Intelligent Monitoring Method and System for Moisture Content and Dust Control of Bulk Cargo in Ports

Abstract

This invention discloses a method and system for intelligent monitoring of moisture content and dust control of bulk cargo in ports. The method includes: firstly, collecting near-infrared spectral signals of different bulk cargoes at different moisture contents to construct a response relationship matrix between bulk cargo moisture content and near-infrared absorbance; secondly, training an initial detection model for the bulk cargo based on the response relationship matrix to obtain a target detection model for bulk cargo moisture content; thirdly, acquiring the critical starting wind speed for bulk cargoes with different moisture contents to obtain a functional relationship between the critical starting wind speed and moisture content; fourthly, acquiring real-time port environmental data and near-infrared spectral signals of the port bulk cargo, and using the target detection model for bulk cargo moisture content to determine real-time moisture content data; and finally, dynamically adjusting the operating parameters of the port bulk cargo dust suppression control system based on the functional relationship and real-time moisture content data. This method achieves the dual goals of dust suppression and water conservation.

Description

Technical Field

[0001] This invention relates to the field of port environmental protection and intelligent control technology, specifically to a method and system for intelligent monitoring of moisture content of bulk cargo and dust linkage control in ports. Background Technology

[0002] Ports, as crucial hubs for transporting coal from north to south and from west to east, face severe dust pollution during bulk cargo transshipment. When bulk cargo is stored in the open, it is easily subject to static dust generation due to environmental factors such as wind speed and humidity. This not only pollutes the atmosphere but also harms the health of workers and causes energy waste. Currently, ports mainly use water spraying for dust suppression, but existing operations rely heavily on experience and fail to fully consider the differences in dust intensity in different areas of the bulk cargo and the impact of moisture content on dust suppression effectiveness. This often results in poor dust suppression or wasted water resources. The moisture content of bulk cargo is a key factor affecting dust generation characteristics; too low a moisture content leads to a surge in dust generation, while too high a content affects the quality of the bulk cargo and may pose a risk of spontaneous combustion. Therefore, accurately monitoring the moisture content of bulk cargo and linking it with dust control is the core of achieving scientific dust suppression, and this has become an urgent technical problem to be solved. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for intelligent monitoring of moisture content in bulk cargo and coordinated dust control in ports, addressing the shortcomings of traditional technologies. It solves the problems of existing port dust suppression operations relying on experience, insufficient accuracy in moisture content detection, and a lack of dynamic adjustment capabilities for dust suppression strategies, achieving the dual goals of effective dust suppression and water conservation.

[0004] One embodiment of this application provides a method for intelligent monitoring of moisture content and dust control in port bulk cargo, applied to a port bulk cargo dust suppression control system. The method includes:

[0005] Near-infrared spectral signals of different bulk commodities at different moisture contents were collected, and a response matrix of the relationship between the moisture content of the bulk commodities and the near-infrared absorbance was constructed. Based on the response relationship matrix, the initial detection model for bulk cargo is trained to obtain the target detection model for bulk cargo moisture content. The critical starting wind speed for bulk cargo with different moisture contents was obtained, and the functional relationship between the critical starting wind speed and the moisture content was obtained. The functional relationship was determined by analyzing the dust generation area distribution and dust generation wind speed variation of single and densely stacked bulk cargo, and determining the optimal moisture content for suppressing dust generation of bulk cargo under different environments. Real-time acquisition of port environmental data and near-infrared spectral signals of bulk cargo at the port; using the bulk cargo moisture content target detection model to determine real-time moisture content data; wherein, the port environmental data includes: ambient wind speed and wind direction data, and dust concentration data around the bulk cargo. Based on the aforementioned functional relationship and the real-time moisture content data, the operating parameters of the port bulk cargo dust suppression control system are dynamically adjusted.

[0006] Optionally, the step of collecting near-infrared spectral signals of different bulk commodities at different moisture contents and constructing a response matrix between the moisture content of the bulk commodities and near-infrared absorbance includes: Near-infrared spectral signals and spectral data of preset characteristic bands are collected for different bulk commodities within different moisture content ranges; wherein, the preset characteristic bands include and ; Based on the near-infrared spectral signals of different bulk commodities in different moisture content ranges and the spectral data of preset characteristic bands, the standard values ​​of moisture content for different bulk commodities are obtained, and they are classified and organized according to bulk commodity type and moisture content gradient to generate a response relationship matrix.

[0007] Optionally, the step of training the initial bulk cargo detection model based on the response relationship matrix to obtain the bulk cargo moisture content target detection model includes: A bulk cargo initial detection model is constructed using near-infrared absorbance data in the response matrix as input features and moisture content standard value as output label. Initialize the parameters of the initial bulk cargo detection model, and train the initial bulk cargo detection model to obtain the trained bulk cargo moisture content target detection model; wherein, the model parameters include polynomial order, scalar regression weights and iterative convergence threshold; the process of training the initial bulk cargo detection model includes using gradient descent to minimize the error between the model prediction value and the actual moisture content, and dynamically adjusting the polynomial order and scalar regression weights.

[0008] Optionally, the initial detection model for bulk cargo is represented as follows:

[0009] in, This indicates the predicted moisture content of bulk cargo. Represents the model constant term. Indicates the number of preset characteristic bands. Denotes the order of the Bernstein polynomial. Indicates the first Each input feature corresponds to The regression coefficients of the Bernstein polynomial. express Step Secondary Bernstein basis functions.

[0010] Optionally, the real-time acquisition of port environmental data and near-infrared spectral signals of bulk cargo, and the determination of real-time moisture content data using the bulk cargo moisture content target detection model, includes: The acquired port environmental data and near-infrared spectral signals of bulk cargo are preprocessed, and the preprocessed near-infrared spectral signals are converted into absorbance data, which are then input into the trained bulk cargo moisture content target detection model to output real-time moisture content data.

[0011] Optionally, real-time moisture content data can be output in the following way:

[0012] in, This indicates real-time moisture content data. This represents the constant term after optimization. This represents the order of the optimized polynomial. This represents the optimized scalar regression weights. Denotes the binomial coefficient. Indicates the first Absorbance data The power represents the feature mapping of absorbance in higher-order dimensions. Indicates the first Near-infrared absorbance data for each characteristic band.

[0013] Another embodiment of this application provides an intelligent monitoring and dust linkage control system for the moisture content of bulk cargo in ports, the system comprising: The acquisition module is used to acquire near-infrared spectral signals of different bulk commodities at different moisture contents, and to construct a response matrix between the moisture content of the bulk commodities and the near-infrared absorbance. The execution module is used to train the initial detection model of bulk cargo based on the response relationship matrix to obtain the target detection model of bulk cargo moisture content; The acquisition module is used to acquire the critical starting wind speed of bulk cargo with different moisture contents, and obtain the functional relationship between the critical starting wind speed and the moisture content; wherein, the functional relationship is determined by analyzing the dust generation area distribution and dust generation wind speed variation law of single and densely stacked bulk cargo, and determining the optimal moisture content for suppressing dust generation of bulk cargo under different environments; The determination module is used to acquire port environmental data and near-infrared spectral signals of bulk cargo in real time, and to determine real-time moisture content data using the bulk cargo moisture content target detection model; wherein, the port environmental data includes: ambient wind speed and wind direction data, and dust concentration data around the bulk cargo. The adjustment module is used to dynamically adjust the operating parameters of the port bulk cargo dust suppression control system based on the functional relationship and the real-time moisture content data.

[0014] Optionally, the acquisition module includes: The acquisition unit is used to acquire near-infrared spectral signals and spectral data of preset characteristic bands for different bulk commodities within different moisture content ranges; wherein, the preset characteristic bands include and ; The generation unit is used to obtain the standard values ​​of moisture content for different bulk commodities based on the near-infrared spectral signals of different bulk commodities in different moisture content ranges and the spectral data of preset characteristic bands, and to classify and organize them according to bulk commodity type and moisture content gradient to generate a response relationship matrix.

[0015] Another embodiment of this application provides a storage medium storing a computer program, wherein the computer program is configured to implement the method described in any of the above-described embodiments when running.

[0016] Another embodiment of this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to implement the method described in any of the above embodiments.

[0017] Compared with existing technologies, this invention first collects near-infrared spectral signals of different bulk cargoes at different moisture contents, and constructs a response relationship matrix between bulk cargo moisture content and near-infrared absorbance. Based on the response relationship matrix, it trains an initial detection model for bulk cargo to obtain a target detection model for bulk cargo moisture content. It then obtains the critical starting wind speed for bulk cargoes with different moisture contents, and derives the functional relationship between the critical starting wind speed and moisture content. Real-time acquisition of port environmental data and near-infrared spectral signals of port bulk cargo is used, and the real-time moisture content data is determined using the aforementioned target detection model. Finally, based on the functional relationship and the real-time moisture content data, the operating parameters of the port bulk cargo dust suppression control system are dynamically adjusted. This invention solves the problems of existing port water spraying dust suppression operations relying on experience, insufficient moisture content detection accuracy, and a lack of dynamic adjustment capabilities for dust suppression strategies, achieving the dual goals of dust suppression effectiveness and water conservation. Attached Figure Description

[0018] Figure 1 Hardware structure block diagram of a computer terminal for a method of intelligent monitoring of moisture content and dust linkage control of bulk cargo in ports, provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating a method for intelligent monitoring of moisture content and dust linkage control of bulk cargo in ports, provided by an embodiment of the present invention. Figure 3 This is a schematic diagram of a port bulk cargo moisture content intelligent monitoring and dust linkage control system provided in an embodiment of the present invention. Detailed Implementation

[0019] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0020] The present invention first provides a method for intelligent monitoring of moisture content of bulk cargo in ports and dust linkage control. This method can be applied to electronic devices, such as computer terminals, specifically ordinary computers, tablets, etc.

[0021] The following detailed explanation uses a computer terminal as an example. Figure 1 This is a hardware structure block diagram of a computer terminal for a method of intelligent monitoring of moisture content and dust linkage control of bulk cargo in ports, provided in an embodiment of the present invention. Figure 1 As shown, the computer device includes a processor, memory, and network interface connected via a system bus, wherein the memory may include non-volatile storage media and internal memory.

[0022] The non-volatile storage medium can store an operating system and a computer program. This computer program includes program instructions that, when executed, cause the processor to perform any intelligent monitoring and dust control method for the moisture content of bulk cargo in ports.

[0023] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0024] The internal memory provides an environment for the execution of computer programs in non-volatile storage media. When the computer program is executed by the processor, it enables the processor to execute any intelligent monitoring and dust linkage control method for the moisture content of bulk cargo in ports.

[0025] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 1 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0026] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0027] See Figure 2 , Figure 2 The flowchart illustrates a method for intelligent monitoring of moisture content and dust control in port bulk cargo, provided by an embodiment of the present invention. Applied to a port bulk cargo dust suppression control system, it may include the following steps: S201: Collect near-infrared spectral signals of different bulk commodities at different moisture contents, and construct a response matrix between the moisture content of the bulk commodities and the near-infrared absorbance.

[0028] Specifically, the step of collecting near-infrared spectral signals of different bulk commodities at different moisture contents and constructing a response matrix between bulk commodity moisture content and near-infrared absorbance may include: 1. Collect near-infrared spectral signals and spectral data of preset characteristic bands for different bulk commodities within different moisture content ranges; wherein, the preset characteristic bands include and ; 2. Based on the near-infrared spectral signals of different bulk commodities in different moisture content ranges and the spectral data of preset characteristic bands, obtain the standard values ​​of moisture content for different bulk commodities, and classify and organize them according to bulk commodity type and moisture content gradient to generate a response relationship matrix.

[0029] For example, typical port bulk cargoes such as coking coal, chemical coal, and thermal coal can be selected as research objects. A series of samples with moisture contents ranging from 1.00% to 22.00% can be prepared through physical hydration and constant-temperature drying. Three parallel samples are set up for each moisture content gradient to ensure data reliability. Near-infrared spectroscopy detection equipment with an IP67 protection rating is used to collect the near-infrared spectral signals of each sample at a measurement distance of 35cm-50cm. The spectral acquisition range is 800nm-2500nm, with a focus on extracting... and The spectral data were sampled at 1 nm intervals, with each sample's spectral signal collected five times and the average value taken to avoid random error interference. Following the national standard drying method, each bulk sample was dried in a 105℃ drying oven to constant weight. The standard moisture content was calculated by weighing the samples before and after drying. The matrix was structured with bulk product type as the row dimension and moisture content gradient as the column dimension, representing the corresponding data for each sample. and The absorbance data for each band, the full-spectrum spectral characteristic parameters, and the standard value of water content were entered one by one to generate a two-dimensional response matrix. The matrix also included annotations of the ambient temperature and humidity during sample collection, providing data support for error correction during subsequent model training.

[0030] S202: Based on the response relationship matrix, train the initial detection model for bulk cargo to obtain the target detection model for bulk cargo moisture content.

[0031] Specifically, the step of training the initial bulk cargo detection model based on the response relationship matrix to obtain the bulk cargo moisture content target detection model may include: Step 1: Construct an initial detection model for bulk cargo using near-infrared absorbance data from the response relationship matrix as input features and moisture content standard value as output label.

[0032] For example, the initial detection model for bulk cargo can be represented as follows:

[0033] in, This indicates the predicted moisture content of bulk cargo. Represents the model constant term. Indicates the number of preset characteristic bands. Denotes the order of the Bernstein polynomial. Indicates the first Each input feature corresponds to The regression coefficients of the Bernstein polynomial. express Step Secondary Bernstein basis functions.

[0034] Step 2: Initialize the parameters of the initial bulk cargo detection model, and train the initial bulk cargo detection model to obtain the trained bulk cargo moisture content target detection model; wherein, the model parameters include the polynomial order, scalar regression weights, and iterative convergence threshold; the training process of the initial bulk cargo detection model includes using the gradient descent method to minimize the error between the model prediction value and the actual moisture content, and dynamically adjusting the polynomial order and scalar regression weights.

[0035] For example, extracting from the response relationship matrix and Near-infrared absorbance data in the band is used as the input feature vector. The standard value of moisture content in the corresponding matrix is ​​used as the output label. An initial detection model for bulk cargo is constructed based on the Bernstein multinomial scalar regression algorithm. The model expression is as follows:

[0036] The order of Bernstein polynomials The initial value can be set to 3-8, the first... Each input feature corresponds to Regression coefficients of the Bernstein polynomial Randomly generated in [ The iterative convergence threshold is defined in the interval [0.5, 0.5]. Set as At the same time, set the learning rate The gradient descent method is used to minimize the mean squared error loss function:

[0037] in, This represents the sample size, and the scalar regression weights are updated iteratively. If the percentage difference in the loss function corresponding to adjacent orders exceeds 0.1-0.2, the polynomial order is dynamically adjusted in the direction of loss reduction. When the following conditions are met... Training terminates when the desired result is obtained, resulting in a bulk cargo moisture content target detection model with a fit of no less than the preset value for different bulk cargoes.

[0038] S203: Obtain the critical starting wind speed for bulk cargo with different moisture contents, and obtain the functional relationship between the critical starting wind speed and the moisture content; wherein, the functional relationship is determined by analyzing the dust generation area distribution and dust generation wind speed variation law of single and densely stacked bulk cargo, and determining the optimal moisture content for suppressing dust generation of bulk cargo under different environments.

[0039] For example, typical port bulk cargoes such as coking coal, chemical coal, and thermal coal can be selected, and a series of samples with a moisture content ranging from 1.00% to 22.00% can be prepared. Bulk cargo models can be constructed at a 1:300 geometric scale. A DC wind tunnel test can be conducted to simulate wind speeds of 6 m / s, 8 m / s, and 10 m / s, and wind incident angles of 0°, 45°, and 90°. The first critical starting wind speed for the initial dust generation on the bulk cargo surface can be recorded. and the second critical starting wind speed for generating a large amount of dust Simultaneously, numerical models of single and densely stacked bulk cargo were established using computational fluid dynamics software. A turbulence model was used to simulate different operating conditions, analyzing the correlation between the proportion of dust-generating area, dust intensity, wind speed, and moisture content. Based on experimental and simulation data, the least squares method was used for function fitting to obtain the functional relationship between the critical starting wind speed and moisture content. First critical starting wind speed:

[0040] Second critical starting wind speed:

[0041] in, , Let be the fitting constant. , It is a power exponent.

[0042] Based on the dust suppression criteria, such as the proportion of dust-generating area ≤ 5% and the dust intensity ≤ preset threshold, the optimal moisture content range that meets the dust suppression requirements and does not affect the properties of bulk cargo is selected, and the functional relationship is calibrated to ensure that it can reflect the effect of moisture content on dust generation of bulk cargo under different environments.

[0043] S204: Real-time acquisition of port environmental data and near-infrared spectral signals of bulk cargo at the port, and determination of real-time moisture content data using the bulk cargo moisture content target detection model; wherein, the port environmental data includes: ambient wind speed and wind direction data, and dust concentration data around the bulk cargo.

[0044] Specifically, the real-time acquisition of port environmental data and near-infrared spectral signals of bulk cargo, and the determination of real-time moisture content data using the bulk cargo moisture content target detection model, may include: The acquired port environmental data and near-infrared spectral signals of bulk cargo are preprocessed, and the preprocessed near-infrared spectral signals are converted into absorbance data, which are then input into the trained bulk cargo moisture content target detection model to output real-time moisture content data.

[0045] Specifically, real-time acquisition of port environmental data and near-infrared spectral signals is performed. For example, a matrix of sensing devices deployed in the yard collects near-infrared spectral signals of bulk cargo at a frequency of 30 seconds per acquisition, simultaneously acquiring ambient wind speed, wind direction, and dust concentration data around the bulk cargo. Random noise is removed from the environmental data using a moving average method, and baseline drift is eliminated from the spectral signals using a third-order polynomial baseline correction method, combined with wavelet threshold denoising to suppress interference signals. The preprocessed spectral signals are converted into absorbance data according to the Lambert-Beer law, with a focus on extracting... and The input feature vector is constructed from the absorbance values ​​of the characteristic bands. The feature vector is input into the trained bulk cargo moisture content target detection model. The model quickly calculates based on the optimized Bernstein polynomial scalar regression algorithm and outputs real-time moisture content data. The detection error is controlled within the preset range. At the same time, it is associated with preprocessed environmental data to form a complete working condition dataset.

[0046] In one alternative implementation, real-time moisture content data can be output in the following manner:

[0047] in, This indicates real-time moisture content data. This represents the constant term after optimization. This represents the order of the optimized polynomial. This represents the optimized scalar regression weights. Denotes the binomial coefficient. Indicates the first Absorbance data The power represents the feature mapping of absorbance in higher-order dimensions. Indicates the first Near-infrared absorbance data for each characteristic band.

[0048] S205: Based on the functional relationship and the real-time moisture content data, dynamically adjust the operating parameters of the port bulk cargo dust suppression control system.

[0049] For example, based on the functional relationship between critical starting wind speed and moisture content, combined with real-time moisture content data and pre-processed environmental wind speed and direction data, the dust generation risk coefficient of bulk cargo under the current operating conditions is calculated:

[0050] in, The ambient wind speed after pretreatment. The first critical starting wind speed corresponds to the real-time moisture content.

[0051] For example, a risk level of R ≥ 0.8 is considered high risk, 0.5 ≤ R < 0.8 is considered medium risk, and R < 0.5 is considered low risk. Dust suppression operation parameters are dynamically adjusted based on the risk level.

[0052] As can be seen, this invention first collects near-infrared spectral signals of different bulk cargoes at different moisture contents, and constructs a response relationship matrix between bulk cargo moisture content and near-infrared absorbance; based on the response relationship matrix, it trains an initial detection model for bulk cargo to obtain a target detection model for bulk cargo moisture content; it obtains the critical starting wind speed for bulk cargoes with different moisture contents, and obtains the functional relationship between the critical starting wind speed and moisture content; it acquires port environmental data and near-infrared spectral signals of port bulk cargo in real time, and uses the aforementioned target detection model for bulk cargo moisture content to determine real-time moisture content data; finally, it dynamically adjusts the operating parameters of the port bulk cargo dust suppression control system based on the aforementioned functional relationship and the real-time moisture content data. It can solve the problems of existing port water spraying dust suppression operations relying on experience, insufficient accuracy in moisture content detection, and lack of dynamic adjustment capability for dust suppression strategies, achieving the dual goals of dust suppression effect and water conservation.

[0053] See Figure 3 , Figure 3 A schematic diagram of a port bulk cargo moisture content intelligent monitoring and dust linkage control system provided in this embodiment of the invention includes: The acquisition module 301 is used to acquire near-infrared spectral signals of different bulk commodities at different moisture contents, and to construct a response relationship matrix between the moisture content of the bulk commodities and the near-infrared absorbance. The execution module 302 is used to train the initial detection model of bulk cargo according to the response relationship matrix to obtain the target detection model of bulk cargo moisture content; The acquisition module 303 is used to acquire the critical starting wind speed of bulk cargo with different moisture contents, and obtain the functional relationship between the critical starting wind speed and the moisture content; wherein, the functional relationship is determined by analyzing the dust generation area distribution and dust generation wind speed variation law of single and densely stacked bulk cargo, and determining the optimal moisture content for suppressing dust generation of bulk cargo under different environments; The determination module 304 is used to acquire port environmental data and near-infrared spectral signals of port bulk cargo in real time, and determine real-time moisture content data using the bulk cargo moisture content target detection model; wherein, the port environmental data includes: ambient wind speed and wind direction data, and dust concentration data around the bulk cargo. The adjustment module 305 is used to dynamically adjust the operating parameters of the port bulk cargo dust suppression control system based on the functional relationship and the real-time moisture content data.

[0054] Specifically, the acquisition module 301 may include: The acquisition unit is used to acquire near-infrared spectral signals and spectral data of preset characteristic bands for different bulk commodities within different moisture content ranges; wherein, the preset characteristic bands include and ; The generation unit is used to obtain the standard values ​​of moisture content for different bulk commodities based on the near-infrared spectral signals of different bulk commodities in different moisture content ranges and the spectral data of preset characteristic bands, and to classify and organize them according to bulk commodity type and moisture content gradient to generate a response relationship matrix.

[0055] Compared with existing technologies, this invention first collects near-infrared spectral signals of different bulk cargoes at different moisture contents, and constructs a response relationship matrix between bulk cargo moisture content and near-infrared absorbance. Based on the response relationship matrix, it trains an initial detection model for bulk cargo to obtain a target detection model for bulk cargo moisture content. It then obtains the critical starting wind speed for bulk cargoes with different moisture contents, and derives the functional relationship between the critical starting wind speed and moisture content. Real-time acquisition of port environmental data and near-infrared spectral signals of port bulk cargo is used, and the real-time moisture content data is determined using the aforementioned target detection model. Finally, based on the functional relationship and the real-time moisture content data, the operating parameters of the port bulk cargo dust suppression control system are dynamically adjusted. This invention solves the problems of existing port water spraying dust suppression operations relying on experience, insufficient moisture content detection accuracy, and a lack of dynamic adjustment capabilities for dust suppression strategies, achieving the dual goals of dust suppression effectiveness and water conservation.

[0056] This invention also provides a storage medium storing a computer program, wherein the computer program is configured to implement the steps in the above method embodiments when running.

[0057] Specifically, in this embodiment, the storage medium can be configured to store a computer program for performing the following steps: S201: Collect near-infrared spectral signals of different bulk commodities at different moisture contents, and construct a response matrix between the moisture content of the bulk commodities and the near-infrared absorbance; S202: Based on the response relationship matrix, train the initial detection model for bulk cargo to obtain the target detection model for bulk cargo moisture content; S203: Obtain the critical starting wind speed for bulk cargo with different moisture contents, and obtain the functional relationship between the critical starting wind speed and the moisture content; wherein, the functional relationship is determined by analyzing the dust generation area distribution and dust generation wind speed variation law of single and densely stacked bulk cargo, and determining the optimal moisture content for suppressing dust generation of bulk cargo under different environments; S204: Real-time acquisition of port environmental data and near-infrared spectral signals of bulk cargo at the port; using the bulk cargo moisture content target detection model to determine real-time moisture content data; wherein, the port environmental data includes: ambient wind speed and wind direction data, and dust concentration data around the bulk cargo. S205: Based on the functional relationship and the real-time moisture content data, dynamically adjust the operating parameters of the port bulk cargo dust suppression control system.

[0058] Specifically, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0059] Compared with existing technologies, this invention first collects near-infrared spectral signals of different bulk cargoes at different moisture contents, and constructs a response relationship matrix between bulk cargo moisture content and near-infrared absorbance. Based on the response relationship matrix, it trains an initial detection model for bulk cargo to obtain a target detection model for bulk cargo moisture content. It then obtains the critical starting wind speed for bulk cargoes with different moisture contents, and derives the functional relationship between the critical starting wind speed and moisture content. Real-time acquisition of port environmental data and near-infrared spectral signals of port bulk cargo is used, and the real-time moisture content data is determined using the aforementioned target detection model. Finally, based on the functional relationship and the real-time moisture content data, the operating parameters of the port bulk cargo dust suppression control system are dynamically adjusted. This invention solves the problems of existing port water spraying dust suppression operations relying on experience, insufficient moisture content detection accuracy, and a lack of dynamic adjustment capabilities for dust suppression strategies, achieving the dual goals of dust suppression effectiveness and water conservation.

[0060] This invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps described in the method embodiments above.

[0061] Specifically, the aforementioned electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the aforementioned processor, and the input / output device is connected to the aforementioned processor.

[0062] Specifically, in this embodiment, the processor can be configured to perform the following steps via a computer program: S201: Collect near-infrared spectral signals of different bulk commodities at different moisture contents, and construct a response matrix between the moisture content of the bulk commodities and the near-infrared absorbance; S202: Based on the response relationship matrix, train the initial detection model for bulk cargo to obtain the target detection model for bulk cargo moisture content; S203: Obtain the critical starting wind speed for bulk cargo with different moisture contents, and obtain the functional relationship between the critical starting wind speed and the moisture content; wherein, the functional relationship is determined by analyzing the dust generation area distribution and dust generation wind speed variation law of single and densely stacked bulk cargo, and determining the optimal moisture content for suppressing dust generation of bulk cargo under different environments; S204: Real-time acquisition of port environmental data and near-infrared spectral signals of bulk cargo at the port; using the bulk cargo moisture content target detection model to determine real-time moisture content data; wherein, the port environmental data includes: ambient wind speed and wind direction data, and dust concentration data around the bulk cargo. S205: Based on the functional relationship and the real-time moisture content data, dynamically adjust the operating parameters of the port bulk cargo dust suppression control system.

[0063] Compared with existing technologies, this invention first collects near-infrared spectral signals of different bulk cargoes at different moisture contents, and constructs a response relationship matrix between bulk cargo moisture content and near-infrared absorbance. Based on the response relationship matrix, it trains an initial detection model for bulk cargo to obtain a target detection model for bulk cargo moisture content. It then obtains the critical starting wind speed for bulk cargoes with different moisture contents, and derives the functional relationship between the critical starting wind speed and moisture content. Real-time acquisition of port environmental data and near-infrared spectral signals of port bulk cargo is used, and the real-time moisture content data is determined using the aforementioned target detection model. Finally, based on the functional relationship and the real-time moisture content data, the operating parameters of the port bulk cargo dust suppression control system are dynamically adjusted. This invention solves the problems of existing port water spraying dust suppression operations relying on experience, insufficient moisture content detection accuracy, and a lack of dynamic adjustment capabilities for dust suppression strategies, achieving the dual goals of dust suppression effectiveness and water conservation.

[0064] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0065] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0066] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0067] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0068] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0069] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0070] The embodiments of the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for intelligent monitoring of moisture content and dust control in port bulk cargo, applied to a port bulk cargo dust suppression control system, characterized in that, The method includes: Near-infrared spectral signals of different bulk commodities at different moisture contents were collected, and a response matrix of the relationship between the moisture content of the bulk commodities and the near-infrared absorbance was constructed. Based on the response relationship matrix, the initial detection model for bulk cargo is trained to obtain the target detection model for bulk cargo moisture content. The critical starting wind speed for bulk cargo with different moisture contents was obtained, and the functional relationship between the critical starting wind speed and the moisture content was obtained. The functional relationship was determined by analyzing the dust generation area distribution and dust generation wind speed variation of single and densely stacked bulk cargo, and determining the optimal moisture content for suppressing dust generation of bulk cargo under different environments. Real-time acquisition of port environmental data and near-infrared spectral signals of bulk cargo at the port; using the bulk cargo moisture content target detection model to determine real-time moisture content data; wherein, the port environmental data includes: ambient wind speed and wind direction data, and dust concentration data around the bulk cargo. Based on the aforementioned functional relationship and the real-time moisture content data, the operating parameters of the port bulk cargo dust suppression control system are dynamically adjusted.

2. The method according to claim 1, characterized in that, The process involves collecting near-infrared spectral signals of different bulk commodities at different moisture contents and constructing a response matrix between the moisture content of the bulk commodities and near-infrared absorbance, including: Near-infrared spectral signals and spectral data of preset characteristic bands are collected for different bulk commodities within different moisture content ranges; wherein, the preset characteristic bands include and ; Based on the near-infrared spectral signals of different bulk commodities in different moisture content ranges and the spectral data of preset characteristic bands, the standard values ​​of moisture content for different bulk commodities are obtained, and they are classified and organized according to bulk commodity type and moisture content gradient to generate a response relationship matrix.

3. The method according to claim 2, characterized in that, The step of training the initial bulk cargo detection model based on the response relationship matrix to obtain the bulk cargo moisture content target detection model includes: A bulk cargo initial detection model is constructed using near-infrared absorbance data in the response matrix as input features and moisture content standard value as output label. Initialize the parameters of the initial bulk cargo detection model, and train the initial bulk cargo detection model to obtain the trained bulk cargo moisture content target detection model; wherein, the model parameters include polynomial order, scalar regression weights and iterative convergence threshold; the process of training the initial bulk cargo detection model includes using gradient descent to minimize the error between the model prediction value and the actual moisture content, and dynamically adjusting the polynomial order and scalar regression weights.

4. The method according to claim 3, characterized in that, The initial detection model for bulk cargo is represented as follows: in, This indicates the predicted moisture content of bulk cargo. Represents the model constant term. Indicates the number of preset characteristic bands. Denotes the order of the Bernstein polynomial. Indicates the first Each input feature corresponds to The regression coefficients of the Bernstein polynomial. express Step Secondary Bernstein basis functions.

5. The method according to claim 4, characterized in that, The method involves acquiring real-time port environmental data and near-infrared spectral signals of bulk cargo, and using the bulk cargo moisture content target detection model to determine real-time moisture content data, including: The acquired port environmental data and near-infrared spectral signals of bulk cargo are preprocessed, and the preprocessed near-infrared spectral signals are converted into absorbance data, which are then input into the trained bulk cargo moisture content target detection model to output real-time moisture content data.

6. The method according to claim 5, characterized in that, Output real-time moisture content data in the following manner: in, This indicates real-time moisture content data. This represents the constant term after optimization. This represents the order of the optimized polynomial. This represents the optimized scalar regression weights. Denotes the binomial coefficient. Indicates the first Absorbance data The power represents the feature mapping of absorbance in higher-order dimensions. Indicates the first Near-infrared absorbance data for each characteristic band.

7. A port bulk cargo moisture content intelligent monitoring and dust linkage control system, characterized in that, The system includes: The acquisition module is used to acquire near-infrared spectral signals of different bulk commodities at different moisture contents, and to construct a response matrix between the moisture content of the bulk commodities and the near-infrared absorbance. The execution module is used to train the initial detection model of bulk cargo based on the response relationship matrix to obtain the target detection model of bulk cargo moisture content; The acquisition module is used to acquire the critical starting wind speed of bulk cargo with different moisture contents, and obtain the functional relationship between the critical starting wind speed and the moisture content; wherein, the functional relationship is determined by analyzing the dust generation area distribution and dust generation wind speed variation law of single and densely stacked bulk cargo, and determining the optimal moisture content for suppressing dust generation of bulk cargo under different environments; The determination module is used to acquire port environmental data and near-infrared spectral signals of bulk cargo in real time, and to determine real-time moisture content data using the bulk cargo moisture content target detection model; wherein, the port environmental data includes: ambient wind speed and wind direction data, and dust concentration data around the bulk cargo. The adjustment module is used to dynamically adjust the operating parameters of the port bulk cargo dust suppression control system based on the functional relationship and the real-time moisture content data.

8. The system according to claim 7, characterized in that, The acquisition module includes: The acquisition unit is used to acquire near-infrared spectral signals and spectral data of preset characteristic bands for different bulk commodities within different moisture content ranges; wherein, the preset characteristic bands include and ; The generation unit is used to obtain the standard values ​​of moisture content for different bulk commodities based on the near-infrared spectral signals of different bulk commodities in different moisture content ranges and the spectral data of preset characteristic bands, and to classify and organize them according to bulk commodity type and moisture content gradient to generate a response relationship matrix.

9. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to implement the method of any one of claims 1 to 6 when it is run.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the method of any one of claims 1 to 6.

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