Purse seine configuration method and system based on illumination distribution analysis and medium

Abstract

The present application relates to a net configuration method and system based on illumination distribution analysis and a medium, and belongs to the technical field of light net. The present application constructs an illumination knowledge graph according to multi-source data information in a target water area, then estimates the illumination attenuation coefficient in different time periods according to the illumination knowledge graph, obtains the illumination data of the target water area within the preset working range in the current time period based on the illumination attenuation coefficient in different time periods, divides the multi-level illumination area according to the illumination data of the target water area within the preset working range in the current time period, constructs a target fish school attracting area, and finally evaluates the target fish school attracting area and configures the working parameters of the light net based on the evaluation result. The present application evaluates the actual illumination of the light net during work according to multi-source data information in the target water area, thereby further optimizing the working parameters of the light net and improving the rationality of the light net arrangement.

Description

Technical Field

[0001] This invention relates to the field of lighting fence deployment technology, and in particular to a fence configuration method, system and medium based on illuminance distribution analysis. Background Technology

[0002] Light-based purse seine is a fisheries technique that uses lights to attract fish and then catches them with purse seines. This technique relies on fish's natural attraction to light; by using strong light at night, fish schools can be effectively concentrated, increasing catch efficiency. Light-based purse seine typically consists of light boats, net boats, and light vessels, with the combined use of underwater and surface lights significantly improving fish attraction efficiency. However, the illumination level of light-based purse seines operating in the water is affected by factors such as external light intensity, water turbidity, and the type of particulate matter in the water. This can lead to the illumination level not reaching the expected effect, resulting in unsatisfactory catch results and indicating an inappropriate configuration of the light-based purse seine. Summary of the Invention

[0003] This invention overcomes the shortcomings of the prior art and provides a method, system and medium for configuring fencing based on illuminance distribution analysis.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] The first aspect of this invention provides a method for configuring fencing based on illuminance distribution analysis, comprising:

[0006] Acquire multi-source data information from the target water area, and construct an illuminance knowledge graph based on the multi-source data information from the target water area;

[0007] Based on the illuminance knowledge graph, the illuminance attenuation coefficient in different time periods is estimated, and the illuminance data of the target water area within the preset working range in the current time period is obtained based on the illuminance attenuation coefficient in different time periods.

[0008] Based on the illuminance data of the target water area within the preset working range during the current time period, multi-level illuminance zones are divided to construct target fish attraction zones;

[0009] The operating parameters of the lighted enclosure are configured based on the evaluation results of the target fish attraction area.

[0010] Furthermore, in the fencing configuration method based on illuminance distribution analysis, multi-source data information in the target water area is obtained, and an illuminance knowledge graph is constructed based on the multi-source data information in the target water area, specifically as follows:

[0011] By deploying data acquisition equipment in the target water area, the equipment collects external light intensity, water turbidity, meteorological data, and particulate matter characteristics at each depth location, thus constructing multi-source data information.

[0012] Based on the multi-source data information, test conditions are constructed, and different illuminance data are tested based on the test conditions to obtain the illuminance attenuation coefficient under different multi-source data information and construct an illuminance knowledge graph.

[0013] The light attenuation coefficients under different multi-source data information are input into the light intensity knowledge graph for storage.

[0014] Furthermore, in the fence configuration method based on illuminance distribution analysis, the illuminance attenuation coefficient in different time periods is estimated according to the illuminance knowledge graph, specifically as follows:

[0015] Acquire multi-source data information of the current target water area within a preset time period, and input the multi-source data information of the current target water area within the preset time period into the illuminance knowledge graph for data matching;

[0016] By matching data, the light attenuation coefficients for different time periods are obtained, and the light attenuation coefficients for different time periods are output.

[0017] Furthermore, in the netting configuration method based on illuminance distribution analysis, the illuminance data of the target water area within the preset working range in the current time period is obtained based on the illuminance attenuation coefficient in different time periods, specifically as follows:

[0018] Based on the light attenuation coefficients in different time periods, obtain the light attenuation coefficients at different depth positions in the current time period, and initialize the illuminance parameters of the light enclosure.

[0019] Simulations were performed based on the illuminance parameters of the light-covered enclosure and the illuminance attenuation coefficients at different depths during the current time period.

[0020] The illuminance data of the target water area within the preset working range during the current time period is obtained through simulation, and the illuminance data of the target water area within the preset working range during the current time period is output.

[0021] Furthermore, in the netting configuration method based on illuminance distribution analysis, multi-level illuminance zones are divided according to the illuminance data of the target water area within the preset working range during the current time period to construct the target fish attraction zone, specifically:

[0022] Set up an illuminance gradient evaluation index, and divide the illuminance data of the target water area within the preset working range in the current time period based on the illuminance gradient evaluation index to obtain multi-level illuminance areas.

[0023] Acquire the illuminance data and illuminance type that attract the target fish population, and obtain the target fish population attraction area based on the multi-level illuminance area, the illuminance data and illuminance type, and output the target fish population attraction area.

[0024] Furthermore, in the netting configuration method based on illuminance distribution analysis, the target fish attraction area is evaluated, specifically including:

[0025] Set a threshold range for the target fish attraction area, and determine whether the target fish attraction area is greater than the threshold range.

[0026] When the target fish attraction area is not larger than the target fish attraction area threshold range, an abnormal target fish attraction area evaluation result is generated.

[0027] When the target fish attraction area is larger than the target fish attraction area threshold range, a normal target fish attraction area evaluation result is generated.

[0028] Furthermore, in the fencing configuration method based on illuminance distribution analysis, the operating parameters of the lighting fencing are configured based on the evaluation results, specifically as follows:

[0029] When the evaluation result is an abnormal target fish attraction area evaluation result, reconfigure the working parameters of the lighted enclosure until it is no longer an abnormal target fish attraction area evaluation result.

[0030] When the assessment results are normal for the target fish attraction area, the operating parameters of the light-filled enclosure should remain unchanged.

[0031] A second aspect of the present invention provides a fence configuration system based on illuminance distribution analysis, including a memory and a processor. The memory includes a fence configuration method program based on illuminance distribution analysis. When the fence configuration method program based on illuminance distribution analysis is executed by the processor, it implements the steps of the fence configuration method based on illuminance distribution analysis as described in any one of the present invention.

[0032] A third aspect of the present invention provides a computer-readable storage medium including a fence configuration method program based on illuminance distribution analysis, wherein when the fence configuration method program based on illuminance distribution analysis is executed by a processor, it implements the steps of the fence configuration method based on illuminance distribution analysis as described in any one of the present invention.

[0033] This invention addresses the shortcomings of the prior art and has the following beneficial effects:

[0034] This invention acquires multi-source data information from a target water area and constructs a light intensity knowledge graph based on this data. Then, it estimates the light attenuation coefficient at different times based on the knowledge graph. Based on this attenuation coefficient, it obtains the light intensity data of the target water area within a preset working range for the current time period. This data is then used to divide the target water area into multi-level light intensity zones, constructing target fish attraction zones. Finally, the target fish attraction zones are evaluated, and the operating parameters of the illuminated enclosure are configured based on the evaluation results. This invention evaluates the actual light intensity of the illuminated enclosure during operation using multi-source data from the target water area, thereby further optimizing the operating parameters of the illuminated enclosure and improving its layout rationality. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained from these drawings without creative effort.

[0036] Figure 1 A flowchart illustrating the overall process of fencing configuration based on illuminance distribution analysis is provided.

[0037] Figure 2 A system block diagram of a fencing configuration system based on illuminance distribution analysis is shown. Detailed Implementation

[0038] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0039] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0040] like Figure 1 As shown, the first aspect of the present invention provides a fence configuration method based on illuminance distribution analysis, comprising:

[0041] S102: Obtain multi-source data information from the target water area and construct an illuminance knowledge graph based on the multi-source data information from the target water area;

[0042] S104: Estimate the light attenuation coefficient in different time periods based on the light attenuation knowledge graph, and obtain the light attenuation data of the target water area within the preset working range in the current time period based on the light attenuation coefficient in different time periods;

[0043] S106: Divide the target water area into multiple illuminance zones based on the illuminance data within the preset working range during the current time period, and construct the target fish attraction zone;

[0044] S108: By evaluating the target fish attraction area, configure the operating parameters of the lighted enclosure based on the evaluation results.

[0045] It should be noted that this invention evaluates the actual illuminance of the light-filled fencing during operation based on multi-source data information from the target water area, thereby further optimizing the working parameters of the light-filled fencing and improving the rationality of its layout.

[0046] Furthermore, in the netting configuration method based on illuminance distribution analysis, multi-source data information in the target water area is obtained, and an illuminance knowledge graph is constructed based on the multi-source data information in the target water area, specifically:

[0047] By deploying data acquisition equipment in the target water area, the equipment collects external light intensity, water turbidity, meteorological data, and particulate matter characteristics at each depth location, thus constructing multi-source data information.

[0048] Test conditions are constructed based on multi-source data information. Tests are conducted on different illuminance data based on the test conditions to obtain the illuminance attenuation coefficient under different multi-source data information and construct an illuminance knowledge graph.

[0049] The light attenuation coefficients under different multi-source data are input into the illuminance knowledge graph for storage.

[0050] It should be noted that the data acquisition equipment includes temperature sensors, humidity sensors, illuminance sensors, infrared cameras, etc. Since external illuminance, water turbidity, meteorological data, and particulate matter characteristics all affect illuminance, the actual illuminance will be reduced to another level, resulting in an illuminance attenuation coefficient. This method is used to construct an illuminance knowledge graph.

[0051] Furthermore, in the fence configuration method based on illuminance distribution analysis, the illuminance attenuation coefficient at different time periods is estimated according to the illuminance knowledge graph, specifically as follows:

[0052] Acquire multi-source data information of the current target water area within a preset time period, and input the multi-source data information of the current target water area within a preset time period into the illuminance knowledge graph for data matching;

[0053] By matching data, the light attenuation coefficients for different time periods are obtained and output.

[0054] Furthermore, in the netting configuration method based on illuminance distribution analysis, illuminance data of the target water area within a preset working range in the current time period is obtained based on the illuminance attenuation coefficient in different time periods. Specifically:

[0055] Based on the light attenuation coefficient in different time periods, obtain the light attenuation coefficient at different depth positions in the current time period, and initialize the illuminance parameters of the light fence.

[0056] Simulations were conducted based on the illuminance parameters of the light-covered fencing and the illuminance attenuation coefficients at different depths during the current time period.

[0057] The simulation acquires the illuminance data of the target water area within the preset working range during the current time period, and outputs the illuminance data of the target water area within the preset working range during the current time period.

[0058] It should be noted that this method can estimate the illuminance data of the target water area within the preset working range during the current period based on the illuminance parameters of the light-filled enclosure (including the type of light, illuminance, etc.).

[0059] Furthermore, in the netting configuration method based on illuminance distribution analysis, multiple illuminance zones are divided according to the illuminance data of the target water area within the preset working range during the current time period to construct the target fish attraction zone, specifically:

[0060] Set up an illuminance gradient evaluation index, and divide the illuminance data of the target water area within the preset working range in the current period based on the illuminance gradient evaluation index to obtain multi-level illuminance areas.

[0061] Acquire the illuminance data and illuminance type that attract the target fish population, and obtain the target fish population attraction area based on the multi-level illuminance area, the illuminance data that attracts the target fish population, and the illuminance type, and output the target fish population attraction area.

[0062] It should be noted that different fish species attract different types of light and have different light intensity data. This method can be used to predict the attraction area of ​​a target fish group.

[0063] Furthermore, in the netting configuration method based on illuminance distribution analysis, the attraction area of ​​the target fish school is evaluated, specifically including:

[0064] Set a threshold range for the target fish attraction area, and determine whether the target fish attraction area is larger than the threshold range.

[0065] When the target fish attraction area is not larger than the target fish attraction area threshold range, an abnormal target fish attraction area evaluation result is generated.

[0066] When the target fish attraction area is larger than the target fish attraction area threshold range, a normal target fish attraction area assessment result is generated.

[0067] Furthermore, in the fencing configuration method based on illuminance distribution analysis, the operating parameters of the lighting fencing are configured based on the evaluation results, specifically as follows:

[0068] When the evaluation result is an abnormal target fish attraction area evaluation result, reconfigure the working parameters of the lighted enclosure until it is no longer an abnormal target fish attraction area evaluation result.

[0069] When the assessment results are normal for the target fish attraction area, the operating parameters of the light-filled enclosure should remain unchanged.

[0070] It should be noted that this method can optimize the operating parameters of the light-filled seine net and improve its fishing effect.

[0071] Furthermore, this method also includes: acquiring historical service data of each light-emitting device in the light enclosure, constructing a light-emitting device fault prediction model based on a deep neural network, and inputting the historical service data of each light-emitting device in the light enclosure into the light-emitting device fault prediction model for training; through training, acquiring the trained light-emitting device fault prediction model, acquiring service data of each light-emitting device within a preset time period, and inputting the service data of each light-emitting device within the preset time period into the trained light-emitting device fault prediction model for prediction; through prediction, acquiring fault information of each light-emitting device in the light enclosure, acquiring the location information of the faulty light-emitting device based on the fault information of each light-emitting device in the light enclosure; updating the target fish attraction area based on the location information of the faulty light-emitting device, and re-optimizing the operating parameters of the light enclosure.

[0072] It should be noted that by using this method to predict potential malfunctions of the light-emitting equipment during use, supplementary lighting can be provided, which can further improve the fishing effect of the light-filled seine net.

[0073] Furthermore, this method also includes: acquiring data on the natural enemy species corresponding to the target catch species through big data, and acquiring remote sensing image data of the fishing location of each light net within a preset time period through remote sensing technology; identifying the remote sensing image data of the fishing location of each light net within the preset time period through AI recognition technology, and determining whether the data on the natural enemy species corresponding to the target catch species exists in the remote sensing image data of the fishing location of each light net within the preset time period; when the data on the natural enemy species corresponding to the target catch species exists in the remote sensing image data of the fishing location of the light net within the preset time period, adjusting the fishing location of the light net until the data on the natural enemy species corresponding to the target catch species does not exist; when the data on the natural enemy species corresponding to the target catch species does not exist in the remote sensing image data of the fishing location of the light net within the preset time period, maintaining the fishing location of the light net unchanged. It should be noted that when there are predators at the fishing location where the light net is located, according to the rules of the food chain, the target prey will flee or not choose that migration location. This indicates that the fishing location where the light net is located is not a suitable area for the target prey to stay for a long time. This method can further optimize the fishing strategy of the light net.

[0074] like Figure 2 As shown, the second aspect of the present invention provides a fence configuration system 4 based on illuminance distribution analysis, including a memory 41 and a processor 42. The memory 41 includes a fence configuration method program based on illuminance distribution analysis. When the fence configuration method program based on illuminance distribution analysis is executed by the processor 42, the following steps are implemented:

[0075] Acquire multi-source data information from the target water area and construct an illuminance knowledge graph based on the multi-source data information from the target water area;

[0076] Based on the illuminance knowledge graph, the illuminance attenuation coefficient in different time periods is estimated, and the illuminance data of the target water area within the preset working range in the current time period is obtained based on the illuminance attenuation coefficient in different time periods.

[0077] Based on the illuminance data of the target water area within the preset working range during the current time period, multi-level illuminance zones are divided to construct target fish attraction zones;

[0078] The operating parameters of the lighted enclosure are configured based on the evaluation results of the target fish attraction area.

[0079] It should be noted that this invention evaluates the actual illuminance of the light-filled fencing during operation based on multi-source data information from the target water area, thereby further optimizing the working parameters of the light-filled fencing and improving the rationality of its layout.

[0080] Furthermore, in the enclosure configuration system based on illuminance distribution analysis, multi-source data information in the target water area is acquired, and an illuminance knowledge graph is constructed based on the multi-source data information in the target water area, specifically:

[0081] By deploying data acquisition equipment in the target water area, the equipment collects external light intensity, water turbidity, meteorological data, and particulate matter characteristics at each depth location, thus constructing multi-source data information.

[0082] Test conditions are constructed based on multi-source data information. Tests are conducted on different illuminance data based on the test conditions to obtain the illuminance attenuation coefficient under different multi-source data information and construct an illuminance knowledge graph.

[0083] The light attenuation coefficients under different multi-source data are input into the illuminance knowledge graph for storage.

[0084] It should be noted that the data acquisition equipment includes temperature sensors, humidity sensors, illuminance sensors, infrared cameras, etc. Since external illuminance, water turbidity, meteorological data, and particulate matter characteristics all affect illuminance, the actual illuminance will be reduced to another level, resulting in an illuminance attenuation coefficient. This method is used to construct an illuminance knowledge graph.

[0085] Furthermore, in the perimeter fencing configuration system based on illuminance distribution analysis, the illuminance attenuation coefficient at different time periods is predicted according to the illuminance knowledge graph, specifically as follows:

[0086] Acquire multi-source data information of the current target water area within a preset time period, and input the multi-source data information of the current target water area within a preset time period into the illuminance knowledge graph for data matching;

[0087] By matching data, the light attenuation coefficients for different time periods are obtained and output.

[0088] Furthermore, in the enclosure configuration system based on illuminance distribution analysis, illuminance data of the target water area within the preset working range is obtained based on the illuminance attenuation coefficient at different time periods. Specifically:

[0089] Based on the light attenuation coefficient in different time periods, obtain the light attenuation coefficient at different depth positions in the current time period, and initialize the illuminance parameters of the light fence.

[0090] Simulations were conducted based on the illuminance parameters of the light-covered fencing and the illuminance attenuation coefficients at different depths during the current time period.

[0091] The simulation acquires the illuminance data of the target water area within the preset working range during the current time period, and outputs the illuminance data of the target water area within the preset working range during the current time period.

[0092] It should be noted that this method can estimate the illuminance data of the target water area within the preset working range during the current period based on the illuminance parameters of the light-filled enclosure (including the type of light, illuminance, etc.).

[0093] Furthermore, in the netting configuration system based on illuminance distribution analysis, multiple illuminance zones are divided according to the illuminance data of the target water area within the preset working range during the current time period, thereby constructing target fish attraction zones, specifically:

[0094] Set up an illuminance gradient evaluation index, and divide the illuminance data of the target water area within the preset working range in the current period based on the illuminance gradient evaluation index to obtain multi-level illuminance areas.

[0095] Acquire the illuminance data and illuminance type that attract the target fish population, and obtain the target fish population attraction area based on the multi-level illuminance area, the illuminance data that attracts the target fish population, and the illuminance type, and output the target fish population attraction area.

[0096] It should be noted that different fish species attract different types of light and have different light intensity data. This method can be used to predict the attraction area of ​​a target fish group.

[0097] Furthermore, in the netting configuration system based on illuminance distribution analysis, the attraction area of ​​the target fish school is evaluated, specifically including:

[0098] Set a threshold range for the target fish attraction area, and determine whether the target fish attraction area is larger than the threshold range.

[0099] When the target fish attraction area is not larger than the target fish attraction area threshold range, an abnormal target fish attraction area evaluation result is generated.

[0100] When the target fish attraction area is larger than the target fish attraction area threshold range, a normal target fish attraction area assessment result is generated.

[0101] Furthermore, in the fencing configuration system based on illuminance distribution analysis, the operating parameters of the lighted fencing are configured based on the evaluation results, specifically as follows:

[0102] When the evaluation result is an abnormal target fish attraction area evaluation result, reconfigure the working parameters of the lighted enclosure until it is no longer an abnormal target fish attraction area evaluation result.

[0103] When the assessment results are normal for the target fish attraction area, the operating parameters of the light-filled enclosure should remain unchanged.

[0104] It should be noted that this method can optimize the operating parameters of the light-filled seine net and improve its fishing effect.

[0105] Furthermore, this method also includes: acquiring historical service data of each light-emitting device in the light enclosure, constructing a light-emitting device fault prediction model based on a deep neural network, and inputting the historical service data of each light-emitting device in the light enclosure into the light-emitting device fault prediction model for training; through training, acquiring the trained light-emitting device fault prediction model, acquiring service data of each light-emitting device within a preset time period, and inputting the service data of each light-emitting device within the preset time period into the trained light-emitting device fault prediction model for prediction; through prediction, acquiring fault information of each light-emitting device in the light enclosure, acquiring the location information of the faulty light-emitting device based on the fault information of each light-emitting device in the light enclosure; updating the target fish attraction area based on the location information of the faulty light-emitting device, and re-optimizing the operating parameters of the light enclosure.

[0106] It should be noted that by using this method to predict potential malfunctions of the light-emitting equipment during use, supplementary lighting can be provided, which can further improve the fishing effect of the light-filled seine net.

[0107] Furthermore, this method also includes: acquiring data on the natural enemy species corresponding to the target catch species through big data, and acquiring remote sensing image data of the fishing location of each light net within a preset time period through remote sensing technology; identifying the remote sensing image data of the fishing location of each light net within the preset time period through AI recognition technology, and determining whether the data on the natural enemy species corresponding to the target catch species exists in the remote sensing image data of the fishing location of each light net within the preset time period; when the data on the natural enemy species corresponding to the target catch species exists in the remote sensing image data of the fishing location of the light net within the preset time period, adjusting the fishing location of the light net until the data on the natural enemy species corresponding to the target catch species does not exist; when the data on the natural enemy species corresponding to the target catch species does not exist in the remote sensing image data of the fishing location of the light net within the preset time period, maintaining the fishing location of the light net unchanged. It should be noted that when there are predators at the fishing location where the light net is located, according to the rules of the food chain, the target prey will flee or not choose that migration location. This indicates that the fishing location where the light net is located is not a suitable area for the target prey to stay for a long time. This method can further optimize the fishing strategy of the light net.

[0108] A third aspect of the present invention provides a computer-readable storage medium including a fence configuration method program based on illuminance distribution analysis, wherein when the fence configuration method program based on illuminance distribution analysis is executed by a processor, it implements any of the steps of the fence configuration method based on illuminance distribution analysis.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0110] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0111] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0112] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0113] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0114] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A fencing configuration method based on illuminance distribution analysis, characterized in that, include: Acquire multi-source data information from the target water area, and construct an illuminance knowledge graph based on the multi-source data information from the target water area; Based on the illuminance knowledge graph, the illuminance attenuation coefficient in different time periods is estimated, and the illuminance data of the target water area within the preset working range in the current time period is obtained based on the illuminance attenuation coefficient in different time periods. Based on the illuminance data of the target water area within the preset working range during the current time period, multi-level illuminance zones are divided to construct target fish attraction zones; The operating parameters of the lighted enclosure are configured based on the evaluation results of the target fish attraction area. Acquire multi-source data information from the target water area, and construct an illuminance knowledge graph based on the multi-source data information from the target water area, specifically as follows: By deploying data acquisition equipment in the target water area, the equipment collects external light intensity, water turbidity, meteorological data, and particulate matter characteristics at each depth location, thus constructing multi-source data information. Based on the multi-source data information, test conditions are constructed, and different illuminance data are tested based on the test conditions to obtain the illuminance attenuation coefficient under different multi-source data information and construct an illuminance knowledge graph. The light attenuation coefficients under different multi-source data information are input into the light intensity knowledge graph for storage.

2. The fencing configuration method based on illuminance distribution analysis according to claim 1, characterized in that, Based on the aforementioned illuminance knowledge graph, the illuminance attenuation coefficient is estimated for different time periods, specifically as follows: Acquire multi-source data information of the current target water area within a preset time period, and input the multi-source data information of the current target water area within the preset time period into the illuminance knowledge graph for data matching; By matching data, the light attenuation coefficients for different time periods are obtained, and the light attenuation coefficients for different time periods are output.

3. The fencing configuration method based on illuminance distribution analysis according to claim 1, characterized in that, Based on the light attenuation coefficients in different time periods, the illuminance data of the target water area within the preset working range in the current time period is obtained, specifically as follows: Based on the light attenuation coefficients in different time periods, obtain the light attenuation coefficients at different depth positions in the current time period, and initialize the illuminance parameters of the light enclosure. Simulations were performed based on the illuminance parameters of the light-covered enclosure and the illuminance attenuation coefficients at different depths during the current time period. The illuminance data of the target water area within the preset working range during the current time period is obtained through simulation, and the illuminance data of the target water area within the preset working range during the current time period is output.

4. The fencing configuration method based on illuminance distribution analysis according to claim 1, characterized in that, Based on the illuminance data of the target water area within the preset working range during the current time period, multiple illuminance zones are divided to construct the target fish attraction zone, specifically: Set up an illuminance gradient evaluation index, and divide the illuminance data of the target water area within the preset working range in the current time period based on the illuminance gradient evaluation index to obtain multi-level illuminance areas. Acquire the illuminance data and illuminance type that attract the target fish population, and obtain the target fish population attraction area based on the multi-level illuminance area, the illuminance data and illuminance type, and output the target fish population attraction area.

5. The fencing configuration method based on illuminance distribution analysis according to claim 1, characterized in that, The evaluation of the target fish attraction area specifically includes: Set a threshold range for the target fish attraction area, and determine whether the target fish attraction area is greater than the threshold range. When the target fish attraction area is not larger than the target fish attraction area threshold range, an abnormal target fish attraction area evaluation result is generated. When the target fish attraction area is larger than the target fish attraction area threshold range, a normal target fish attraction area evaluation result is generated.

6. The fencing configuration method based on illuminance distribution analysis according to claim 1, characterized in that, Based on the evaluation results, the operating parameters of the lighted fencing are configured as follows: When the evaluation result is an abnormal target fish attraction area evaluation result, reconfigure the working parameters of the lighted enclosure until it is no longer an abnormal target fish attraction area evaluation result. When the assessment results are normal for the target fish attraction area, the operating parameters of the light-filled enclosure should remain unchanged.

7. A fencing configuration system based on illuminance distribution analysis, characterized in that, The device includes a memory and a processor. The memory includes a fence configuration method program based on illuminance distribution analysis. When the processor executes the fence configuration method program based on illuminance distribution analysis, it implements the steps of the fence configuration method based on illuminance distribution analysis as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The method includes a fence configuration method program based on illuminance distribution analysis, which, when executed by a processor, implements the steps of the fence configuration method based on illuminance distribution analysis as described in any one of claims 1-6.

Images