Riverway environmental protection is used to dredging and conveying system

Abstract

The application discloses a kind of riverway environmental protection with dredging conveying system, including data monitoring unit, pre-processing unit, core analysis unit and signal control unit, by data monitoring unit acquisition obtains riverway data, from the efficiency of dredging conveying work, energy consumption and water quality multi-angle integrated monitoring, guarantee the comprehensiveness of data acquisition, preliminary analysis and depth calculation of data are carried out again by pre-processing unit, to comprehensively obtain the evaluation coefficient corresponding to efficiency, energy consumption and water quality, realize the overall evaluation of riverway multi-angle, again by core analysis unit constructs riverway dredging efficiency analysis model, generates dredging conveying optimal scheme and dredging regulation signal and riverway management signal, in turn by signal control unit signal regulation processing is handled, realize the low energy consumption and high efficiency of dredging work, and guarantee riverway overall dredging conveying work's role unity and effect stability, finally realize the synchronous promotion of dredging efficiency and water quality.

Description

Technical Field

[0001] This invention relates to the field of river management technology, and in particular to a dredging and conveying system for environmental protection in rivers. Background Technology

[0002] River dredging can prevent siltation caused by the deposition of silt, debris and other blockages at the bottom of the river. Dredging can keep the river clear, prevent floods, improve water quality, maintain the ecological environment and ensure navigation safety. It can also help reduce water flow resistance, improve the self-purification capacity of the water body and improve the river ecosystem. With the intelligent development of equipment and technology, modern society has gradually developed and applied artificial intelligence algorithms to optimize dredging operation plans and combined them with sensor technology to monitor river siltation in real time, so as to realize intelligent dredging and sewage management.

[0003] The existing river environmental dredging and transportation work suffers from low dredging efficiency and high equipment energy consumption. In the process of systematic monitoring of dredging work, due to incomplete data collection, it is difficult to ensure the unified role and stable effect of the overall river dredging and transportation work. This leads to high river management and maintenance costs, and dredging work will affect water quality and thus cause interference to the ecological environment.

[0004] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to address the problems of low dredging efficiency and high equipment energy consumption in existing river environmental dredging and transportation operations, as well as the shortcomings of incomplete data collection, high river management and maintenance costs, and the impact of dredging on water quality and ecological environment during systematic monitoring of dredging operations. This invention acquires river data through a data monitoring unit, integrating multi-faceted monitoring of dredging and transportation efficiency, energy consumption, and water quality to ensure comprehensive data collection. A preprocessing unit performs preliminary analysis and in-depth calculations on the data to comprehensively obtain evaluation coefficients for efficiency, energy consumption, and water quality, achieving a multi-faceted overall assessment of the river. A core analysis unit constructs a river dredging efficiency analysis model, generating optimal dredging and transportation solutions, dredging control signals, and river management signals. These signals are then processed and controlled by a signal control unit to ensure low energy consumption and high efficiency in dredging operations, and to guarantee the unified function and stable effect of the overall river dredging and transportation work, ultimately achieving a simultaneous upgrade in dredging efficiency and water quality.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A river dredging and conveying system for environmental protection includes a data monitoring unit, a preprocessing unit, a core analysis unit, and a signal control unit, wherein the data monitoring unit, the preprocessing unit, the core analysis unit, and the signal control unit are connected by signals.

[0008] The data monitoring unit is used to collect and acquire river data, which includes efficiency information, energy consumption information, and water quality information.

[0009] The preprocessing unit is used for preliminary analysis of river data: by sequentially analyzing efficiency information, energy consumption information and water quality information, efficiency evaluation coefficient, energy consumption evaluation coefficient and water quality evaluation coefficient are obtained respectively.

[0010] The core analysis unit constructs a river dredging efficiency analysis model: by establishing a change function between the efficiency evaluation coefficient and the energy consumption evaluation coefficient, the river dredging efficiency evaluation index is obtained, thereby generating the optimal dredging and transportation scheme and the corresponding dredging control signal; then, by combining the river dredging efficiency evaluation index with the water quality evaluation coefficient, the river environmental management prediction index is obtained, and the corresponding river management signal is generated.

[0011] The signal control unit is used to receive dredging control signals and river management signals and process them accordingly.

[0012] Furthermore, the specific process for collecting river channel data is as follows:

[0013] A1: Efficiency information includes river flow velocity, river level, and sediment content;

[0014] A three-dimensional model of the river channel is constructed using 3D modeling technology, and the model is labeled: First, the river channel is divided into N0 regions, and any one of the regions is labeled as a. Then, N1 points are extracted from region a, and any one of the points is labeled as b. The efficiency information of the location of point b is collected, and the river flow velocity, river water level and sediment content corresponding to the location of point b are labeled as Vb, Lb and Hb, respectively.

[0015] By combining the river flow velocity, river water level, and sediment content at N1 points, the river flow velocity Va, river water level La, and sediment content Ha corresponding to region a are obtained.

[0016] A2: Energy consumption information includes power, operating time, and electricity consumption;

[0017] The N2 dredging and conveying devices used for dredging and conveying work in area a of the river channel are integrated. Any one of the dredging and conveying devices is labeled as c, the power of device c is labeled as Pc, and the working time of device c is labeled as Tc. The power consumption of device c is obtained from this. Then, by combining the power consumption of N2 dredging and conveying devices, the power consumption Wa of area a is calculated.

[0018] A3: Water quality information includes pollutant content, microbial content, and turbidity;

[0019] Pollutants include inorganic substances such as acids, alkalis, and salts, heavy metal pollution, and radioactive pollutants; microorganisms include algae, bacteria, fungi, and viruses; the pollutant content and microbial content are obtained by sampling and testing at point b by a professional testing unit, and the turbidity is collected by a turbidimeter, and the turbidity at point b is marked as HZb.

[0020] The river channel is assumed to contain N3 types of pollutants and N4 types of microorganisms. The content of any pollutant D is labeled as d, and the content of any microorganism E is labeled as e. Then, the pollutant content WRb and the microorganism content WSb at point b are obtained by integrating them.

[0021] Then, by integrating the pollutant content WRb, microbial content WSb, and turbidity HZb of N1 points, the pollutant content WRa, microbial content WSa, and turbidity HZa of region a are obtained.

[0022] Furthermore, the preprocessing unit performs preliminary analysis on the river data by constructing a preliminary analysis model. The specific construction process of the preliminary analysis model is as follows:

[0023] B1: Input the parameter set information corresponding to N0 regions, mark any parameter set as ψ, the parameter set ψ includes n0 parameter elements, mark any parameter element as φ, and mark the data value of parameter element φ as Jφ;

[0024] B2: First, calculate the mean value Pjφ of the parameter element φ by using the data values ​​Jφ of the parameter element φ in N0 regions;

[0025] B3: Then, by calculating the standard deviation of the data values ​​Jφ of the parameter element φ in N0 regions, the fluctuation coefficient σj of the parameter element φ is obtained;

[0026] B4: Then, the kurtosis coefficient FDφ and skewness coefficient PDφ of the parameter element φ are calculated and obtained;

[0027] B5: Integrate the mean Pjφ, fluctuation coefficient σj, kurtosis coefficient FDφ, and skewness coefficient PDφ of parameter element φ into a preprocessed dataset of parameter element φ, and output the preprocessed dataset of parameter element φ.

[0028] Furthermore, the efficiency information is substituted into the preliminary analysis model, the corresponding preprocessed dataset is output, and deep computation is performed to obtain the efficiency evaluation coefficient. The specific process is as follows:

[0029] C1: Input the efficiency information of N0 regions as parameter set information into the preliminary analysis model. The efficiency information includes river flow velocity Va, river water level La and sediment content Ha. Sequentially obtain the preprocessed datasets of river flow velocity, river water level and sediment content.

[0030] Then, through in-depth calculations, the river flow velocity influence coefficient YV, the river channel water level influence coefficient YL, and the sediment content influence coefficient YH were obtained respectively.

[0031] C2: The efficiency evaluation coefficient Xxl is obtained by combining the river flow velocity influence coefficient YV, the river channel water level influence coefficient YL, and the sediment content influence coefficient YH.

[0032] Furthermore, the energy consumption information is substituted into the preliminary analysis model, the corresponding preprocessed dataset is output, and deep calculations are performed to obtain the energy consumption assessment coefficient. The specific process is as follows:

[0033] D1: Substitute the corresponding power consumption Wa of N0 regions into the preliminary analysis model, obtain and output the preprocessed dataset of power consumption;

[0034] Constructing a deep analysis model: Input the preprocessed dataset of parameter M, which includes the mean Pma, fluctuation coefficient σm, kurtosis coefficient FDm and skewness coefficient PDm of parameter M, and then obtain the influence coefficient YM of parameter M;

[0035] D2: Substitute the preprocessed dataset of electricity consumption into the deep analysis model to obtain the electricity consumption influence coefficient YW;

[0036] D3: Then, the energy consumption assessment coefficient Xnh is obtained through the energy consumption impact coefficient YW.

[0037] Furthermore, the water quality information is substituted into the preliminary analysis model, the corresponding preprocessed dataset is output, and in-depth calculations are performed to obtain the water quality assessment coefficient. The specific process is as follows:

[0038] E1: Input the water quality information of N0 regions as a set of parameters into the preliminary analysis model. The water quality information includes pollutant content WRa, microbial content WSa, and turbidity HZa.

[0039] E1-1: Input the pollutant content WRa of N0 regions into the preliminary analysis model to obtain and output the preprocessed dataset of pollutant content; then substitute the preprocessed dataset of pollutant content into the deep analysis model to obtain the pollutant content influence coefficient YWR.

[0040] E1-2: Input the microbial content WSa of N0 regions into the preliminary analysis model to obtain and output the preprocessed dataset of microbial content; then substitute the preprocessed dataset of microbial content into the deep analysis model to obtain the microbial content influence coefficient YWS;

[0041] E1-3: Input the turbidity HZa of N0 regions into the preliminary analysis model to obtain and output the preprocessed turbidity dataset; then substitute the preprocessed turbidity dataset into the deep analysis model to obtain the turbidity influence coefficient YHZ;

[0042] E2: The water quality assessment coefficient Xsz is obtained by combining the pollutant content influence coefficient YWR, the microbial content influence coefficient YWS, and the turbidity influence coefficient YHZ.

[0043] Furthermore, the specific process for generating dredging control signals is as follows:

[0044] Establish the variation curve between the efficiency evaluation coefficient Xxl and the energy consumption evaluation coefficient Xnh, and fit and construct the variation function F1 between the efficiency evaluation coefficient Xxl and the energy consumption evaluation coefficient Xnh to obtain the river dredging efficiency evaluation index XN.

[0045] Then, a standard range of the river dredging efficiency assessment index XN is set, and corresponding dredging control signals are generated by comparing the ranges.

[0046] Furthermore, the specific process for generating river management signals is as follows:

[0047] The river environmental management prediction index GL is obtained by combining the river dredging efficiency assessment index XN with the water quality assessment coefficient Xsz.

[0048] Then, a risk range for the river environmental management prediction index GL is set, and corresponding river management signals are generated by comparing the ranges.

[0049] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0050] This invention collects river data through a data monitoring unit, integrating monitoring from multiple perspectives, including efficiency, energy consumption, and water quality, to ensure comprehensive data collection. A preprocessing unit then performs preliminary analysis and in-depth calculations to comprehensively obtain evaluation coefficients for efficiency, energy consumption, and water quality, enabling a multi-faceted overall assessment of the river. A core analysis unit then constructs a river dredging efficiency analysis model, generating optimal dredging and transportation schemes, dredging control signals, and river management signals. These signals are then processed and controlled by a signal control unit to achieve low energy consumption and high efficiency in dredging operations, ensuring the unified function and stable effect of the overall river dredging and transportation work. Ultimately, this achieves simultaneous upgrades in dredging efficiency and water quality. Attached Figure Description

[0051] Figure 1 A schematic diagram of the module of the present invention is shown;

[0052] Figure 2 A schematic diagram of the process of the present invention is shown. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] Example 1:

[0055] like Figure 1-2 As shown, a river dredging and conveying system for environmental protection includes a data monitoring unit, a preprocessing unit, a core analysis unit, and a signal control unit, wherein the data monitoring unit, the preprocessing unit, the core analysis unit, and the signal control unit are connected by signals.

[0056] The work steps are as follows:

[0057] Sa: The data monitoring unit collects and acquires river data, including efficiency information, energy consumption information, and water quality information.

[0058] The specific process for collecting river channel data is as follows:

[0059] A1: Efficiency information includes river flow velocity, river level, and sediment content;

[0060] A three-dimensional model of the river channel is constructed using 3D modeling technology, and the model is labeled: First, the river channel is divided into N0 regions, and any one of the regions is labeled as a. Then, N1 points are extracted from region a, and any one of the points is labeled as b. The efficiency information of the location of point b is collected, and the river flow velocity, river water level and sediment content corresponding to the location of point b are labeled as Vb, Lb and Hb, respectively.

[0061] By combining the river flow velocity, river water level, and sediment content at N1 points, the river flow velocity Va, river water level La, and sediment content Ha corresponding to region a are obtained.

[0062] Wherein, the river flow velocity Va in the preset region a is: River water level La in region a: Sediment content Ha in region a:

[0063] Among them, existing radar current meter, radar water level meter and sediment content collector are used to collect information on river flow velocity, river water level and sediment content in sequence.

[0064] A2: Energy consumption information includes power, operating time, and electricity consumption;

[0065] The N2 dredging and conveying devices used for dredging and conveying work in area a of the river channel are integrated. Each dredging and conveying device is labeled as c, the power of device c is labeled as Pc, and the working time of device c is labeled as Tc. The power consumption of device c is obtained from this. Then, by combining the power consumption of the N2 dredging and conveying devices, the power consumption Wa of area a is calculated.

[0066] A3: Water quality information includes pollutant content, microbial content, and turbidity;

[0067] Pollutants include inorganic substances such as acids, alkalis, and salts, heavy metal pollution, and radioactive pollutants; microorganisms include algae, bacteria, fungi, and viruses; the pollutant content and microbial content are obtained by sampling and testing at point b by a professional testing unit, and the turbidity is collected by a turbidimeter, and the turbidity at point b is marked as HZb.

[0068] Given that there are N3 types of pollutants and N4 types of microorganisms in the river channel, the content of any pollutant D is labeled as d, and the content of any microorganism E is labeled as e. The pollutant content WRb at point b is then integrated and obtained. And the microbial content WSb at point b:

[0069] Wherein, μ is the weighting coefficient of the content d of pollutant D and μ is greater than 0. The weighting coefficient μ reflects the degree of influence of pollutant D on river water pollution; υ is the weighting coefficient of the content e of microorganism E and e is greater than 0. The weighting coefficient υ reflects the degree of influence of microorganism E on river water quality; the weighting coefficients μ and υ are obtained by pre-setting after being calculated from a large amount of relevant experimental data.

[0070] By integrating the pollutant content WRb, microbial content WSb, and turbidity HZb at N1 points, the pollutant content WRa of region a is obtained: Microbial content WSa in region a: Turbidity HZa in region a:

[0071] Sb: Preliminary analysis of river data by the preprocessing unit: By sequentially analyzing efficiency information, energy consumption information and water quality information, efficiency evaluation coefficient, energy consumption evaluation coefficient and water quality evaluation coefficient are obtained respectively;

[0072] Sb-1: The preprocessing unit performs preliminary analysis on the river data by constructing a preliminary analysis model. The specific construction process of the preliminary analysis model is as follows:

[0073] B1: Input the parameter set information corresponding to N0 regions, mark any parameter set as ψ, the parameter set ψ includes n0 parameter elements, mark any parameter element as φ, and mark the data value of parameter element φ as Jφ;

[0074] B2: First, calculate the mean value Pjφ of the parameter element φ by using the data values ​​Jφ of the parameter element φ in N0 regions: The higher the mean value Pjφ, the higher the overall level of the parameter element φ in the N0 regions;

[0075] B3: Then, by calculating the standard deviation of the data values ​​Jφ of the parameter element φ in N0 regions, the fluctuation coefficient σj of the parameter element φ is obtained. The higher the fluctuation coefficient σj of parameter element φ, the higher the difference in data value Jφ of parameter element φ between regions, indicating that the overall dredging and transportation work of the river is more inconsistent, resulting in a worse dredging and transportation effect.

[0076] B4: Then, the kurtosis coefficient FDφ of the parameter element φ is calculated and obtained.

[0077]

[0078] The larger the kurtosis coefficient FDφ of parameter element φ, the steeper the distribution of parameter element φ in the N0 regions of the river channel, the more the distribution area of ​​the data value Jφ of parameter element φ close to the mean Pjφ, and the less the distribution area far away from the mean Pjφ of parameter element φ; conversely, the more gentle the distribution of parameter element φ in the N0 regions of the river channel, the more even the distribution of parameter element φ in the overall river channel, and thus the more unified and stable the effect of the overall dredging and transportation work in the river channel.

[0079] B5: Further calculate and obtain the skewness coefficient PDφ of the parameter element φ:

[0080]

[0081] When the skewness coefficient PDφ of parameter element φ is greater than 0, it indicates that the mean of parameter element φ is higher than the mode of parameter element φ, and the distribution of parameter element φ in the N0 regions of the river channel shows a right-skewed trend. The higher the value of the skewness coefficient PDφ, the greater the degree of right-skewness. When the skewness coefficient PDφ of parameter element φ is less than 0, it indicates that the mean of parameter element φ is lower than the mode of parameter element φ, and the distribution of parameter element φ in the N0 regions of the river channel shows a left-skewed trend. The lower the value of the skewness coefficient PDφ, the greater the degree of left-skewness. When the skewness coefficient PDφ of parameter element φ is closer to 0, it indicates that the mean of parameter element φ is closer to the mode of parameter element φ, which further indicates that the overall dredging and transportation work of the river channel is more unified and the effect is more stable.

[0082] B5: Integrate the mean Pjφ, fluctuation coefficient σj, kurtosis coefficient FDφ, and skewness coefficient PDφ of parameter element φ into a preprocessed dataset of parameter element φ, and output the preprocessed dataset of parameter element φ.

[0083] Sb-2: The efficiency information is substituted into the preliminary analysis model, the corresponding preprocessed dataset is output, and in-depth calculations are performed to obtain the efficiency evaluation coefficients. The specific process is as follows:

[0084] C1: Input the efficiency information of N0 regions as a set of parameters into the preliminary analysis model. The efficiency information includes river flow velocity Va, river level La, and sediment content Ha.

[0085] C1-1: Calculate the mean value Pva of the river flow velocity Va in N0 regions, then obtain the fluctuation coefficient σv of the river flow velocity, and further calculate the kurtosis coefficient FDv and skewness coefficient PDv of the river flow velocity; integrate and label the mean value Pva, fluctuation coefficient σv, kurtosis coefficient FDv and skewness coefficient PDv of the river flow velocity as a preprocessed dataset of river flow velocity, and output the preprocessed dataset of river flow velocity;

[0086] Then, deep computation is performed using the preprocessed river flow velocity dataset to obtain the river flow velocity influence coefficient YV:

[0087]

[0088] Wherein, α1, α2, α3, and α4 are the weighting coefficients of the mean river velocity Pva, fluctuation coefficient σv, kurtosis coefficient FDv, and skewness coefficient PDv, respectively, and α1, α2, α3, and α4 are all greater than 0; since dredging work is accompanied by river channel clearing, making the river flow smoother, it will be accompanied by an increase in river velocity. The higher the mean river velocity Pva, the higher the overall level of river velocity in N0 areas and the higher the efficiency of dredging work; the higher the fluctuation coefficient σv, the higher the river velocity. The greater and more inconsistent the differences in river flow velocity between regions, the less consistent the overall dredging and transportation work becomes, resulting in lower efficiency and poorer results. Conversely, the smaller the kurtosis coefficient (FDv) of the river flow velocity, the more even the distribution of river flow velocity in the N0 regions of the river channel, and the more uniform the overall river flow velocity distribution. Furthermore, the closer the skewness coefficient (PDv) is to 0, the closer the mean of the river flow velocity is to the mode of the river flow velocity. In summary, this indicates that the overall dredging and transportation work is more unified and has a more stable effect.

[0089] C1-2: Calculate the mean value of the river water level La in N0 regions to obtain the mean value Pla of the river water level, then obtain the fluctuation coefficient σl of the river water level, and then calculate the kurtosis coefficient FDl and skewness coefficient PDl of the river water level; integrate the mean value Pla, fluctuation coefficient σl, kurtosis coefficient FDl and skewness coefficient PDl of the river water level and label them as a preprocessed dataset of the river water level, and output the preprocessed dataset of the river water level;

[0090] Then, by performing in-depth calculations on the preprocessed river water level dataset, the river water level influence coefficient YL is obtained:

[0091]

[0092] Among them, α5, α6, α7, and α8 are the weighting coefficients of the mean river water level Pla, fluctuation coefficient σl, kurtosis coefficient FDl, and skewness coefficient PDl, respectively, and α5, α6, α7, and α8 are all greater than 0. Since dredging work is accompanied by a drop in river water level, the lower the mean river water level Pla, the lower the overall river water level in the N0 regions, and the higher the efficiency of dredging work. The lower the fluctuation coefficient σl, the smaller the kurtosis coefficient FDl, and the closer the skewness coefficient PDl is to 0, the smaller the difference in river water level between regions, and the more unified and stable the overall dredging and transportation work of the river.

[0093] C1-3: Calculate the mean value Pha of the sediment content Ha in N0 regions, then obtain the fluctuation coefficient σh of the sediment content, and then calculate the kurtosis coefficient FDh and skewness coefficient PDh of the sediment content; integrate the mean value Pha, fluctuation coefficient σh, kurtosis coefficient FDh and skewness coefficient PDh of the sediment content and label them as a preprocessed dataset of sediment content, and output the preprocessed dataset of sediment content;

[0094] Then, deep calculations are performed on the preprocessed dataset of sediment content to obtain the sediment content influence coefficient YH:

[0095]

[0096] Among them, α9, α10, α11, and α12 are the weighting coefficients of the mean sediment content Pha, fluctuation coefficient σh, kurtosis coefficient FDh, and skewness coefficient PDh, respectively, and α9, α10, α11, and α12 are all greater than 0. Since dredging work is accompanied by a decrease in sediment content, the lower the mean sediment content Pha, the lower the overall sediment content level of N0 regions and the higher the efficiency of dredging work. The lower the fluctuation coefficient σh, the smaller the kurtosis coefficient FDh, and the closer the skewness coefficient PDh is to 0, the smaller the difference in sediment content between regions, and the more unified and stable the overall dredging and transportation work of the river.

[0097] C2: The efficiency evaluation coefficient Xxl is obtained by combining the river flow velocity influence coefficient YV, the river channel water level influence coefficient YL, and the sediment content influence coefficient YH: Xxl = YV β1 +YL β2 +YH β3 ;

[0098] Wherein, β1, β2, and β3 are the weighting factor coefficients of the river flow velocity influence coefficient YV, the river channel water level influence coefficient YL, and the sediment content influence coefficient YH, respectively, and β1, β2, and β3 are all greater than 0; when the river flow velocity influence coefficient YV, the river channel water level influence coefficient YL, and the sediment content influence coefficient YH are higher, the efficiency evaluation coefficient Xxl is higher, which indicates that the dredging and transportation efficiency of the river is higher and the effect is better;

[0099] Sb-3: Substitute energy consumption information into the preliminary analysis model, output the corresponding preprocessed dataset, and perform deep calculations to obtain energy consumption assessment coefficients. The specific process is as follows:

[0100] D1: Substitute the corresponding power consumption Wa of N0 regions into the preliminary analysis model: Calculate the mean of power consumption Wa in N0 regions to obtain the mean power consumption Pwa, then obtain the fluctuation coefficient σw of power consumption, and further calculate the kurtosis coefficient FDw and skewness coefficient PDw of power consumption; integrate and label the mean power consumption Pwa, fluctuation coefficient σw, kurtosis coefficient FDw and skewness coefficient PDw as a preprocessed dataset of power consumption, and output the preprocessed dataset of power consumption.

[0101] Constructing a deep analysis model: The preprocessed dataset of parameter M is input, including the mean Pma, volatility coefficient σm, kurtosis coefficient FDm, and skewness coefficient PDm. The influence coefficient YM of parameter M is then obtained: YM = γ1*Pma + γ2*σm + γ3*FDm + γ4*PDm 2 ;

[0102] Wherein, γ1, γ2, γ3 and γ4 are the weighting coefficients of the mean Pma, fluctuation coefficient σm, kurtosis coefficient FDm and skewness coefficient PDm of parameter M, respectively, and γ1, γ2, γ3 and γ4 are all greater than 0.

[0103] D2: Substitute the preprocessed dataset of power consumption into the deep analysis model to obtain the power consumption influence coefficient YW. The higher the mean value of power consumption Pwa, the higher the energy consumption of dredging and transportation work, resulting in high cost and low efficiency. When the fluctuation coefficient σw of power consumption, the higher the kurtosis coefficient FDw, and the more the skewness coefficient PDw deviates from 0, the higher the energy consumption difference of dredging and transportation work between river areas, the more inconsistent the overall effect, and the more unstable the effect.

[0104] D3: Then, obtain the energy consumption assessment coefficient Xnh through the energy consumption impact coefficient YW: Xnh = YW β4 ;

[0105] Wherein, β4 is the conversion coefficient of the energy consumption influence coefficient YW and β4 is greater than 0; when the energy consumption influence coefficient YW is higher, the energy consumption assessment coefficient Xnh is higher, indicating that the efficiency of dredging and transportation work is worse.

[0106] Sb-4: Substitute water quality information into the preliminary analysis model, output the corresponding preprocessed dataset, and perform in-depth calculations to obtain the water quality assessment coefficient. The specific process is as follows:

[0107] E1: Input the water quality information of N0 regions as a set of parameters into the preliminary analysis model. The water quality information includes pollutant content WRa, microbial content WSa, and turbidity HZa.

[0108] E1-1: Input the pollutant content WRa of N0 regions into the preliminary analysis model to obtain the mean Pwra, fluctuation coefficient σwr, kurtosis coefficient FDwr, and skewness coefficient PDwr of the pollutant content. Integrate and label them as the preprocessed dataset of pollutant content, and output the preprocessed dataset of pollutant content. Then, substitute the preprocessed dataset of pollutant content into the deep analysis model to obtain the pollutant content influence coefficient YWR.

[0109] The higher the mean value of pollutant content Pwra, the worse the water quality and the worse the effect of dredging and river management. When the fluctuation coefficient σwr, the kurtosis coefficient FDwr, and the square value of the skewness coefficient PDwr are higher, the pollutant content influence coefficient YWR is higher, which indicates that the difference in pollutants between river areas is greater, and thus the management effect of dredging is worse.

[0110] E1-2: Input the microbial content WSa of N0 regions into the preliminary analysis model to obtain the mean Pwsa, fluctuation coefficient σws, kurtosis coefficient FDws, and skewness coefficient PDws of the microbial content. Integrate and label them as the pre-processed dataset of microbial content, and output the pre-processed dataset of microbial content. Then, substitute the pre-processed dataset of microbial content into the deep analysis model to obtain the influence coefficient YWS of microbial content.

[0111] The higher the mean value of microbial content Pwsa, the worse the water quality and the worse the effect of dredging and river management. When the fluctuation coefficient σws, kurtosis coefficient FDws, and square value of skewness coefficient PDws are higher, the influence coefficient YWS of microbial content is higher, which indicates that the difference in microbial content between river areas is higher, and thus the management effect of dredging is worse.

[0112] E1-3: Input the turbidity HZa of N0 regions into the preliminary analysis model to obtain the mean Phza, fluctuation coefficient σhz, kurtosis coefficient FDhz, and skewness coefficient PDhz of turbidity. Integrate and label these values ​​as a preprocessed dataset of turbidity, and output the preprocessed dataset of turbidity. Then, substitute the preprocessed dataset of turbidity into the deep analysis model to obtain the turbidity influence coefficient YHZ.

[0113] The higher the mean turbidity Phza, the worse the water quality and the worse the effect of dredging and river management. When the turbidity fluctuation coefficient σhz, the kurtosis coefficient FDhz, and the square value of the skewness coefficient PDhz are higher, the turbidity influence coefficient YHZ is higher, which indicates that the difference in pollutants between river areas is greater, and thus the management effect of dredging is worse.

[0114] E2: The water quality assessment coefficient Xsz is obtained by combining the pollutant content influence coefficient YWR, the microbial content influence coefficient YWS, and the turbidity influence coefficient YHZ.

[0115] Among them, β5, β6 and β7 are the weighting factor coefficients of pollutant content influence coefficient YWR, microbial content influence coefficient YWS and turbidity influence coefficient YHZ, respectively, and β5, β6 and β7 are all greater than 0; when the pollutant content influence coefficient YWR, microbial content influence coefficient YWS and turbidity influence coefficient YHZ are higher, the water quality assessment coefficient Xsz is lower, indicating that the effect of river environmental protection management is worse;

[0116] Sc: The core analysis unit constructs a river dredging efficiency analysis model: by establishing a variation function between the efficiency evaluation coefficient and the energy consumption evaluation coefficient, the river dredging efficiency evaluation index is obtained, thereby generating the optimal dredging and transportation scheme and corresponding dredging control signals to ensure low energy consumption and high efficiency in dredging work; then, by combining the river dredging efficiency evaluation index with the water quality evaluation coefficient, the river environmental management prediction index is obtained, generating corresponding river management signals, thereby simultaneously ensuring dredging efficiency and water quality;

[0117] Sc-1: The specific process of generating dredging control signals is as follows:

[0118] Establish the variation curve between the efficiency evaluation coefficient Xxl and the energy consumption evaluation coefficient Xnh, and fit and construct the variation function F1 between the efficiency evaluation coefficient Xxl and the energy consumption evaluation coefficient Xnh: Xxl=F1(Xnh). Substituting the energy consumption evaluation coefficient Xnh into the variation function F1, the corresponding efficiency evaluation coefficient Xxl can be obtained.

[0119] Thus, the river dredging efficiency assessment index XN is obtained:

[0120] When the energy consumption assessment coefficient Xnh is lower and the efficiency assessment coefficient Xxl is higher, the river dredging efficiency assessment index XN is higher, indicating that the dredging and transportation work is more efficient. Therefore, by calculating the maximum value of the river dredging efficiency assessment index XN, the dredging and transportation work plan corresponding to the current time node can be obtained and integrated and marked as the optimal dredging and transportation plan, thereby ensuring low energy consumption and high efficiency of dredging work.

[0121] Furthermore, a standard range for the river dredging efficiency assessment index XN is established, and corresponding dredging control signals are generated through range comparison:

[0122] There are R pre-defined standard intervals. Any standard interval is labeled Hr, where r is the index of the standard interval, 0 < r ≤ R. When the river dredging efficiency assessment index XN is within the standard interval Hr, an r-level dredging control signal is generated, assessing the river dredging efficiency level as r, and outputting it to the backend for visualization. The lower the river dredging efficiency assessment index XN, the lower the level of the dredging control signal, indicating a lower level of river dredging efficiency and a poorer assessment of the dredging and transportation efficiency, requiring timely adjustments to the river dredging management plan.

[0123] Sc-2: The specific process of generating river management signals is as follows:

[0124] The river environmental management prediction index GL is obtained by combining the river dredging efficiency assessment index XN with the water quality assessment coefficient Xsz: GL = XN * Xsz;

[0125] When the river dredging efficiency assessment index XN and the water quality assessment coefficient Xsz are higher, the river environmental management prediction index GL is higher, which means that the prediction effect of river environmental management is better, thereby ensuring both dredging efficiency and water quality.

[0126] Then, a risk range for the river environmental management prediction index GL is set, and corresponding river management signals are generated by comparing the ranges:

[0127] There are Q preset risk intervals, and any risk interval is marked as Hq, where q is the index of the risk interval, 0 < q ≤ Q. When the river environmental management prediction index GL is located in the risk interval Hq, a q-level river management signal is generated, the river management prediction level is assessed as q, and it is output to the background for visualization. The lower the river environmental management prediction index GL, the lower the level of the river management signal, which indicates a worse assessment of the river management prediction effect and a more severe reduction in the predicted river management effect.

[0128] Sd: The signal control unit receives dredging and control signals and river management signals and processes them accordingly. The specific process is as follows:

[0129] When a dredging control signal is received, the corresponding dredging control text is immediately edited and displayed according to the signal level, thereby prompting the back-end staff to perform corresponding control operations on the dredging and transportation work. For example, based on the existing dredging equipment and manpower in the river, the current dredging and transportation work plan is continuously adjusted to approach the optimal dredging and transportation plan, thereby ensuring low energy consumption and high efficiency of the dredging work.

[0130] Upon receiving a river management signal, the corresponding river management text is immediately edited and displayed according to the signal level. This prompts back-end staff to make corresponding adjustments to the future dredging and transportation work plan to achieve river management goals. For example, this could involve adding or removing a certain number of dredging equipment, replacing or upgrading dredging equipment with appropriate energy consumption and work efficiency parameters, and adjusting the workload between different river areas to ensure consistency in dredging work across areas. This ensures the unified function and stable effect of the overall river dredging and transportation work, ultimately achieving a simultaneous upgrade in dredging efficiency and water quality.

[0131] In summary, this invention acquires river data through a data monitoring unit, integrating multi-faceted monitoring of dredging and transportation efficiency, energy consumption, and water quality to ensure comprehensive data collection. A preprocessing unit then performs preliminary analysis and in-depth calculations to comprehensively obtain evaluation coefficients for efficiency, energy consumption, and water quality, achieving a multi-dimensional overall assessment of the river. A core analysis unit constructs a river dredging efficiency analysis model, generating optimal dredging and transportation schemes, dredging control signals, and river management signals. These signals are then processed and controlled by a signal control unit to ensure low energy consumption and high efficiency in dredging operations, and to guarantee the unified function and stable effect of the overall river dredging and transportation work. Ultimately, this achieves simultaneous upgrades in dredging efficiency and water quality.

[0132] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value.

[0133] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0134] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A river dredging and conveying system for environmental protection, characterized in that: It includes a data monitoring unit, a preprocessing unit, a core analysis unit, and a signal control unit, wherein the data monitoring unit, the preprocessing unit, the core analysis unit, and the signal control unit are connected by signals; The data monitoring unit is used to collect and acquire river data, which includes efficiency information, energy consumption information, and water quality information. The preprocessing unit is used for preliminary analysis of river data: by sequentially analyzing efficiency information, energy consumption information and water quality information, efficiency evaluation coefficient, energy consumption evaluation coefficient and water quality evaluation coefficient are obtained respectively. The core analysis unit constructs a river dredging efficiency analysis model: by establishing a change function between the efficiency evaluation coefficient and the energy consumption evaluation coefficient, the river dredging efficiency evaluation index is obtained, thereby generating the optimal dredging and transportation scheme and the corresponding dredging control signal; then, by combining the river dredging efficiency evaluation index with the water quality evaluation coefficient, the river environmental management prediction index is obtained, and the corresponding river management signal is generated. The river environmental management prediction index GL is obtained by combining the river dredging efficiency assessment index XN with the water quality assessment coefficient Xsz. Then, a risk range for the river environmental management prediction index GL is set, and corresponding river management signals are generated by comparing the ranges. The signal control unit is used to receive dredging control signals and river management signals and process them accordingly. When a dredging control signal is received, the corresponding dredging control text is immediately edited and displayed according to the signal level, thereby prompting the back-end staff to perform corresponding control operations on the dredging and transportation work; When a river management signal is received, the corresponding river management text is immediately edited and displayed according to the signal level, thereby prompting the back-end staff to make corresponding adjustments to the future dredging and transportation work plan in order to achieve river management. A1: Efficiency information includes river flow velocity, river level, and sediment content; A2: Energy consumption information includes power, operating time, and electricity consumption; A3: Water quality information includes pollutant content, microbial content, and turbidity. The energy consumption information is substituted into the preliminary analysis model, the corresponding preprocessed dataset is output, and deep calculations are performed to obtain the energy consumption assessment coefficient. The specific process is as follows: D1: Substitute the corresponding power consumption Wa of N0 regions into the preliminary analysis model, obtain and output the preprocessed dataset of power consumption; Constructing a deep analysis model: Input the preprocessed dataset of parameter M, which includes the mean Pma, fluctuation coefficient σm, kurtosis coefficient FDm and skewness coefficient PDm of parameter M, and then obtain the influence coefficient YM of parameter M; D2: Substitute the preprocessed dataset of electricity consumption into the deep analysis model to obtain the electricity consumption influence coefficient YW; D3: Then, the energy consumption assessment coefficient Xnh is obtained through the energy consumption impact coefficient YW; The water quality information is substituted into the preliminary analysis model, the corresponding preprocessed dataset is output, and in-depth calculations are performed to obtain the water quality assessment coefficient. The specific process is as follows: E1: Input the water quality information of N0 regions as a set of parameters into the preliminary analysis model. The water quality information includes pollutant content WRa, microbial content WSa, and turbidity HZa. E1-1: Input the pollutant content WRa of N0 regions into the preliminary analysis model to obtain and output the preprocessed dataset of pollutant content; then substitute the preprocessed dataset of pollutant content into the deep analysis model to obtain the pollutant content influence coefficient YWR. E1-2: Input the microbial content WSa of N0 regions into the preliminary analysis model to obtain and output the preprocessed dataset of microbial content; then substitute the preprocessed dataset of microbial content into the deep analysis model to obtain the microbial content influence coefficient YWS; E1-3: Input the turbidity HZa of N0 regions into the preliminary analysis model to obtain and output the preprocessed turbidity dataset; then substitute the preprocessed turbidity dataset into the deep analysis model to obtain the turbidity influence coefficient YHZ; E2: The water quality assessment coefficient Xsz is obtained by combining the pollutant content influence coefficient YWR, the microbial content influence coefficient YWS, and the turbidity influence coefficient YHZ; The specific process for generating dredging control signals is as follows: Establish the variation curve between the efficiency evaluation coefficient Xxl and the energy consumption evaluation coefficient Xnh, and fit and construct the variation function F1 between the efficiency evaluation coefficient Xxl and the energy consumption evaluation coefficient Xnh to obtain the river dredging efficiency evaluation index XN. Then, a standard range of the river dredging efficiency assessment index XN is set, and corresponding dredging control signals are generated by comparing the ranges.

2. The river dredging and conveying system for environmental protection according to claim 1, characterized in that: The specific process for collecting river channel data is as follows: A three-dimensional model of the river channel is constructed using 3D modeling technology, and the model is labeled: First, the river channel is divided into N0 regions, and any one of the regions is labeled as a. Then, N1 points are extracted from region a, and any one of the points is labeled as b. The efficiency information of the location of point b is collected, and the river flow velocity, river water level and sediment content corresponding to the location of point b are labeled as Vb, Lb and Hb, respectively. By combining the river flow velocity, river water level, and sediment content at N1 points, the river flow velocity Va, river water level La, and sediment content Ha corresponding to region a are obtained. The N2 dredging and conveying devices used for dredging and conveying work in area a of the river channel are integrated. Any one of the dredging and conveying devices is labeled as c, the power of device c is labeled as Pc, and the working time of device c is labeled as Tc. The power consumption of device c is obtained from this. Then, by combining the power consumption of N2 dredging and conveying devices, the power consumption Wa of area a is calculated. Pollutants include inorganic substances such as acids, alkalis, and salts, heavy metal pollution, and radioactive pollutants; microorganisms include algae, bacteria, fungi, and viruses; the pollutant content and microbial content are obtained by sampling and testing at point b by a professional testing unit, and the turbidity is collected by a turbidimeter, and the turbidity at point b is marked as HZb. The river channel is assumed to contain N3 types of pollutants and N4 types of microorganisms. The content of any pollutant D is labeled as d, and the content of any microorganism E is labeled as e. Then, the pollutant content WRb and the microorganism content WSb at point b are obtained by integrating them. Then, by integrating the pollutant content WRb, microbial content WSb, and turbidity HZb of N1 points, the pollutant content WRa, microbial content WSa, and turbidity HZa of region a are obtained.

3. The river dredging and conveying system for environmental protection according to claim 2, characterized in that: The preprocessing unit performs preliminary analysis on the river data by constructing a preliminary analysis model. The specific construction process of the preliminary analysis model is as follows: B1: Input the parameter set information corresponding to N0 regions, mark any parameter set as ψ, the parameter set ψ includes n0 parameter elements, mark any parameter element as φ, and mark the data value of parameter element φ as Jφ; B2: First, calculate the mean value Pjφ of the parameter element φ by using the data values ​​Jφ of the parameter element φ in N0 regions; B3: Then, by calculating the standard deviation of the data values ​​Jφ of the parameter element φ in N0 regions, the fluctuation coefficient σj of the parameter element φ is obtained; B4: Then, the kurtosis coefficient FDφ and skewness coefficient PDφ of the parameter element φ are calculated and obtained; B5: Integrate the mean Pjφ, fluctuation coefficient σj, kurtosis coefficient FDφ, and skewness coefficient PDφ of parameter element φ into a preprocessed dataset of parameter element φ, and output the preprocessed dataset of parameter element φ.

4. A river dredging and conveying system for environmental protection according to claim 3, characterized in that: The efficiency information is substituted into the preliminary analysis model, the corresponding preprocessed dataset is output, and in-depth calculations are performed to obtain the efficiency evaluation coefficients. The specific process is as follows: C1: Input the efficiency information of N0 regions as parameter set information into the preliminary analysis model. The efficiency information includes river flow velocity Va, river water level La and sediment content Ha. Sequentially obtain the preprocessed datasets of river flow velocity, river water level and sediment content. Then, through in-depth calculations, the river flow velocity influence coefficient YV, the river channel water level influence coefficient YL, and the sediment content influence coefficient YH were obtained respectively. C2: The efficiency evaluation coefficient Xxl is obtained by combining the river flow velocity influence coefficient YV, the river channel water level influence coefficient YL, and the sediment content influence coefficient YH.

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