Aquatic biodiversity assessment method, computer device, and readable storage medium

Abstract

The present application provides a method for evaluating aquatic biodiversity, a computer device, and a readable storage medium, which relate to the technical field of environmental governance. By performing data cleaning and data preprocessing on the aquatic biodiversity data of multiple aquatic ecological quadrat points corresponding to each other in the target water area, the actual relative abundance data, actual habitat condition data, and actual ecological niche of all aquatic species in the target water area are obtained. Then, on the basis of the obtained various data, by introducing ecological niche simulation technology, the mutual relationships and roles of different aquatic species in the ecosystem of the target water area are adaptively simulated, so that the biological effect impact conditions caused by various water environment factors in the target water area on different aquatic species evaluated finally are comprehensive and accurate enough, facilitating researchers to more comprehensively and accurately understand the change trend of aquatic biodiversity in the specified water area and timely propose governance decisions for the specified water area.

Description

Technical Field

[0001] This application relates to the technical field of environmental governance. Specifically, it relates to a method for assessing aquatic biodiversity, a computer device, and a readable storage medium. Background Art

[0002] With the continuous development of science and technology, humans have gradually begun to pay attention to the health status of water environments. The quality of the water in a water source will directly affect the survival status of aquatic organisms (such as submerged plants, floating-leaved plants, floating plants, zooplankton, and fish, etc.) and the physical health of other terrestrial organisms that depend on this water source for survival. In addition to bringing important values to human society and economy (such as providing food, medicine, raw materials, maintaining ecosystem functions, etc.), the aquatic biodiversity of a water source can also reflect many problems in the aquatic environment corresponding to the water source. When different pollutants exist in the water body, they will correspondingly cause different biological effects, and sometimes even cause irreversible harm to one or more aquatic species (for example, microplastics will have a greater impact on fish such as male medaka and juvenile seahorses, and common pesticides will cause acute biological toxicity to various fish and algae after entering the water, and endocrine disruptors such as typical environmental estrogens will cause the proliferation of various cells in organisms). Therefore, in the process of water environment governance, the assessment results of aquatic biodiversity are usually used as biological indicators for assessing the degree of water pollution, nutritional status, and ecological health. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a method for assessing aquatic biodiversity, a computer device, and a readable storage medium, which can adaptively simulate the mutual relationships and roles of different aquatic species in the ecosystem of a replaceable designated water area by introducing niche simulation technology, so that the impact status of various water environment factors in the designated water area on different aquatic species caused by the final assessment has sufficient comprehensiveness and accuracy, facilitating researchers to more comprehensively and accurately understand the changing trend of aquatic biodiversity in the designated water area and timely propose governance decisions for the designated water area.

[0004] To achieve the above purpose, the technical solutions adopted in the embodiments of this application are as follows:

[0005] In the first aspect, this application provides a method for assessing aquatic biodiversity, and the method includes:

[0006] Obtain the aquatic biodiversity data of multiple aquatic ecological quadrat points in a target water area respectively corresponding to multiple aquatic ecological samples;

[0007] Perform data cleaning and data preprocessing on the obtained aquatic biodiversity data to obtain the actual relative abundance data, actual habitat status data, and actual niche of all aquatic species in the target water area;

[0008] Construct a target niche model that is simultaneously adapted to all aquatic species in the target water area according to the actual relative abundance data and actual niches of each aquatic species.

[0009] Call the target niche model to perform niche simulation based on the actual relative abundance data of each aquatic species to obtain the predicted niche of each aquatic species.

[0010] Conduct a reverse analysis of biological effects based on the actual habitat condition data, actual niches, and predicted niches of each aquatic species to obtain the actual water environment impact status of each aquatic species when surviving in the target water area.

[0011] In an alternative embodiment, the actual relative abundance data corresponding to each aquatic species includes the actual relative abundance values corresponding to the corresponding aquatic species at all aquatic ecological samples. Then, the step of constructing a target niche model that is simultaneously adapted to all aquatic species in the target water area according to the actual relative abundance data and actual niches of each aquatic species includes:

[0012] For each aquatic species among all aquatic species, calculate the actual average abundance value of the aquatic species in the target water area according to all the actual relative abundance values corresponding to the aquatic species.

[0013] Construct an initial niche model involving all aquatic species regarding species average abundance and species niche.

[0014] Construct a target log-likelihood function of the model parameters to be solved for the initial niche model according to the actual average abundance values and actual niches of each aquatic species.

[0015] Solve for the model parameters with the goal of minimizing the target log-likelihood function to obtain the target model parameters.

[0016] Substitute the target model parameters into the initial niche model to obtain the target niche model.

[0017] In an alternative embodiment, for each aquatic species among all aquatic species, the step of calculating the actual average abundance value of the aquatic species in the target water area according to all the actual relative abundance values corresponding to the aquatic species includes:

[0018] Analyze the data distribution characteristics of all the actual relative abundance values corresponding to the aquatic species to obtain the actual abundance value distribution characteristics of the aquatic species.

[0019] Search for a target average abundance value calculation strategy that matches the actual abundance value distribution characteristic from the pre-stored average abundance value calculation strategies corresponding to different abundance value distribution characteristics;

[0020] Calculate the average abundance value based on all the actual relative abundance values corresponding to the aquatic species according to the target average abundance value calculation strategy to obtain the actual average abundance value of the aquatic species.

[0021] In an optional implementation manner, the target log-likelihood function is represented by the following formula:

[0022] ;

[0023] Wherein, is used to represent the target log-likelihood function, is used to represent the total number of aquatic species in the target water area, is used to represent the th actual average abundance value of an aquatic species, is used to represent the actual output parameter of the initial niche model when the input parameter is the actual niche of the th aquatic species.

[0024] In an optional implementation manner, the step of calling the target niche model to perform niche simulation based on the actual relative abundance data of all aquatic species to obtain the predicted niches of all aquatic species respectively includes:

[0025] For each aquatic species among all aquatic species, substitute the actual average abundance value of the aquatic species in the target water area into the target niche model for model prediction to obtain the predicted niche corresponding to the actual average abundance value output by the target niche model.

[0026] In an optional implementation manner, the method further includes:

[0027] Obtain the historical average abundance values, historical habitat condition data, and historical water environment impact conditions of all aquatic species in the target water area;

[0028] For each aquatic species among all aquatic species, perform species survival anomaly detection according to the historical average abundance value, historical habitat condition data, historical water environment impact condition, actual relative abundance data, actual habitat condition data, and actual water environment impact condition corresponding to the aquatic species;

[0029] When it is detected that the aquatic species has a species survival anomaly, an anomaly alarm is issued for the aquatic species.

[0030] In an optional implementation manner, the method further includes:

[0031] Calculate biodiversity indicators based on the actual relative abundance data and actual habitat condition data of all aquatic species in the target water area, and obtain the actual diversity indicator data corresponding to the target water area;

[0032] Compare the calculated actual diversity indicator data with the pre-stored standard diversity indicator data, and conduct a water area ecological health assessment based on the data comparison result and the actual impact status of the water environment corresponding to each aquatic species, to obtain the actual ecological health status and actual ecological change trend status of the target water area.

[0033] In an alternative embodiment, the method further includes:

[0034] Obtain the reference diversity indicator data of the reference water area;

[0035] Compare the actual diversity indicator data with the reference diversity indicator data to obtain the water area ecological difference status between the target water area and the reference water area.

[0036] In a second aspect, the present application provides a computer device, including a processor and a memory. The memory stores a computer program that can be executed by the processor, and the processor can execute the computer program to implement the aquatic biodiversity assessment method according to any one of the foregoing embodiments.

[0037] In a third aspect, the present application provides a readable storage medium, on which a computer program is stored. When the computer program is executed by a computer device, it implements the aquatic biodiversity assessment method according to any one of the foregoing embodiments.

[0038] In this case, the beneficial effects of the embodiments of the present application may include the following:

[0039] In this application, by performing data cleaning and data preprocessing on the aquatic biodiversity data of multiple aquatic ecological quadrat points in the target water area, the actual relative abundance data, actual habitat status data, and actual ecological niches of all aquatic species in the target water area are obtained. Then, based on the actual relative abundance data and actual ecological niches of all aquatic species, a target ecological niche model that is simultaneously adapted to all aquatic species in the target water area is constructed, and this target ecological niche model is called to perform ecological niche simulation based on the actual relative abundance data of all aquatic species to obtain the predicted ecological niches of all aquatic species. Subsequently, reverse biological effect analysis is carried out according to the actual habitat status data, actual ecological niches, and predicted ecological niches of all aquatic species to obtain the actual water environment impact status suffered by all aquatic species when surviving in the target water area. Thus, by introducing ecological niche simulation technology, the mutual relationships and roles of different aquatic species in the ecosystem of the replaceable designated water area are adaptively simulated, enabling the biological effect impact status caused by various water environment factors in the designated water area evaluated finally to have sufficient comprehensiveness and accuracy, facilitating researchers to more comprehensively and accurately understand the changing trend of aquatic biodiversity in the designated water area and timely propose governance decisions for the designated water area.

[0040] To make the above objects, features, and advantages of this application more obvious and understandable, the following specific preferred embodiments are given and described in detail in conjunction with the accompanying drawings as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other relevant drawings can also be obtained based on these drawings.

[0042] Figure 1 Schematic diagram of the composition of the computer device provided for the embodiments of this application;

[0043] Figure 2 One of the schematic flowcharts of the aquatic biodiversity assessment method provided for the embodiments of this application;

[0044] Figure 3 For Figure 2 Schematic flowchart of the sub-steps included in step S230 in

[0045] Figure 4 Another schematic flowchart of the aquatic biodiversity assessment method provided for the embodiments of this application;

[0046] Figure 5It is the third flowchart diagram of the aquatic biodiversity assessment method provided by the embodiments of the present application;

[0047] Figure 6 It is the fourth flowchart diagram of the aquatic biodiversity assessment method provided by the embodiments of the present application.

[0048] Icon: 10 - Computer device; 11 - Memory; 12 - Processor; 13 - Communication unit. Detailed implementation manners

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. The components of the embodiments of the present application usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0050] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but merely represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0051] It should be noted that: Similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0052] In the description of the present application, it should be understood that relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise", or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article, or device. Without further limitation, an element defined by the phrase "including a..." does not exclude the presence of additional identical elements in the process, method, article, or device including the element. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood in specific circumstances.

[0053] The following will describe in detail some implementation manners of the present application with reference to the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0054] Please refer to Figure 1 , Figure 1 which is a schematic diagram of the components of the computer device 10 provided in the embodiments of the present application. In the embodiments of the present application, the computer device 10 can be used to comprehensively and accurately evaluate the aquatic biodiversity in the target water area designated by the researcher, ensuring that the final aquatic biodiversity evaluation result can facilitate the researcher to more comprehensively and accurately understand the change trend of the aquatic biodiversity in the designated water area (i.e., the target water area), so as to timely propose governance decisions for the designated water area. Among them, the computer device 10 can be, but is not limited to, a tablet computer, a personal computer, a server, etc.

[0055] In the embodiments of the present application, the computer device 10 may include a memory 11, a processor 12, and a communication unit 13. Each of the memory 11, the processor 12, and the communication unit 13 is directly or indirectly electrically connected to each other to achieve data transmission or interaction. For example, these components of the memory 11, the processor 12, and the communication unit 13 can be electrically connected to each other through one or more communication buses or signal lines.

[0056] In this embodiment, the memory 11 can be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc. Among them, the memory 11 is used to store a computer program, and after receiving an execution instruction, the processor 12 can execute the computer program accordingly.

[0057] In this embodiment, the processor 12 may be an integrated circuit chip with signal processing capabilities. The processor 12 may be a general-purpose processor, including at least one of a central processing unit (CPU), a graphics processing unit (GPU), a network processor (NP), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc., and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application.

[0058] In this embodiment, the communication unit 13 is used to establish a communication connection between the computer device 10 and other electronic devices through a network, and transmit and receive data through the network, where the network includes a wired communication network and a wireless communication network. For example, the computer device 10 can obtain the aquatic biodiversity data of multiple aquatic ecological quadrat points in the target water area through the communication unit 13, where the sampling time points of the multiple aquatic ecological samples corresponding to the same aquatic ecological quadrat point are different from each other, and the aquatic biodiversity data of a single aquatic ecological sample may include information such as the composition of biological species involved in the corresponding aquatic ecological sample, the actual absolute abundance value and actual relative abundance value of each biological species, the species distribution status of each biological species at the corresponding aquatic ecological quadrat point, and the actual habitat characteristics of each biological species at the corresponding aquatic ecological quadrat point.

[0059] In this embodiment, the computer device 10 can store a computer program for implementing the aquatic biodiversity assessment operation in the memory 11, and by driving the processor 12 to execute the computer program, during the assessment of the aquatic biodiversity of the target water area, introduce the niche simulation technology to adaptively simulate the mutual relationships and roles of different aquatic species in the ecosystem of the target water area, so that the biological effect impact status caused by various water environment factors in the target water area finally evaluated has sufficient comprehensiveness and accuracy, facilitating researchers to more comprehensively and accurately understand the change trend of aquatic biodiversity in the specified water area and timely propose governance decisions for the specified water area.

[0060] It can be understood that Figure 1 The block diagram shown is only a schematic diagram of the composition of the computer device 10, and the computer device 10 may also include more or fewer components than those shown Figure 1 in the figure, or have a structure different from that shown Figure 1The different configurations shown. Figure 1 Each component shown in can be implemented by hardware, software, or a combination thereof.

[0061] In this application, to ensure that the computer device 10 can introduce niche simulation technology to adaptively simulate the interrelationships and roles of different aquatic species in the ecosystem of a replaceable specified water area, so that the biological effect impact conditions caused by various water environment factors in the specified water area evaluated finally are comprehensive and accurate enough, which is convenient for researchers to more comprehensively and accurately understand the change trend of aquatic biodiversity in the specified water area and timely propose governance decisions for the specified water area. The embodiments of this application implement the foregoing functions by providing an aquatic biodiversity assessment method applied to the above computer device 10. The aquatic biodiversity assessment method provided by this application will be elaborated in detail below.

[0062] Please refer to Figure 2 , Figure 2 is one of the schematic flowcharts of the aquatic biodiversity assessment method provided by the embodiments of this application. In the embodiments of this application, Figure 1 the aquatic biodiversity assessment method shown can include step S210 to step S250.

[0063] Step S210, obtaining the aquatic biodiversity data of multiple aquatic ecological quadrat points in the target water area respectively corresponding to multiple aquatic ecological samples.

[0064] In this embodiment, the target water area can be specified by the researcher by accessing various map data (such as satellite maps, topographic maps, etc.). The multiple aquatic ecological quadrat points in the target water area can be determined by the researcher by identifying the water body distribution of the target water area. The aquatic biodiversity data of each aquatic ecological sample can be obtained through field surveys, ecological monitoring, remote sensing technology, indoor sampling means, etc.

[0065] Step S220, performing data cleaning and data preprocessing on the obtained aquatic biodiversity data to obtain the actual relative abundance data, actual habitat condition data, and actual ecological niche of all aquatic species in the target water area.

[0066] In this embodiment, the computer device 10 can check whether there are missing values, outliers or error values in the obtained aquatic biodiversity data related to the target water area by performing data cleaning on all the aquatic biodiversity data. Then, for the missing values in the aquatic biodiversity data, deep learning algorithms are used for data filling or data deletion. Then, the computer device 10 performs data preprocessing on all the aquatic biodiversity data after the data cleaning operation to observe whether the corresponding data are on the same scale, whether they are normally distributed, independently distributed or identically distributed, and ensures the data consistency of all the aquatic biodiversity data after data preprocessing through data transformation and data standardization, so as to obtain the actual relative abundance data, actual habitat condition data, actual ecological niche and actual absolute abundance data of each aquatic species in the target water area, where the actual relative abundance data corresponding to each aquatic species includes the actual relative abundance values corresponding to the aquatic species at all aquatic ecological samples respectively, and the actual absolute abundance data corresponding to each aquatic species includes the actual absolute abundance values corresponding to the aquatic species at all aquatic ecological samples respectively.

[0067] Step S230, construct a target niche model that is simultaneously adapted to all aquatic species in the target water area according to the actual relative abundance data and actual ecological niche of each aquatic species.

[0068] In this embodiment, after determining the actual relative abundance data and actual ecological niche of each aquatic species in the target water area, the computer device 10 can perform an ecosystem characteristic analysis on the actual relative abundance data and actual ecological niche of each aquatic species in the target water area, and select a target model architecture adapted to the ecosystem characteristic analysis result from a variety of preset niche model architectures (for example, Lotka-Volterra model architecture, niche model architecture based on Maximum Likelihood Estimation (MLE)). Then, according to the actual relative abundance data and actual ecological niche of each aquatic species in the target water area, niche model construction processing is performed based on the selected target model architecture to obtain a target niche model that can be simultaneously adapted to all aquatic species in the target water area.

[0069] Optionally, please refer to Figure 3 , Figure 3 Yes Figure 2The flowchart of the sub-steps included in step S230 in [the context]. In the embodiments of the present application, step S230 may include sub-steps S231 to S235 to construct a target niche model adapted to the aquatic biodiversity data actually sampled and observed at the target water area by using the maximum likelihood estimation method, so as to ensure that the corresponding target niche model can consider the impacts of various water environment factors on species survival from a more comprehensive perspective and can reflect the complexity of species distribution in the real ecosystem as much as possible.

[0070] Sub-step S231, for each aquatic species among all aquatic species, calculate the actual average abundance value of the aquatic species in the target water area according to all the actual relative abundance values corresponding to the aquatic species.

[0071] In this embodiment, after the computer device 10 determines the actual relative abundance data of each aquatic species in the determined target water area, it can analyze the data distribution characteristics of all the actual relative abundance values included in the actual relative abundance data of the aquatic species to determine the actual abundance value distribution characteristics of the aquatic species in the target water area, and search for a target abundance value calculation strategy that matches the determined actual abundance value distribution characteristics among the pre-stored average abundance value calculation strategies corresponding to different abundance value distribution characteristics, and finally calculate the average abundance value of all the actual relative abundance values of the aquatic species in the target water area according to the found target abundance value calculation strategy to obtain the actual average abundance value of the aquatic species in the target water area.

[0072] Among them, the data abundance value distribution characteristics corresponding to the multiple average abundance value calculation strategies pre-stored at the computer device 10 may include, but are not limited to, "Poisson distribution characteristics", "log-normal distribution characteristics", etc.; the average abundance value calculation strategy corresponding to the "Poisson distribution characteristics" may be "directly calculate the actual average value among all the actual relative abundance values involved in the corresponding aquatic species"; the average abundance value calculation strategy corresponding to the "log-normal distribution characteristics" may be "aimed at maximizing the log-normal distribution likelihood function of the corresponding aquatic species with respect to the actual relative abundance values, solve the expected value parameter μ and the standard deviation σ in the log-normal distribution likelihood function, and then based on the characteristic that the logarithmic form of the relative abundance average value follows a log-normal distribution with respect to the aforementioned expected value parameter μ and the aforementioned standard deviation σ in the log-normal distribution, use the probability density function between the aforementioned logarithmic form and the aforementioned expected value parameter μ and the aforementioned standard deviation σ to solve the actual average abundance value of the corresponding aquatic species", where the log-normal distribution likelihood function can be represented by " ". for representing the actual relative abundance value of the corresponding aquatic species at the th aquatic ecological sample, for representing the total number of aquatic ecological samples actually involved by the corresponding aquatic species within the target water area.

[0073] Sub-step S232, constructing an initial niche model regarding the species average abundance and species niche involving all aquatic species.

[0074] In this embodiment, the initial niche model can be constructed by preliminarily evaluating the ecosystem at the target water area and selecting a linear function or a non-linear function according to the preliminary evaluation result. Optionally, in an implementation manner of this embodiment, to improve the construction efficiency of the target niche model, the initial niche model can be directly constructed using a linear function. At this time, the initial niche model can be directly expressed as " ", where is used to characterize the species average abundance of the th aquatic species, is used to characterize the species niche of the th aquatic species, and are both model parameters to be solved of the initial niche model.

[0075] Sub-step S233, constructing an objective log-likelihood function regarding the model parameters to be solved of the initial niche model according to the actual average abundance values and actual niches of all aquatic species respectively.

[0076] In this embodiment, during the niche simulation process, it is usually defaulted that the species abundance follows a certain probability distribution (for example, Poisson distribution). Therefore, the objective likelihood function regarding the model parameters to be solved that can use the maximum likelihood estimation method for all aquatic species in the target water area can be expressed as " ", where is used to represent the total number of aquatic species in the target water area, is used to represent the actual average abundance value of the th aquatic species, is used to represent the actual output parameter of the initial niche model when the input parameter is the actual niche of the th aquatic species.

[0077] For the maximum likelihood estimation operation of the above target likelihood function, the maximum likelihood estimation operation can be converted into a minimization problem with low data processing complexity by taking the logarithm transformation of the target likelihood function and then multiplying by a negative sign. At this time, the target log-likelihood function can be expressed by the following formula:

[0078] ;

[0079] where, is used to represent the target log-likelihood function, is used to represent the total number of aquatic species in the target water area, is used to represent the actual average abundance value of the th aquatic species, is used to represent the actual output parameter of the initial niche model when the input parameter is the actual niche of the th aquatic species.

[0080] Sub-step S234, solve the model parameters with the goal of minimizing the target log-likelihood function to obtain the target model parameters.

[0081] Sub-step S235, substitute the target model parameters into the initial niche model to obtain the target niche model.

[0082] Thus, this application can execute the above sub-steps S231 to S235, and use the maximum likelihood estimation method to construct a target niche model adapted to the aquatic biodiversity data actually sampled and observed at the target water area, so as to ensure that the corresponding target niche model can consider the impacts of various water environment factors on species survival from a more comprehensive perspective and can reflect the complexity of species distribution in the real ecosystem as much as possible.

[0083] Step S240, call the target niche model to perform niche simulation based on the actual relative abundance data of all aquatic species to obtain the predicted niches of all aquatic species.

[0084] In this embodiment, after the computer device 10 constructs an adapted target niche model for the target water area, it can correspondingly call the target niche model to perform niche simulation based on the actual relative abundance data of all aquatic species in the target water area, so as to determine the predicted niches of all aquatic species in the target water area. Among them, during the niche simulation process, the target niche model can adaptively simulate the mutual relationships and roles of different aquatic species in the ecosystem of a replaceable designated water area, so as to provide more real ecological information for the assessment operation of aquatic biodiversity, which helps to directly observe the niche distribution of aquatic organisms and capture the interactions between aquatic species.

[0085] Optionally, in an implementation manner of this embodiment, the step of calling the target niche model to perform niche simulation based on the actual relative abundance data of all aquatic species to obtain the predicted niches of all aquatic species may include:

[0086] For each aquatic species among all aquatic species, substitute the actual average abundance value of this aquatic species in the target water area into the target niche model for model prediction, and obtain the predicted niche corresponding to the actual average abundance value output by the target niche model.

[0087] Step S250, perform reverse analysis of biological effects based on the actual habitat condition data, actual niches, and predicted niches of all aquatic species, so as to obtain the actual water environment impact conditions suffered by all aquatic species when surviving in the target water area.

[0088] In this embodiment, the actual water environment impact conditions include the biological effect impact conditions actually caused by various water environment factors (for example, water body substance composition, water body turbidity, water body pollutant content, etc.) of the target water area on the corresponding aquatic species. The computer device 10 can use the maximum likelihood estimation method to estimate water environment factors on the basis of having determined the actual habitat condition data, the actual niches, and the predicted niches of all aquatic species in the target water area, so as to determine the actual water environment impact conditions suffered by all aquatic species surviving in the target water area.

[0089] Thus, by performing the above steps S210 to S250, in the process of evaluating the aquatic biodiversity of the target water area, the niche simulation technology can be introduced to adaptively simulate the mutual relationships and roles of different aquatic species in the ecosystem of the target water area, so that the biological effect impact conditions caused by various water environment factors in the target water area finally evaluated on different aquatic species are comprehensive and accurate enough, facilitating researchers to more comprehensively and accurately understand the change trend of aquatic biodiversity in the designated water area and timely propose governance decisions for the designated water area.

[0090] Optionally, please refer to Figure 4 , Figure 4 which is the second flowchart of the aquatic biodiversity assessment method provided by the embodiments of the present application. In the embodiments of the present application, compared with the Figure 2 shown aquatic biodiversity assessment method, Figure 4 the shown aquatic biodiversity assessment method may further include steps S260 to S280 to perform abnormal monitoring of aquatic biodiversity in the target water area.

[0091] Step S260, obtain the historical average abundance values, historical habitat condition data, and historical water environment impact conditions of all aquatic species in the target water area.

[0092] Among them, the historical water environment impact condition is used to describe the biological effect impact conditions actually caused by various water environment factors in the target water area to the corresponding aquatic species during the historical time period.

[0093] Step S270, for each aquatic species among all aquatic species, perform species survival anomaly detection according to the historical average abundance value, historical habitat condition data, historical water environment impact condition, actual relative abundance data, actual habitat condition data, and actual water environment impact condition corresponding to the aquatic species.

[0094] Among them, for each aquatic species in the target water area, the computer device 10 can compare the historical data (e.g., historical average abundance value, historical habitat condition data, historical water environment impact condition) and actual data (e.g., actual relative abundance data, actual habitat condition data, and actual water environment impact condition) that match the data type of the aquatic species to determine whether the data difference degree between the historical data and the actual data of the corresponding data type exceeds the set range, and when the above data difference degree exceeds the set range, determine that the corresponding aquatic species has an abnormal condition at the ecological level of the above data type, so as to complete the species survival anomaly detection operation for all aquatic species in the target water area.

[0095] Step S230: When it is detected that there is an abnormal situation in the survival of the aquatic species, an abnormal alarm is issued for the aquatic species.

[0096] Among them, when there is an abnormal situation in the survival of a certain aquatic species in the target water area at a specific ecological level (for example, abundance, habitat, water environment impact status, etc.), the computer device 10 will send an abnormal alarm message for the aquatic species to the terminal device held by the R & D personnel by means of text messages, voice broadcasts, etc., so as to achieve the effect of abnormal alarm. The abnormal alarm message is used to disclose which aquatic species in the target water area has an abnormal situation in the survival at what ecological level.

[0097] Thus, the present application can perform abnormal monitoring of the aquatic biodiversity of the target water area by executing the above steps S260 to S280.

[0098] Optionally, please refer to Figure 5 , Figure 5 which is the third schematic flow chart of the aquatic biodiversity assessment method provided by the embodiment of the present application. In the embodiment of the present application, compared with the aquatic biodiversity assessment methods shown in Figure 2 or Figure 4 , Figure 5 the aquatic biodiversity assessment method shown in

[0099] can further include steps S310 to S320 to help researchers more comprehensively understand the diversity characteristics of the ecosystem at the target water area and provide important data support for the protection and management of the ecological environment.

[0100] Step S310: Calculate biodiversity indicators based on the actual relative abundance data and actual habitat status data of all aquatic species in the target water area to obtain actual diversity indicator data corresponding to the target water area. Among them, the types of biodiversity indicators involved in the actual diversity indicator data corresponding to the target water area can include species richness, species diversity index, relative richness of the ecosystem, Alpha diversity, Beta diversity, etc.; the actual indicator data corresponding to species richness can be obtained by counting the number of species actually observed in the target water area; the actual indicator data corresponding to the species diversity index can be obtained by performing Shannon-Wiener index calculation and Simpson index calculation based on the actual relative abundance data of all aquatic species in the target water area; the actual indicator data corresponding to the relative richness of the ecosystem can be evaluated by Pielou's Evenness index; the actual indicator data corresponding to Beta diversity can calculate indicators such as Jaccard index and Bray-Curtis index to measure the degree of species composition difference between different aquatic ecological quadrat points in the target water area.

[0101] Step S320: Compare the calculated actual diversity index data with the pre-stored standard diversity index data, and conduct a water area ecological health assessment based on the data comparison result and the actual water environment impact status corresponding to each aquatic species, so as to obtain the actual ecological health status and the actual ecological change trend status of the target water area.

[0102] Among them, the types of biodiversity indicators involved in the standard diversity index data are consistent with the types of biodiversity indicators involved in the actual diversity index data; the computer device 10 can compare the calculated actual diversity index data with the pre-stored standard diversity index data, and combine the actual water environment impact status corresponding to each aquatic species in the target water area to predict water pollutants, so as to predict what water pollutants exist in the target water area that can cause the current formed ecological effect, which is convenient for judging the health status and change trend of the ecological system at the target water area.

[0103] In an implementation manner of this embodiment, a parameter mean comparison method can be used for data comparison, that is, by calculating the data means of the actual diversity index data and the standard diversity index data respectively, and conducting a statistical test to evaluate whether the difference in the biodiversity level at the target water area has statistical significance.

[0104] Therefore, by executing the above steps S310 to S320, this application can help researchers more comprehensively understand the diversity characteristics of the ecological system at the target water area, and provide important data support for the protection and management of the ecological environment.

[0105] Optionally, please refer to Figure 6 , Figure 6 FIG. is the fourth flow chart of the aquatic biodiversity assessment method provided by the embodiment of this application. In the embodiment of this application, compared with the Figure 5 shown aquatic biodiversity assessment method, Figure 6 the shown aquatic biodiversity assessment method may further include steps S330 to S340 to evaluate whether the difference in the biodiversity level of the target water area relative to the reference water area is significant, so as to help researchers more intuitively understand the biodiversity status of the ecological system at the target water area, which is convenient for researchers to make reasonable and effective governance decisions.

[0106] Step S330: Obtain the reference diversity index data of the reference water area.

[0107] Among them, the types of biodiversity indicators involved in the reference diversity index data are consistent with the types of biodiversity indicators involved in the actual diversity index data.

[0108] Step S340: Compare the actual diversity index data with the reference diversity index data to obtain the water ecosystem difference status between the target water area and the reference water area.

[0109] Among them, the computer device 10 can directly compare the actual diversity index data and the reference diversity index data one by one at the data level according to the index type, or compare the respective data means of the actual diversity index data and the standard diversity index data, and then use traditional statistical methods (such as t-test, analysis of variance, etc.) to evaluate the statistical significance of the water ecosystem difference status between the target water area and the reference water area.

[0110] Thus, by executing the above steps S330 to S340, this application can evaluate whether the difference in the biodiversity level of the target water area relative to the reference water area is significant, helping researchers more intuitively understand the biodiversity status of the ecosystem at the target water area and facilitating researchers to make reasonable and effective governance decisions.

[0111] It can be understood that after the computer device 10 in this application finishes executing Figure 2 , Figure 4 , Figure 5 and Figure 6 any one of the aquatic biodiversity assessment methods shown in the drawings, the corresponding aquatic biodiversity assessment results corresponding to the target water area can be visually presented in the form of charts, maps, etc., facilitating scientific researchers, decision-makers and the public to understand the status and trends of aquatic biodiversity. At the same time, the computer device 10 can also upload the processed data during the aquatic biodiversity assessment process to the cloud device for storage, so that users can observe and analyze the data at any time.

[0112] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of the devices, methods, and computer program products according to the embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0113] In addition, the functional modules in each embodiment of this application can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part. If the function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of this application. The aforementioned storage medium includes various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0114] In summary, in an aquatic biodiversity assessment method, computer device, and readable storage medium provided by the present application, the present application performs data cleaning and data preprocessing on the aquatic biodiversity data of multiple aquatic ecological quadrat points corresponding to each other in a target water area to obtain the actual relative abundance data, actual habitat condition data, and actual ecological niche of all aquatic species in the target water area. Then, based on the actual relative abundance data and actual ecological niche of all aquatic species, a target ecological niche model that is simultaneously adapted to all aquatic species in the target water area is constructed, and the target ecological niche model is called to perform ecological niche simulation based on the actual relative abundance data of all aquatic species to obtain the predicted ecological niche of all aquatic species. Then, reverse biological effect analysis is performed according to the actual habitat condition data, actual ecological niche, and predicted ecological niche of all aquatic species to obtain the actual water environment impact status suffered by all aquatic species when surviving in the target water area. Thus, by introducing ecological niche simulation technology in the process of aquatic biodiversity assessment, the mutual relationships and roles of different aquatic species in the ecosystem of a replaceable designated water area are adaptively simulated, so that the biological effect impact status caused by various water environment factors in the designated water area on different aquatic species evaluated finally has sufficient comprehensiveness and accuracy, facilitating researchers to more comprehensively and accurately understand the change trend of aquatic biodiversity in the designated water area and timely propose governance decisions for the designated water area.

[0115] As described above, the above are only various implementation manners of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for evaluating aquatic biodiversity, characterized in that, The method includes: Obtaining the aquatic biodiversity data of multiple aquatic ecological quadrat points in the target water area respectively corresponding to multiple aquatic ecological samples; Performing data cleaning and data preprocessing on the obtained aquatic biodiversity data to obtain the actual relative abundance data, actual habitat condition data, and actual ecological niche of all aquatic species in the target water area, where the actual relative abundance data corresponding to each aquatic species includes the actual relative abundance values corresponding to the corresponding aquatic species at all aquatic ecological samples; Constructing a target niche model that is simultaneously adapted to all aquatic species in the target water area according to the actual relative abundance data and actual ecological niche of all aquatic species; Invoking the target niche model to perform niche simulation based on the actual relative abundance data of all aquatic species to obtain the predicted ecological niche of all aquatic species; Performing reverse analysis of biological effects according to the actual habitat condition data, actual ecological niche, and predicted ecological niche of all aquatic species to obtain the actual water environment impact status suffered by all aquatic species when surviving in the target water area; Among them, the step of constructing a target niche model that is simultaneously adapted to all aquatic species in the target water area according to the actual relative abundance data and actual ecological niche of all aquatic species includes: For each aquatic species among all aquatic species, analyzing the data distribution characteristics of all actual relative abundance values corresponding to the aquatic species to obtain the actual abundance value distribution characteristics of the aquatic species, then searching from the pre-stored average abundance value calculation strategies corresponding to different abundance value distribution characteristics for the target abundance value calculation strategy that matches the actual abundance value distribution characteristics, and then calculating the average abundance value based on all actual relative abundance values corresponding to the aquatic species according to the target abundance value calculation strategy to obtain the actual average abundance value of the aquatic species in the target water area; Constructing an initial niche model involving all aquatic species regarding species average abundance and species ecological niche; Constructing a target log-likelihood function for the model parameter to be solved regarding the initial niche model according to the actual average abundance values and actual ecological niche of all aquatic species; Solving the model parameters with the goal of minimizing the target log-likelihood function to obtain the target model parameters; Substituting the target model parameters into the initial niche model to obtain the target niche model.

2. The method according to claim 1, wherein The target log-likelihood function is expressed by the following formula: ； Among them, is used to represent the target log-likelihood function, is used to represent the total number of aquatic species in the target water area, is used to represent the actual average abundance value of the ith aquatic species, is used to represent the actual output parameter of the initial niche model when the input parameter is the actual niche of the ith aquatic species.

3. The method according to claim 1, wherein The step of invoking the target niche model to perform niche simulation based on the actual relative abundance data of all aquatic species to obtain the predicted ecological niche of all aquatic species includes: For each aquatic species among all aquatic species, substituting the actual average abundance value of the aquatic species in the target water area into the target niche model for model prediction to obtain the predicted ecological niche corresponding to the actual average abundance value output by the target niche model.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: Obtaining the historical average abundance values, historical habitat condition data, and historical water environment impact status of all aquatic species in the target water area; For each aquatic species among all aquatic species, species survival anomaly detection is performed based on the historical average abundance value corresponding to the aquatic species, historical habitat condition data, historical water environment impact condition, actual relative abundance data, actual habitat condition data, and actual water environment impact condition corresponding to the aquatic species; When it is detected that the aquatic species has a species survival anomaly, an anomaly warning is issued for the aquatic species.

5. The method according to any one of claims 1 to 3, characterized in that, The method further includes: Calculating biodiversity indicators based on the actual relative abundance data and actual habitat condition data of all aquatic species in the target water area to obtain actual diversity indicator data corresponding to the target water area; Comparing the calculated actual diversity indicator data with pre-stored standard diversity indicator data, and performing a water area ecological health assessment based on the data comparison result and the actual water environment impact conditions corresponding to all aquatic species to obtain the actual ecological health condition and actual ecological change trend condition of the target water area.

6. The method according to claim 5, characterized in that, The method further includes: Obtaining reference diversity indicator data of a reference water area; Comparing the actual diversity indicator data with the reference diversity indicator data to obtain the water area ecological difference condition between the target water area and the reference water area.

7. A computer device, characterized in that, It includes a processor and a memory, the memory stores a computer program that can be executed by the processor, and the processor can execute the computer program to implement the aquatic biodiversity assessment method according to any one of claims 1-6.

8. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a computer device, the aquatic biodiversity assessment method according to any one of claims 1-6 is implemented.

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