A method for assessing water ecological risk, a computer device, and a readable storage medium

Abstract

The present application provides a method for assessing aquatic ecological risks, a computer device, and a readable storage medium, which relate to the technical field of environmental governance. After obtaining the current aquatic ecological data, socioeconomic data, and historical aquatic ecological data of the target basin, the present application comprehensively considers the aquatic ecological risk factors that may be involved in the target basin by introducing the principles of Bayesian statistics and combining with the DPSIR model framework, and constructs an aquatic ecological risk assessment system based on the above-mentioned obtained data, so that the constructed aquatic ecological risk assessment system can more accurately express the internal relationships and interactions of the aquatic ecosystem at the target basin, to ensure the accuracy, credibility, and timeliness of the final aquatic ecological risk assessment results, facilitate researchers to more comprehensively and accurately understand the health status of the aquatic ecosystem at the target basin, and timely propose or adjust the governance decisions for the target basin.

Description

Technical Field

[0001] The present application relates to the technical field of environmental governance, and in particular, to a water ecological risk assessment method, a computer device, and a readable storage medium. Background Art

[0002] As an important part of Ecological Risk Assessment (ERA), Water Ecological Risk Assessment (WERA) plays a crucial role in aspects such as identifying risk factors in water ecosystems and water environment governance. It can effectively analyze the impacts of basin hydrological changes, geological disasters, environmental degradation, and human activities on water ecosystems in a designated basin, and carry out research on basin water ecological risk assessment and water ecological restoration. With the continuous development of science and technology, the water environment problems in basins around the world are becoming increasingly serious. The types and quantities of risk sources are increasing continuously, and water environment risk problems are prominent, seriously threatening human health and water use safety.

[0003] Therefore, the protection of basin ecological environment has gradually become the consensus of the whole society. Water ecological risk assessment has become an important means for preventing current water environment accidents, ensuring water quality safety and ecological safety, and can provide important basis for maintaining ecological balance and making decisions on water ecological environment protection. Currently, conventional water ecological risk assessment schemes usually adopt mathematical statistics methods or mathematical model methods. Among them, the data collection workload of mathematical statistics methods is huge, and the overall assessment efficiency is different. While in the case of complex types of water environment risk sources and strong environmental sensitivity in a designated basin, the mathematical model method actually has a series of problems in aspects such as data collection, analysis, and model accuracy, and cannot meet the actual water ecological risk assessment requirements. Summary of the Invention

[0004] In view of this, the purpose of the present application is to provide a water ecological risk assessment method, a computer device, and a readable storage medium, which can comprehensively consider the water ecological risk factors that may be involved in any designated basin by introducing Bayesian statistical principles and combining with the DPSIR (Drivers-Pressure-State-Impact-Response) model framework, so that the constructed water ecological risk assessment system can more accurately express the internal relationships and interactions of the water ecosystem in any designated basin, to ensure the accuracy, credibility, and real-time nature of the final water ecological risk assessment results, facilitate researchers to more comprehensively and accurately understand the health status of the water ecosystem in the designated basin, and timely propose or adjust the governance decisions for the designated basin.

[0005] To achieve the above object, the technical solution adopted in the embodiments of the present application is as follows:

[0006] In a first aspect, the present application provides a method for assessing water ecological risk, and the method includes:

[0007] Obtain the current water ecological data, social and economic data, and historical water ecological data of the target basin;

[0008] Based on the current water ecological data, the social and economic data, and the historical water ecological data, construct a DPSIR model to obtain a target DPSIR model adapted to the target basin;

[0009] Perform Bayesian model conversion on the target DPSIR model to obtain a target Bayesian model adapted to the target basin, and the actual conditional probabilities of the water ecological state results corresponding to all leaf nodes in the target Bayesian model occurring under the influence of different risk events, where each risk event matches a traversal path of an ancestor node corresponding to the corresponding leaf node;

[0010] Perform data statistics on the current water ecological data, the social and economic data, and the historical water ecological data to obtain the result occurrence probabilities of the water ecological state results corresponding to all leaf nodes at the target basin;

[0011] According to the result occurrence probabilities corresponding to all leaf nodes and the actual conditional probabilities of all leaf nodes under the influence of different risk events, calculate the event occurrence probabilities of different risk events at the target basin based on the total probability theorem.

[0012] In an optional implementation manner, the step of constructing a DPSIR model based on the current water ecological data, the social and economic data, and the historical water ecological data to obtain a target DPSIR model adapted to the target basin includes:

[0013] According to various pre-stored DPSIR model architecture element types, extract framework elements from the current water ecological data, the social and economic data, and the historical water ecological data to obtain all model framework elements involved in the target DPSIR model;

[0014] Identify the water ecological causal relationships in the current water ecological data, the social and economic data, and the historical water ecological data, and configure the causal relationships between all model framework elements in the target DPSIR model according to the obtained water ecological causal relationship identification results to form the causal relationship chain of the target DPSIR model.

[0015] In an alternative embodiment, the step of performing Bayesian model conversion on the target DPSIR model to obtain a target Bayesian model adapted to the target basin, and the actual conditional probabilities of the occurrence of the water ecological state results corresponding to all leaf nodes in the target Bayesian model under the influence of different risk events includes:

[0016] Taking all model framework elements in the target DPSIR model as a risk node in the target Bayesian model respectively, and configuring the parent-child relationships between all risk nodes in the target Bayesian model according to the causal relationship chain of the target DPSIR model;

[0017] Determining the connection probabilities from all risk nodes in the target Bayesian model to their corresponding child nodes according to the current water ecological data, the socio-economic data, and the historical water ecological data;

[0018] Calculating the actual conditional probabilities of all leaf nodes in the target Bayesian model under the influence of different risk events according to the connection probabilities from all risk nodes to their corresponding child nodes.

[0019] In an alternative embodiment, the step of calculating the actual conditional probabilities of all leaf nodes in the target Bayesian model under the influence of different risk events according to the connection probabilities from all risk nodes to their corresponding child nodes includes:

[0020] For each leaf node in the target Bayesian model, determining all ancestor node traversal paths corresponding to the leaf node in the target Bayesian model;

[0021] For each ancestor node traversal path corresponding to the leaf node, performing a multiplication operation on the connection probabilities of all ancestor nodes involved in the ancestor node traversal path to obtain the actual conditional probability of the leaf node under the influence of the risk event matching the ancestor node traversal path.

[0022] In an alternative embodiment, the operation relationship between the result occurrence probability of the i-th leaf node in the target Bayesian model, the actual conditional probability of the i-th leaf node under the influence of different risk events, and the event occurrence probabilities of different risk events is expressed by the following formula:

[0023]

[0024] where P(A i ) is used to represent the result occurrence probability of the water ecological state result corresponding to the i-th leaf node, P(B j ) is used to represent the event occurrence probability of the j-th risk event associated with the i-th leaf node, P(Ai |B j ) is used to represent the actual conditional probability of the water ecological status result corresponding to the i-th leaf node under the influence of the j-th risk event, and m is used to represent the total number of risk events associated with the i-th leaf node.

[0025] In an alternative embodiment, the method further includes:

[0026] For each occurred water ecological status result that matches the current water ecological data, determine all target risk events associated with the occurred water ecological status result;

[0027] For each determined target risk event, based on the result occurrence probability of the occurred water ecological status result, the actual conditional probability of the occurred water ecological status result under the influence of the target risk event, and the event occurrence probability of the target risk event, calculate the result triggering probability of the target risk event causing the occurred water ecological status result based on Bayes' theorem.

[0028] In an alternative embodiment, the operation relationship among the result occurrence probability of the k-th occurred water ecological status result, the actual conditional probability of the k-th occurred water ecological status result under the influence of the q-th target risk event, the event occurrence probability of the q-th target risk event, and the result triggering probability of the q-th target risk event causing the k-th occurred water ecological status result is represented by the following formula:

[0029]

[0030] where P(C k ) is used to represent the result occurrence probability of the k-th occurred water ecological status result, P(B q ) is used to represent the event occurrence probability of the q-th target risk event, P(C k |B q ) is used to represent the actual conditional probability of the k-th occurred water ecological status result under the influence of the q-th target risk event, and P(B q |C k ) is used to represent the result triggering probability of the q-th target risk event causing the k-th occurred water ecological status result.

[0031] In an alternative embodiment, the method further includes:

[0032] Graphically display the event occurrence probabilities of different risk events at the target watershed, and / or the result triggering probabilities of different target risk events causing different occurred water ecological status results.

[0033] 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 water ecological risk assessment method described in any one of the foregoing embodiments.

[0034] 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 water ecological risk assessment method described in any one of the foregoing embodiments.

[0035] In this case, the beneficial effects of the embodiments of the present application may include the following:

[0036] Based on the current water ecological data, social and economic data, and historical water ecological data of the target basin, the present application constructs a DPSIR model to obtain a target DPSIR model adapted to the target basin. Then, by performing Bayesian model conversion on the target DPSIR model, a target Bayesian model adapted to the target basin is obtained, as well as the actual conditional probabilities of the water ecological state results corresponding to all leaf nodes in the target Bayesian model occurring under the influence of different risk events. Subsequently, based on the current water ecological data, social and economic data, and historical water ecological data through data statistical means, the occurrence probabilities of the water ecological state results corresponding to all the foregoing leaf nodes at the target basin are determined. Finally, according to the occurrence probabilities of the results corresponding to all leaf nodes and the actual conditional probabilities of all leaf nodes under the influence of different risk events, the occurrence probabilities of different risk events at the target basin are calculated based on the total probability theorem. Thus, by introducing the principles of Bayesian statistics and combining the DPSIR model architecture, all water ecological risk factors that may be involved in any designated basin are comprehensively considered, enabling the constructed water ecological risk assessment system to more accurately express the internal relationships and interactions of the water ecological system at any designated basin, ensuring the accuracy, credibility, and real-time nature of the final water ecological risk assessment results, facilitating researchers to more comprehensively and accurately understand the health status of the water ecological system at the designated basin, and timely proposing or adjusting the governance decisions for the designated basin.

[0037] To make the above objects, features, and advantages of the present application more obvious and understandable, the following specific preferred embodiments are given, and in conjunction with the accompanying drawings, the detailed description is as follows. Description of the Drawings

[0038] To more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative efforts.

[0039] Figure 1 It is a schematic diagram of the composition of the computer device provided by the embodiment of the present application;

[0040] Figure 2 It is one of the schematic flowcharts of the water ecological risk assessment method provided by the embodiment of the present application;

[0041] Figure 3 It is Figure 2 a schematic flowchart of the sub-steps included in step S220 in;

[0042] Figure 4 It is Figure 2 a schematic flowchart of the sub-steps included in step S230 in;

[0043] Figure 5 It is another schematic flowchart of the water ecological risk assessment method provided by the embodiment of the present application;

[0044] Figure 6 It is the third schematic flowchart of the water ecological risk assessment method provided by the embodiment of the present application.

[0045] Icons: 10 - computer device; 11 - memory; 12 - processor; 13 - communication unit. Detailed implementation manners

[0046] 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. Usually, the components of the embodiments of the present application described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0047] 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 present application claimed, 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 of the present application without creative efforts fall within the scope of protection of the present application.

[0048] 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.

[0049] 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 "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the existence of additional identical elements in the process, method, article or device comprising 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.

[0050] The following will describe in detail some embodiments of the present application with reference to the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments may be combined with each other.

[0051] Please refer to Figure 1 , Figure 1 which is a schematic diagram of the composition of the computer device 10 provided in the embodiment of the present application. In the embodiment of the present application, the computer device 10 can be used to comprehensively, accurately and in real time evaluate the water ecosystem risk factors of the target watershed designated by the researcher, ensuring that the finally obtained water ecological risk assessment result can facilitate the researcher to more comprehensively and accurately understand the health status of the water ecosystem in the designated watershed (i.e., the target watershed), so as to timely propose or adjust the governance decision for the designated watershed. Among them, the computer device 10 can be, but is not limited to, a tablet computer, a personal computer, a server, etc.

[0052] In the embodiment of the present application, the computer device 10 may include a memory 11, a processor 12, and a communication unit 13. Each element 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 elements of the memory 11, the processor 12, and the communication unit 13 may be electrically connected to each other through one or more communication buses or signal lines.

[0053] In this embodiment, the memory 11 may 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.

[0054] 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., which can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application.

[0055] 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 current water ecological data, social and economic data, and historical water ecological data of the target basin through the communication unit 13. The current water ecological data may include the current water body physical parameter data (such as temperature, flow velocity, transparency, etc.), water body chemical parameter data (such as pH value, dissolved oxygen content, nitrogen and phosphorus content, heavy metal concentration, etc.), and water body biological parameter data (such as population quantity and diversity, the situation of biological indicator species, etc.) of the target basin. The social and economic data may include the social and economic factors that may affect the water body (such as industrial development, urbanization process, agricultural activities, etc.) and economic policy measure factors (such as relevant economic policies and regulations, regulatory measures, and historical management result data, etc.) of the target basin. The historical water ecological data may include the water body physical parameter data, water body chemical parameter data, and water body biological parameter data of the target basin in a historical time period.

[0056] In this embodiment, the computer device 10 can store a computer program for implementing the water ecological risk assessment operation in the memory 11, and by driving the processor 12 to execute the computer program, in the process of the water ecological risk assessment for the target basin, introduce the Bayesian statistics principle combined with the DPSIR model architecture to comprehensively consider the water ecological risk factors that may be involved in the target basin, so that the constructed water ecological risk assessment system can more accurately express the internal relationships and interactions of the water ecological system at the target basin, to ensure the accuracy, credibility, and real-time nature of the final water ecological risk assessment result, facilitate researchers to more comprehensively and accurately understand the health status of the water ecological system at the target basin, and timely propose or adjust the governance decision for the target basin.

[0057] 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 shown, or have a different configuration from that Figure 1 shown. Figure 1 Each component shown can be implemented by hardware, software, or a combination thereof.

[0058] In this application, to ensure that the computer device 10 can introduce the principles of Bayesian statistics and combine with the DPSIR model architecture to comprehensively consider the water ecological risk factors that may be involved in any specified watershed, so that the constructed water ecological risk assessment system can more accurately express the internal relationships and interactions of the water ecological system at any specified watershed, to ensure the accuracy, credibility and real-time nature of the final water ecological risk assessment results, and to facilitate researchers to more comprehensively and accurately understand the health status of the water ecological system at the specified watershed and timely propose or adjust the governance decisions for the specified watershed, the embodiments of this application achieve the foregoing functions by providing a water ecological risk assessment method applied to the above computer device 10. The following elaborates in detail on the water ecological risk assessment method provided in this application.

[0059] Please refer to Figure 2 , Figure 2 which is one of the flow diagrams of the water ecological risk assessment method provided by the embodiments of this application. In the embodiments of this application, the water ecological risk assessment method may include steps S210 to S250.

[0060] Step S210, obtaining the current water ecological data, social and economic data, and historical water ecological data of the target watershed.

[0061] In this embodiment, the target watershed can be specified by researchers by accessing various map data (such as satellite maps, topographic maps, etc.).

[0062] Step S220, constructing a DPSIR model based on the current water ecological data, social and economic data, and historical water ecological data to obtain a target DPSIR model adapted to the target watershed.

[0063] In this embodiment, the computer device 10 may pre-store various types of DPSIR model architecture elements, which are respectively "driving force factors", "pressure factors", "state factors", "impact factors", and "response factors": "Driving force factors" are the fundamental causes leading to environmental changes, usually involving social, economic, technological, and political factors (for example, regional population growth, increasing industrialization level, etc.), and usually indirectly affect the water ecological risk; "Pressure factors" are the impact factors directly resulting in the change of ecological quality caused by "driving force factors" (for example, increasing industrial wastewater discharge, increasing pesticide use, overusing land), usually manifested as various activities on the environment, which can exert external forces on the environmental system to cause environmental changes; "State factors" are the results of monitoring and measuring the environmental system (for example, pollutant concentration, biological community structure), which can represent the actual environmental conditions (or ecological system conditions), and provide information about environmental health and quality; "Impact factors" are used to evaluate the possible impacts of water body state changes on the ecology, economy, and society caused by "pressure factors"; "Response factors" are activities taken to reduce the negative impacts of "driving force factors" and "pressure factors" on the environment, while improving or maintaining the environmental state, and response measures for preventing and mitigating negative impacts can be formulated (for example, improving sewage treatment capacity, implementing ecological restoration projects, etc.). Therefore, in essence, the DPSIR model construction operation can comprehensively explore the changing trend and driving mechanism of the ecological risk of the corresponding basin, belonging to an important analysis framework for multi-dimensional evaluation of basin ecological risk, and can effectively reflect the mutual relationship among the multi-dimensional water ecological risk factors of the corresponding basin, to help researchers better understand and address the related challenges of complex environmental problems.

[0064] Therefore, after the computer device 10 obtains the current water ecological data, social and economic data, and historical water ecological data of the target basin, it can first perform data preprocessing (including operations such as data denoising, data correction, and data fusion) on the current water ecological data, the social and economic data, and the historical water ecological data to ensure data accuracy and data consistency, and then construct a DPSIR model based on the preprocessed current water ecological data, social and economic data, and historical water ecological data to ensure that the constructed target DPSIR model can comprehensively reflect the mutual relationship among the multi-dimensional water ecological risk factors of the target basin.

[0065] Optionally, please refer to Figure 3 , Figure 3 is Figure 2Schematic diagram of the sub-steps included in step S220. In the embodiment of the present application, step S220 may include sub-steps S221 to S222 to ensure that the constructed target DPSIR model can comprehensively reflect the mutual relationship between the multi-dimensional water ecological risk factors of the target basin.

[0066] Sub-step S221: Extract the framework elements from the current water ecological data, socio-economic data, and historical water ecological data according to various pre-stored DPSIR model architecture element types to obtain all the model framework elements involved in the target DPSIR model.

[0067] Sub-step S222: Identify the water ecological causal relationships in the current water ecological data, socio-economic data, and historical water ecological data, and configure the causal relationships between all the model framework elements in the target DPSIR model according to the obtained water ecological causal relationship identification results to form the causal relationship chain of the target DPSIR model.

[0068] Thus, the present application can ensure that the constructed target DPSIR model can comprehensively reflect the mutual relationship between the multi-dimensional water ecological risk factors of the target basin by executing the above sub-steps S221 to S222.

[0069] Step S230: Perform Bayesian model conversion on the target DPSIR model to obtain a target Bayesian model adapted to the target basin, and the actual conditional probabilities of the water ecological state results corresponding to all the leaf nodes in the target Bayesian model under the influence of different risk events.

[0070] In this embodiment, each leaf node in the target Bayesian model can correspond to at least one risk event. The at least one risk event corresponding to different leaf nodes can be at least partially the same or completely different. All the risk events corresponding to a single leaf node respectively match a traversal path of ancestor nodes at the position of the leaf node in the target Bayesian model, that is, a risk event corresponding to a single leaf node is substantially equivalent to the set of all nodes involved in a traversal path of ancestor nodes of the leaf node.

[0071] After obtaining the target DPSIR model corresponding to the target basin, the computer device 10 will convert the target DPSIR model into a Bayesian model based on the principles of Bayesian statistics, taking each model framework element in the target DPSIR model as a risk node in the Bayesian model, and then transplanting the causal relationship chain of the target DPSIR model into the Bayesian model, so that the parent-child relationship between the risk nodes in the Bayesian model can effectively represent the interdependent relationship between the multi-dimensional water ecological risk factors in the target basin. Furthermore, based on the pre-processed current water ecological data, social and economic data, and historical water ecological data, the connection probability of each risk node in the corresponding Bayesian model from being a parent node to the corresponding child node is determined, and then the actual conditional probability of each leaf node in the corresponding Bayesian model under the influence of at least one ancestral node traversal path (i.e., at least one risk event) is calculated, thereby obtaining the target Bayesian model, as well as the actual conditional probability of the water ecological state result corresponding to each leaf node in the target Bayesian model under the influence of different risk events, to ensure that the water ecological risk assessment system represented by the target Bayesian model can more accurately express the internal relationship and interaction of the water ecological system at the target basin.

[0072] Optionally, please refer to Figure 4 , Figure 4 is Figure 2 The flowchart of the sub-steps included in step S230 in. In the embodiment of the present application, step S230 may include sub-steps S231 to S233 to ensure that the water ecological risk assessment system represented by the constructed target Bayesian model can more accurately express the internal relationship and interaction of the water ecological system at the target basin.

[0073] Sub-step S231: Take all model framework elements in the target DPSIR model as a risk node in the target Bayesian model respectively, and configure the parent-child relationship between all risk nodes in the target Bayesian model according to the causal relationship chain of the target DPSIR model.

[0074] Sub-step S232: Determine the connection probability of each risk node in the target Bayesian model to the corresponding child node according to the current water ecological data, social and economic data, and historical water ecological data.

[0075] Among them, the connection probability from each risk node in the target Bayesian model to its corresponding child node when it is a parent node can be assigned by domain experts based on experience according to the preprocessed current water ecological data, the social and economic data, and the historical water ecological data, or can be assigned by the computer device 10 through state classification of the preprocessed current water ecological data, the social and economic data, and the historical water ecological data, and then assigning conditional probabilities according to the state classification results (for example, when the state classification result is "good", it indicates that the probability of the corresponding risk node causing the corresponding child node to appear is relatively low, and the corresponding conditional probability can be assigned any probability value between 0.2 and 0.4; when the state classification result is "bad", it indicates that the probability of the corresponding risk node causing the corresponding child node to appear is relatively high, and the corresponding conditional probability can be assigned any probability value between 0.7 and 0.9).

[0076] Sub-step S233: Calculate the actual conditional probabilities of all leaf nodes in the target Bayesian model under the influence of different risk events according to the connection probabilities from each risk node to its corresponding child node.

[0077] Among them, the step of calculating the actual conditional probabilities of all leaf nodes in the target Bayesian model under the influence of different risk events according to the connection probabilities from each risk node to its corresponding child node includes:

[0078] For each leaf node in the target Bayesian model, determine all the ancestral node traversal paths corresponding to this leaf node in the target Bayesian model;

[0079] For each ancestral node traversal path corresponding to this leaf node, perform a multiplication operation on the connection probabilities of all the ancestral nodes involved in this ancestral node traversal path to obtain the actual conditional probability of this leaf node under the influence of the risk event matching this ancestral node traversal path.

[0080] Thus, by executing the above sub-steps S231 to S233, the present application can ensure that the water ecological risk assessment system represented by the constructed target Bayesian model can more accurately express the internal relationships and interactions of the water ecological system at the target basin.

[0081] Step S240: Perform data statistics on the current water ecological data, social and economic data, and historical water ecological data to obtain the occurrence probabilities of the water ecological state results corresponding to all leaf nodes at the target basin.

[0082] In this embodiment, the computer device 10 can perform probability statistics on the preprocessed current water ecological data, the social and economic data, and the historical water ecological data based on statistical principles to determine the occurrence probability of the water ecological state results corresponding to each leaf node involved in the target Bayesian model at the target basin.

[0083] Step S250: Calculate the occurrence probability of each different risk event at the target basin based on the total probability theorem according to the occurrence probability of each leaf node and the actual conditional probability of each leaf node under the influence of different risk events.

[0084] In this embodiment, the first operation relationship among the occurrence probability of the result of the i-th leaf node in the target Bayesian model, the actual conditional probability of the i-th leaf node under the influence of different risk events, and the occurrence probability of each different risk event is expressed by the following formula:

[0085]

[0086] Among them, P(A i ) is used to represent the occurrence probability of the water ecological state result corresponding to the i-th leaf node, P(B j ) is used to represent the occurrence probability of the j-th risk event associated with the i-th leaf node, P(A i |B j ) is used to represent the actual conditional probability of the water ecological state result corresponding to the i-th leaf node under the influence of the j-th risk event, and m is used to represent the total number of risk events associated with the i-th leaf node.

[0087] Therefore, the computer device 10 can solve the joint equations of the above first operation relationships involving the same risk events corresponding to multiple leaf nodes by considering the event overlap situation among at least one risk event corresponding to each different leaf node in the target Bayesian model, so as to obtain the occurrence probability of each risk event involved in the target Bayesian model at the target basin, ensuring the accuracy, reliability, and real-time nature of the corresponding water ecological risk assessment results, facilitating researchers to more comprehensively and accurately understand the health status of the water ecological system at the specified basin, and timely proposing or adjusting the governance decision for the specified basin.

[0088] Therefore, by performing the above steps S210 to S250, in the process of water ecological risk assessment for the target basin, the Bayesian statistical principle is introduced and combined with the DPSIR model framework to comprehensively consider the water ecological risk factors that may be involved in any designated basin, so that the constructed water ecological risk assessment system can more accurately express the internal relationships and interactions of the water ecological system at any designated basin, ensuring the accuracy, credibility and real-time nature of the final water ecological risk assessment results, facilitating researchers to more comprehensively and accurately understand the health status of the water ecological system at the designated basin, and timely proposing or adjusting the governance decisions for the designated basin.

[0089] Optionally, please refer to Figure 5 , Figure 5 which is the second schematic flowchart of the water ecological risk assessment method provided by the embodiments of the present application. In the embodiments of the present application, compared with the water ecological risk assessment method shown in Figure 2 , the water ecological risk assessment method shown in Figure 5 may further include step S260 and step S270 to help researchers more comprehensively understand the actual triggering probabilities of different risk events causing the current water ecological status at the target basin, facilitating R & D personnel to further determine which risk events have actually occurred at the target basin, and timely taking effective measures to prevent the spread of water environmental pollution.

[0090] Step S260: For each occurred water ecological state result that matches the current water ecological data, determine all target risk events associated with the occurred water ecological state result.

[0091] Wherein, the target risk event is any risk event directly associated with the leaf node corresponding to the occurred water ecological state result.

[0092] Step S270: For each determined target risk event, based on the result occurrence probability of the occurred water ecological state result, the actual conditional probability of the occurred water ecological state result under the influence of the target risk event, and the event occurrence probability of the target risk event, calculate the result triggering probability of the target risk event causing the occurred water ecological state result based on Bayes' theorem.

[0093] In this embodiment, the second operation relationship among the result occurrence probability of the k-th occurred water ecological state result, the actual conditional probability of the k-th occurred water ecological state result under the influence of the q-th target risk event, the event occurrence probability of the q-th target risk event, and the result triggering probability of the q-th target risk event causing the k-th occurred water ecological state result is expressed by the following formula:

[0094]

[0095] Among them, P(C k ) is used to represent the occurrence probability of the k-th occurred water ecological state result, P(B q ) is used to represent the occurrence probability of the q-th target risk event, P(C k |B q ) is used to represent the actual conditional probability of the k-th occurred water ecological state result under the influence of the q-th target risk event, and P(B q |C k ) is used to represent the result triggering probability that the q-th target risk event causes the k-th occurred water ecological state result.

[0096] Therefore, after the computer device 10 determines any occurred water ecological state result and any corresponding target risk event, it can calculate the probability of the target risk event occurring on the basis of the existence of the occurred water ecological state result according to the above second operation relationship, that is, the result triggering probability that the target risk event causes the occurred water ecological state result, so as to help researchers more comprehensively understand the actual triggering probabilities of different risk events in the target basin causing the current water ecological situation, facilitate R & D personnel to further determine which risk events actually occur in the target basin, and take effective measures in time to prevent the spread of water environmental pollution.

[0097] Thus, this application can help researchers more comprehensively understand the actual triggering probabilities of different risk events in the target basin causing the current water ecological situation by executing the above steps S260 and S270, facilitate R & D personnel to further determine which risk events actually occur in the target basin, and take effective measures in time to prevent the spread of water environmental pollution.

[0098] Optionally, please refer to Figure 6 , Figure 6 which is the third flow chart of the water ecological risk assessment method provided by the embodiment of this application. In the embodiment of this application, compared with the water ecological risk assessment method shown in Figure 5 , the water ecological risk assessment method shown in Figure 6 may further include step S280 to assist researchers in intuitively understanding the health status of the water ecological system and the water ecological risk assessment results in the target basin.

[0099] Step S280: Display in a chart the occurrence probabilities of different risk events in the target basin respectively, and / or the result triggering probabilities that different target risk events cause different occurred water ecological state results.

[0100] In this embodiment, the computer device 10 can display the occurrence probabilities of different risk events at the target basin and / or the result triggering probabilities of different target risk events causing different existing water ecological state results through conventional chart methods such as bar charts and pie charts, so as to effectively present the water ecological risk assessment results through a visual intuitive display method, facilitate improving the information understanding ability and information utilization ability of researchers for the water ecological risk assessment results, and assist researchers in further intuitively understanding the health status of the water ecological system at the target basin.

[0101] Thus, this application can assist researchers in intuitively understanding the health status of the water ecological system and the water ecological risk assessment results at the target basin by executing the above step S280.

[0102] In the embodiments provided in this application, it should be understood that the disclosed device and method 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 device, method, and computer program product according to the embodiments of this application. In this regard, each block in the flowchart or block diagram can 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 from that marked in the accompanying drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can 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 for executing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0103] In addition, in each embodiment of the present application, each functional module 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 such an understanding, the technical solution of the present 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 the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0104] In summary, in a water ecological risk assessment method, a computer device, and a readable storage medium provided by the present application, the present application constructs a DPSIR model based on the current water ecological data, social and economic data, and historical water ecological data of the target basin to obtain a target DPSIR model adapted to the target basin. Then, by performing Bayesian model conversion on the target DPSIR model, a target Bayesian model adapted to the target basin is obtained, and the actual conditional probabilities of the water ecological state results corresponding to all leaf nodes in the target Bayesian model occurring under the influence of different risk events are obtained. Then, by means of data statistics, based on the current water ecological data, social and economic data, and historical water ecological data, the result occurrence probabilities of the water ecological state results corresponding to all the aforementioned leaf nodes at the target basin are determined. Finally, according to the result occurrence probabilities corresponding to all leaf nodes and the actual conditional probabilities of all leaf nodes under the influence of different risk events, the event occurrence probabilities of different risk events at the target basin are calculated based on the total probability theorem. Thus, by introducing the principle of Bayesian statistics and combining the DPSIR model architecture, all water ecological risk factors that may be involved in any designated basin are comprehensively considered, so that the constructed water ecological risk assessment system can more accurately express the internal relationships and interactions of the water ecological system at any designated basin, to ensure the accuracy, credibility, and real-time nature of the final water ecological risk assessment results, and to facilitate researchers to more comprehensively and accurately understand the health status of the water ecological system at the designated basin and timely propose or adjust the governance decisions for the designated basin.

[0105] As described above, these are only various embodiments of the present application. However, 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 shall be subject to the protection scope of the claims.

Claims

1. A method for assessing aquatic ecological risk, characterized in that, The method includes: Obtaining the current water ecological data, socioeconomic data, and historical water ecological data of the target basin; Constructing a DPSIR model based on the current water ecological data, the socioeconomic data, and the historical water ecological data to obtain a target DPSIR model adapted to the target basin; Performing Bayesian model conversion on the target DPSIR model to obtain a target Bayesian model adapted to the target basin, and the actual conditional probabilities of the water ecological state results corresponding to all leaf nodes in the target Bayesian model under the influence of different risk events, where each risk event matches a traversal path of ancestor nodes corresponding to a leaf node; Performing data statistics on the current water ecological data, the socioeconomic data, and the historical water ecological data to obtain the result occurrence probabilities of the water ecological state results corresponding to all leaf nodes at the target basin; Based on the result occurrence probabilities corresponding to all leaf nodes and the actual conditional probabilities of all leaf nodes under the influence of different risk events, calculating the event occurrence probabilities of different risk events at the target basin based on the total probability theorem.

2. The method according to claim 1, wherein The step of constructing a DPSIR model based on the current water ecological data, the socioeconomic data, and the historical water ecological data to obtain a target DPSIR model adapted to the target basin includes: Extracting framework elements from the current water ecological data, the socioeconomic data, and the historical water ecological data according to multiple pre-stored DPSIR model architecture element types to obtain all model framework elements involved in the target DPSIR model; Identifying the water ecological causal relationships in the current water ecological data, the socioeconomic data, and the historical water ecological data, and configuring the causal relationships between all model framework elements in the target DPSIR model according to the obtained water ecological causal relationship identification results to form the causal relationship chain of the target DPSIR model.

3. The method according to claim 1, characterized in that, The step of performing Bayesian model conversion on the target DPSIR model to obtain a target Bayesian model adapted to the target basin, and the actual conditional probabilities of the water ecological state results corresponding to all leaf nodes in the target Bayesian model under the influence of different risk events includes: Regarding all model framework elements in the target DPSIR model as a risk node in the target Bayesian model respectively, and configuring the parent-child relationships between all risk nodes in the target Bayesian model according to the causal relationship chain of the target DPSIR model; Determining the connection probabilities of all risk nodes in the target Bayesian model to their corresponding child nodes according to the current water ecological data, the socioeconomic data, and the historical water ecological data; Calculating the actual conditional probabilities of all leaf nodes in the target Bayesian model under the influence of different risk events according to the connection probabilities of all risk nodes to their corresponding child nodes.

4. The method according to claim 3, wherein The step of calculating the actual conditional probabilities of all leaf nodes in the target Bayesian model under the influence of different risk events according to the connection probabilities of all risk nodes to their corresponding child nodes respectively includes: For each leaf node in the target Bayesian model, determine all the traversal paths of the ancestor nodes corresponding to this leaf node in the target Bayesian model; For each traversal path of the ancestor nodes corresponding to this leaf node, perform a multiplication operation on the connection probabilities of all the ancestor nodes involved in this traversal path of the ancestor nodes to obtain the actual conditional probability of this leaf node under the influence of the risk event matching this traversal path of the ancestor nodes.

5. The method according to claim 1, characterized in that, The operation relationship among the occurrence probability of the result of the i-th leaf node in the target Bayesian model, the actual conditional probability of the i-th leaf node under the influence of different risk events, and the occurrence probabilities of different risk events respectively is expressed by the following formula: Among them, P(A i ) is used to represent the occurrence probability of the result of the water ecological state result corresponding to the i-th leaf node, P(B j ) is used to represent the occurrence probability of the j-th risk event associated with the i-th leaf node, P(Ai|Bj) is used to represent the actual conditional probability of the water ecological state result corresponding to the i-th leaf node under the influence of the j-th risk event, and m is used to represent the total number of risk events associated with the i-th leaf node.

6. The method according to any one of claims 1-5, characterized in that The method further includes: For each water ecological state result that has occurred and matches the current water ecological data, determine all the target risk events associated with this water ecological state result that has occurred; For each determined target risk event, based on Bayes' theorem, calculate the result triggering probability of this target risk event causing this water ecological state result that has occurred according to the occurrence probability of this water ecological state result that has occurred, the actual conditional probability of this water ecological state result that has occurred under the influence of this target risk event, and the occurrence probability of this target risk event.

7. The method according to claim 6, characterized in that, The operation relationship among the occurrence probability of the result of the k-th water ecological state result that has occurred, the actual conditional probability of the k-th water ecological state result that has occurred under the influence of the q-th target risk event, the occurrence probability of the q-th target risk event, and the result triggering probability of the q-th target risk event causing the k-th water ecological state result that has occurred is expressed by the following formula: Among them, P(C k ) is used to represent the occurrence probability of the k-th occurred water ecological status result, P(B q ) is used to represent the occurrence probability of the q-th target risk event, P(C k |B q ) is used to represent the actual conditional probability of the k-th occurred water ecological status result under the influence of the q-th target risk event, and P(B q |C k ) is used to represent the result triggering probability of the q-th target risk event causing the k-th occurred water ecological status result.

8. The method according to claim 6, characterized in that The method further includes: Graphically display the occurrence probabilities of different risk events at the target basin respectively, and / or the result triggering probabilities of different target risk events causing different water ecological state results that have occurred.

9. 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 water ecological risk assessment method according to any one of claims 1-8.

10. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a computer device, it implements the water ecological risk assessment method according to any one of claims 1-8.

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