A control method for fishery water environment monitoring equipment

Abstract

The present invention discloses a method for controlling fishery water environment monitoring equipment, and relates to the technical field of equipment control. The method comprises: obtaining a primary monitoring area and a secondary monitoring area; obtaining a primary monitoring equipment layout array and a secondary monitoring equipment layout array; setting a monitoring bandwidth; obtaining a primary monitoring indicator set sequence and a secondary monitoring indicator set sequence; obtaining a primary monitoring area water state coefficient and a secondary monitoring area water state coefficient; and performing water environment monitoring on the primary monitoring area and the secondary monitoring area according to the adjusted monitoring bandwidth. The present invention solves the technical problems in the prior art that the control of fishery water environment monitoring equipment has a low degree of fit with the actual water environment and poor control reliability, and achieves the technical effect of improving the degree of fit between the monitoring bandwidth of the water environment monitoring equipment and the actual situation and improving the accuracy of equipment control.

Description

Technical Field

[0001] The invention relates to the technical field of equipment control, and in particular to a method for controlling fishery water environment monitoring equipment. Background Art

[0002] With the expansion of fish farming, water environment monitoring has become an important means to ensure the healthy development of fisheries. Existing technologies usually monitor water environment parameters (such as dissolved oxygen, temperature, pH value, etc.) through sensors. However, these methods mostly adopt fixed monitoring strategies and lack accurate analysis of dynamic changes in the water environment. Especially in key areas such as sewage outlets and intensive aquaculture areas, monitoring equipment is difficult to adapt to the complexity of water environment fluctuations.

[0003] The existing technology has technical problems such as low fit between the control of fishery water environment monitoring equipment and the actual water environment conditions and poor control reliability. Summary of the invention

[0004] The present application provides a method for controlling fishery water environment monitoring equipment, which is used to solve the technical problems in the prior art that the control of fishery water environment monitoring equipment has a low degree of conformity with the actual water environment conditions and poor control reliability.

[0005] In view of the above problems, the present application provides a method for controlling a fishery water environment monitoring device, the method comprising:

[0006] Obtain the surrounding facility information of the target fishery breeding area, divide the area based on the surrounding facility information, and obtain the primary monitoring area and the secondary monitoring area;

[0007] Deploy environmental monitoring equipment in the primary monitoring area and the secondary monitoring area to obtain a primary monitoring equipment deployment array and a secondary monitoring equipment deployment array;

[0008] The historical water environment monitoring database of the interactive target fishery breeding area is searched with the primary monitoring area and the secondary monitoring area as indexes, and the primary monitoring bandwidth set and the secondary monitoring bandwidth set are set according to the search results;

[0009] In the preset equipment control analysis window, the monitoring data of the first-level monitoring equipment array and the second-level monitoring equipment array are respectively extracted according to the preset water environment monitoring indicators to obtain the first-level monitoring indicator set sequence and the second-level monitoring indicator set sequence;

[0010] Based on the first-level monitoring bandwidth set and the second-level monitoring bandwidth set, the first-level monitoring indicator set sequence and the second-level monitoring indicator set sequence are subjected to fast and slow flow cross-fusion analysis to obtain the water body state coefficient of the first-level monitoring area and the water body state coefficient of the second-level monitoring area;

[0011] Based on the water state coefficient of the primary monitoring area and the water state coefficient of the secondary monitoring area, the primary monitoring bandwidth set and the secondary monitoring bandwidth set are adjusted, and water environment monitoring of the primary monitoring area and the secondary monitoring area is carried out according to the obtained primary adjusted monitoring bandwidth set and the secondary adjusted monitoring bandwidth set.

[0012] Furthermore, the historical water environment monitoring database of the interactive target fishery breeding area is searched with the primary monitoring area and the secondary monitoring area as indexes, and the primary monitoring bandwidth set and the secondary monitoring bandwidth set are set according to the search results, including:

[0013] Using the primary monitoring area and the secondary monitoring area as indexes, the historical water environment monitoring database is searched to obtain the primary historical water environment abnormal interval set and the secondary historical water environment abnormal interval set;

[0014] The maximum value in the first-level historical water environment anomaly interval set is used as the first-level slow flow monitoring bandwidth, and the minimum value in the first-level historical water environment anomaly interval set is used as the first-level fast flow monitoring bandwidth;

[0015] The maximum value in the secondary historical water environment anomaly interval set is used as the secondary slow flow monitoring bandwidth, and the minimum value in the secondary historical water environment anomaly interval set is used as the secondary fast flow monitoring bandwidth;

[0016] The first-level slow flow monitoring bandwidth and the first-level fast flow monitoring bandwidth are taken as the first-level monitoring bandwidth set;

[0017] The secondary slow flow monitoring bandwidth and the secondary fast flow monitoring bandwidth are taken as the primary monitoring bandwidth set.

[0018] Furthermore, based on the first-level monitoring bandwidth set and the second-level monitoring bandwidth set, the first-level monitoring indicator set sequence and the second-level monitoring indicator set sequence are subjected to fast and slow flow cross-fusion analysis to obtain the first-level monitoring area water body state coefficient and the second-level monitoring area water body state coefficient, including:

[0019] A first-level slow flow indicator feature analysis branch and a first-level fast flow indicator feature analysis branch are constructed according to the first-level slow flow monitoring bandwidth and the first-level fast flow monitoring bandwidth of the first-level monitoring bandwidth set;

[0020] The feature convolution analysis of the first-level monitoring indicator set sequence is performed using the first-level slow flow indicator feature analysis branch and the first-level fast flow indicator feature analysis branch, respectively, to extract the first-level regional slow flow spatial feature vector and the first-level regional fast flow temporal feature vector in the analysis results;

[0021] A cross-fusion analysis is performed based on the first-level regional slow flow spatial feature vector and the first-level regional fast flow temporal feature vector, and the attention weight of the first-level slow flow indicator feature analysis branch is updated to obtain the first-level slow flow update indicator feature analysis branch;

[0022] The feature convolution analysis of the first-level monitoring indicator set sequence is performed using the first-level slow flow update indicator feature analysis branch and the first-level fast flow indicator feature analysis branch, and the analysis results are input into the first-level fully connected network layer to identify the water state coefficient, and the water state coefficient of the first-level monitoring area is obtained;

[0023] According to the secondary slow flow monitoring bandwidth and the secondary fast flow monitoring bandwidth of the secondary monitoring bandwidth set, a fast and slow flow cross-fusion analysis is performed on the secondary monitoring indicator set sequence to obtain the water state coefficient of the secondary monitoring area.

[0024] Furthermore, a cross-fusion analysis is performed based on the first-level regional slow flow spatial feature vector and the first-level regional fast flow temporal feature vector, and the attention weight of the first-level slow flow indicator feature analysis branch is updated to obtain the first-level slow flow update indicator feature analysis branch, including:

[0025] Obtaining the attention weight of the first-level slow flow branch of the first-level slow flow indicator feature analysis branch;

[0026] Using the cross-fusion formula, the first-level slow flow spatial feature vector and the first-level fast flow temporal feature vector are cross-fused and analyzed, and the first-level slow flow branch attention weight is updated to obtain the first-level updated slow flow branch attention weight;

[0027] Based on the attention weight of the first-level updated slow flow branch, the attention weight of the first-level slow flow indicator feature analysis branch is updated to obtain the first-level slow flow update indicator feature analysis branch.

[0028] Furthermore, the cross-fusion formula is:

[0029] ;

[0030] in, Update the attention weight of the slow stream branch for the first level, is the spatial eigenvector of slow flow in the first-order region, is the normalized value of the similarity between the spatial feature vector of the slow flow in the first-level region and the temporal feature vector of the fast flow in the first-level region, is the transpose of the time series feature vector of the first-level regional fast flow, is the attention weight of the first-level slow-flow branch, is the dimension of the fast flow time series feature vector in the first-level region.

[0031] Furthermore, the preset water environment monitoring indicators include temperature, pH value, dissolved oxygen content, and ammonia nitrogen content.

[0032] Further, based on the water state coefficient of the primary monitoring area and the water state coefficient of the secondary monitoring area, the primary monitoring bandwidth set and the secondary monitoring bandwidth set are adjusted, and water environment monitoring of the primary monitoring area and the secondary monitoring area is performed according to the obtained primary adjusted monitoring bandwidth set and the secondary adjusted monitoring bandwidth set, including:

[0033] Calculate the difference between the water state coefficient of the first-level monitoring area and the water state coefficient of the first-level monitoring area of ​​the preset standard to obtain the first-level coefficient difference;

[0034] Calculate the difference between the water state coefficient of the secondary monitoring area and the water state coefficient of the preset standard secondary monitoring area to obtain the secondary coefficient difference;

[0035] When both the first-level coefficient difference and the second-level coefficient difference do not meet the preset difference threshold, the first-level coefficient difference is divided by the preset standard first-level monitoring area water state coefficient, and the difference between the obtained ratio and 1 is calculated, and the calculated result is multiplied by the first-level monitoring bandwidth set to obtain the first-level adjustment monitoring bandwidth set, wherein the first-level adjustment monitoring bandwidth set includes the first-level slow flow adjustment monitoring bandwidth and the first-level fast flow adjustment monitoring bandwidth;

[0036] When both the first-level coefficient difference and the second-level coefficient difference do not meet the preset difference threshold, the second-level coefficient difference is divided by the preset standard second-level monitoring area water state coefficient, and the difference between the obtained ratio and 1 is calculated, and the calculated result is multiplied by the second-level monitoring bandwidth set to obtain the second-level adjustment monitoring bandwidth set, where the second-level adjustment monitoring bandwidth set includes the second-level slow flow monitoring adjustment bandwidth and the second-level fast flow adjustment monitoring bandwidth.

[0037] Further, when both the primary coefficient difference and the secondary coefficient difference satisfy a preset difference threshold, the primary monitoring bandwidth set and the secondary monitoring bandwidth set are not adjusted.

[0038] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0039] The present application obtains the surrounding facility information of the target fishery breeding area, divides the area based on the surrounding facility information, obtains the primary monitoring area and the secondary monitoring area, and then deploys environmental monitoring equipment in the primary monitoring area and the secondary monitoring area to obtain the primary monitoring equipment deployment array and the secondary monitoring equipment deployment array, and then interacts with the historical water environment monitoring database of the target fishery breeding area, respectively uses the primary monitoring area and the secondary monitoring area as indexes to search the historical water environment monitoring database, sets the primary monitoring bandwidth set and the secondary monitoring bandwidth set according to the search results, and in the preset equipment control analysis window, respectively deploys the primary monitoring equipment array according to the preset water environment monitoring indicators The monitoring data is extracted from the array of the secondary monitoring equipment layout to obtain the first-level monitoring indicator set sequence and the second-level monitoring indicator set sequence, and then the first-level monitoring indicator set sequence and the second-level monitoring indicator set sequence are cross-fused and analyzed based on the first-level monitoring bandwidth set and the second-level monitoring bandwidth set to obtain the water state coefficient of the first-level monitoring area and the water state coefficient of the second-level monitoring area. Based on the size of the water state coefficient of the first-level monitoring area and the water state coefficient of the second-level monitoring area, the first-level monitoring bandwidth set and the second-level monitoring bandwidth set are adjusted, and the water environment monitoring of the first-level monitoring area and the second-level monitoring area is carried out according to the obtained first-level adjusted monitoring bandwidth set and the second-level adjusted monitoring bandwidth set. The technical effect of improving the fit between the layout of monitoring equipment and the monitoring bandwidth of monitoring equipment and the actual water environment conditions, and improving the accuracy and reliability of equipment control is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Attached Figure 1 It is a flow chart of a method for controlling a fishery water environment monitoring device provided by an embodiment of the present invention;

[0041] Attached Figure 2 It is a flow chart of obtaining the water state coefficient of the primary monitoring area and the water state coefficient of the secondary monitoring area in a fishery water environment monitoring equipment control method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0043] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0044] Embodiment, as attached Figure 1 As shown, the present application provides a method for controlling a fishery water environment monitoring device, wherein the method comprises:

[0045] Step S100: Acquire the surrounding facility information of the target fishery breeding area, divide the area based on the surrounding facility information, and obtain the primary monitoring area and the secondary monitoring area;

[0046] In one possible embodiment, the target fishery breeding area refers to the water area where fishery breeding activities are located, including fish ponds, breeding cages or natural waters, which may be affected by surrounding facilities (such as sewage discharge outlets, concentrated breeding areas). Peripheral facility information refers to key facility information near the target breeding area, including but not limited to the distribution locations of sewage discharge outlets, water inlets and outlets, concentrated breeding areas, etc. These facilities may have an impact on the water environment. The first-level monitoring area is a high-risk area, such as an area near a sewage discharge outlet or a concentrated breeding area, where the water quality fluctuates greatly. The second-level monitoring area is an area with lower risks or a stable environment.

[0047] By analyzing the information of surrounding facilities in the target fishery breeding area, the potential impact of each facility on the water environment is evaluated. For example, based on the discharge volume and flow rate of the sewage outlet, the first-level monitoring area that may be affected by pollution is delineated; for intensive breeding areas, the impact range is delineated in combination with the breeding density and water fluidity, and the affected area is divided into the first-level monitoring area. In addition, the areas in the target fishery breeding area other than the first-level monitoring area are divided into the second-level monitoring area.

[0048] For example, when the surrounding facility information shows that there is only a sewage outlet in the target fishery breeding area, the daily discharge of the sewage outlet is obtained, such as 1,000 tons, assuming that the pollutant concentration is 50 mg / L and the water flow rate is 2 meters / second, and the discharge radius calculation formula is used to calculate the radius of the first-level monitoring area. The discharge radius calculation formula is: After calculation, the radius of the first-level monitoring area is 500 meters, so the first-level monitoring area is an area with the sewage outlet as the center and a radius of 500 meters. The area except the first-level monitoring area in the target fishery breeding area is divided into the second-level monitoring area.

[0049] By dividing the monitoring areas into different levels, the key monitoring areas are clearly defined, providing a scientific basis for the deployment of subsequent monitoring equipment. The first-level monitoring area, as a key monitoring area, helps capture sudden changes in water quality; the second-level monitoring area is used for supplementary monitoring to ensure a complete assessment of the overall water environment, achieving the technical effect of providing a basis for the subsequent control of monitoring equipment according to the conditions of different monitoring areas.

[0050] Step S200: deploying environmental monitoring equipment in the primary monitoring area and the secondary monitoring area to obtain a primary monitoring equipment deployment array and a secondary monitoring equipment deployment array;

[0051] In one embodiment, the first-level monitoring area and the second-level monitoring area are traversed to perform area size statistics to obtain the area of ​​the first-level monitoring area and the area of ​​the second-level monitoring area. Furthermore, the area of ​​the equipment monitoring area is determined according to the model of the environmental monitoring equipment. The preset first-level monitoring area layout density and the preset second-level monitoring area layout density are obtained, wherein the preset first-level monitoring area layout density and the preset second-level monitoring area layout density are respectively the layout density of the monitoring equipment in the first-level monitoring area set by the technicians in this field, and the layout density of the monitoring equipment in the second-level monitoring area set by the technicians in this field. Among them, since the water environment in the second-level monitoring area is relatively stable, the preset first-level monitoring area layout density is greater than the preset second-level monitoring area layout density. For example: one monitoring device is arranged for every 100 square meters in the first-level monitoring area, and one monitoring device is arranged for every 500 square meters in the second-level monitoring area.

[0052] The number of first-level monitoring devices and the number of second-level monitoring devices are obtained by dividing the area of ​​the first-level monitoring area by the preset first-level monitoring area layout density, and the area of ​​the second-level monitoring area by the preset second-level monitoring area layout density. According to the number of first-level monitoring devices and the number of second-level monitoring devices, the devices are evenly laid out in the first-level monitoring area and the second-level monitoring area to obtain the first-level monitoring device layout array and the second-level monitoring device layout array. By laying out the monitoring equipment, the technical effect of providing the hardware foundation for the subsequent monitoring equipment control is achieved.

[0053] Step S300: Interact with the historical water environment monitoring database of the target fishery breeding area, use the primary monitoring area and the secondary monitoring area as indexes, search the historical water environment monitoring database, and set the primary monitoring bandwidth set and the secondary monitoring bandwidth set according to the search results;

[0054] Further, the historical water environment monitoring database of the interactive target fishery breeding area is searched with the primary monitoring area and the secondary monitoring area as indexes, and the primary monitoring bandwidth set and the secondary monitoring bandwidth set are set according to the search results. Step S300 of the embodiment of the present application also includes:

[0055] Using the primary monitoring area and the secondary monitoring area as indexes, the historical water environment monitoring database is searched to obtain the primary historical water environment abnormal interval set and the secondary historical water environment abnormal interval set;

[0056] The maximum value in the first-level historical water environment anomaly interval set is used as the first-level slow flow monitoring bandwidth, and the minimum value in the first-level historical water environment anomaly interval set is used as the first-level fast flow monitoring bandwidth;

[0057] The maximum value in the secondary historical water environment anomaly interval set is used as the secondary slow flow monitoring bandwidth, and the minimum value in the secondary historical water environment anomaly interval set is used as the secondary fast flow monitoring bandwidth;

[0058] The first-level slow flow monitoring bandwidth and the first-level fast flow monitoring bandwidth are taken as the first-level monitoring bandwidth set;

[0059] The secondary slow flow monitoring bandwidth and the secondary fast flow monitoring bandwidth are taken as the primary monitoring bandwidth set.

[0060] Among them, the first-level historical water environment anomaly interval set is a set obtained by extracting the occurrence time of all abnormal events in the first-level monitoring area, and then calculating the time intervals between adjacent events. The second-level historical water environment anomaly interval set is a set obtained by extracting the occurrence time of all abnormal events in the second-level monitoring area, and then calculating the time intervals between adjacent events. Furthermore, the maximum value in the first-level historical water environment anomaly interval set is used as the first-level slow flow monitoring bandwidth, and the minimum value in the first-level historical water environment anomaly interval set is used as the first-level fast flow monitoring bandwidth. The maximum value in the second-level historical water environment anomaly interval set is used as the second-level slow flow monitoring bandwidth, and the minimum value in the second-level historical water environment anomaly interval set is used as the second-level fast flow monitoring bandwidth. Then the first-level slow flow monitoring bandwidth and the first-level fast flow monitoring bandwidth are used as the first-level monitoring bandwidth set, and the second-level slow flow monitoring bandwidth and the second-level fast flow monitoring bandwidth are used as the first-level monitoring bandwidth set.

[0061] Preferably, the first-level slow flow monitoring bandwidth is the maximum value in the first-level historical water environment anomaly interval set, which is used to capture trend changes over a longer period of time. The first-level fast flow monitoring bandwidth is the minimum value in the first-level historical water environment anomaly interval set, which is used to capture rapid fluctuations in a short period of time. The second-level slow flow monitoring bandwidth is the maximum value in the second-level historical water environment anomaly interval set, which is used to capture trend changes over a longer period of time. The second-level fast flow monitoring bandwidth is the minimum value in the second-level historical water environment anomaly interval set, which is used to monitor rapid fluctuations in the second-level monitoring area. By extracting anomaly intervals from historical data and dynamically setting the fast and slow flow monitoring bandwidths, the goal of optimizing monitoring frequency configuration for regional characteristics is achieved.

[0062] Step S400: In the preset equipment control analysis window, the monitoring data of the first-level monitoring equipment layout array and the second-level monitoring equipment layout array are respectively extracted according to the preset water environment monitoring indicators to obtain the first-level monitoring indicator set sequence and the second-level monitoring indicator set sequence;

[0063] Furthermore, the preset water environment monitoring indicators include temperature, pH value, dissolved oxygen content, and ammonia nitrogen content.

[0064] In one possible embodiment, the preset device control analysis window is a time period for performing device control analysis pre-set by a technician in this field. The preset water environment monitoring indicators are key parameters that reflect the water environment conditions, including temperature, pH value, dissolved oxygen content, and ammonia nitrogen content. Among them, temperature is the water temperature, which mainly affects the dissolved oxygen content in the water and the metabolic activities of aquatic organisms. The pH value is the acidity and alkalinity of the water body, reflecting whether the environment is suitable for the survival of farmed organisms. The dissolved oxygen content is the amount of oxygen dissolved in water, which is an important indicator for measuring the health of water bodies. The ammonia nitrogen content reflects the level of nitrogen-containing pollutants produced by farming activities or pollution sources, and high concentrations may be toxic to fish.

[0065] According to the preset water environment monitoring indicators, the monitoring data of the first-level monitoring equipment array and the second-level monitoring equipment array in the preset equipment control analysis window are extracted to obtain the first-level monitoring indicator set sequence and the second-level monitoring indicator set sequence. Among them, the first-level monitoring indicator set sequence reflects the changes of the water environment of the first-level monitoring area in the preset equipment control analysis window over time. And the second-level monitoring indicator set sequence reflects the changes of the water environment of the second-level monitoring area in the preset equipment control analysis window over time.

[0066] By obtaining the first-level monitoring indicator set sequence and the second-level monitoring indicator set sequence, the technical effect of providing data support for subsequent water state coefficient analysis is achieved.

[0067] Step S500: Based on the first-level monitoring bandwidth set and the second-level monitoring bandwidth set, a fast and slow flow cross-fusion analysis is performed on the first-level monitoring indicator set sequence and the second-level monitoring indicator set sequence to obtain the first-level monitoring area water body state coefficient and the second-level monitoring area water body state coefficient;

[0068] Further, as attached Figure 2 As shown, based on the first-level monitoring bandwidth set and the second-level monitoring bandwidth set, the first-level monitoring indicator set sequence and the second-level monitoring indicator set sequence are subjected to fast and slow flow cross-fusion analysis to obtain the first-level monitoring area water body state coefficient and the second-level monitoring area water body state coefficient. The step S500 of the embodiment of the present application also includes:

[0069] A first-level slow flow indicator feature analysis branch and a first-level fast flow indicator feature analysis branch are constructed according to the first-level slow flow monitoring bandwidth and the first-level fast flow monitoring bandwidth of the first-level monitoring bandwidth set;

[0070] The feature convolution analysis of the first-level monitoring indicator set sequence is performed using the first-level slow flow indicator feature analysis branch and the first-level fast flow indicator feature analysis branch, respectively, to extract the first-level regional slow flow spatial feature vector and the first-level regional fast flow temporal feature vector in the analysis results;

[0071] A cross-fusion analysis is performed based on the first-level regional slow flow spatial feature vector and the first-level regional fast flow temporal feature vector, and the attention weight of the first-level slow flow indicator feature analysis branch is updated to obtain the first-level slow flow update indicator feature analysis branch;

[0072] The feature convolution analysis of the first-level monitoring indicator set sequence is performed using the first-level slow flow update indicator feature analysis branch and the first-level fast flow indicator feature analysis branch, and the analysis results are input into the first-level fully connected network layer to identify the water state coefficient, and the water state coefficient of the first-level monitoring area is obtained;

[0073] According to the secondary slow flow monitoring bandwidth and the secondary fast flow monitoring bandwidth of the secondary monitoring bandwidth set, a fast and slow flow cross-fusion analysis is performed on the secondary monitoring indicator set sequence to obtain the water state coefficient of the secondary monitoring area.

[0074] In one embodiment of the present application, by performing a fast and slow flow cross-fusion analysis on the first-level monitoring indicator set sequence and the second-level monitoring indicator set sequence according to the first-level slow flow monitoring bandwidth of the first-level monitoring bandwidth set, the first-level fast flow monitoring bandwidth and the second-level slow flow monitoring bandwidth of the second-level monitoring bandwidth set, the water environment conditions in the first-level monitoring area and the second-level monitoring area are reliably analyzed to obtain the first-level monitoring area water state coefficient and the second-level monitoring area water state coefficient. Among them, the larger the first-level monitoring area water state coefficient and the second-level monitoring area water state coefficient, the higher the water environment quality of the corresponding monitoring area.

[0075] Preferably, multiple sample monitoring indicator set sequences, a first-level slow flow monitoring bandwidth, and multiple sample area spatial feature vectors and multiple sample area time series feature vectors obtained after feature extraction of multiple sample monitoring indicator set sequences according to the first-level slow flow monitoring bandwidth are obtained as training data, wherein the multiple sample area spatial feature vectors and the multiple sample area time series feature vectors correspond one to one. The training data is used to perform supervised training on a framework built based on a convolutional neural network, the input data is a monitoring indicator set sequence, the output data is multiple sample area spatial feature vectors and multiple sample area time series feature vectors, and the network parameters are updated according to the output results during training until the training converges, and a trained first-level slow flow indicator feature analysis branch is obtained.

[0076] Furthermore, based on the same construction principle of obtaining the first-level slow flow indicator characteristic analysis branch, a first-level fast flow indicator characteristic analysis branch is constructed according to the first-level fast flow monitoring bandwidth. Among them, the first-level slow flow indicator characteristic analysis branch is used to perform spatiotemporal feature convolution analysis on the first-level monitoring indicator set sequence according to the first-level slow flow monitoring bandwidth to obtain the first-level regional slow flow spatial feature vector and the first-level regional slow flow time series feature vector. The first-level fast flow indicator characteristic analysis branch is used to perform spatiotemporal feature convolution analysis on the first-level monitoring indicator set sequence according to the first-level fast flow monitoring bandwidth to obtain the first-level regional fast flow spatial feature vector and the first-level regional fast flow time series feature vector.

[0077] Preferably, the first-level regional slow flow spatial feature vector reflects the spatial distribution of the preset water environment monitoring index in the first-level monitoring area after feature analysis according to the first-level slow flow monitoring bandwidth. The first-level regional slow flow spatial feature vector can be [temperature slow flow spatial distribution 1 , pH slow flow spatial distribution 1 , spatial distribution of dissolved oxygen content in slow flow 1 , slow flow spatial distribution of ammonia nitrogen content 1 】 T , where the temperature slow flow spatial distribution 1 It reflects the temperature spatial distribution obtained after characteristic analysis according to the first-level slow flow monitoring bandwidth in the first-level monitoring area. pH value slow flow spatial distribution 1 It reflects the spatial distribution of pH value obtained after characteristic analysis according to the first-level slow flow monitoring bandwidth in the first-level monitoring area. 1 It reflects the spatial distribution of dissolved oxygen content in the first-level monitoring area after characteristic analysis according to the first-level slow flow monitoring bandwidth. 1 It reflects the spatial distribution of ammonia nitrogen content in the primary monitoring area obtained after characteristic analysis according to the primary slow flow monitoring bandwidth.

[0078] The first-level regional slow flow time series feature vector reflects the time series feature distribution of the preset water environment monitoring indicators in the first-level monitoring area after feature analysis according to the first-level slow flow monitoring bandwidth. The first-level regional slow flow time series feature vector can be [temperature slow flow time series feature distribution 1 , pH value slow flow time series characteristic distribution 1 , slow flow time series distribution of dissolved oxygen content 1 , slow flow time series distribution of ammonia nitrogen content 1 】 T .

[0079] The spatial characteristic vector of the fast flow in the first-level region reflects the spatial distribution of the preset water environment monitoring indicators in the first-level monitoring area after the characteristic analysis is performed according to the first-level fast flow monitoring bandwidth. The spatial characteristic vector of the fast flow in the first-level region can be [the spatial distribution of temperature fast flow 1 , pH fast flow spatial distribution 1 , fast flow spatial distribution of dissolved oxygen content 1 , fast flow spatial distribution of ammonia nitrogen content 1 】 T The first-level regional fast flow time series feature vector reflects the time series feature distribution of the preset water environment monitoring indicators in the first-level monitoring area after feature analysis according to the first-level fast flow monitoring bandwidth. The first-level regional fast flow spatial feature vector can be [temperature fast flow time series feature distribution 1 , pH value fast flow time series characteristic distribution 1 , fast flow time series distribution of dissolved oxygen content 1 , fast flow time series distribution of ammonia nitrogen content 1 】 T .

[0080] Preferably, the first-level slow flow indicator feature analysis branch and the first-level fast flow indicator feature analysis branch are used to perform feature convolution analysis on the first-level monitoring indicator set sequence respectively, and the first-level regional slow flow spatial feature vector and the first-level regional slow flow temporal feature vector, as well as the first-level regional fast flow spatial feature vector and the first-level regional fast flow temporal feature vector are obtained. The first-level regional slow flow spatial feature vector and the first-level regional fast flow temporal feature vector are extracted respectively for cross-fusion analysis, so that according to the degree of association between the first-level regional fast flow temporal feature vector and the first-level regional slow flow spatial feature vector, the first-level slow flow indicator feature analysis branch is updated with attention weights to obtain the first-level slow flow update indicator feature analysis branch. The perception ability of the first-level slow flow update indicator feature analysis branch to spatial and temporal features is improved.

[0081] Then, the first-level slow flow update index feature analysis branch and the first-level fast flow index feature analysis branch are used to perform feature convolution analysis on the first-level monitoring index set sequence, respectively, to obtain the first-level regional slow flow update spatial feature vector and the first-level regional slow flow update time series feature vector, as well as the first-level regional fast flow spatial feature vector and the first-level regional fast flow time series feature vector. The first-level regional slow flow update spatial feature vector and the first-level regional slow flow update time series feature vector, as well as the first-level regional fast flow spatial feature vector and the first-level regional fast flow time series feature vector are input into the first-level fully connected network layer after training to identify the water body state coefficient, and the first-level monitoring area water body state coefficient is obtained.

[0082] Optionally, obtain multiple sample area slow flow update spatial feature vectors and multiple sample area slow flow update temporal feature vectors, as well as multiple sample area fast flow spatial feature vectors and multiple sample area fast flow temporal feature vectors, and obtain the corresponding multiple sample area water state coefficients as training sample data, use the training sample data to perform supervised training on a convolutional neural network-based framework, and learn the many-to-one mapping relationship during training until the training converges to obtain a trained first-level fully connected network layer.

[0083] Based on the same principle as obtaining the water state coefficient of the primary monitoring area, the secondary monitoring indicator set sequence is cross-fused with slow and fast flows according to the secondary slow flow monitoring bandwidth and the secondary fast flow monitoring bandwidth to obtain the water state coefficient of the secondary monitoring area. The accuracy of slow flow feature extraction is enhanced through attention weight update, the focus on key change areas is optimized, and further analysis is performed to obtain the water state coefficient of the primary monitoring area and the water state coefficient of the secondary monitoring area, achieving the technical effect of providing a direct basis for subsequent monitoring equipment adjustments and ensuring the efficiency and accuracy of equipment control.

[0084] Further, a cross-fusion analysis is performed based on the first-level regional slow flow spatial feature vector and the first-level regional fast flow temporal feature vector, and the attention weight of the first-level slow flow index feature analysis branch is updated to obtain a first-level slow flow update index feature analysis branch. Step S500 of the embodiment of the present application further includes:

[0085] Obtaining the attention weight of the first-level slow flow branch of the first-level slow flow indicator feature analysis branch;

[0086] Using the cross-fusion formula, the first-level slow flow spatial feature vector and the first-level fast flow temporal feature vector are cross-fused and analyzed, and the first-level slow flow branch attention weight is updated to obtain the first-level updated slow flow branch attention weight;

[0087] Based on the attention weight of the first-level updated slow flow branch, the attention weight of the first-level slow flow indicator feature analysis branch is updated to obtain the first-level slow flow update indicator feature analysis branch.

[0088] Furthermore, the cross-fusion formula is:

[0089] ;

[0090] in, Update the attention weight of the slow stream branch for the first level, is the spatial eigenvector of slow flow in the first-order region, is the normalized value of the similarity between the spatial feature vector of the slow flow in the first-level region and the temporal feature vector of the fast flow in the first-level region, is the transpose of the time series feature vector of the first-level regional fast flow, is the attention weight of the first-level slow-flow branch, is the dimension of the fast flow time series feature vector in the first-level region.

[0091] In one possible embodiment, the attention weight of the first-level slow flow branch reflects the weight of each slow flow feature in the first-level slow flow feature analysis branch, which is used to measure the importance of different features in the slow flow data. The higher the weight, the greater the contribution of the feature to the water body status assessment. The attention weight of the first-level slow flow branch is obtained according to the network parameters of the trained first-level slow flow feature analysis branch. Using the cross-fusion formula, the slow flow spatial feature vector of the first-level area is combined with the fast flow temporal feature vector, and the degree of correlation between the two is calculated, and then the attention weight of the slow flow feature is adjusted according to the degree of correlation to more accurately reflect the important impact of fast flow changes on slow flow features.

[0092] According to the output result of the cross-fusion formula, the attention weight of the first-level slow flow branch is updated to generate the attention weight of the first-level updated slow flow branch. This dynamically adjusts the focus of the slow flow feature under specific environmental conditions and strengthens the feature expression of key change points. The updated attention weight is used to update the network parameters of the first-level slow flow indicator feature analysis branch, that is, to replace the weight parameters and generate the first-level slow flow update indicator feature analysis branch for further extracting high-quality features of slow flow data.

[0093] By dynamically adjusting the attention weight, the first-level slow flow update indicator feature analysis branch pays more attention to the key features related to fast flow changes, thereby improving the accuracy of long-term trend analysis. After cross-fusion analysis, a deep interaction between fast and slow flow characteristics is achieved, making the two complement each other and achieving the technical effect of enhancing the overall perception of changes in water state.

[0094] Step S600: Based on the water state coefficient of the first-level monitoring area and the water state coefficient of the second-level monitoring area, the first-level monitoring bandwidth set and the second-level monitoring bandwidth set are adjusted, and water environment monitoring of the first-level monitoring area and the second-level monitoring area is performed according to the obtained first-level adjusted monitoring bandwidth set and the second-level adjusted monitoring bandwidth set.

[0095] Further, based on the magnitude of the water state coefficient of the primary monitoring area and the water state coefficient of the secondary monitoring area, the primary monitoring bandwidth set and the secondary monitoring bandwidth set are adjusted, and water environment monitoring is performed on the primary monitoring area and the secondary monitoring area according to the obtained primary adjusted monitoring bandwidth set and the secondary adjusted monitoring bandwidth set. Step S600 of the embodiment of the present application further includes:

[0096] Calculate the difference between the water state coefficient of the first-level monitoring area and the water state coefficient of the first-level monitoring area of ​​the preset standard to obtain the first-level coefficient difference;

[0097] Calculate the difference between the water state coefficient of the secondary monitoring area and the water state coefficient of the preset standard secondary monitoring area to obtain the secondary coefficient difference;

[0098] When both the first-level coefficient difference and the second-level coefficient difference do not meet the preset difference threshold, the first-level coefficient difference is divided by the preset standard first-level monitoring area water state coefficient, and the difference between the obtained ratio and 1 is calculated, and multiplied by the first-level monitoring bandwidth set to obtain the first-level adjustment monitoring bandwidth set, where the first-level adjustment monitoring bandwidth set includes the first-level slow flow adjustment monitoring bandwidth and the first-level fast flow adjustment monitoring bandwidth;

[0099] When both the first-level coefficient difference and the second-level coefficient difference do not meet the preset difference threshold, the second-level coefficient difference is divided by the preset standard second-level monitoring area water state coefficient, and the difference between the obtained ratio and 1 is calculated and multiplied by the first-level monitoring bandwidth set to obtain the first-level adjustment monitoring bandwidth set, where the first-level adjustment monitoring bandwidth set includes the first-level slow flow adjustment monitoring bandwidth and the first-level fast flow adjustment monitoring bandwidth.

[0100] Further, when both the primary coefficient difference and the secondary coefficient difference satisfy a preset difference threshold, the primary monitoring bandwidth set and the secondary monitoring bandwidth set are not adjusted.

[0101] In one embodiment, the size of the water state coefficient of the primary monitoring area and the water state coefficient of the secondary monitoring area respectively reflect the comprehensive health status of the water environment in the primary monitoring area and the secondary monitoring area. Furthermore, the size of the monitoring bandwidth can be adjusted for the primary slow flow monitoring bandwidth, the primary fast flow monitoring bandwidth, the secondary slow flow monitoring bandwidth and the secondary fast flow monitoring bandwidth in the primary monitoring bandwidth set and the secondary monitoring bandwidth set of the primary monitoring equipment array and the secondary monitoring equipment array. Preferably, a high state coefficient indicates that the water body is healthy and the parameters are stable, so the monitoring bandwidth can be reduced after adjustment. A low state coefficient indicates that the water body risk is high, the parameters fluctuate violently, and a higher monitoring bandwidth is required.

[0102] In a possible embodiment, the difference between the water state coefficient of the primary monitoring area and the water state coefficient of the preset standard primary monitoring area is calculated to obtain the primary coefficient difference, wherein the primary coefficient difference reflects the difference between the water state of the primary monitoring area and the water state set by those skilled in the art.

[0103] Calculate the difference between the water state coefficient of the secondary monitoring area and the water state coefficient of the preset standard secondary monitoring area to obtain the secondary coefficient difference. Among them, the secondary coefficient difference reflects the gap between the water state of the primary monitoring area and the water state set by technical personnel in this field. When both the primary coefficient difference and the secondary coefficient difference do not meet the preset difference threshold (the maximum difference pre-set by technical personnel in this field), the primary coefficient difference is divided by the preset standard primary monitoring area water state coefficient, and the difference between the obtained ratio and 1 is calculated to obtain the amplitude required for bandwidth adjustment. Multiply the calculation result with the primary slow flow monitoring bandwidth and the primary fast flow monitoring bandwidth in the primary monitoring bandwidth set to obtain the primary adjusted slow flow monitoring bandwidth and the primary adjusted fast flow monitoring bandwidth. The primary adjusted slow flow monitoring bandwidth and the primary adjusted fast flow monitoring bandwidth are used as the primary adjusted monitoring bandwidth set.

[0104] When both the primary coefficient difference and the secondary coefficient difference do not meet the preset difference threshold, the secondary coefficient difference is divided by the preset standard secondary monitoring area water state coefficient, and the difference between the obtained ratio and 1 is calculated, and the calculated result is multiplied by the secondary slow flow monitoring bandwidth and the secondary fast flow monitoring bandwidth of the secondary monitoring bandwidth set to obtain the secondary adjusted slow flow monitoring bandwidth and the secondary adjusted fast flow monitoring bandwidth. The secondary adjusted slow flow monitoring bandwidth and the secondary adjusted fast flow monitoring bandwidth are used as the primary adjusted monitoring bandwidth set.

[0105] Furthermore, when both the primary coefficient difference and the secondary coefficient difference meet the preset difference threshold, the primary monitoring bandwidth set and the secondary monitoring bandwidth set are not adjusted. This achieves the technical effect of dynamically adjusting the monitoring bandwidth according to the real-time state coefficient, making the equipment sampling more efficient and accurate, and improving the reliability of equipment control.

[0106] In summary, the embodiments of the present application have at least the following technical effects:

[0107] 1. This application achieves the technical effect of improving the matching degree between monitoring data and actual water environment by dividing the target fishery breeding area according to the information of sewage discharge outlets, intensive breeding areas and other facilities, and determining the monitoring equipment layout array and monitoring bandwidth according to the actual situation of the monitoring area.

[0108] 2. This application generates a water state coefficient by combining the cross-fusion analysis of fast and slow flow data, and dynamically adjusts the monitoring bandwidth of the monitoring equipment according to the water state coefficient, thereby achieving the technical effect of reducing unnecessary equipment operation and data collection burdens, improving resource utilization efficiency, and improving equipment control accuracy.

[0109] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0110] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0111] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for controlling fishery water environment monitoring equipment, characterized in that: The method comprises: Acquire the surrounding facility information of the target fishery breeding area, divide the area based on the surrounding facility information, and obtain the primary monitoring area and the secondary monitoring area; Deploy environmental monitoring equipment in the primary monitoring area and the secondary monitoring area to obtain a primary monitoring equipment deployment array and a secondary monitoring equipment deployment array; Interacting with the historical water environment monitoring database of the target fishery breeding area, using the primary monitoring area and the secondary monitoring area as indexes, searching the historical water environment monitoring database, and setting the primary monitoring bandwidth set and the secondary monitoring bandwidth set according to the search results; In the preset equipment control analysis window, the monitoring data of the first-level monitoring equipment layout array and the second-level monitoring equipment layout array are respectively extracted according to the preset water environment monitoring indicators to obtain a first-level monitoring indicator set sequence and a second-level monitoring indicator set sequence; Based on the first-level monitoring bandwidth set and the second-level monitoring bandwidth set, a fast-slow flow cross-fusion analysis is performed on the first-level monitoring indicator set sequence and the second-level monitoring indicator set sequence to obtain a first-level monitoring area water body state coefficient and a second-level monitoring area water body state coefficient; Based on the magnitude of the water state coefficient of the primary monitoring area and the water state coefficient of the secondary monitoring area, the primary monitoring bandwidth set and the secondary monitoring bandwidth set are adjusted, and water environment monitoring of the primary monitoring area and the secondary monitoring area is performed according to the obtained primary adjusted monitoring bandwidth set and the secondary adjusted monitoring bandwidth set; The historical water environment monitoring database of the target fishery breeding area is interacted with, the primary monitoring area and the secondary monitoring area are used as indexes, the historical water environment monitoring database is searched, and the primary monitoring bandwidth set and the secondary monitoring bandwidth set are set according to the search results, including: Using the primary monitoring area and the secondary monitoring area as indexes, searching the historical water environment monitoring database to obtain a primary historical water environment abnormal interval set and a secondary historical water environment abnormal interval set; The maximum value in the first-level historical water body environment anomaly interval set is used as the first-level slow flow monitoring bandwidth, and the minimum value in the first-level historical water body environment anomaly interval set is used as the first-level fast flow monitoring bandwidth; The maximum value in the secondary historical water body environment anomaly interval set is used as the secondary slow flow monitoring bandwidth, and the minimum value in the secondary historical water body environment anomaly interval set is used as the secondary fast flow monitoring bandwidth; The first-level slow flow monitoring bandwidth and the first-level fast flow monitoring bandwidth are used as the first-level monitoring bandwidth set; The secondary slow flow monitoring bandwidth and the secondary fast flow monitoring bandwidth are used as the primary monitoring bandwidth set; Wherein, based on the first-level monitoring bandwidth set and the second-level monitoring bandwidth set, the first-level monitoring indicator set sequence and the second-level monitoring indicator set sequence are subjected to fast and slow flow cross-fusion analysis to obtain the first-level monitoring area water body state coefficient and the second-level monitoring area water body state coefficient, including: Constructing a first-level slow flow indicator feature analysis branch and a first-level fast flow indicator feature analysis branch according to the first-level slow flow monitoring bandwidth and the first-level fast flow monitoring bandwidth of the first-level monitoring bandwidth set; Using the first-level slow flow indicator feature analysis branch and the first-level fast flow indicator feature analysis branch to perform feature convolution analysis on the first-level monitoring indicator set sequence, respectively, and extract the first-level regional slow flow spatial feature vector and the first-level regional fast flow temporal feature vector in the analysis results; Performing cross-fusion analysis on the first-level regional slow flow spatial feature vector and the first-level regional fast flow temporal feature vector, and updating the attention weight of the first-level slow flow indicator feature analysis branch to obtain a first-level slow flow update indicator feature analysis branch; Using the first-level slow flow update index feature analysis branch and the first-level fast flow index feature analysis branch to perform feature convolution analysis on the first-level monitoring index set sequence, and input the analysis result into the first-level fully connected network layer to identify the water state coefficient, so as to obtain the water state coefficient of the first-level monitoring area; According to the secondary slow flow monitoring bandwidth and the secondary fast flow monitoring bandwidth of the secondary monitoring bandwidth set, a fast and slow flow cross-fusion analysis is performed on the secondary monitoring indicator set sequence to obtain the water state coefficient of the secondary monitoring area; Among them, a cross-fusion analysis is performed according to the first-level regional slow flow spatial feature vector and the first-level regional fast flow temporal feature vector, and the attention weight of the first-level slow flow indicator feature analysis branch is updated to obtain a first-level slow flow update indicator feature analysis branch, including: Obtaining a primary slow flow branch attention weight of the primary slow flow indicator feature analysis branch; Using a cross-fusion formula, a cross-fusion analysis is performed on the first-level region slow flow spatial feature vector and the first-level region fast flow temporal feature vector, and the first-level slow flow branch attention weight is updated to obtain the first-level updated slow flow branch attention weight; The attention weight of the first-level slow flow indicator feature analysis branch is updated based on the attention weight of the first-level updated slow flow branch to obtain a first-level slow flow updated indicator feature analysis branch.

2. A method for controlling fishery water environment monitoring equipment as claimed in claim 1, characterized in that: The cross-fusion formula is: ； in, Update the attention weight of the slow stream branch for the first level, is the spatial eigenvector of slow flow in the first-order region, is the normalized value of the similarity between the spatial feature vector of the slow flow in the first-level region and the temporal feature vector of the fast flow in the first-level region, is the transpose of the time series feature vector of the first-level regional fast flow, is the attention weight of the first-level slow-flow branch, is the dimension of the fast flow time series feature vector in the first-level region.

3. A method for controlling fishery water environment monitoring equipment as claimed in claim 1, characterized in that: The preset water environment monitoring indicators include temperature, pH value, dissolved oxygen content, and ammonia nitrogen content.

4. A method for controlling fishery water environment monitoring equipment as claimed in claim 1, characterized in that: Based on the water state coefficient of the primary monitoring area and the water state coefficient of the secondary monitoring area, the primary monitoring bandwidth set and the secondary monitoring bandwidth set are adjusted, and water environment monitoring of the primary monitoring area and the secondary monitoring area is performed according to the obtained primary adjusted monitoring bandwidth set and the secondary adjusted monitoring bandwidth set, including: Calculate the difference between the water state coefficient of the first-level monitoring area and the water state coefficient of the preset standard first-level monitoring area to obtain the first-level coefficient difference; Calculate the difference between the water state coefficient of the secondary monitoring area and the water state coefficient of the preset standard secondary monitoring area to obtain the secondary coefficient difference; When both the first-level coefficient difference and the second-level coefficient difference do not meet the preset difference threshold, the first-level coefficient difference is divided by the preset standard first-level monitoring area water state coefficient, and the difference between the obtained ratio and 1 is calculated, and the calculated result is multiplied by the first-level monitoring bandwidth set to obtain the first-level adjustment monitoring bandwidth set, wherein the first-level adjustment monitoring bandwidth set includes the first-level slow flow adjustment monitoring bandwidth and the first-level fast flow adjustment monitoring bandwidth; When both the first-level coefficient difference and the second-level coefficient difference do not meet the preset difference threshold, the second-level coefficient difference is divided by the preset standard second-level monitoring area water state coefficient, and the difference between the obtained ratio and 1 is calculated, and the calculated result is multiplied by the second-level monitoring bandwidth set to obtain the second-level adjustment monitoring bandwidth set, wherein the second-level adjustment monitoring bandwidth set includes the second-level slow flow monitoring adjustment bandwidth and the second-level fast flow adjustment monitoring bandwidth.

5. A method for controlling fishery water environment monitoring equipment as claimed in claim 4, characterized in that: When both the primary coefficient difference and the secondary coefficient difference satisfy a preset difference threshold, the primary monitoring bandwidth set and the secondary monitoring bandwidth set are not adjusted.

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