitting methods.

[0028] According to another aspect of the present invention, there is provided a dust concentration prediction device, the device comprising:

[0029] A basic information acquisition module, which is used to obtain the geographical coordinate information of the dust prediction point and confirm various factors affecting the dust concentration;

[0030] A relationship equation and weight coefficient determination module, wherein the relationship equation and weight coefficient determination module is used to construct a relationship equation between each factor and the dust concentration and a weight coefficient of the relationship equation between each factor and the dust concentration according to each factor affecting the dust concentration;

[0031] A dust concentration model building module, the dust concentration model building module is used to fit the equations of the various factors and the dust concentration and the weight coefficients of the relationship equations of the various factors and the dust concentration to build a dust concentration model for the dust prediction point;

[0032] A dust concentration generation module is used to obtain the parameters of the relationship equation between various factors and dust concentration and bring them into the dust concentration model of the dust prediction point to obtain the dust concentration of the dust prediction point.

[0033] According to another aspect of the present invention, there is provided an electronic device, comprising: at least one processor, and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any dust concentration prediction method in the embodiments of the present invention.

[0034] According to another aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute any dust concentration prediction method in the embodiments of the present invention.

[0035] Compared with the prior art, the dust concentration prediction method according to the embodiment of the present invention has the following beneficial effects:

[0036] The embodiments of the present invention construct a dust concentration prediction model and use meteorological data, geographic information and other relevant factors to obtain dust concentration data. No or only a small amount of on-site monitoring equipment is required, thereby significantly reducing the cost of obtaining dust concentration data. The present invention uses the dust concentration prediction model to perform predictions, which can avoid monitoring errors caused by traditional monitoring equipment due to factors such as equipment damage and aging caused by wind and sand, thereby improving the accuracy of obtaining dust concentration data. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flow chart of a dust concentration prediction method provided by an embodiment of the present invention;

[0038] Figure 2 It is a wind direction map of Turpan in the past 20 years provided by an embodiment of the present invention;

[0039] Figure 3 This is a wind speed map of Turpan in the past 20 years provided by an embodiment of the present invention;

[0040] Figure 4 is a schematic diagram of a dust concentration prediction device provided by an embodiment of the present invention;

[0041] Figure 5 is a block diagram of an electronic device for implementing an embodiment of the present invention.

[0042] In the figure, 10, basic information acquisition module; 20, relationship equation and weight coefficient determination module; 30, dust concentration model construction module; 40, dust concentration generation module; 600, electronic device; 601, calculation unit; 602, ROM; 603, RAM; 604, bus; 605, I / O interface; 606, input unit; 607, output unit; 608, storage unit; 609, communication unit. DETAILED DESCRIPTION

[0043] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0044] like Figure 1 As shown, in an optional embodiment of the present invention, the method for predicting dust concentration includes:

[0045] S1, obtain the geographical coordinate information of the dust prediction point and confirm the various factors affecting the dust concentration;

[0046] S2, constructing a relationship equation between each factor and the dust concentration and a weight coefficient of the relationship equation between each factor and the dust concentration according to each factor affecting the dust concentration;

[0047] S3, fitting the equations of the various factors and the dust concentration and the weight coefficients of the relationship equations of the various factors and the dust concentration to construct a dust concentration model for the dust prediction point;

[0048] S4, obtaining the parameters of the relationship equation between various factors and dust concentration and bringing them into the dust concentration model of the dust prediction point to obtain the dust concentration of the dust prediction point.

[0049] Specifically, taking the Turpan prediction point as an example, the present invention is further described:

[0050] The geographical coordinates of the Turpan prediction point are obtained in S1, and the factors affecting the sandstorm weather are counted, including the concentration of the sandstorm source, the temperature, humidity, wind speed, wind direction and visibility of the sandstorm prediction point; Turpan, Xinjiang is the prediction location, with geographical coordinates of longitude 89.2503° and latitude 42.97274°, about 60km from the northwest of the Kumtag Desert, about 130km from the southern edge of the Gurbantunggut Desert, and about 320km from the northeast edge of the Taklimakan Desert.

[0051] Among them, the effective dust source nearby is determined according to the geographical coordinates of the Turpan prediction point, and the effective dust source is determined according to the average wind speed of the prediction point:

[0052]

[0053] In this example, effective dust sources are screened:

[0054] Further, according to the wind direction of the prediction point, it is determined whether the wind direction of the dust source and the prediction point is against the wind or against the wind. The wind against the dust source refers to the wind blowing from the dust source to the prediction point, which means it is valid and is included in the weight.

[0055] The wind against the dust source refers to the wind blowing from the prediction point towards the dust source. If the dust source does not work or is invalid, then the dust source will be ignored.

[0056] In S2, the weight factors of each factor are obtained by using the hierarchical analysis method as shown in the following table

[0057] Among them, CR = 0.057113274 < 0.1, indicating that the consistency is passed. The above assigned weight coefficients are reasonable.

[0058] The dust concentration equation for dust sources includes:

[0059]

[0060] f(ss) is the dust concentration of the dust source, ai is the weight coefficient of the i-th dust source, i is the i-th dust source affecting the dust environment. Xi is the parameter when the dust volume of the i-th dust source reaches the prediction point;

[0061] Among them, the fitting equation of f(ss) is:

[0062] X=SV d

[0063]

[0064] The data is fitted to show the relationship between sand content and wind speed:

[0065]

[0066] The dry settling rate is derived based on the combined effects of buoyancy, gravity, air resistance, etc. on particles.

[0067]

[0068] Where S is the atmospheric sand content, i.e. the dust concentration in the sand source area, V is the dry deposition rate, and v is the w is the average wind speed, the dust source formula is as follows:

[0069]

[0070] The dust propagation distance formula is as follows:

[0071]

[0072] D(t) represents the dust propagation distance in km, and D0 represents the straight-line distance from the dust source to the prediction point.

[0073] like Figure 2 and Figure 3 As shown in the figure, except for the relationship equation between the dust source concentration factor and the dust concentration, the relationship equations between other factors and the dust concentration are obtained based on historical data through linear or nonlinear fitting methods. By fitting PM2.5 data for the past 20 years with temperature, humidity, wind speed, wind direction and visibility, the fitting equation is as follows:

[0074] F(T)=57.93-0.65T

[0075] F(H)=37.16+0.4H

[0076] F(ws)=46.68+0.01ws

[0077] F(wd)=51.29-0.002wd

[0078] F(C)=179.51V^(-0.398)

[0079] At a certain moment in Turpan, Xinjiang, the ambient temperature is 15°C, the humidity is 40%, the wind speed is 5m / s, the wind direction is 135°, and the visibility is 30km. 4m / s<wind speed (5m / s)<7m / s, which means that the dust source within 300km is an effective dust source. Turpan is about 60km away from the northern edge of the Kumtag Desert, about 130km away from the southern edge of the Gurbantungt Desert, and about 300km away from the northeastern edge of the Taklimakan Desert. According to the location of Turpan, if the wind direction is in the direction of the wind, it will be included in the calculation, and if the wind direction is against the wind, it will not be included. That is, if the wind direction at the prediction point is north wind, N2 will be included in the calculation, and other dust sources will be ignored. If the wind direction is south wind, N1 and N3 will be included in the calculation, and other dust sources will be ignored.

[0080] The weight coefficients obtained by using the hierarchical analysis method are as follows:

[0081] N1 N2 N3 Kumtag Desert Gurbantunggut Desert Taklimakan Desert 0.2828 0.6434 0.0738

[0082] CR=0.056475713<0.1, indicating that the consistency is passed.

[0083] The Turpan dust source equation is as follows:

[0084] f(ss)=0.2828X1+0.6434X2+0.0738X3

[0085] In addition to the parameters of the equation of the relationship between the dust source concentration factor and the dust concentration, the parameters of the equation of the relationship between other factors and the dust concentration are obtained based on historical data through linear or nonlinear fitting methods.

[0086] In S3, the dust concentration model is specifically:

[0087]

[0088] Among them, F(C) is the dust concentration, k is the weight coefficient, f(x) is the relationship equation between each factor and the dust concentration, and x is the parameter of the relationship equation between each factor and the dust concentration.

[0089] The Turpan prediction model is obtained by summarizing the equations as follows:

[0090]

[0091] In S4, various parameters are brought into the Turpan prediction model to obtain the predicted value of the dust concentration with Turpan as the prediction point.

[0092] Literature research shows that the Gurbantunggut Desert is mainly composed of medium sand (0.25-0.5 mm, 8.7%), fine sand (0.1-0.25 mm, 68.2%), very fine sand (0.05-0.1 mm, 19.1%) and silt sand (<0.05 mm, 4%). The main components of sand are silicon dioxide, metal oxides, salts, etc. To simplify the calculation, the sand is considered to be silicon dioxide. The density of sand is 2650 kg / m3, C = 0.45, and the aerodynamic viscosity η is 1.84×10 -5 Pa·s, air density ρ air 1.185kg / m 3 , the settlement velocity and settlement distance of several types of sand are calculated as follows:

[0093] Medium sand settling velocity: V = 3m / s ~ 4.23m / s

[0094] Fine sand settling velocity: V = 1.89m / s ~ 3m / s

[0095] Very fine sand settling velocity: V = 1.34m / s ~ 1.89m / s

[0096] Powder sand settling velocity: V<1.34m / s

[0097] For example, if the temperature in Turpan is 15°C, the humidity is 40%, the wind speed is 5m / s, the wind direction is 90°, and the visibility is 30km, then it can be judged that the dust source is effective within 300km, and the wind speed is 90°, then the Gurbantunggut Desert is an effective dust source, and the dust concentration calculated by the prediction model is 59.769ug / m 3 .

[0098] In an optional embodiment of the present invention, the various factors affecting the dust concentration specifically include the concentration of the dust source, the temperature, humidity, wind speed, wind direction and visibility at the dust prediction point.

[0099] In an optional embodiment of the present invention, the dust concentration model is specifically:

[0100]

[0101] Among them, F(C) is the dust concentration, k is the weight coefficient, f(x) is the relationship equation between each factor and the dust concentration, and x is the parameter of the relationship equation between each factor and the dust concentration.

[0102] Furthermore, the dust concentration equation of the dust source includes:

[0103]

[0104] f(ss) is the dust concentration of the dust source, ai is the weight coefficient of the i-th dust source, i is the i-th dust source affecting the dust environment. Xi is the parameter when the dust volume of the i-th dust source reaches the prediction point;

[0105] Among them, the fitting equation of f(ss) is:

[0106] X=SV d

[0107]

[0108] The data is fitted to show the relationship between sand content and wind speed:

[0109]

[0110] The dry settling rate is derived based on the combined effects of buoyancy, gravity, air resistance, etc. on particles.

[0111]

[0112] Where S is the atmospheric sand content, i.e. the dust concentration in the sand source area, V is the dry deposition rate, and v is the w is the average wind speed, the dust source formula is as follows:

[0113]

[0114] The dust propagation distance formula is as follows:

[0115]

[0116] D(t) represents the dust propagation distance in km, and D0 represents the straight-line distance from the dust source to the prediction point.

[0117] In an optional embodiment of the present invention, the weight coefficient is determined by a hierarchy analysis method.

[0118] In an optional embodiment of the present invention, in addition to the relationship equation between the concentration factor of the dust source and the dust concentration, the relationship equations of other factors and the dust concentration are obtained based on historical data through a linear or nonlinear fitting method.

[0119] According to another aspect of the present invention, there is provided a dust concentration prediction device, the device comprising:

[0120] A basic information acquisition module 10, which is used to obtain the geographical coordinate information of the dust prediction point and confirm various factors affecting the dust concentration;

[0121] A relationship equation and weight coefficient determination module 20, wherein the relationship equation and weight coefficient determination module 20 is used to construct a relationship equation between each factor and the dust concentration and a weight coefficient of the relationship equation between each factor and the dust concentration according to each factor affecting the dust concentration;

[0122] A dust concentration model building module 30, the dust concentration model building module 30 is used to fit the equations of the various factors and the dust concentration and the weight coefficients of the relationship equations of the various factors and the dust concentration to build a dust concentration model for the dust prediction point;

[0123] A dust concentration generating module 40 is used to obtain the parameters of the relationship equation between various factors and dust concentration and bring them into the dust concentration model of the dust prediction point to obtain the dust concentration of the dust prediction point.

[0124] The embodiments of the present invention construct a dust concentration prediction model and use meteorological data, geographic information and other relevant factors to obtain dust concentration data. No or only a small amount of on-site monitoring equipment is required, thereby significantly reducing the cost of obtaining dust concentration data. The present invention uses the dust concentration prediction model to perform predictions, which can avoid monitoring errors caused by traditional monitoring equipment due to factors such as equipment damage and aging caused by wind and sand, thereby improving the accuracy of obtaining dust concentration data.

[0125] According to an embodiment of the present invention, the present invention also provides an electronic device, a readable storage medium and a computer program product.

[0126] Figure 4 A schematic block diagram of an example electronic device 600 that can be used to implement an embodiment of the present invention is shown. The electronic device 600 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0127] like Figure 4As shown, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0128] Multiple components in the electronic device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the electronic device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0129] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 601 performs the various methods and processes described above, such as a power generation prediction method. For example, in some embodiments, a power generation prediction method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of a power generation prediction method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform a power generation prediction method in any other appropriate manner (e.g., by means of firmware).

[0130] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0132] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0133] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0134] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0135] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0136] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[0137] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A dust concentration prediction method, characterized in that: The method comprises: S1, obtain the geographical coordinate information of the dust prediction point and confirm the various factors affecting the dust concentration; S2, constructing a relationship equation between each factor and the dust concentration and a weight coefficient of the relationship equation between each factor and the dust concentration according to each factor affecting the dust concentration; S3, fitting the equations of the various factors and the dust concentration and the weight coefficients of the relationship equations of the various factors and the dust concentration to construct a dust concentration model for the dust prediction point; S4, obtaining the parameters of the relationship equation between various factors and dust concentration and bringing them into the dust concentration model of the dust prediction point to obtain the dust concentration of the dust prediction point.

2. The dust concentration prediction method according to claim 1, characterized in that: The factors affecting dust concentration include the concentration of dust sources, temperature, humidity, wind speed, wind direction and visibility at the dust forecast point.

3. The dust concentration prediction method according to claim 2, characterized in that: The dust concentration model is specifically: Among them, F(C) is the dust concentration, k is the weight coefficient, f(x) is the relationship equation between each factor and the dust concentration, and x is the parameter of the relationship equation between each factor and the dust concentration.

4. The dust concentration prediction method according to claim 3, characterized in that: The dust concentration equation for dust sources includes: f(ss) is the dust concentration of the dust source, ai is the weight coefficient of the i-th dust source, i is the i-th dust source affecting the dust environment. Xi is the parameter when the dust volume of the i-th dust source reaches the prediction point; Among them, the fitting equation of f(ss) is: X=SV d The dry settling rate is derived based on the combined effects of buoyancy, gravity, air resistance, etc. on particles. Where S is the atmospheric sand content, i.e. the dust concentration in the sand source area, V is the dry deposition rate, and v is w is the average wind speed, The dust propagation distance formula is as follows: D(t) represents the dust propagation distance in km, and D0 represents the straight-line distance from the dust source to the prediction point.

5. The dust concentration prediction method according to claim 4, characterized in that: The weight coefficient is determined by the hierarchical analysis method.

6. The dust concentration prediction method according to claim 1, characterized in that: Except for the relationship equation between the concentration factor of dust source and dust concentration, the relationship equations between other factors and dust concentration are obtained based on historical data through linear or nonlinear fitting methods.

7. A dust concentration prediction device, characterized in that: The dust concentration prediction setting includes: A basic information acquisition module, which is used to obtain the geographical coordinate information of the dust prediction point and confirm various factors affecting the dust concentration; A relationship equation and weight coefficient determination module, wherein the relationship equation and weight coefficient determination module is used to construct a relationship equation between each factor and the dust concentration and a weight coefficient of the relationship equation between each factor and the dust concentration according to each factor affecting the dust concentration; A dust concentration model building module, the dust concentration model building module is used to fit the equations of the various factors and the dust concentration and the weight coefficients of the relationship equations of the various factors and the dust concentration to build a dust concentration model for the dust prediction point; A dust concentration generation module is used to obtain the parameters of the relationship equation between various factors and dust concentration and bring them into the dust concentration model of the dust prediction point to obtain the dust concentration of the dust prediction point.

8. An electronic device, characterized in that: include: at least one processor, and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

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