Method, device, medium and equipment for predicting dust concentration in transfer station

Abstract

The application provides a transfer station dust concentration prediction method, device, medium and equipment, and belongs to the technical field of dust concentration testing. The method comprises the following steps: acquiring a real-time dust concentration value of a target measurement point in a transfer station; acquiring a set of conveying parameters of the material on the associated conveying belt conveying the material to the target measurement point, the set of conveying parameters comprising material properties and instantaneous material flow at multiple future time points; predicting a dust concentration sequence of the target measurement point based on the real-time dust concentration value and the set of conveying parameters, the dust concentration sequence comprising dust concentration prediction values at multiple future time points; detecting whether there is a dust concentration prediction value exceeding a preset dust concentration threshold in the dust concentration sequence; and if so, adjusting the set of conveying parameters so that the dust concentration prediction values formed based on the adjusted set of conveying parameters are all below the dust concentration threshold. The application can realize early prediction and intervention of dust concentration exceeding the standard.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of dust concentration testing, and particularly relates to a transfer station dust concentration prediction method, device, medium and equipment. BACKGROUND

[0002] In the industrial fields of steel and metallurgy, the transfer station, as the core node of the raw material conveying system, undertakes the transfer function of bulk materials such as iron ore, coke and coal between different belt conveyors and silos. Due to the behaviors of height difference impact and gravity falling of the materials in the unloading and transfer process, the transfer station becomes a high-risk area of dust pollution. Dust not only contains harmful components such as free silicon dioxide, and long-term exposure can cause workers to suffer from occupational diseases such as pneumoconiosis, and high-concentration dust can also cause explosion risk, which seriously threatens production safety and environmental quality.

[0003] The dust pollution of the transfer station has significant particularity, which specifically shows that: 1. Dynamic superposition effect: Different types of materials (such as coke, injection coal, sintered ore, etc.) have differences in particle size, humidity and flow rate, and when they converge in a closed space, the dust release amount is not simply linear superposition, but presents a nonlinear coupling characteristic. For example, when fine particle coal dust and coarse particle ore dust are mixed and diffused, a more complex concentration field distribution will be formed due to air flow disturbance, increasing the difficulty of pollution control. 2. Instantaneous peak risk: The falling impact of the materials at the unloading port and the belt transfer point is the main inducement of dust explosion. 3. Monitoring lag and blind area: Traditional monitoring relies on point sensors (such as laser scattering type and filter membrane weighing type), which can only collect historical concentration data at fixed points and cannot predict the potential pollution contribution of the materials that have not arrived (such as the material flow about to enter the transfer point). When the sensor feedbacks a high concentration signal, the dust has already diffused to the working area, forming a "passive response" dilemma.

[0004] The existing dust concentration monitoring and prevention and control scheme has obvious limitations: 1. Response lag: The fixed concentration limit (such as 50 mg / m³) is used as the trigger threshold, and when the concentration exceeds the limit, the high-concentration dust has already diffused continuously. Even if the ventilation and dust reduction measures are started immediately, the workers are still inevitably exposed to the dangerous environment, and the health risk cannot be completely avoided. 2. Rough measures: When the conveying belt in the transfer station is operating at high load, even if the dust removal effect of the related dust removal equipment is opened to the maximum value, there is still a risk of dust concentration exceeding the limit. In this case, the material conveying is forcibly terminated as soon as the concentration exceeds the limit, without considering the collaborative response requirements of the upstream and downstream equipment (such as belt machine deceleration, silo buffering, etc.), which is easy to cause secondary problems such as material accumulation and equipment impact damage, causing additional production loss.

[0005] Therefore, in view of the dynamic and instantaneous nature of dust pollution in transfer stations and the shortcomings of existing monitoring methods, there is an urgent need to develop an intelligent monitoring solution that can predict pollution trends in advance, accurately cover risk areas, and provide a response window for preventive measures, in order to achieve active prevention of dust pollution and efficient coordination of safety production. SUMMARY

[0006] The present application aims to provide a transfer station dust concentration prediction method, device, medium and equipment to solve at least one of the above technical problems.

[0007] In a first aspect, the present application provides a transfer station dust concentration prediction method, comprising:

[0008] Obtaining the real-time dust concentration value of the target measurement point in the transfer station, the target measurement point being arranged in the sealing cover of the material inlet, the outlet of the crusher or the inspection passage;

[0009] Obtaining the set of conveying parameters of the material on the associated conveying belt conveying the material to the target measurement point, the set of conveying parameters including the material properties and the instantaneous flow of the material at multiple future time points;

[0010] Predicting the dust concentration sequence of the target measurement point based on the real-time dust concentration value and the set of conveying parameters, the dust concentration sequence including the predicted dust concentration values at multiple future time points;

[0011] Detecting whether there is a predicted dust concentration value exceeding the preset dust concentration threshold in the dust concentration sequence;

[0012] If so, adjusting the set of conveying parameters so that the predicted dust concentration values in the dust concentration sequence formed based on the adjusted set of conveying parameters do not exceed the dust concentration threshold.

[0013] Optionally, the prediction of the dust concentration sequence of the target measurement point based on the real-time dust concentration value and the set of conveying parameters comprises:

[0014] Selecting a target prediction function matching the target measurement point from a plurality of preset dust concentration prediction functions;

[0015] Determining the parameter value of the associated parameter in the target prediction function according to the spatial characteristics of the target measurement point and the set of conveying parameters;

[0016] Predicting the dust concentration sequence of the target measurement point based on the real-time dust concentration value, the set of conveying parameters and the target prediction function with the determined parameter value of the associated parameter.

[0017] Optionally, the adjusting of the set of conveying parameters is such that none of the predicted values of dust concentration in the sequence of dust concentrations formed based on the adjusted set of conveying parameters exceeds the threshold value of dust concentration, including:

[0018] adjusting the value of the associated parameter in the target prediction function to a maximum constraint value, the maximum constraint value being the value of the associated parameter corresponding to the case where the maximization of dust removal measures is initiated at the target measurement point;

[0019] back-calculation of a material limiting flow rate based on the real-time value of dust concentration and the target prediction function under the maximum constraint value, such that none of the predicted values of dust concentration in the sequence of dust concentrations formed at the material limiting flow rate exceeds the threshold value of dust concentration;

[0020] controlling the associated conveying belt to convey material to the target measurement point at the material limiting flow rate and initiate the maximization of dust removal measures at the target measurement point.

[0021] Optionally, the associated conveying belts include a plurality, and the controlling of the associated conveying belts to convey material to the target measurement point at the material limiting flow rate includes: selecting one or several associated conveying belts that can reduce the instantaneous flow rate of material from the plurality of associated conveying belts as target conveying belts; and controlling the target conveying belts to convey material to the target measurement point at the material limiting flow rate.

[0022] Optionally, the associated conveying belts include a plurality, and the controlling of the associated conveying belts to convey material to the target measurement point at the material limiting flow rate includes:

[0023] calculating an influence coefficient of material on dust concentration for each associated conveying belt;

[0024] selecting at least one associated conveying belt with an influence coefficient exceeding a preset coefficient threshold value as a target conveying belt;

[0025] controlling the target conveying belt to convey material to the target measurement point at the material limiting flow rate.

[0026] Optionally, before the selecting of the target prediction function matching the target measurement point from the plurality of preset dust concentration prediction functions, the method further includes:

[0027] performing simulation according to the spatial characteristics of the target measurement point to construct a dust concentration prediction function matching the target measurement point;

[0028] obtaining a set of historical values of dust concentration at the target measurement point and a set of historical conveying parameters;

[0029] The dust concentration prediction function is trained by fitting based on the set of historical dust concentration values and the set of historical conveying parameters, to determine an adaptive parameter value of the dust concentration prediction function under different material conveying historical information.

[0030] Optionally, the method further comprises: obtaining material conveying planning information related to the transfer station; and identifying, from the material conveying planning information, an associated conveying belt corresponding to the target measurement point.

[0031] Optionally, after the conveying parameter set is adjusted such that the predicted dust concentration values in the dust concentration sequence formed based on the adjusted conveying parameter set are all below the dust concentration threshold, the method further comprises: when the duration of material transportation from the associated conveying belt to the target measurement point according to the adjusted conveying parameter set reaches a first duration, restoring the conveying parameter set to the conveying parameter set before the adjustment.

[0032] In a second aspect, the present application provides a dust concentration prediction device for a transfer station, the device comprising:

[0033] a dust concentration detection module configured to obtain a real-time dust concentration value of a target measurement point in the transfer station, the target measurement point being arranged in a seal cover of a drop port, an outlet of a crusher, or an inspection passage;

[0034] a conveying parameter set obtaining module configured to obtain a conveying parameter set of material on an associated conveying belt conveying material to the target measurement point, the conveying parameter set comprising material properties and instantaneous material flow at multiple future time points;

[0035] a dust concentration prediction module configured to predict a dust concentration sequence of the target measurement point based on the real-time dust concentration value and the conveying parameter set, the dust concentration sequence comprising predicted dust concentration values at multiple future time points;

[0036] a parameter adjustment module configured to detect whether there is a predicted dust concentration value exceeding a preset dust concentration threshold in the dust concentration sequence, and if so, adjust the conveying parameter set such that the predicted dust concentration values in the dust concentration sequence formed based on the adjusted conveying parameter set are all below the dust concentration threshold.

[0037] In a third aspect, the present application provides a computer-readable storage medium having stored executable instructions, which, when executed by a processor, cause the processor to perform the method described in any one of the embodiments of the present application.

[0038] In a fourth aspect, the present application provides an electronic device, comprising: one or more processors; a memory for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to perform the method described in any of the embodiments of the present application.

[0039] The transport station dust concentration prediction method, device, medium and equipment in the present application have the following beneficial effects:

[0040] 1. Based on the real-time dust concentration value obtained by real-time monitoring of the target measurement point, in combination with the material properties on the associated conveying belt and the material instantaneous flow of the conveying parameter set at multiple future time points, the dust concentration sequence in the future time period is predicted. Based on the dust concentration sequence, it can be known whether there is a risk of exceeding the dust concentration threshold in the future preset time period, thereby realizing the early prediction of the pollutant exceeding the standard.

[0041] 2. When it is identified that the dust concentration prediction value exceeds the corresponding dust concentration threshold, the conveying parameter set is adjusted in advance, so that the dust concentration prediction value in the dust concentration sequence formed based on the adjusted conveying parameter set does not exceed the dust concentration threshold, so that the relevant staff can intervene in time to avoid the occurrence of dust concentration exceeding the standard. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope of the present application.

[0043] Figure 1 A flowchart of the transport station dust concentration prediction method in an embodiment;

[0044] Figure 2 A flowchart of predicting the dust concentration sequence of the target measurement point based on the real-time dust concentration value and the conveying parameter set in an embodiment;

[0045] Figure 3 A flowchart of adjusting the conveying parameter set so that the dust concentration prediction value in the dust concentration sequence formed based on the adjusted conveying parameter set does not exceed the dust concentration threshold in an embodiment;

[0046] Figure 4 A flowchart of the prediction function construction process in an embodiment;

[0047] Figure 5 A structural diagram of the transport station dust concentration prediction device in an embodiment;

[0048] Figure 6 Figure 2 is a schematic diagram of a structure of a dust concentration prediction device in a transfer station according to another embodiment;

[0049] Figure 7 Figure 3 is a schematic diagram of a structure of a dust concentration prediction device in a transfer station according to another embodiment;

[0050] Figure 8 Figure 4 is a schematic diagram of a structure of an electronic device according to an embodiment. DETAILED DESCRIPTION

[0051] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0052] All the terms used in the present application (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted to have meanings consistent with the context of the present specification, and should not be interpreted in an idealized or overly formal manner.

[0053] For example, the terms "first", "second", and the like used in the present application can be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from another element.

[0054] For another example, the terms "include", "contain", and the like used in the present application indicate the existence of a feature, step, operation and / or component, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0055] A dust concentration prediction method for a transfer station is provided in the present application, as shown in Figure 1, the method comprises: Figure 1

[0056] Step 110, obtaining a real-time dust concentration value of a target measurement point in the transfer station.

[0057] In the present embodiment, the target measurement point is arranged in the sealing cover of the material inlet, the outlet of the crusher or the inspection passage. Multiple measurement points can be arranged in the transfer station, and these measurement points can be arranged at any suitable position of the transfer station, such as in the sealing cover of the material inlet, the outlet of the crusher or the inspection passage.

[0058] ​The drop port is the junction of material falling from the upstream equipment (such as the silo, belt conveyor) to the downstream belt conveyor / chute, and a large amount of dust is generated due to high-speed impact (the falling speed is usually 5-8 m / s), which is one of the core dust sources of the transfer station. In order to reduce dust overflow, a sealing cover is usually installed at the drop port to form a relatively closed space. During the crushing process, the materials collide and extrude each other, generating a large amount of fine particle dust. The outlet of the crusher is usually connected to the discharge chute, and the dust escapes during the falling process of the materials, and the airflow disturbance at the outlet is strong (because the temperature of the crushed materials is slightly higher, a weak upward airflow is formed), so the dust diffusion range is wide. The inspection channel is the walking path of workers or machines for daily inspection of equipment (belt conveyor, valve, sensor), which is usually arranged along the two sides of the belt conveyor, with a width of 0.8-1.2 m and a height of 2-2.5 m (to ensure that the personnel walk upright). There is no direct dust source in the channel, but it is affected by the diffusion of upstream dust sources such as the drop port and the crusher, and the dust concentration is closely related to the ventilation conditions (natural ventilation or mechanical ventilation). There may be vortex zones in the channel (such as behind the belt support and at the corner of the wall), and dust is easy to accumulate, which is a high-risk area for long-term exposure of personnel.

[0059] These measurement points are arranged with corresponding dust sensors. Through the dust sensor, the real-time dust concentration value at the corresponding position can be measured. The dust sensor can be any suitable measuring device such as a distributed laser dust sensor or a beta-ray absorption dust meter, which can send the measured real-time dust concentration value to the electronic device through wireless transmission. The electronic device can receive the information uploaded by each dust sensor in real time, parse the real-time dust concentration value, the identifier of the dust sensor and the timestamp from the information, and based on the correspondence between the identifier and the measurement point, it can be determined that the real-time dust concentration value is the value of which measurement point at which time.

[0060] In one embodiment, before step 110, it further includes: obtaining material conveying planning information related to the transfer station; identifying the associated conveying belt corresponding to the target measurement point from the material conveying planning information.

[0061] The material conveying planning information is structured data containing the belt scheduling plan, material type, flow demand, time period division, etc. of the transfer station in the future period of time, which provides a time reference for associated belt identification and parameter recovery. The corresponding associated conveying belt can be determined from the material conveying planning information.

[0062] Step 120: obtaining the conveying parameter set of the material on the associated conveying belt conveying the material to the target measurement point.

[0063] In this embodiment, the conveying parameter set includes material properties and material instantaneous flow at multiple future time points. The associated conveying belt refers to a belt conveyor that directly or indirectly conveys material to the area where the target measurement point is located, and its conveying behavior will affect the dust concentration of the target measurement point. The discharge section of the directly associated belt conveyor is directly connected to the equipment where the target measurement point is located, and the indirectly associated belt conveyor can be the belt conveyor that needs to be transferred and finally enters the equipment where the target measurement point is located. For example, when the target measurement point is at the discharge port, the associated conveying belt can be the upstream belt of the discharge port and the belt conveyor conveying material with the upstream belt; when the target measurement point is at the crusher, the associated conveying belt includes the feeding belt of the crusher and the belt conveyor transferring material to the feeding belt.

[0064] The conveying parameter set is a set of parameters describing the conveying behavior of the associated conveying belt and the characteristics of the conveyed material, which is the core input for predicting dust concentration. It can include material properties (static characteristics) and material instantaneous flow (dynamic characteristics). Material properties refer to inherent physical properties of the material that affect dust generation and dispersion, reflecting the physical properties of the material and affecting the dust generation potential; material instantaneous flow (dynamic characteristics): reflects the amount of material conveyed per unit time, affecting the dust generation intensity, and its unit is usually "tons / hour (t / h)" or "kilograms / second (kg / s)". Material properties can specifically include one or more of material type, particle size distribution of the material, material humidity, material density, etc. The material type can include iron ore, coke, sintered ore, and injection coal, and different types of materials have different hardness and brittleness (such as coke, which is more brittle and more likely to generate dust when broken / impacted); particle size distribution is used to reflect the size range and proportion of material particles (such as <1mm fine particles accounting for 30%, 1-5mm medium particles accounting for 50%), the higher the proportion of fine particles, the more likely it is to generate dust; material humidity reflects the moisture content of the material (such as coke humidity 8%, iron ore humidity 12%), the higher the humidity, the stronger the adhesion between particles, and the less dust escapes; material density represents the mass of material per unit volume (such as iron ore density 4.8t / m³, coke density 0.8t / m³), density affects the impact strength of material falling (the denser the material, the stronger the impact, the more dust). Material instantaneous flow can be measured in real time by related detection equipment such as belt scales and saved.

[0065] The system pre-plans and stores the material properties and material instantaneous flow of the material conveyed by each belt conveyor, and after determining the associated conveying belt, the conveying parameter set of the material transported on the associated conveying belt can be obtained.

[0066] Step 130, predicting the dust concentration sequence of the target measurement point based on the real-time dust concentration value and the conveying parameter set.

[0067] The dust concentration sequence is a time sequence composed of predicted values of the dust concentration of the target measurement point at multiple discrete future time points, and reflects the trend of the dust concentration over time. The dust concentration sequence includes predicted values of the dust concentration at multiple future time points, which are time points within a prediction time window. The prediction time window covers a preset future time length, and the starting time point is the current time point. The time length of the preset future time length is a preset fixed length, such as 10 minutes, 20 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, etc. Taking the current time point as t0 and the prediction step as (1 minute, for example), the number of predicted values of the dust concentration in the dust concentration sequence is n, that is, the dust concentration sequence D of the future n time points is represented as where d(t i ) is the predicted value of the dust concentration at time point t i (mg / m 3 ).

[0068] The electronic device pre-constructs a prediction model and / or a prediction function for dust concentration prediction. The prediction model can be a long short-term memory (LSTM) model.

[0069] Taking the LSTM model as an example, the electronic device pre-collects a large amount of sample training data, which is a pre-processed multivariate time series data of each measurement point formed by historical collection or simulation through a generative adversarial network model. The multivariate time series data includes dust concentration values, a set of conveying parameters corresponding to the dust concentration values (such as instantaneous material flow, material type, particle size distribution, material humidity, material density, etc.), and spatial features of each measurement point (such as environmental parameters including spatial structure of the measurement point, wind speed, temperature, humidity, ventilation state, etc.). The sample training data is formed by pre-processing the sample training data, such as outlier cleaning, normalization, and time series segmentation.

[0070] The LSTM model can use a tensorized LSTM layer to upgrade the hidden state of the traditional LSTM to a tensor form, and realize parallel processing of multivariate time series through tensor operations. The LSTM layer and the MLP layer in the model dynamically adjust the parameter sharing weight between tasks through an attention mechanism; the multi-task 1dCNN layer in the model is used to extract short-term features of local time series, such as sudden change signals of the dust concentration and short-term fluctuations of the wind speed, and each task (measurement point) has an independent upper network but shares the bottom convolution kernel.

[0071] During the iterative training of the model, the historical dust concentration sequence data T and the local original data L in the sample training data are input as inputs, and the dust concentration sequence , …}. wherein T = { , … }, L = { }, represents the measured dust concentration value at time t-w, represents the corresponding dust concentration value of the i-th measurement point at time t+k (which can also be a measured dust concentration value), t represents time, and w and k can be time window sizes, such as 10-30 minutes; represents the output dust concentration prediction value of the i-th measurement point at time t+k.

[0072] For each iteration output dust concentration sequence, the corresponding loss function is called to calculate the loss value, and the corresponding parameter is adjusted based on the calculated loss value. According to the adjusted parameters, the iterative training is performed again until the calculated loss value is less than the preset loss threshold, or the number of iterations reaches the preset number of times threshold. After completing the iterative training, the trained dust concentration prediction model can be finally obtained.

[0073] When predicting the dust concentration sequence using the dust concentration prediction model, the real-time dust concentration value, the set of conveying parameters, the spatial characteristics of the target measurement point, and the historical dust concentration value (such as the measured / calculated dust concentration value within the previous w time windows (such as 30 minutes, 60 minutes)) can be used as the input of the model. Through the LSTM layer, MLP layer, 1dCNN layer, etc. in the dust concentration prediction model, the dust concentration sequence of each target measurement point can be finally output.

[0074] The dust concentration prediction function can be a function constructed based on one or a combination of fluid mechanics diffusion models (such as Gaussian diffusion model), impact dust raising prediction model, or related empirical formula. It can be understood that the spatial characteristics of different types of measurement points have great differences. For example, the spatial characteristics of the three types of measurement points in the material falling port sealing cover, the crusher outlet, and the inspection passage are obviously different. Based on this, the electronic device sets a corresponding prediction function for each type of measurement point.

[0075] For example, the dust concentration in the inspection passage follows the convection diffusion equation. Based on the convection diffusion equation and combined with the spatial characteristics of the inspection passage, a prediction function matching the inspection passage can be constructed. For the material falling port sealing cover and the crusher outlet, it is more consistent with the impact dust raising prediction model. Therefore, based on the impact dust raising prediction model and combined with the spatial characteristics of the material falling port sealing cover and the crusher outlet, prediction functions suitable for the material falling port sealing cover and the crusher outlet can be constructed, respectively.

[0076] By taking the real-time dust concentration value, the set of conveying parameters, and the spatial characteristics of the target measurement point as inputs of the corresponding prediction model and / or prediction function, a dust concentration sequence of the target measurement point can be output.

[0077] In step 140, it is detected whether there is a dust concentration prediction value exceeding the preset dust concentration threshold in the dust concentration sequence.

[0078] In this embodiment, the dust concentration threshold is a critical concentration value set in the dust concentration monitoring and control of the transfer station to ensure the safety of operation, the health of personnel, and the continuity of production, and is a core basis for determining whether dust pollution needs to be intervened. Its essence is to divide a "safe interval" and a "risk interval" through a quantitative index to guide the system to take measures such as early warning and regulation when the dust concentration reaches or exceeds the value. The sizes of the dust concentration thresholds corresponding to different measurement points are not necessarily the same, and the electronic device pre-sets a correspondence table between different measurement points and dust concentration thresholds. According to the target measurement point, the corresponding dust concentration threshold can be found in the correspondence table.

[0079] The sizes of each dust concentration prediction value in the dust concentration sequence and the corresponding dust concentration threshold are compared to identify whether there is a dust concentration prediction value exceeding the dust concentration threshold. If there is, it means that the dust concentration at the target measurement point will exceed the standard at a certain time in the future, and intervention is needed at this time.

[0080] In step 150, if there is, the set of conveying parameters is adjusted so that the dust concentration prediction values in the dust concentration sequence formed based on the adjusted set of conveying parameters are all less than or equal to the dust concentration threshold.

[0081] Optionally, for the prediction result that the dust concentration will exceed the standard, the adjustment time of the set of conveying parameters and the specific parameters that need to be adjusted can be determined. For example, the instantaneous flow of the material can be reduced to achieve rapid dust control, and optimization of static parameters (material properties) can be supplemented, and finally the adjustment effect is verified by the prediction model. Among them, the adjustment time can be as early as possible to avoid the increase of adjustment difficulty caused by adjustment lag.

[0082] For example, the instantaneous flow of the material can be reduced in steps to avoid material accumulation or equipment impact caused by sudden flow reduction. For example, if the initial predicted concentration is 40 mg / m³ (the threshold is 30 mg / m³), the belt speed is first reduced from 2.5 m / s to 2.2 m / s (the instantaneous flow of the material is reduced from 800 t / h to 700 t / h), and the concentration is re-predicted. If the dust concentration prediction value is still greater than 30 mg / m³ (still exceeds the standard) after adjustment, it is reduced to 2.0 m / s (the flow is 600 t / h), and the dust concentration prediction values obtained are all less than or equal to 30 mg / m³. 3 3 .​

[0083] If there is no dust concentration prediction value in the dust concentration sequence that is less than the dust concentration threshold value, the material conveying continues according to the set material conveying planning information, and the conveying parameter set is not adjusted.

[0084] The dust concentration prediction method of the transfer station in the present application is based on the real-time dust concentration value obtained by real-time monitoring of the target measurement point, and combines the material properties on the associated conveying belt and the material instantaneous flow at multiple future time points and other conveying parameter sets to predict a dust concentration sequence in a future period of time. Based on the dust concentration sequence, it can be known whether there is a risk of exceeding the dust concentration threshold value in the future preset time period, thereby realizing early prediction of excessive pollutants. Further, when it is identified that the dust concentration prediction value exceeds the corresponding dust concentration threshold value, the conveying parameter set is adjusted in advance, so that the dust concentration prediction value in the dust concentration sequence formed based on the adjusted conveying parameter set does not exceed the dust concentration threshold value, so that relevant personnel can intervene in time to avoid the occurrence of dust concentration exceeding.

[0085] In one embodiment, as shown in FIG. 2, the dust concentration sequence of the target measurement point is predicted based on the real-time dust concentration value and the conveying parameter set, including: Figure 2

[0086] Step 210: selecting a target prediction function matched with the target measurement point from a plurality of preset dust concentration prediction functions.

[0087] In the present embodiment, the electronic device presets a plurality of dust concentration prediction functions, each of which is adapted to one or more measurement points. It can be understood that the dust generation and diffusion characteristics of different types of positions such as the sealing cover inside the drop port, the crusher outlet, and the inspection channel are significantly different, so different prediction functions need to be matched to ensure prediction accuracy. The electronic device classifies the spatial features of each measurement point, sets a prediction function for measurement points with the same common spatial features, and establishes a correspondence between the prediction function and the measurement point. Based on the correspondence, a prediction function matched with the target measurement point can be selected as the target prediction function. These prediction functions can be functions formed based on one or more combinations of power functions, quadratic functions, etc.

[0088] Step 220: determining the parameter values of the associated parameters in the target prediction function according to the spatial features of the target measurement point and the conveying parameter set.

[0089] ​In this embodiment, spatial characteristics refer to spatial attributes, environmental, or geometric features of the physical environment at the measurement point that are relevant to dust diffusion and concentration distribution. These characteristics may include one or more of position and distance characteristics, spatial geometric characteristics, airflow and ventilation characteristics, surface and sedimentation characteristics, and spatial topological characteristics. Position and distance characteristics can be expressed as the spatial position of the measurement point relative to the dust source (e.g., the drop point of a conveyor or the starting point of a conveyor belt), as well as its distance from key equipment (e.g., fans or baffles). Spatial geometric characteristics refer to the geometric shape, size, or structure of the area where the measurement point is located, which influence the boundary conditions of dust diffusion. Airflow and ventilation characteristics refer to the airflow direction, wind speed, and ventilation method of the environment at the measurement point, which directly influence the direction and rate of dust diffusion. Surface and sedimentation characteristics refer to the surface properties (e.g., material and slope) surrounding the measurement point, which influence the dust sedimentation rate and the likelihood of secondary dust entrainment. Spatial topological characteristics refer to the overall layout or structural hierarchy of the area where the measurement point is located, such as whether it is located at a corner, intersection, or other special location. Some of these spatial characteristics can be determined through design drawings, on-site mapping, or 3D modeling, while others can be determined from the system's control parameters. Some of these spatial characteristics can be adjusted based on the operation of the dust removal equipment in the transfer station. For example, by activating the relevant dust removal equipment, the airflow and ventilation characteristics, material humidity, drop height, drop speed, etc. can be changed to reduce the dust concentration in the environment or reduce the rate at which the dust concentration increases.

[0090] The values ​​of the associated parameters in the corresponding preset functions vary depending on the spatial characteristics and transport parameter sets. Associated parameters refer to parameters in the prediction function that are directly related to the spatial characteristics of the measurement point (such as geometry, ventilation conditions, dust source layout) and / or the transport parameter set. These parameters must be individually calibrated based on the environmental characteristics and transport parameter set of the specific measurement point. Even if the same prediction function is applied to different measurement points, the associated parameter values ​​will vary due to the differences in spatial characteristics.

[0091] The associated parameters may include material-related parameters and / or space-related parameters. According to the pre-established correspondence between the spatial characteristics and the transport parameter set and the associated parameters, the parameter values ​​of the associated parameters in the current objective function may be queried or calculated.

[0092] For example, the pre-established prediction functions can be the following, each prediction being adapted to a different type of measurement point. For example, prediction function 1 is adapted to the measurement point inside the sealing cover of the drop port, prediction function 2 is adapted to the measurement point at the crusher outlet, and prediction function 3 is adapted to the measurement point at the inspection channel. The pre-established prediction functions are as follows:

[0093] Prediction function 1: ;

[0094] Prediction function 2: ;

[0095] Prediction function 3: .

[0096] in, Indicates the tth i Dust concentration at the moment, Indicates that at the tth i+1 Dust concentration at the moment, Indicates that at t i The instantaneous flow rate of the material at the time t i+1 Time and t i The time interval at the moment is , 、 、 、 The concentration attenuation coefficient is calibrated experimentally, and its corresponding value varies under different spatial characteristics. For example, in the same space, the value with dust removal measures activated is smaller than the value without dust removal measures activated (different ventilation volumes); 、 、 Indicates the dust emission coefficient, which is related to spatial characteristics such as the falling speed and falling height of the material; Indicates the humidity correction factor, which is related to the humidity of the material. The higher the humidity, the The bigger; Indicates the material particle size correction coefficient. The smaller the particle size distribution of the material, the The bigger; It represents the air volume correction factor, which is determined based on various factors such as the channel cross-sectional area and channel ventilation volume in the environment.

[0097] Step 230 : predicting a dust concentration sequence of a target measurement point based on the real-time dust concentration value, the transport parameter set, and a target prediction function that determines the parameter values ​​of the associated parameters.

[0098] When the parameter values ​​of the associated parameters are determined, the dust concentration at each moment can be determined based on the above parameter values ​​and the corresponding dust concentration values ​​and the instantaneous flow rate of the material, thereby forming a corresponding dust concentration sequence.

[0099] In this embodiment, by matching a dedicated prediction function to each target measurement point and adapting the prediction function to different parameter values ​​based on different spatial characteristics, the calculated dust concentration prediction value is more accurate. Furthermore, using the preset function to predict dust concentration effectively reduces the complexity of dust concentration prediction and improves engineering practicality.

[0100] In one embodiment,Figure 3 As shown, the set of conveying parameters is adjusted so that the predicted values of the dust concentration in the sequence of dust concentrations formed based on the adjusted set of conveying parameters all do not exceed the dust concentration threshold, including:

[0101] Step 310, adjust the value of the associated parameter in the target prediction function to the maximum constraint value.

[0102] Step 320, based on the real-time dust concentration value and the target prediction function under the maximum constraint value, deduce the material limiting flow rate so that the predicted values of the dust concentration in the sequence of dust concentrations formed under the material limiting flow rate all do not exceed the dust concentration threshold.

[0103] Step 330, control the associated conveying belt to convey material to the target measurement point at the material limiting flow rate, and start the maximum dust removal measure at the target measurement point.

[0104] In this embodiment, the maximum constraint value is the value of the associated parameter corresponding to the case where the maximum dust removal measure is started at the target measurement point. That is, the maximum constraint value is the limit value of the associated parameter at the target measurement point when the maximum dust removal measure is started, and the dust spread is weakest / the dust concentration growth rate is slowest when the maximum dust removal measure is started. Different dust removal equipment is provided at different measurement points, and each dust removal equipment has different dust removal capacity. The dust removal mode can include one or more of mechanical dust removal, pneumatic dust removal, and fan dust removal. For mechanical dust removal, a closed cover can be used to close the material falling area (such as a closed material guide chute), to reduce dust overflow; a scraper sweeper can be used to clean the residual material close to the belt surface, to reduce secondary dust generated by falling material; a buffer chute can be installed at the inlet, to reduce the falling speed of the material and reduce impact dust. For pneumatic dust removal, the dust-containing air in the closed cover can be sucked into a pipeline, and clean air can be discharged after filtering the dust by a filter bag; atomizing nozzles can be installed around the material falling point, to adsorb dust particles by fine water mist (suitable for non-flammable and non-explosive scenes). The amount of air drawn by the air volume adjustable fan can be automatically adjusted according to the real-time dust concentration, to balance the negative pressure in the closed cover and prevent dust overflow; dust suction ports can be installed at the top of the silo or the inlet, to generate negative pressure by the fan, to suck the floating dust in the silo into the dust removal equipment (such as a cyclone dust collector), and axial flow fans or air ducts can be installed at appropriate positions in the inspection channel, to form directional airflow to guide the diffused dust to the dust removal equipment.

[0105] Based on the relationship between the value of the associated parameter and the spatial feature, and in combination with the dust removal capacity of each dust removal mode, the value of the associated parameter corresponding to the maximum dust removal capacity (i.e., the maximum constraint value) is determined.

[0106] After determining the maximum constraint value, the target prediction function is used to calculate the instantaneous material flow as an unknown variable under the condition that each calculated dust concentration prediction value does not exceed the corresponding dust concentration threshold, and the appropriate instantaneous material flow is calculated. The calculated instantaneous material flow is used as the material limiting flow.

[0107] By associating the limit constraint of the parameter and the flow backstepping, the balance between "safety compliance" and "production efficiency" is achieved, which is especially suitable for industrial scenes with large dust concentration fluctuations and high prevention and control requirements.

[0108] In one embodiment, before selecting the target prediction function matching the target measurement point from the preset plurality of dust concentration prediction functions, the above method further includes a prediction function construction process. As shown in Figure 4 The process includes:

[0109] Step 410, according to the spatial characteristics of the target measurement point, simulation is carried out to construct a dust concentration prediction function matching the target measurement point.

[0110] In this embodiment, the diffusion law of dust in the space of the target measurement point is simulated by CFD (Computational Fluid Dynamics), and the quantitative relationship between concentration and influencing factors (such as flow, height) is extracted, and the mathematical form of the prediction function is constructed accordingly. Specifically, related software such as ANSYS Fluent can be called to construct the geometric model of the target measurement point (such as containing the details of the feeding port, the sealing cover, the observation window, the belt, etc.) and define the boundary conditions, such as the inner wall of the sealing cover as a no-slip boundary, the observation window as a pressure outlet (communicating with the atmosphere), and the feeding point as a dust source (setting the particle size distribution and material properties consistent).

[0111] After forming the geometric model of the target measurement point, multi-condition CFD simulation can be realized, and for each condition, the simulation is carried out, and the dust concentration curve with time under each condition is output. Each condition has corresponding different variables, and these variables include a set of related conveying parameters such as instantaneous material flow, feeding height, feeding speed, ventilation volume, and material properties.

[0112] Regression analysis is performed on the simulation data to construct the initial form of the prediction function, such as the three initial prediction functions described above.

[0113] Step 420, a set of historical dust concentration values and a set of historical conveying parameters at the target measurement point are obtained.

[0114] Step 430, based on the set of historical dust concentration values and the set of historical conveying parameters, the dust concentration prediction function is fitted and trained to determine the adaptive parameter values of the dust concentration prediction function under different historical material conveying information.

[0115] For the formed initial prediction function, the electronic device further acquires a set of historical dust concentration values measured at the measurement point, and a set of corresponding historical conveying parameters and related spatial features, and fits the training set data through a related machine learning algorithm, solves the values of the undetermined parameters in the prediction function under different working conditions, and verifies the adaptability of the parameters under different historical information (such as high humidity, low flow). Wherein, the machine learning algorithm can be a least square ensemble network search, so that the mean square error between the calculated prediction value (i.e. the dust concentration prediction value) and the actual value (i.e. the historical dust concentration value) is minimized, thereby obtaining the relationship between the associated parameters in the model and the spatial features and the conveying parameter set, and obtaining the adaptive parameter values of the dust concentration prediction function under different material conveying historical information.

[0116] In this embodiment, the prediction function of the dust concentration is formed by simulating the construction function and the historical data fitting training, which can improve the accuracy of the prediction function construction.

[0117] In one embodiment, the control of the associated conveying belt to the target measurement point according to the material limit flow for material conveying includes: selecting one or more associated conveying belts that can reduce the material instantaneous flow from a plurality of associated conveying belts as target conveying belts; and controlling the target conveying belts to the target measurement point according to the material limit flow for material conveying.

[0118] In this embodiment, the associated conveying belts at the measurement point can include multiple, and the multiple associated conveying belts are all conveying materials. For example, there are three conveying belts that are aggregated in a certain bin and discharged from the outlet chute, which finally affect the dust concentration of the target measurement point. When it is predicted that the dust concentration will exceed the standard, one or more of the three conveying belts can be selected for control.

[0119] Further, each conveying belt is usually conveying material according to the pre-designed material instantaneous flow, and the material instantaneous flow on part of the conveying belts can be adjusted, and part of the conveying belts cannot be adjusted. The electronic device adjusts the material from the associated conveying belts that can reduce the material instantaneous flow, and controls it to convey material according to the calculated material limit flow. The material limit flow is within the allowable conveying flow range of the corresponding conveying belt.

[0120] In one embodiment, the control of the associated conveying belt to the target measurement point according to the material limit flow for material conveying includes: calculating the influence coefficient of the material on the dust concentration on each associated conveying belt; selecting at least one associated conveying belt with an influence coefficient exceeding a preset coefficient threshold as a target conveying belt; and controlling the target conveying belt to the target measurement point according to the material limit flow for material conveying.

[0121] In this embodiment, the influence coefficient is a parameter quantifying the degree of influence of the change in the material flow of a single conveying belt on the dust concentration at the target measurement point. The greater the influence coefficient, the more significant the influence of the conveying belt on the dust concentration.

[0122] For each associated conveying belt at the measurement point, the influence of each associated conveying belt on the dust concentration is separated based on the target prediction function by the control variable method, and the corresponding influence coefficient is calculated based on the influence of each associated conveying belt.

[0123] The coefficient threshold value can be adaptively set in combination with relevant process requirements and historical data. After obtaining the influence coefficients of the various associated conveying belts, the associated conveying belts corresponding to the influence coefficients that exceed the coefficient threshold value are selected as the target conveying belts, and the selected target conveying belts are the conveying belts that can reduce the instantaneous material flow.

[0124] For the selected target conveying belts, the flow distribution of the various associated conveying belts is performed according to the calculated material limit flow, so that the total flow of the distributed associated conveying belts is adjusted to the material limit flow. Specifically, the distribution flow of each target conveying belt can be distributed based on one or more factors such as the task priority of the corresponding conveying belt, the minimum allowed flow, etc. The calculated flow reduction amount is distributed to the target conveying belts, and precise control is achieved through the PLC system to adjust the material conveying speed and other parameters of the conveying belts, so that the adjusted dust concentration value within a future preset time period does not exceed the dust concentration threshold value.

[0125] In this embodiment, by quantifying the influence weight of each belt, "precise positioning and targeted regulation" is achieved, which can efficiently control the dust concentration and minimize the interference with production in the multi-belt collaborative feeding scene.

[0126] In one embodiment, after adjusting the conveying parameter set so that the predicted values of the dust concentration in the dust concentration sequence formed based on the adjusted conveying parameter set do not exceed the dust concentration threshold value, the method further includes: when the duration of the associated conveying belt transporting material to the target measurement point according to the adjusted conveying parameter set reaches a first duration, restoring the conveying parameter set to the conveying parameter set before adjustment.

[0127] In this embodiment, the first duration can be determined according to one or more factors such as the above-mentioned material conveying planning information, the measured dust concentration value at the future time, etc.

[0128] For example, the instantaneous flow of the material is adjusted at 9:30, and according to the material conveying plan information, it is necessary to ensure that the instantaneous flow of the material is restored to the set flow before 10:00. Then the first duration can be 30 minutes. And further, if at a certain time before 10:00, the dust concentration value of the target measurement point is reduced to a very low safety value, then at that time, the conveying parameter set can be restored to the conveying parameter set before the adjustment, so that the associated conveying belt continues to convey the material according to the set conveying parameter set from that time.

[0129] By restoring the conveying parameter set after running for the first duration, the balance between the temporary and planned adjustment of the parameters can be considered, so that the dust control can meet the real-time safety requirements and adapt to the long-term production plan, and the coordination and efficiency of the transfer station operation are improved.

[0130] In one embodiment, as shown in Figure 5 A dust concentration prediction device of a transfer station is provided, and the device comprises:

[0131] The dust concentration detection module 510 is configured to obtain a real-time dust concentration value of a target measurement point in the transfer station, and the target measurement point is arranged in a seal cover of a drop port, an outlet of a crusher, or an inspection passage.

[0132] The conveying parameter set acquisition module 520 is configured to obtain a conveying parameter set of the material on an associated conveying belt conveying the material to the target measurement point, and the conveying parameter set comprises material attributes and instantaneous flows of the material at multiple future time points.

[0133] The dust concentration prediction module 530 is configured to predict a dust concentration sequence of the target measurement point based on the real-time dust concentration value and the conveying parameter set, and the dust concentration sequence comprises dust concentration prediction values at multiple future time points.

[0134] The parameter adjustment module 540 is configured to detect whether there is a dust concentration prediction value exceeding a preset dust concentration threshold value in the dust concentration sequence, and if so, adjust the conveying parameter set, so that the dust concentration prediction values in the dust concentration sequence formed based on the adjusted conveying parameter set are all less than the dust concentration threshold value.

[0135] In one embodiment, the dust concentration prediction module 530 is further configured to select a target prediction function matched with the target measurement point from a plurality of preset dust concentration prediction functions, determine parameter values of associated parameters in the target prediction function according to spatial features of the target measurement point and the conveying parameter set, and predict the dust concentration sequence of the target measurement point based on the real-time dust concentration value, the conveying parameter set, and the target prediction function with the determined parameter values of the associated parameters.

[0136] In one embodiment, the parameter adjustment module 540 is further configured to adjust the value of the associated parameter in the target prediction function to a maximum constraint value, the maximum constraint value being a value of the associated parameter corresponding to a case where the maximized dust removal measure is initiated at the target measurement point; and to back-calculate the material limiting flow based on the real-time dust concentration value and the target prediction function under the maximum constraint value, such that the predicted dust concentration values in the dust concentration sequence formed under the material limiting flow are all less than the dust concentration threshold; and to control the associated conveying belt to convey the material to the target measurement point at the material limiting flow, and to initiate the maximized dust removal measure at the target measurement point.

[0137] In one embodiment, the parameter adjustment module 540 is further configured to select one or more associated conveying belts that can reduce the material instantaneous flow from the plurality of associated conveying belts as target conveying belts; and to control the target conveying belts to convey the material to the target measurement point at the material limiting flow.

[0138] In one embodiment, the parameter adjustment module 540 is further configured to calculate an influence coefficient of the material on the dust concentration on each associated conveying belt; to select at least one associated conveying belt with an influence coefficient exceeding a preset coefficient threshold as a target conveying belt; and to control the target conveying belt to convey the material to the target measurement point at the material limiting flow.

[0139] In one embodiment, as shown in Figure 6 the device further comprises:

[0140] a prediction function construction module 550 configured to simulate according to the spatial features of the target measurement point to construct a dust concentration prediction function matched with the target measurement point; to obtain a set of historical dust concentration values at the target measurement point and a set of historical conveying parameters; and to fit and train the dust concentration prediction function based on the set of historical dust concentration values and the set of historical conveying parameters to determine the adaptive parameter values of the dust concentration prediction function under different material conveying historical information.

[0141] In one embodiment, as shown in Figure 7 the device further comprises:

[0142] an associated conveying belt determination module 560 configured to obtain material conveying planning information related to the transfer station; and to identify the associated conveying belt corresponding to the target measurement point from the material conveying planning information.

[0143] The parameter adjustment module 540 is further configured to restore the set of conveying parameters to the set of conveying parameters before the adjustment when the duration of the material transportation of the associated conveying belt to the target measurement point according to the adjusted set of conveying parameters reaches a first duration.

[0144] In one embodiment, a computer-readable storage medium is provided, on which executable instructions are stored. When the instructions are executed by a processor, the processor executes the steps in the above-mentioned method embodiments.

[0145] In one embodiment, an electronic device is also provided, comprising one or more processors; a memory, wherein one or more programs are stored in the memory, wherein when the one or more programs are executed by one or more processors, the one or more processors execute the steps in the above-mentioned method embodiments.

[0146] In one embodiment, Figure 8 , which shows a schematic diagram of the structure of an electronic device for implementing an embodiment of the present application. The electronic device includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 802 or programs loaded from a storage unit 808 into a random access memory (RAM) 803. The RAM 803 also stores various programs and data required for the operation of the electronic device. The CPU 801, ROM 802, and RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0147] The following components are connected to the I / O interface 805: an input section 806 including a keyboard, mouse, and the like; an output section 807 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and speakers; a storage section 808 including a hard disk; and a communication section 809 including a network interface card such as a LAN card or a modem. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. Removable media 811, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 810 as needed, so that computer programs read from the removable media can be installed in the storage section 808 as needed.

[0148] In particular, according to embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer-readable medium carrying instructions. In such embodiments, the instructions can be downloaded and installed from a network via the communication portion 809 and / or installed from removable media 811. When the instructions are executed by the central processing unit (CPU) 801, the various method steps described in this application are performed.

[0149] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, rather than limit the present application; even though the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some or all of the technical features thereof can be replaced equivalently; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0150] In addition, those skilled in the art will appreciate that a combination of features of different embodiments means that such combination is within the scope of the present application and forms a different embodiment. For example, all of the above embodiments can be used in any combination. The information disclosed in this section is merely intended to deepen the understanding of the general background of the present application, and should not be regarded as acknowledging or implying in any form that the information constitutes the prior art known to those skilled in the art.

Claims

1. A method for predicting dust concentration at a transfer station, characterized in that: The method comprises: Obtain real-time dust concentration values ​​at target measurement points within the transfer station, where the target measurement points are located inside the material discharge port seal, at the crusher outlet, or in the inspection channel; Acquire a set of conveying parameters of a material on an associated conveyor belt conveying the material to the target measurement point, the set of conveying parameters including material properties and instantaneous flow rates of the material at multiple future moments; Predicting a dust concentration sequence at the target measurement point based on the real-time dust concentration value and the transport parameter set, including: selecting a target prediction function that matches the target measurement point from a plurality of preset dust concentration prediction functions, determining parameter values ​​of associated parameters in the target prediction function based on spatial characteristics of the target measurement point and the transport parameter set, and predicting a dust concentration sequence at the target measurement point based on the real-time dust concentration value, the transport parameter set, and the target prediction function having determined the parameter values ​​of the associated parameters, wherein the dust concentration sequence includes predicted dust concentration values ​​at a plurality of future moments; Detecting whether there is a dust concentration prediction value exceeding a preset dust concentration threshold in the dust concentration sequence; If so, the conveying parameter set is adjusted so that the dust concentration prediction values ​​in the dust concentration sequence formed based on the adjusted conveying parameter set do not exceed the dust concentration threshold, including: adjusting the value of the associated parameter in the target prediction function to the maximum constraint value, the maximum constraint value being the value of the associated parameter corresponding to the case where the maximized dust removal measure is initiated at the target measurement point, inverting the material limit flow rate based on the real-time dust concentration value and the target prediction function under the maximum constraint value, so that the dust concentration prediction values ​​in the dust concentration sequence formed under the material limit flow rate do not exceed the dust concentration threshold, controlling the associated conveyor belt to transport the material to the target measurement point according to the material limit flow rate, and initiating the maximized dust removal measure at the target measurement point.

2. The method according to claim 1, characterized in that The associated conveyor belts include a plurality of belts, and controlling the associated conveyor belts to convey the material to the target measurement point according to the material restricted flow rate includes: Select one or more associated conveyor belts that can reduce the instantaneous flow of materials from multiple associated conveyor belts as target conveyor belts; The target conveying belt is controlled to convey the material to the target measuring point according to the material limit flow rate.

3. The method according to claim 1, characterized in that The associated conveyor belts include a plurality of belts, and controlling the associated conveyor belts to convey the material to the target measurement point according to the material restricted flow rate includes: Calculate the influence coefficient of the material on each associated conveyor belt on the dust concentration; Selecting at least one associated conveyor belt whose influence coefficient exceeds a preset coefficient threshold as a target conveyor belt; The target conveying belt is controlled to convey the material to the target measuring point according to the material limit flow rate.

4. The method according to claim 1, wherein Before selecting a target prediction function that matches the target measurement point from a plurality of preset dust concentration prediction functions, the method further includes: Performing simulation based on the spatial characteristics of the target measurement point to construct a dust concentration prediction function that matches the target measurement point; Obtaining a historical dust concentration value set and a historical transport parameter set at the target measurement point; The dust concentration prediction function is fitted and trained based on the historical dust concentration value set and the historical transportation parameter set to determine the adaptive parameter values ​​of the dust concentration prediction function under different material transportation historical information.

5. The method according to any one of claims 1 to 4, characterized in that The method further comprises: Obtaining material transportation planning information related to the transfer station; Identifying an associated conveyor belt corresponding to the target measurement point from the material conveyance planning information; After adjusting the transport parameter set so that the dust concentration prediction values ​​in the dust concentration sequence formed based on the adjusted transport parameter set do not exceed the dust concentration threshold, the method further includes: When the duration of the associated conveyor belt transporting the material to the target measurement point according to the adjusted conveyor parameter set reaches a first duration, the conveyor parameter set is restored to the conveyor parameter set before adjustment.

6. A dust concentration prediction device for a transfer station, characterized in that: The device comprises: A dust concentration detection module is used to obtain real-time dust concentration values ​​at target measurement points within the transfer station. The target measurement points are set inside the sealing cover of the drop port, the crusher outlet, or the inspection channel; A conveying parameter set acquisition module is used to acquire a conveying parameter set of a material on an associated conveyor belt that conveys the material to the target measurement point, wherein the conveying parameter set includes material properties and instantaneous material flow rates at multiple future moments; a dust concentration prediction module, configured to predict a dust concentration sequence at the target measurement point based on the real-time dust concentration value and the transport parameter set, comprising: selecting a target prediction function that matches the target measurement point from a plurality of preset dust concentration prediction functions, determining parameter values ​​of associated parameters in the target prediction function based on spatial characteristics of the target measurement point and the transport parameter set, and predicting a dust concentration sequence at the target measurement point based on the real-time dust concentration value, the transport parameter set, and the target prediction function having determined parameter values ​​of the associated parameters, wherein the dust concentration sequence includes predicted dust concentration values ​​at multiple future moments; A parameter adjustment module is used to detect whether there is a dust concentration prediction value exceeding a preset dust concentration threshold in the dust concentration sequence; if so, adjust the conveying parameter set so that the dust concentration prediction values ​​in the dust concentration sequence formed based on the adjusted conveying parameter set do not exceed the dust concentration threshold, including: adjusting the value of the associated parameter in the target prediction function to the maximum constraint value, the maximum constraint value is the value of the associated parameter corresponding to the case where the maximized dust removal measure is initiated at the target measurement point, and inferring the material limit flow rate based on the real-time dust concentration value and the target prediction function under the maximum constraint value, so that the dust concentration prediction values ​​in the dust concentration sequence formed under the material limit flow rate do not exceed the dust concentration threshold, controlling the associated conveyor belt to transport material to the target measurement point according to the material limit flow rate, and initiating the maximized dust removal measure at the target measurement point.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores executable instructions, which, when executed by a processor, enable the processor to perform the method according to any one of claims 1 to 5.

8. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs, which, when executed by the one or more processors, causes the one or more processors to perform the method according to any one of claims 1 to 5.

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