Evaluation and optimization method of the resilience function of ecological infrastructure in response to pollution during flood season

Abstract

The application provides a kind of ecological infrastructure to respond to flood season pollution Resilience Function Evaluation and Optimization Method, which can be applied to the technical field of urban resilience calculation.The method comprises the following steps: obtaining the multi-modal monitoring data of the ecological infrastructure in the to-be-tested region in the target period;Based on the multi-modal monitoring data, the pollution risk of each of the plurality of to-be-tested sub-regions of the to-be-tested region is calculated;Based on the plurality of multi-modal time series data, the function state of the ecological infrastructure in the to-be-tested region at each of the plurality of sampling time points is determined respectively;Based on the multi-modal monitoring data, the pollution risk of each of the plurality of to-be-tested sub-regions and the function state of the ecological infrastructure in the to-be-tested region at each of the plurality of sampling time points, correlation analysis is carried out on the to-be-tested region, and the correlation analysis result is obtained;Determine the resilience level of the ecological infrastructure to determine the functional short board of the ecological infrastructure and the resilience improvement potential of the ecological infrastructure, and determine the optimization target of the ecological infrastructure.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban resilience computing, in particular to a resilience function evaluation and optimization method for ecological infrastructure to deal with flood season pollution. BACKGROUND

[0002] With the acceleration of urbanization and the frequent occurrence of extreme rainfall events, flood season pollution has become a major challenge for urban water environment management. Urban ecological infrastructure can play a key role in reducing runoff and purifying water quality, thereby better dealing with flood season pollution. However, the existing evaluation methods for the ability and resilience of ecological infrastructure to deal with flood season pollution have low evaluation accuracy, which makes it difficult to provide effective data reference for the management and configuration of ecological infrastructure. SUMMARY

[0003] In view of the above problems, the present application provides a resilience function evaluation and optimization method for ecological infrastructure to deal with flood season pollution.

[0004] According to a first aspect of the present application, a resilience function evaluation and optimization method for ecological infrastructure to deal with flood season pollution is provided, comprising: obtaining multi-modal monitoring data of ecological infrastructure in a target period in a to-be-tested region, wherein the target period includes the flood season of the to-be-tested region, and the multi-modal monitoring data includes multi-modal time series data of the to-be-tested region at a plurality of sampling time points in the target period; calculating the pollution risk of each to-be-tested sub-region of the to-be-tested region based on the multi-modal monitoring data, wherein the pollution risk represents the cumulative amount of pollutants in the water body entering the to-be-tested sub-region after the pollutants are reduced by the ecological infrastructure in the to-be-tested sub-region; determining the functional state of the ecological infrastructure in the to-be-tested region at each sampling time point based on the plurality of multi-modal time series data; performing correlation analysis on the to-be-tested region based on the multi-modal monitoring data, the pollution risk of each to-be-tested sub-region and the functional state of the ecological infrastructure in the to-be-tested region at each sampling time point, to obtain a correlation analysis result, wherein the correlation analysis result is used to represent the monitoring data in the multi-modal monitoring data that has the strongest correlation with the pollution risk and / or the functional state; and determining the resilience level of the ecological infrastructure based on the correlation analysis result, to determine the functional short board of the ecological infrastructure and the resilience improvement potential of the ecological infrastructure, and to determine the optimization target of the ecological infrastructure.

[0005] According to an embodiment of the present application, the pollution risk of each of the plurality of sub-regions of the to-be-tested region is calculated based on the multi-modal monitoring data, including: determining the pollution intensity of each of the plurality of sub-regions and the pollution control rate of each of the plurality of sub-regions in the target period based on the multi-modal monitoring data; determining the pollution degree of each of the plurality of sub-regions based on the pollution intensity of each of the plurality of sub-regions; and determining the pollution risk of each of the plurality of sub-regions based on the plurality of pollution control rates and the plurality of pollution degrees.

[0006] According to an embodiment of the present application, the pollution intensity is determined by one of the following manners: determining the area corresponding to each of the plurality of underlying surface types in the to-be-tested sub-region based on the underlying surface type of each position in the to-be-tested sub-region; determining the area proportion corresponding to each of the plurality of underlying surface types based on the total area of the to-be-tested sub-region and the area corresponding to each of the plurality of underlying surface types; determining the pollution intensity based on the area proportion and the average runoff concentration corresponding to the underlying surface type; determining the pollution input amount in the target water body based on the monitoring data of the target water body in the multi-modal monitoring data, wherein the target water body represents a water body for containing the precipitation flowing through the to-be-tested sub-region; and determining the pollution intensity based on the pollution input amount.

[0007] According to an embodiment of the present application, the pollution input amount is determined by one of the following manners: determining the pollution accumulation amount in the target water body based on the monitoring data of the target water body; determining the scouring coefficient, the scouring index of the to-be-tested sub-region and the unit area runoff of the target water body based on the multi-modal monitoring data; determining the pollution runoff input amount based on the scouring coefficient, the scouring index, the unit area runoff and the pollution accumulation amount; determining the pollution input amount based on the pollution runoff input amount of the to-be-tested sub-region and the pollution life input amount of the to-be-tested sub-region; determining the pollution concentration of each of the plurality of pollution types, the degradation coefficient of each of the plurality of pollution types and the water volume of the target water body in the target water body based on the multi-modal monitoring data; and determining the pollution input amount based on the plurality of pollution concentrations, the plurality of degradation coefficients and the water volume.

[0008] According to an embodiment of the present application, the pollution degree of each of the plurality of sub-regions is determined based on the pollution intensity of each of the plurality of sub-regions, including: determining the maximum first pollution intensity and the minimum second pollution intensity from the pollution intensity of each of the plurality of sub-regions; and normalizing the pollution intensity of each of the plurality of sub-regions based on the first pollution intensity and the second pollution intensity to obtain the pollution degree of each of the plurality of sub-regions.

[0009] According to an embodiment of the present application, based on the plurality of multi-modal time-series data, the respective functional states of the ecological infrastructure in the to-be-tested region at the plurality of sampling time points are determined, including: sequentially analyzing each sampling time point according to the time-series relationship between the plurality of sampling time points, to determine the respective pollutant contents of the plurality of pollutant species contained in the water body corresponding to the to-be-tested region at the sampling time point; determining the water quality indexes of the water body corresponding to the to-be-tested region at the plurality of sampling time points based on the respective preset weights of the plurality of pollutant species and the respective pollutant contents of the plurality of pollutant species; and determining the respective functional states of the ecological infrastructure in the to-be-tested region at the plurality of sampling time points based on the plurality of water quality indexes.

[0010] According to an embodiment of the present application, based on the plurality of water quality indexes, the respective functional states of the ecological infrastructure in the to-be-tested region at the plurality of sampling time points are determined, including: for each sampling time point, determining a preceding sampling time point adjacent to the sampling time point and located before the sampling time point from the plurality of sampling time points; comparing the water quality index of the sampling time point with the water quality index of the preceding sampling time point to obtain a comparison result; in the case that the comparison result indicates that the water quality of the sampling time point is better than that of the preceding sampling time point, determining that the functional state indicates that the function of the ecological infrastructure in the to-be-tested region at the sampling time point is improved; and in the case that the comparison result indicates that the water quality of the sampling time point is worse than that of the preceding sampling time point, determining that the functional state indicates that the function of the ecological infrastructure in the to-be-tested region at the sampling time point is not improved.

[0011] According to an embodiment of the present application, the method for evaluating and optimizing the resilience function of the ecological infrastructure in coping with flood season pollution further includes: in response to the ecological infrastructure in the to-be-tested region being updated, determining a to-be-evaluated time period in which the update is located, the to-be-evaluated time period being determined according to the adjacent preceding sampling time point and the sampling time point; and determining the update effect of the update based on the functional state determined according to the adjacent preceding sampling time point and the sampling time point corresponding to the to-be-evaluated time period.

[0012] According to an embodiment of the present application, based on the correlation analysis result, the resilience level of the ecological infrastructure is determined to determine the functional short board of the ecological infrastructure and the resilience improvement potential of the ecological infrastructure, and the optimization target of the ecological infrastructure is determined, including: based on the correlation analysis result, a resilience evaluation index system for the ecological infrastructure is constructed, wherein the resilience evaluation index system for the ecological infrastructure is used to quantify the resilience level of the urban ecological infrastructure under different conditions, and the resilience level represents the ability of the ecological infrastructure to cope with pollution caused by the target period; based on the resilience evaluation index system, it is determined whether the resilience level of the ecological infrastructure represents a functional short board of the ecological infrastructure, wherein the functional short board includes a to-be-tested sub-region with a pollution risk higher than a risk threshold, and a to-be-evaluated time period with an update effect indicating that the update does not improve the resilience of the ecological infrastructure; in the case where the to-be-tested sub-region with a pollution risk higher than the risk threshold is determined, the to-be-tested sub-region with a pollution risk higher than the risk threshold is determined as a to-be-optimized region; according to the correlation analysis result of the to-be-optimized region, the optimization target of the ecological infrastructure in the to-be-optimized sub-region is determined; in the case where the to-be-evaluated time period with an update effect indicating that the update does not improve the resilience of the ecological infrastructure is determined, the to-be-evaluated time period is determined as a to-be-optimized time period; and according to the correlation analysis result of the to-be-optimized time period, the optimization target of the ecological infrastructure in the to-be-optimized region is determined.

[0013] According to an embodiment of the present application, based on the resilience evaluation index system, it is determined whether the resilience level of the ecological infrastructure represents a functional short board of the ecological infrastructure, including: based on the resilience evaluation index system, the resilience level of the ecological infrastructure under different conditions is quantified, wherein the resilience level represents the ability of the ecological infrastructure to cope with pollution caused by the target period; and based on the resilience level and a preset resilience threshold range of the ecological infrastructure, it is determined whether the resilience level of the ecological infrastructure represents a functional short board of the ecological infrastructure.

[0014] According to an embodiment of the present application, based on the correlation analysis result, the resilience level of the ecological infrastructure is determined to determine the functional short board of the ecological infrastructure and the resilience improvement potential of the ecological infrastructure, and the optimization target of the ecological infrastructure is determined, including: based on the correlation analysis result, a resilience evaluation index system for the ecological infrastructure is constructed, wherein the resilience evaluation index system for the ecological infrastructure is used to quantify the resilience level of the urban ecological infrastructure under different conditions, and the resilience level represents the ability of the ecological infrastructure to cope with pollution caused by the target period; based on the resilience evaluation index system, it is determined whether the resilience level of the ecological infrastructure represents a functional short board of the ecological infrastructure, wherein the functional short board includes a to-be-tested sub-region with a pollution risk higher than a risk threshold, and a to-be-evaluated time period with an update effect indicating that the update does not improve the resilience of the ecological infrastructure; in the case where the to-be-tested sub-region with a pollution risk higher than the risk threshold is determined, the to-be-tested sub-region with a pollution risk higher than the risk threshold is determined as a to-be-optimized region; according to the correlation analysis result of the to-be-optimized region, the optimization target of the ecological infrastructure in the to-be-optimized sub-region is determined; in the case where the to-be-evaluated time period with an update effect indicating that the update does not improve the resilience of the ecological infrastructure is determined, the to-be-evaluated time period is determined as a to-be-optimized time period; and according to the correlation analysis result of the to-be-optimized time period, the optimization target of the ecological infrastructure in the to-be-optimized region is determined.

[0015] A second aspect of the present invention provides a resilience optimization device for ecological infrastructure in response to flood season pollution, comprising: a data acquisition module for acquiring multimodal monitoring data of ecological infrastructure in a test area during a target period, wherein the target period includes the flood season of the test area, and the multimodal monitoring data includes multimodal time-series data of the test area at multiple sampling time points within the target period; a risk calculation module for calculating the pollution risk of each of multiple test sub-regions of the test area based on the multimodal monitoring data, wherein the pollution risk represents the cumulative amount of pollutants entering the water body of the test sub-region after the ecological infrastructure in the test sub-region has reduced the pollutants; and a state determination module for determining the state based on the multiple multimodal time-series data respectively. The system determines the functional status of ecological infrastructure in the test area at multiple sampling time points; a correlation analysis module is used to perform correlation analysis on the test area based on multimodal monitoring data, the pollution risks of multiple test sub-areas, and the functional status of ecological infrastructure in the test area at multiple sampling time points, and obtain correlation analysis results, where the correlation analysis results are used to represent the monitoring data with the strongest correlation with pollution risk and / or functional status in the multimodal monitoring data; and a target determination module is used to determine the resilience level of ecological infrastructure based on the correlation analysis results, in order to identify the functional shortcomings of ecological infrastructure, assess the resilience improvement potential of each unit and the overall system, and determine the optimization target of ecological infrastructure.

[0016] A third aspect of the present invention provides an electronic device comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0017] A fourth aspect of the present invention also provides a computer-readable storage medium having a computer program or instructions stored thereon, wherein the computer program or instructions, when executed by a processor, implement the steps of the above-described method.

[0018] A fifth aspect of the present invention also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0019] According to embodiments of the present invention, by fusing multimodal monitoring data, an integrated assessment of flood season pollution risk and the functional status and resilience of ecological infrastructure is achieved. Based on correlation analysis to identify key influencing factors, optimization objectives can be scientifically determined, improving the accuracy and foresight of decision-making and providing a data-driven system solution for enhancing the resilience of ecological infrastructure. Attached Figure Description

[0020] The above and other objects, features and advantages of the present application will become more apparent from the following description when taken in conjunction with the accompanying drawings, in which:

[0021] Figure 1 A scenario diagram of application of the method for evaluating and optimizing the resilience function of ecological infrastructure in response to flood season pollution according to an embodiment of the present application is shown.

[0022] Figure 2 A flowchart of the method for evaluating and optimizing the resilience function of ecological infrastructure in response to flood season pollution according to an embodiment of the present application is shown.

[0023] Figure 3 A schematic diagram of differentiation characteristics of flood season and non-flood season and rainfall matching analysis involved in ecological infrastructure configuration according to an embodiment of the present application is shown.

[0024] Figure 4 A schematic diagram of the function state of ecological infrastructure and its corresponding pollution risk according to an embodiment of the present application is shown.

[0025] Figure 5 A resilience evaluation result according to an embodiment of the present application is shown.

[0026] Figure 6 A structural block diagram of the device for evaluating and optimizing the resilience function of ecological infrastructure in response to flood season pollution according to an embodiment of the present application is shown.

[0027] Figure 7 A block diagram of an electronic device suitable for implementing the method for evaluating and optimizing the resilience function of ecological infrastructure in response to flood season pollution according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0028] Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. It is to be understood, however, that the description is merely exemplary of the present application, and is not intended to limit the scope of the present application. In the following detailed description of the embodiments of the present application, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without these specific details. In other instances, well-known structures and techniques have not been described in detail in order to avoid obscuring aspects of the present application.

[0029] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the present application. As used herein, the term "includes" and "comprising" and the like are meant to be inclusive in a manner that there are no other non-mentioned items.

[0030] All terms used herein, including technical and scientific terms, have the meanings commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the use of terms herein should be interpreted in the context of the specification and should not be interpreted in an idealized or overly formal way.

[0031] In the case of using expressions similar to "at least one of A, B, and C, etc.", in general, it should be interpreted as the meaning that the person skilled in the art generally understands the expression (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B, and C, etc.).

[0032] In the technical solutions of the present application, the user information (including but not limited to user personal information, user image information, user equipment information, such as location information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved are information and data authorized by the user or authorized by all parties, and the collection, storage, use, processing, transmission, provision, invention and application of related data comply with relevant laws, regulations and standards, necessary security measures are taken, do not violate public order and good customs, and corresponding operation entrances are provided for users to choose authorization or refusal.

[0033] In the scenario of using personal information for automated decision-making, the method, device and system provided by the embodiments of the present application all provide corresponding operation entrances for users to choose to agree or refuse the automated decision-making result; if the user chooses to refuse, the expert decision-making process is entered. The expression "automated decision-making" here refers to the activity of automatically analyzing, evaluating the behavior habits, interests and hobbies, or economic, health, credit status, etc. of a person through a computer program, and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by personnel who are engaged in a certain field of work, have special experience, knowledge and skills, and reach a certain professional level.

[0034] In the related art, when assessing the resilience of ecological infrastructure to respond to flood season pollution, only a single sensor is usually used to assess and calculate the resilience of the ecological infrastructure, which is difficult to comprehensively reflect the real response of the ecological infrastructure to the pollution situation.

[0035] The embodiment of the present application provides a method for evaluating and optimizing the resilience function of ecological infrastructure in flood season pollution response, comprising: acquiring multi-modal monitoring data of ecological infrastructure in a to-be-tested region in a target period, wherein the target period comprises a flood season of the to-be-tested region, and the multi-modal monitoring data comprises multi-modal time series data of the to-be-tested region at a plurality of sampling time points in the target period; calculating a pollution risk of each to-be-tested sub-region of the to-be-tested region based on the multi-modal monitoring data, wherein the pollution risk represents a cumulative amount of pollutants in a water body entering the to-be-tested sub-region after the pollutants are reduced by the ecological infrastructure in the to-be-tested sub-region; determining a function state of the ecological infrastructure in the to-be-tested region at each sampling time point based on the plurality of multi-modal time series data; performing correlation analysis on the to-be-tested region based on the multi-modal monitoring data, the pollution risk of each to-be-tested sub-region and the function state of the ecological infrastructure in the to-be-tested region at each sampling time point, to obtain a correlation analysis result, wherein the correlation analysis result is used to represent monitoring data in the multi-modal monitoring data that has the strongest correlation with the pollution risk and / or the function state; and determining a resilience level of the ecological infrastructure based on the correlation analysis result, to determine a function short board of the ecological infrastructure and a resilience improvement potential of the ecological infrastructure, and to determine an optimization target of the ecological infrastructure.

[0036] Figure 1 An application scenario diagram of the method for evaluating and optimizing the resilience function of ecological infrastructure in flood season pollution response is shown.

[0037] As shown in Figure 1 , the application scenario 100 according to the embodiment can include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104 and a server 105. The network 104 is a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103 and the server 105. The network 104 can include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0038] A user can use the first terminal device 101, the second terminal device 102 and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102 and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0039] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0040] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0041] It should be noted that the method for assessing and optimizing the resilience of ecological infrastructure to flood season pollution provided in this embodiment of the invention can generally be executed by server 105. Correspondingly, the device for assessing and optimizing the resilience of ecological infrastructure to flood season pollution provided in this embodiment of the invention can generally be located in server 105. The method for assessing and optimizing the resilience of ecological infrastructure to flood season pollution provided in this embodiment of the invention can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the device for assessing and optimizing the resilience of ecological infrastructure to flood season pollution provided in this embodiment of the invention can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0042] It should be understood that Figure 1 The number of first terminal devices, second terminal devices, third terminal devices, networks, and servers shown in the diagram is merely illustrative. Depending on implementation needs, any number of first terminal devices, second terminal devices, third terminal devices, networks, and servers can be included.

[0043] The following will be based on Figure 1 The described scene, through Figures 2-5 The present invention provides a detailed description of the method for assessing and optimizing the resilience of ecological infrastructure in response to flood season pollution, according to embodiments of the present invention.

[0044] Figure 2 A flowchart is shown for a method for assessing and optimizing the resilience of ecological infrastructure in response to flood season pollution, according to an embodiment of the present invention.

[0045] like Figure 2As shown, the embodiment resilience function evaluation and optimization method of the ecological infrastructure for flood season pollution includes operation S210~operation S250.

[0046] In operation S210, multi-modal monitoring data of the ecological infrastructure in the to-be-tested region in a target period is acquired.

[0047] The to-be-tested region can be any region that needs to be optimized for resilience to flood season pollution. For example, the to-be-tested region can be a region composed of multiple cities, a region composed of one city, a partial region of one city, etc.

[0048] The to-be-tested region is provided with ecological infrastructure, which can be used to filter and treat pollutants carried in rainwater during the flood season to control the total amount of pollutants entering the river.

[0049] The target period includes the flood season of the to-be-tested region, and the multi-modal monitoring data includes multi-modal time series data of the to-be-tested region at multiple sampling time points within the target period. The multi-modal monitoring data includes but is not limited to meteorological data, hydrological data, water quality data, ecological data, drainage data, etc. The above multi-modal monitoring data can be acquired from a database for the to-be-tested region.

[0050] Specifically, the water quality data can refer to water quality detection data, including monthly water quality monitoring records of each river in the region, each record containing river name, sampling month, total phosphorus (TP), ammonia nitrogen (NH3-N), chemical oxygen demand (COD Cr ), permanganate index (COD Mn ), dissolved oxygen (DO), etc. Key water quality indicators.

[0051] The drainage data can refer to drainage zoning and ecological infrastructure information, including a plurality of refined drainage zoning based on city drainage pipe network data, and an ecological infrastructure project account corresponding to each zoning, including construction time, design scale, service area, service state, maintenance record, etc. Information of low-impact development facilities such as rain gardens, permeable pavements, sunken green spaces, and ecological wetlands. At the same time, the geographic location coordinates of each facility and its design technical report are integrated for subsequent function matching and performance comparison.

[0052] The meteorological data and hydrological data can include high spatiotemporal resolution rainfall data of the research area weather station and radar inversion, with a time resolution of daily scale and spatial coverage of the whole region. The following rainfall characteristic indicators are extracted: monthly cumulative precipitation, field rainfall, maximum daily rainfall, longest continuous rainfall days, longest continuous no-rain days, maximum continuous rainfall, ≥2mm rainfall days in a month, ≥13.5mm rainfall days in a month, ≥22.5mm rainfall days in a month, ≥33.5mm rainfall days in a month, for representing the occurrence frequency and cumulative effect of different intensity rainfall events.

[0053] The original acquired data can be cleaned to eliminate abnormal values and missing values, reasonably filled in by linear interpolation or inverse distance weighting method, unified the time base of all data, aligned the monthly water quality data with the monthly scale meteorological data, and time-matched the field rainfall event and the water quality response of the corresponding drainage partition. Based on the geographic information system platform, all facilities, monitoring sections, drainage partitions and other spatial elements are vector modeled to realize the spatial coordinate unification and topology relationship construction.

[0054] In operation S220, the pollution risk of each of the plurality of to-be-measured sub-regions in the to-be-measured region is calculated based on the multi-modal monitoring data.

[0055] The to-be-measured sub-regions are obtained by regionally dividing the to-be-measured region, there is no overlapping part between the plurality of to-be-measured sub-regions, and the plurality of to-be-measured sub-regions can completely cover the to-be-measured region.

[0056] The pollution risk represents the cumulative amount of pollutants in the water body entering the to-be-measured sub-region after the ecological infrastructure in the to-be-measured sub-region reduces the pollutants. The pollution risk can reflect the ability of the ecological infrastructure in the to-be-measured region to respond to pollution.

[0057] In operation S230, the function state of the ecological infrastructure in the to-be-measured region at each of the plurality of sampling time points is determined based on the plurality of multi-modal time series data.

[0058] The function state of the ecological infrastructure can be used to represent the current response ability of the ecological infrastructure to the pollutants, and the function state can include an effective period, an inefficient period and a failure period.

[0059] The effective period means that the water quality is significantly better after the ecological infrastructure project is put into use than before the ecological infrastructure project is put into use. This refers to the period during which the ecological infrastructure project is normally operated within its expected performance standard. During this period, the water environment improvement function of the ecological infrastructure is operated with the best efficiency and effect.

[0060] The inefficient period means that the water quality is slightly better after the ecological infrastructure project is put into use than before the ecological infrastructure project is put into use, but not significantly. This refers to the period during which the performance or efficiency of the ecological infrastructure project is significantly lower than the expected standard, or there is a strong interference factor: for example, accidental point source pollution, algal explosion factor. During this period, the ecological infrastructure project may still have a water environment improvement function, but its effect or output is poor. Or there is a strong interference of other factors that cannot be observed as a sample.

[0061] The failure period indicates that the water quality of the ecological infrastructure project in the same drainage partition is worse after the ecological infrastructure project is put into use. This refers to the period during which the ecological infrastructure project loses the effect of water environment improvement. During this period, they may completely lose their function or be unable to continue to provide reliable services.

[0062] By analyzing the respective functional states of the ecological infrastructure at multiple sampling time points, it can be determined whether the ecological infrastructure is effective at multiple sampling time points, so as to further analyze the adjacent two sampling time points where the functional state changes, and determine the factors that cause the functional state of the ecological infrastructure to change.

[0063] In operation S240, based on the multi-modal monitoring data, the respective pollution risks of the multiple to-be-measured sub-regions, and the respective functional states of the ecological infrastructure in the to-be-measured region at multiple sampling time points, the to-be-measured region is subjected to correlation analysis, and a correlation analysis result is obtained.

[0064] The correlation analysis result is used to represent the monitoring data in the multi-modal monitoring data that is most strongly associated with the pollution risk and / or the functional state.

[0065] Through correlation analysis, the monitoring data that is most strongly associated with the pollution risk and / or the functional state can be determined, and therefore, the monitoring data that causes the to-be-measured region to have a high pollution risk or causes the ecological infrastructure to be in a failure period can be further determined.

[0066] In operation S250, based on the correlation analysis result, the resilience level of the ecological infrastructure is determined to determine the functional short board of the ecological infrastructure and the resilience improvement potential of the ecological infrastructure, and to determine the optimization target of the ecological infrastructure.

[0067] Through the correlation analysis result, the resilience level of the ecological infrastructure can be evaluated, and the functional short board of the ecological infrastructure can be determined, so as to determine the subsequent optimization target of the ecological infrastructure. Since the monitoring data that causes the to-be-measured region to have a high pollution risk or causes the ecological infrastructure to be in a failure period can be determined through the correlation analysis result, it can be determined that the current ecological infrastructure in the to-be-measured region has relatively poor response capability to the above monitoring data and needs to be improved. Therefore, the optimization target, i.e., improving the response capability of the ecological infrastructure to the above monitoring data, can be determined.

[0068] According to the embodiments of the present application, by fusing multi-modal monitoring data, integrated evaluation of flood season pollution risk and ecological infrastructure functional state is realized. Based on correlation analysis to identify key influencing factors, the optimization target can be scientifically determined, the precision and foresight of decision-making are improved, and a data-driven system solution for resilience improvement of ecological infrastructure is provided.

[0069] Figure 3 A schematic diagram showing the differentiation characteristics of flood season and non-flood season and the ecological infrastructure configuration according to an embodiment of the present application involves rainfall matching analysis.

[0070] As shown in Figure 3 By analyzing the different interval field rainfall occurrence probability corresponding to the A-B section, it can be known that the flood season usually corresponds to a higher rainfall interval, and by analyzing the ecological infrastructure project proportion corresponding to the B-C section, it can be known that the current ecological infrastructure is mainly used to cover the non-flood season rainfall, and the response capability of the ecological infrastructure to the flood season rainfall needs to be improved, so the target period in the present application includes the flood season of the to-be-tested region.

[0071] According to an embodiment of the present application, based on the multi-modal monitoring data, the pollution risk of each of the plurality of to-be-tested sub-regions of the to-be-tested region is calculated, including: based on the multi-modal monitoring data, respectively determining the pollution intensity of each of the plurality of to-be-tested sub-regions and the pollution control rate of each of the plurality of to-be-tested sub-regions within the target period; based on the pollution intensity of each of the plurality of to-be-tested sub-regions, determining the pollution degree of each of the plurality of to-be-tested sub-regions; and based on the plurality of pollution control rates and the plurality of pollution degrees, determining the pollution risk of each of the plurality of to-be-tested sub-regions.

[0072] The pollution intensity of the to-be-tested sub-region can represent the total amount of pollutants entering the river system after the control or filtering effect of the ecological infrastructure. The pollution degree of the to-be-tested sub-region can represent the ranking of the total amount of pollutants in the to-be-tested sub-region among the total amounts of pollutants of the plurality of to-be-tested sub-regions.

[0073] In the process of analyzing the optimization target, for the plurality of to-be-tested sub-regions of each to-be-tested region, if there are common problems and individualized problems among the plurality of to-be-tested sub-regions, the ideal situation is to determine the individualized problems of each of the plurality of to-be-tested sub-regions, and to determine the common problems among the plurality of to-be-tested sub-regions through a small number of to-be-tested sub-regions, and further obtain the optimization target, which can reduce the calculation resource investment for analyzing the common problems while ensuring the discovery of the individualized problems of the to-be-tested sub-regions.

[0074] In one example, the ecological infrastructure of a certain to-be-tested region has poor resilience and pollution response capability, that is, the total amount of pollutants entering the river after precipitation of each to-be-tested sub-region of the to-be-tested region is relatively high, in which case, if the total amount of pollutants, i.e. the pollution intensity, is used for judgment, then in the subsequent determination of the optimization target, all to-be-tested sub-regions will be analyzed, and the analysis of all to-be-tested sub-regions will obtain the same analysis result, i.e. the common problems among the plurality of to-be-tested sub-regions, which will result in a large amount of calculation resources being used to calculate the same problem without contributing to the optimization target.

[0075] Therefore, the pollution intensity of the to-be-tested sub-region can be normalized to obtain the pollution degree of the to-be-tested sub-region, and in the subsequent optimization target analysis process, the to-be-tested sub-region with a higher pollution degree can be selected from multiple to-be-tested sub-regions, and the to-be-tested sub-region is analyzed, and the monitoring data of the to-be-tested sub-region with a relatively poor response capability is determined to determine the optimization target.

[0076] For the region without ecological infrastructure and the region with water-permeable underlying surface, the initial loss and the subsequent loss value can be calculated according to the preset rainfall, that is, the sum of the plant interception, the depression storage and the infiltration amount in the rainfall process of the region. The annual runoff total control rate is the area weighted average of the annual runoff total control rate of each water-permeable underlying surface, and the rainfall runoff coefficient of rainfall can be obtained from the multi-modal monitoring data , and the annual runoff total control rate VCR is calculated according to .

[0077] (1)

[0078] The underlying surface type of the to-be-tested sub-region and the reduction degree of the underlying surface type to a certain pollutant can be obtained from the multi-modal monitoring data . The underlying surface type represents the material layer directly contacted with the outside world of the to-be-tested sub-region, for example, can include water surface, green land, impervious surface and the like.

[0079] It should be noted that all the calculation processes of the present application are respectively performed for different pollutants, and will not be described in detail in the following description.

[0080] According to VCR and the reduction degree , the pollutant control rate can be calculated by formula (2):

[0081] (2)

[0082] The pollutant control rate can be used to represent the possibility of the pollutant entering the river channel in the to-be-tested sub-region and the potential impact degree of the pollutant.

[0083] Based on the pollutant control rate and the pollution degree of each to-be-tested sub-region, the pollution risk of each to-be-tested sub-region can be determined by formula (3) :

[0084] (3)

[0085] Fs represents the pollution degree.

[0086] According to the embodiment of the present application, the pollution risk is decomposed into the product of the pollution degree and the control rate, realizing the quantitative evaluation of the risk. The decomposition method reflects the pollution source intensity and considers the reduction capacity of the ecological infrastructure, making the risk evaluation more comprehensive and objective, and providing a basis for differentiated management.

[0087] According to the embodiment of the present application, the pollution intensity is determined by one of the following methods: determining the areas corresponding to the multiple underlying surface types in the to-be-tested sub-region based on the underlying surface types at each position in the to-be-tested sub-region; determining the area proportion of the multiple underlying surface types based on the total area of the to-be-tested sub-region and the areas corresponding to the multiple underlying surface types; determining the pollution intensity based on the area proportion and the average runoff concentration corresponding to the underlying surface type; determining the pollution input quantity in the target water body based on the monitoring data of the target water body in the multi-modal monitoring data, wherein the target water body represents a water body for containing the precipitation flowing through the to-be-tested sub-region; and determining the pollution intensity based on the pollution input quantity.

[0088] In one example, the areas corresponding to the multiple underlying surface types in the to-be-tested sub-region can be determined based on the underlying surface types at each position in the to-be-tested sub-region, and the area proportion of each underlying surface type can be determined according to the areas corresponding to the multiple underlying surface types and the total area A of the to-be-tested sub-region. j As shown in formula (4):

[0089] (4)

[0090] wherein A j represents the area corresponding to the jth underlying surface type in the to-be-tested sub-region.

[0091] The pollution intensity S can be determined by formula (5) based on the area proportion and the average runoff concentration corresponding to the underlying surface type:

[0092] (5)

[0093] wherein m represents the total number of underlying surface types in the to-be-tested sub-region, EMC j represents the average runoff concentration corresponding to the jth underlying surface type in the to-be-tested sub-region.

[0094] In another example, in the case where it is difficult to obtain one or several parameters such as the underlying surface type, the area corresponding to the underlying surface type, and the average runoff concentration corresponding to the underlying surface type through the multi-modal monitoring data, the target water body corresponding to the to-be-tested sub-region can be determined first, i.e., the rainwater at the to-be-tested sub-region after the precipitation will enter the water body.

[0095] The pollution input quantity in the target water body is determined based on the monitoring data of the target water body and the pollutant intensity is determined according to the pollutant input, that is .

[0096] According to an embodiment of the present application, two pollutant intensity calculation paths are provided, one is a source resolution method based on underlying surface and the other is a load calculation method based on water body monitoring. The former is suitable for rapid evaluation when data is limited, and the latter improves the calculation accuracy by using measured data, enhancing the adaptability and reliability of the method.

[0097] According to an embodiment of the present application, the pollutant input is determined by one of the following ways: determining the pollutant accumulation in the target water body based on monitoring data of the target water body; determining the scouring coefficient, scouring index and unit area runoff of the target water body of the to-be-measured sub-region based on multi-modal monitoring data; determining the pollutant runoff input based on the scouring coefficient, scouring index, unit area runoff and pollutant accumulation; determining the pollutant input based on the pollutant runoff input of the to-be-measured sub-region and the pollutant life input of the to-be-measured sub-region; determining the pollutant concentration of each of a plurality of pollutant species, the degradation coefficient of each of a plurality of pollutant species and the water volume of the target water body in the target water body based on multi-modal monitoring data; and determining the pollutant input based on a plurality of pollutant concentrations, a plurality of degradation coefficients and the water volume.

[0098] In one example, the pollutant input into the target water body can be composed of two parts, one part is the runoff pollution input of the pollutant, that is, the pollutant input into the target water body brought by precipitation, and the other part is the pollutant input into the target water body caused by reasons other than precipitation in the to-be-measured sub-region, which includes life pollution and the like.

[0099] Based on the monitoring data of the target water body, the pollutant accumulation B in the target water body can be determined by formula (6):

[0100] (6)

[0101] Wherein, C1 represents the maximum accumulation of the pollutant, C2 represents the half-saturation constant, that is, the number of days required to reach half of the maximum accumulation, and t represents the time, that is, the current number of days.

[0102] Based on multi-modal monitoring data, the scouring coefficient, scouring index and unit area runoff of the target water body of the to-be-measured sub-region are determined. In order to accurately reflect the influence of different rainfall intensity and duration on pollutant scouring and accumulation, an exponential function can be used for calculation, that is, by inputting the scouring coefficient and the scouring index, the change trend of the pollutant scouring amount with the change of rainfall conditions is calculated. Specifically, the pollutant runoff input W is determined based on the scouring coefficient, the scouring index, the unit area runoff and the pollutant accumulation. off As shown in formula (7):

[0103] (7)

[0104] wherein R c represents a wash-off coefficient, n represents a wash-off exponent, and r represents runoff per unit area.

[0105] Based on the pollution runoff input of the to-be-tested sub-region and the pollution life input of the to-be-tested sub-region, the pollution input W total , as shown in formula (8):

[0106] (8)

[0107] wherein W p represents the pollution life input of the to-be-tested sub-region, which can be obtained from the multi-modal monitoring data, and a represents the probability of the occurrence of a wrong connection of a pipe network in the to-be-tested sub-region, wherein the wrong connection of the pipe network includes, for example, in a separate drainage system, a sewage pipe and a rainwater pipe are connected reversely, a rainwater and a sewage pipe are cross-connected due to structural defects, domestic sewage of a residential building enters a community rainwater pipe, domestic sewage enters a rainwater pipe through a rainwater inlet, and the like.

[0108] In another example, in a case where it is difficult to obtain the one or more parameters from the multi-modal monitoring data, the pollution concentration of each of the plurality of pollution types, the degradation coefficient of each of the plurality of pollution types, and the water volume of the target water body can be determined based on the multi-modal monitoring data. And through formula (9), based on the plurality of pollution concentrations, the plurality of degradation coefficients and the water volume, the pollution input W total is determined.

[0109] (9)

[0110] wherein K represents the degradation coefficient of the pollution, V represents the water volume of the target water body, and C s represents the pollution concentration.

[0111] According to the embodiments of the present application, the calculation method of the pollution input is refined, and the processes of runoff wash-off, life discharge and water self-purification are comprehensively considered. Through multi-path accounting, the comprehensiveness of the input evaluation is ensured, and a solid data foundation is laid for subsequent risk calculation.

[0112] According to an embodiment of the present application, the pollution degree of each of the plurality of sub-regions to be measured is determined based on the pollutant intensity of each of the plurality of sub-regions to be measured, including: determining the maximum first pollutant intensity and the minimum second pollutant intensity from the pollutant intensity of each of the plurality of sub-regions to be measured; and normalizing the pollutant intensity of each of the plurality of sub-regions to be measured based on the first pollutant intensity and the second pollutant intensity to obtain the pollution degree of each of the plurality of sub-regions to be measured.

[0113] After determining the pollutant intensity of each of the plurality of sub-regions to be measured in the above manner, the maximum first pollutant intensity S max and the minimum second pollutant intensity S min may be determined from the pollutant intensity of each of the plurality of sub-regions to be measured in the sub-region to be measured, and the pollutant intensity of each sub-region to be measured is normalized based on S max and S min to obtain the pollution degree F s of each sub-region to be measured.

[0114] (10)

[0115] According to an embodiment of the present application, the pollution degree is obtained by extreme value normalization processing, which eliminates the size difference between different sub-regions. This standardization method makes the pollution levels of different regions comparable, facilitating the identification of high-risk hotspots.

[0116] Through the above processing, the sub-region to be measured can be divided into a plurality of non-overlapping sub-regions to be measured, and the pollution degree of each sub-region to be measured is determined, and the sub-region to be measured is split and analyzed in the spatial dimension. In addition, the sub-region to be measured can also be split and analyzed in the time dimension, and the sub-region to be measured can be split and analyzed in multiple dimensions to obtain more accurate and scientific correlation analysis results. The split and analysis of the sub-region to be measured in the time dimension will be explained below.

[0117] According to an embodiment of the present application, based on a plurality of multi-modal time series data, the functional state of the ecological infrastructure in the sub-region to be measured at each of a plurality of sampling time points is determined, including: sequentially analyzing each sampling time point according to the time sequence relationship between the plurality of sampling time points to determine the pollutant content of each of a plurality of pollutant species contained in the water body corresponding to the sub-region to be measured at the sampling time point; determining the water quality index of the water body corresponding to the sub-region to be measured at the plurality of sampling time points based on the preset weight of each of the plurality of pollutant species and the pollutant content of each of the plurality of pollutant species; and determining the functional state of the ecological infrastructure in the sub-region to be measured at each of the plurality of sampling time points based on the plurality of water quality indexes.

[0118] The pollution content P of each of the plurality of pollution types contained in the water body corresponding to the to-be-tested region at each sampling time point can be determined by sequentially analyzing each sampling time point according to the time sequence relationship between the plurality of sampling time points. i According to the preset weights C of the plurality of pollution types respectively, the water quality index WQI of the water body corresponding to the to-be-tested region at each sampling time point is determined. i Wherein, i in P i and C i represents the i-th pollution type, and the water quality index WQI of the water body corresponding to the to-be-tested region at each sampling time point is determined.

[0119] (11)

[0120] Wherein, n represents the total number of pollution types.

[0121] Based on the plurality of water quality indexes, the functional state of the ecological infrastructure in the to-be-tested region at each of the plurality of sampling time points is determined. Specifically, it can include: for each sampling time point, determining a preceding sampling time point adjacent to the sampling time point and located before the sampling time point from the plurality of sampling time points; comparing the water quality index of the sampling time point with the water quality index of the preceding sampling time point to obtain a comparison result; in the case that the comparison result indicates that the water quality of the sampling time point is better than that of the preceding sampling time point, determining that the functional state indicates that the function of the ecological infrastructure in the to-be-tested region at the sampling time point is improved; and in the case that the comparison result indicates that the water quality of the sampling time point is worse than that of the preceding sampling time point, determining that the functional state indicates that the function of the ecological infrastructure in the to-be-tested region at the sampling time point is not improved.

[0122] In one example, T adjacent sampling time points, i.e. t0, t1, …, t T , are included. For any one of them, t x , the preceding sampling time point is t x-1 . Comparing the water quality index of the sampling time point with the water quality index of the preceding sampling time point can obtain a comparison result indicating the size relationship between the two.

[0123] Since the higher the above-mentioned water quality index, the better the water quality, in the case that the water quality index of the sampling time point is greater than that of the preceding sampling time point, it can be determined that the comparison result indicates that the water quality of the sampling time point is better than that of the preceding sampling time point, and further determined that the functional state indicates that the function of the ecological infrastructure in the to-be-tested region at the sampling time point is improved.

[0124] Conversely, in the case that the water quality index at the sampling time point is less than the water quality index at the previous sampling time point, it can be determined that the comparison result indicates that the water quality at the sampling time point is worse than the water quality at the previous sampling time point, and it is further determined that the functional state indicates that the ecological infrastructure at the sampling time point in the to-be-tested region has no promotion in function.

[0125] In another example, the significance score can also be obtained through hypothesis testing calculation. In the case that it is determined that the water quality index at the sampling time point is greater than the water quality index at the previous sampling time point, the functional state of the ecological infrastructure at the sampling time point is determined according to the size relationship between the significance score and the score threshold. In the case that it is determined that the water quality index at the sampling time point is less than the water quality index at the previous sampling time point, the functional state of the ecological infrastructure at the sampling time point is determined as the ineffective period.

[0126] For example, the score threshold can be 0.05. In the case that the significance score is less than the score threshold, it can be determined that the functional state of the ecological infrastructure at the sampling time point is the effective period. In the case that the significance score is greater than the score threshold, it can be determined that the functional state of the ecological infrastructure at the sampling time point is the low-efficiency period.

[0127] According to the embodiment of the present application, the functional state of the ecological infrastructure is dynamically evaluated based on the multi-pollutant water quality index, which realizes continuous monitoring of the operation effect of the facility. Compared with single-point evaluation, time series analysis can better reflect the true performance and change trend of the facility in the entire flood season. By comparing the water quality at the previous time point to judge the functional change, the promotion or decay of the facility function can be found in time. This dynamic evaluation method provides decision support for timely intervention and operation adjustment.

[0128] Generally, the functional state of the ecological infrastructure will not change frequently during normal use. The main reason for affecting the functional state and causing the functional state to change is that the ecological infrastructure is actively upgraded or updated. Therefore, the above functional state can be determined again after the ecological infrastructure in the to-be-tested region is updated, to determine the update effect brought by the update.

[0129] Specifically, the method for evaluating and optimizing the resilience function of the ecological infrastructure in responding to pollution in the flood season further includes: in response to the update of the ecological infrastructure in the to-be-tested region, determining a to-be-evaluated time period in which the update is located, the to-be-evaluated time period being determined according to the adjacent previous sampling time point and the sampling time point; and determining an update effect of the update based on the functional state determined according to the adjacent previous sampling time point and the sampling time point corresponding to the to-be-evaluated time period.

[0130] After the ecological infrastructure is updated, based on a time when the ecological infrastructure is updated, adjacent previous sampling time points and a sampling time point are determined from a plurality of sampling time points, so that the time when the ecological infrastructure is updated is between the to-be-evaluated time period determined according to the previous sampling time point and the sampling time point.

[0131] The function state of the previous sampling time point and the function state of the sampling time point are determined respectively, and the update effect of the update can be determined according to the difference between the two function states.

[0132] For example, in the case that the function state of the previous sampling time point represents a failure period / low efficiency period, and the function state of the sampling time point represents an effective period, it can be determined that the update effect of the update is good, and the improvement of the ecological infrastructure is obvious. In the case that the function state of the sampling time point does not change compared with the function state of the previous sampling time point, it can be determined that the update effect of the update is not good, and the improvement of the ecological infrastructure is not obvious or even no improvement.

[0133] According to the embodiment of the present application, the facility update effect is associated with the function state change of a specific time window, and the precise evaluation of the update measure is realized. The mechanism provides key technical support for the closed-loop management of evaluation, optimization and re-evaluation.

[0134] According to the embodiment of the present application, based on the correlation analysis result, the resilience level of the ecological infrastructure is determined to determine the function short board of the ecological infrastructure and the resilience improvement potential of the ecological infrastructure, and the optimization target of the ecological infrastructure is determined, including: based on the correlation analysis result, a resilience evaluation index system for the ecological infrastructure is constructed, wherein the resilience evaluation index system for the ecological infrastructure is used to quantify the resilience level of the urban ecological infrastructure under different conditions, and the resilience level represents the ability of the ecological infrastructure to cope with pollution caused by the target period; based on the resilience evaluation index system, it is determined whether the resilience level of the ecological infrastructure represents a function short board of the ecological infrastructure, wherein the function short board includes a to-be-tested sub-region with a pollution risk higher than a risk threshold, and a to-be-evaluated time period with an update effect representing that the update does not improve the resilience of the ecological infrastructure; in the case that the to-be-tested sub-region with the pollution risk higher than the risk threshold is determined, the to-be-tested sub-region with the pollution risk higher than the risk threshold is determined as a to-be-optimized region; according to the correlation analysis result of the to-be-optimized region, the optimization target of the ecological infrastructure in the to-be-optimized sub-region is determined; in the case that the to-be-evaluated time period with the update effect representing that the update does not improve the resilience of the ecological infrastructure is determined, the to-be-evaluated time period is determined as a to-be-optimized time period; and according to the correlation analysis result of the to-be-optimized time period, the optimization target of the ecological infrastructure in the to-be-optimized region is determined.

[0135] According to an embodiment of the present application, based on the resilience evaluation index system, it is determined whether the resilience level of the ecological infrastructure indicates that the ecological infrastructure has a functional short board, comprising: based on the resilience evaluation index system, quantifying the resilience level of the ecological infrastructure under different conditions, wherein the resilience level indicates the ability of the ecological infrastructure to cope with pollution caused by the target period; and based on the resilience level and the preset resilience threshold range of the ecological infrastructure, determining whether the resilience level of the ecological infrastructure indicates that the ecological infrastructure has a functional short board.

[0136] According to an embodiment of the present application, the resilience level of the ecological infrastructure can be evaluated by the resilience performance and the resilience score to quantify it.

[0137] For example, the anti-interference ability of the ecological infrastructure in response to precipitation can be evaluated according to formula (12) to obtain the resilience performance AUC of the ecological infrastructure:

[0138] (12)

[0139] Wherein, a and b represent the lower limit value and the upper limit value of the design pressure respectively, x represents the various pressures from the outside world, f(x) represents the resilience performance of the ecological infrastructure under the various pressures from the outside world, N represents the number of intermediate points obtained by dividing the range between the upper limit value and the lower limit value according to the preset granularity, and n represents each intermediate point.

[0140] After determining the resilience performance of the ecological infrastructure, the resilience performance can be normalized by formula (13) to obtain the normalized resilience score Res:

[0141] (13)

[0142] Wherein, i represents the type of the pollutant, that is, for each type of pollutant, a resilience score can be obtained. The greater the resilience score, the higher the resilience, and the better the function and effect of the ecological infrastructure.

[0143] According to an embodiment of the present application, for the ecological infrastructure, a corresponding resilience threshold range can be preset, in the case that the resilience level of the ecological infrastructure is within the resilience threshold range, it can be determined that the ecological infrastructure does not have a functional short board, and in the case that the resilience level of the ecological infrastructure is outside the resilience threshold range, it can be determined that the ecological infrastructure has a functional short board.

[0144] For the spatial dimension, according to the pollution risk of each of the plurality of to-be-tested sub-regions, in the case that it is determined that there is a to-be-tested sub-region with a pollution risk higher than the risk threshold, the to-be-tested sub-region with a pollution risk higher than the risk threshold is determined as the to-be-optimized region.

[0145] For the time dimension, in a case where it is determined that there is an evaluation time period for which the update effect indicates that the update does not improve the resilience of the ecological infrastructure, the evaluation time period is determined as an optimization time period, or an update for the ecological infrastructure occurring in the evaluation time period is determined as an optimization update.

[0146] Correlation analysis is respectively performed on the optimization sub-region and the optimization time period or the optimization update, and based on a correlation analysis result, an optimization target for the optimization sub-region and the optimization time period or the optimization update is determined.

[0147] According to an embodiment of the present application, the optimization object is identified based on the risk threshold and the update effect double standard, and the target allocation of resources is realized. High-risk areas and low-efficiency measures are preferentially processed, and the input-output ratio of resilience optimization is significantly improved.

[0148] According to an embodiment of the present application, based on the multi-modal monitoring data, the pollution risks of the plurality of to-be-tested sub-regions, and the functional states of the ecological infrastructure in the to-be-tested region at the plurality of sampling time points, correlation analysis is performed on the to-be-tested region to obtain a correlation analysis result, including: taking the multi-modal monitoring data as a plurality of independent variables, taking the pollution risks of the plurality of to-be-tested sub-regions and the functional states of the ecological infrastructure in the to-be-tested region at the plurality of sampling time points as a plurality of dependent variables, and constructing a correlation model between the independent variables and the dependent variables; using a correlation analysis method, performing correlation analysis on the correlation model to obtain a correlation strength relationship between the plurality of independent variables and the plurality of dependent variables; and based on the correlation strength relationship, determining the correlation analysis result.

[0149] Taking the multi-modal monitoring data as a plurality of independent variables, and taking the pollution risks of the plurality of to-be-tested sub-regions and the functional states of the ecological infrastructure in the to-be-tested region at the plurality of sampling time points as a plurality of dependent variables, a correlation model can be constructed based on the independent variables and the dependent variables. The above correlation model can determine one or more independent variables with the highest contribution weight to the change of each dependent variable by causing the change of the dependent variable through the change of the plurality of independent variables.

[0150] Therefore, using a correlation method, correlation analysis is performed on the correlation model, and a correlation strength relationship between the plurality of independent variables and the plurality of dependent variables can be obtained. The correlation strength relationship is used to represent the contribution weight of each independent variable to the change of each dependent variable. The higher the contribution weight of a certain independent variable to a certain dependent variable, the greater the correlation strength between the independent variable and the dependent variable.

[0151] Figure 4 A schematic diagram of the functional state of the ecological infrastructure and its corresponding pollution risk according to an embodiment of the present application is shown.

[0152] As Figure 4PRCPTOT, SDII, Rx1d, CDD, Rxnd, R2.0, R22.5, and R33.5, as shown in FIG. 1, wherein PRCPTOT represents the total annual precipitation, SDII represents the simple daily intensity index, Rx1d represents the maximum 1-day precipitation, CDD represents the number of consecutive dry days, Rxnd represents the maximum n-day precipitation, R2.0 represents the number of days with precipitation greater than 2.0 mm, R22.5 represents the number of days with precipitation greater than 22.5 mm, and R33.5 represents the number of days with precipitation greater than 33.5 mm. The above data can be obtained through multi-modal monitoring data.

[0153] Through correlation analysis of the water quality changes of different functional states (i.e., effective period, low-effective period, and invalid period) of the ecological infrastructure and the monitoring data, it can be concluded that R13.5, CWD, and VCR are the main factors affecting the resilience of the ecological infrastructure project. In the effective period, R13.5 is significantly correlated with the water quality index WQI, and moderate to heavy rain of more than 13.5 mm per month is the key factor limiting the upper limit of the water environment improvement function of the ecological infrastructure project. In the invalid period, CWD is significantly correlated with the water quality index WQI, indicating that the water environment function of the ecological infrastructure project in the invalid period is hindered by continuous rainfall.

[0154] According to the one-to-one correlation strength relationship between the multiple independent variables and the multiple dependent variables, the correlation analysis result can be determined, and ultimately the resilience improvement potential of the evaluation system can be realized, the functional short board of the system can be identified, and the optimization target of the ecological infrastructure can be determined.

[0155] According to the embodiments of the present application, by constructing a multi-variable correlation model, the correlation strength between each monitoring index and the risk and functional state of the ecological infrastructure is systematically revealed. The identified key factors directly point to the root cause, making subsequent optimization measures more targeted and effective.

[0156] Specifically, formula (12) and formula (13) can be constructed according to the following Figure 5 The constructed curve, and then the resilience score is calculated.

[0157] Figure 5 The resilience evaluation result according to the embodiments of the present application is schematically shown.

[0158] As Figure 5 shown, wherein R13.5 represents the number of days with precipitation greater than 13.5 mm, CWD represents the longest rainy period, and VCR represents the annual total runoff control rate. Figure 5 (a) in FIG. 1 shows the relationship between the stress caused by R13.5 and the resilience of the ecological infrastructure. Figure 5 (b) in FIG. 1 shows the relationship between the stress caused by CWD and the resilience of the ecological infrastructure. Figure 5The (c) in the figure shows the relationship between the stress caused by the VCR and the resilience of the ecological infrastructure. Through analysis, it is known that the current ecological infrastructure can normally strain under R13.5 and VCR when coping with 50% stress, and is in a relatively safe and effective state. However, when coping with CWD of 20% or more, the strain capacity of the ecological infrastructure will be at a poor level, and there is a risk of failure.

[0159] Based on the above-mentioned ecological infrastructure resilience function evaluation and optimization method for coping with flood season pollution, the present application also provides an ecological infrastructure resilience function evaluation and optimization device for coping with flood season pollution. The following will be described in detail in combination with Figure 6 The device is described in detail.

[0160] Figure 6 The structure block diagram of the ecological infrastructure resilience function evaluation and optimization device for coping with flood season pollution according to the embodiment of the present application is schematically shown.

[0161] As Figure 6 shown, the ecological infrastructure resilience function evaluation and optimization device 600 for coping with flood season pollution of the embodiment includes a data acquisition module 610, a risk calculation module 620, a state determination module 630, a correlation analysis module 640 and a target determination module 650.

[0162] The data acquisition module 610 is used to acquire the multi-modal monitoring data of the ecological infrastructure in the target period in the region to be tested, wherein the target period includes the flood season of the region to be tested, and the multi-modal monitoring data includes the multi-modal time series data of the region to be tested at a plurality of sampling time points in the target period. In an embodiment, the data acquisition module 610 can be used to perform the operation S210 described above, and will not be described here.

[0163] The risk calculation module 620 is used to calculate the pollution risk of each of the plurality of sub-regions to be tested in the region to be tested based on the multi-modal monitoring data, wherein the pollution risk represents the cumulative amount of pollutants in the water body entering the sub-region to be tested after the pollutants are reduced by the ecological infrastructure in the sub-region to be tested. In an embodiment, the risk calculation module 620 can be used to perform the operation S220 described above, and will not be described here.

[0164] The state determination module 630 is used to determine the functional state of the ecological infrastructure in the region to be tested at each of the plurality of sampling time points based on the plurality of multi-modal time series data. In an embodiment, the state determination module 630 can be used to perform the operation S230 described above, and will not be described here.

[0165] The correlation analysis module 640 is configured to perform correlation analysis on the to-be-tested region based on the multi-modal monitoring data, the pollution risks of the to-be-tested sub-regions respectively, and the functional states of the ecological infrastructure in the to-be-tested region at the plurality of sampling time points, to obtain a correlation analysis result, where the correlation analysis result is used to represent monitoring data in the multi-modal monitoring data that has the strongest correlation with the pollution risks and / or the functional states. In an embodiment, the correlation analysis module 640 can be configured to perform the operation S240 described above, and details are not described herein again.

[0166] The target determination module 650 is configured to determine the resilience level of the ecological infrastructure based on the correlation analysis result, to determine the functional short board of the ecological infrastructure and the resilience improvement potential of the ecological infrastructure, and to determine the optimization target of the ecological infrastructure. In an embodiment, the target determination module 650 can be configured to perform the operation S250 described above, and details are not described herein again.

[0167] According to an embodiment of the present application, the risk calculation module 620 includes a control rate calculation sub-module, a degree calculation sub-module, and a risk calculation sub-module.

[0168] The control rate calculation sub-module is configured to determine the pollutant intensity of each of the to-be-tested sub-regions and the pollutant control rate of each of the to-be-tested sub-regions in the target period based on the multi-modal monitoring data.

[0169] The degree calculation sub-module is configured to determine the pollution degree of each of the to-be-tested sub-regions based on the pollutant intensity of each of the to-be-tested sub-regions.

[0170] The risk calculation sub-module is configured to determine the pollution risk of each of the to-be-tested sub-regions based on the plurality of pollutant control rates and the plurality of pollution degrees.

[0171] According to an embodiment of the present application, the resilience function evaluation and optimization 600 of the ecological infrastructure for responding to flood season pollution further includes an area calculation module, a proportion calculation module, a first intensity calculation module, an input quantity calculation module, and a second intensity calculation module.

[0172] The area calculation module is configured to determine the areas corresponding to the plurality of underlying surface types in the to-be-tested sub-region based on the underlying surface types of each position in the to-be-tested sub-region.

[0173] The proportion calculation module is configured to determine the area proportions corresponding to the plurality of underlying surface types based on the total area of the to-be-tested sub-region and the areas corresponding to the plurality of underlying surface types.

[0174] The first intensity calculation module is configured to determine the pollutant intensity based on the area proportions and the average runoff concentration corresponding to the underlying surface types.

[0175] The input amount calculation module is configured to determine the pollutant input amount in the target water body based on the monitoring data for the target water body in the multi-modal monitoring data, wherein the target water body represents a water body for containing the precipitation flowing through the to-be-measured sub-region.

[0176] The second intensity calculation module is configured to determine the pollutant intensity based on the pollutant input amount.

[0177] According to an embodiment of the present application, the input amount calculation module comprises a cumulative amount calculation submodule, a runoff amount calculation submodule, a runoff input calculation submodule, a first input amount calculation submodule, a data determination submodule and a second input amount calculation submodule.

[0178] The cumulative amount calculation submodule is configured to determine the pollutant cumulative amount in the target water body based on the monitoring data for the target water body.

[0179] The runoff amount calculation submodule is configured to determine the wash-off coefficient, the wash-off index of the to-be-measured sub-region and the unit area runoff amount of the target water body based on the multi-modal monitoring data.

[0180] The runoff input calculation submodule is configured to determine the pollutant runoff input amount based on the wash-off coefficient, the wash-off index, the unit area runoff amount and the pollutant cumulative amount.

[0181] The first input amount calculation submodule is configured to determine the pollutant input amount based on the pollutant runoff input amount of the to-be-measured sub-region and the pollutant living input amount of the to-be-measured sub-region.

[0182] The data determination submodule is configured to determine the pollutant concentration of each of the plurality of pollutant species, the degradation coefficient of each of the plurality of pollutant species and the water volume of the target water body based on the multi-modal monitoring data.

[0183] The second input amount calculation submodule is configured to determine the pollutant input amount based on the plurality of pollutant concentrations, the plurality of degradation coefficients and the water volume.

[0184] According to an embodiment of the present application, the degree calculation submodule comprises an intensity determination unit and a degree calculation unit.

[0185] The intensity determination unit is configured to determine the maximum first pollutant intensity and the minimum second pollutant intensity from the pollutant intensities of the plurality of to-be-measured sub-regions.

[0186] The degree calculation unit is configured to normalize the pollutant intensities of the plurality of to-be-measured sub-regions based on the first pollutant intensity and the second pollutant intensity to obtain the pollution degrees of the plurality of to-be-measured sub-regions.

[0187] According to an embodiment of the present application, the state determination module 630 comprises a content determination submodule, a water quality determination submodule and a state determination submodule.

[0188] The content determination submodule is configured to analyze each sampling time point in sequence according to the time sequence relationship between the sampling time points, and determine the pollutant content of each pollutant type contained in the water body corresponding to the to-be-measured region at the sampling time point.

[0189] The water quality determination submodule is configured to determine the water quality index of the water body corresponding to the to-be-measured region at the sampling time points based on the preset weights of the plurality of pollutant types and the pollutant contents of the plurality of pollutant types.

[0190] The state determination submodule is configured to determine the functional state of the ecological infrastructure in the to-be-measured region at the sampling time points based on the plurality of water quality indexes.

[0191] According to an embodiment of the present application, the state determination submodule comprises a time point determination unit, a water quality comparison unit, a first state determination unit, and a second state determination unit.

[0192] The time point determination unit is configured to determine, for each sampling time point, a preceding sampling time point adjacent to the sampling time point and located before the sampling time point from the plurality of sampling time points.

[0193] The water quality comparison unit is configured to compare the water quality index of the sampling time point with the water quality index of the preceding sampling time point to obtain a comparison result.

[0194] The first state determination unit is configured to determine that the functional state indicates that the function of the ecological infrastructure in the to-be-measured region at the sampling time point is improved in the case that the comparison result indicates that the water quality at the sampling time point is better than the water quality at the preceding sampling time point.

[0195] The second state determination unit is configured to determine that the functional state indicates that the function of the ecological infrastructure in the to-be-measured region at the sampling time point is not improved in the case that the comparison result indicates that the water quality at the sampling time point is worse than the water quality at the preceding sampling time point.

[0196] According to an embodiment of the present application, the ecological infrastructure resilience function evaluation and optimization during flood season pollution 600 further comprises a time period determination module and an update effect determination module.

[0197] The time period determination module is configured to determine a to-be-evaluated time period in which an update of the ecological infrastructure in the to-be-measured region occurs in response to the update, the to-be-evaluated time period being determined according to the adjacent preceding sampling time point and the sampling time point.

[0198] The update effect determination module is configured to determine an update effect of the update based on the functional state determined according to the adjacent preceding sampling time point and the sampling time point corresponding to the to-be-evaluated time period.

[0199] According to an embodiment of the present application, the target determining module 650 comprises a system construction submodule, a short board determining submodule, a region determining submodule, a first target determining submodule, a time period determining submodule, a second target determining submodule, and a third target determining submodule.

[0200] The system construction submodule is configured to construct a resilience evaluation index system for the ecological infrastructure based on the correlation analysis result, wherein the resilience evaluation index system for the ecological infrastructure is configured to quantify resilience levels of the urban ecological infrastructure under different situations, and the resilience level represents the ability of the ecological infrastructure to cope with pollution caused by the target period.

[0201] The short board determining submodule is configured to determine whether the resilience level of the ecological infrastructure represents a functional short board of the ecological infrastructure based on the resilience evaluation index system, wherein the functional short board comprises a to-be-tested sub-region with a pollution risk higher than a risk threshold and a to-be-evaluated time period with an update effect indicating that the update does not improve the resilience of the ecological infrastructure.

[0202] The region determining submodule is configured to determine the to-be-tested sub-region with the pollution risk higher than the risk threshold as a to-be-optimized region in a case where the to-be-tested sub-region with the pollution risk higher than the risk threshold is determined.

[0203] The first target determining submodule is configured to determine an optimization target for the ecological infrastructure in the to-be-optimized sub-region according to the correlation analysis result of the to-be-optimized region.

[0204] The time period determining submodule is configured to determine the to-be-evaluated time period as a to-be-optimized time period in a case where the to-be-evaluated time period with the update effect indicating that the update does not improve the resilience of the ecological infrastructure is determined.

[0205] The second target determining submodule is configured to determine an optimization target for the ecological infrastructure in the to-be-optimized region according to the correlation analysis result of the to-be-optimized time period.

[0206] According to an embodiment of the present application, the short board determining submodule comprises a resilience quantifying unit and a short board determining unit.

[0207] The resilience quantifying unit is configured to quantify the resilience level of the ecological infrastructure under different situations based on the resilience evaluation index system, wherein the resilience level represents the ability of the ecological infrastructure to cope with pollution caused by the target period.

[0208] The short board determining unit is configured to determine whether the resilience level of the ecological infrastructure represents a functional short board of the ecological infrastructure based on the resilience level and a preset resilience threshold range of the ecological infrastructure.

[0209] According to an embodiment of the present application, the correlation analysis module 640 comprises a model construction submodule, a correlation analysis submodule and an analysis result determination submodule.

[0210] The model construction submodule is configured to construct a correlation model between the independent variables and the dependent variables by taking the multi-modal monitoring data as the independent variables and taking the pollution risks of the to-be-tested sub-regions and the functional states of the ecological infrastructure in the to-be-tested region at the sampling time points as the dependent variables.

[0211] The correlation analysis submodule is configured to perform correlation analysis on the correlation model by using a correlation analysis method to obtain a correlation strength relationship between the independent variables and the dependent variables.

[0212] The analysis result determination submodule is configured to determine the correlation analysis result based on the correlation strength relationship.

[0213] According to an embodiment of the present application, any plurality of modules among the data acquisition module 610, the risk calculation module 620, the state determination module 630, the correlation analysis module 640 and the target determination module 650 can be combined in one module for implementation, or any one of the modules can be split into a plurality of modules. Alternatively, at least part of the functions of one or more of the modules can be combined with at least part of the functions of other modules, and implemented in one module. According to an embodiment of the present application, at least one of the data acquisition module 610, the risk calculation module 620, the state determination module 630, the correlation analysis module 640 and the target determination module 650 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on board, a system on package, an application specific integrated circuit (ASIC), or any other reasonable manner of integrating or packaging a circuit, etc. hardware or firmware, or implemented in any one of software, hardware and firmware or in a proper combination of any of them. Alternatively, at least one of the data acquisition module 610, the risk calculation module 620, the state determination module 630, the correlation analysis module 640 and the target determination module 650 can be at least partially implemented as a computer program module which can perform corresponding functions when the computer program module is run.

[0214] Figure 7 A block diagram of an electronic device suitable for implementing the method for evaluating and optimizing the resilience function of the ecological infrastructure in response to flood season pollution according to an embodiment of the present application is shown.

[0215] As Figure 7As shown, the electronic device 700 according to an embodiment of the present application includes a processor 701 which can perform various appropriate actions and processes in accordance with a program stored in a read only memory (ROM) 702 or a program loaded into a random access memory (RAM) 703 from a storage section 708. The processor 701 can include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor, and / or a related chipset, and / or a special purpose microprocessor (e.g., an application specific integrated circuit (ASIC)), and so on. The processor 701 can also include an on-board memory for cache use. The processor 701 can include a single processing unit or multiple processing units to perform the various actions of the method processes according to embodiments of the present application.

[0216] In the RAM 703, various programs and data required for the operation of the electronic device 700 are stored. The processor 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. The processor 701 performs various operations of the method processes according to embodiments of the present application by executing the programs in the ROM 702 and / or the RAM 703. Note that the programs can also be stored in one or more memories other than the ROM 702 and the RAM 703. The processor 701 can also perform various operations of the method processes according to embodiments of the present application by executing the programs stored in the one or more memories.

[0217] According to an embodiment of the present application, the electronic device 700 can also include an input / output (I / O) interface 705 which is also connected to the bus 704. The electronic device 700 can also include one or more of the following components connected to the input / output (I / O) interface 705: an input section 706 including a keyboard, a mouse, etc.; an output section 707 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the input / output (I / O) interface 705 as necessary. A removable medium 711 such as a magnetic disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 710 as necessary, so that a computer program read out therefrom is installed into the storage section 708 as necessary.

[0218] The application further provides a computer readable storage medium, which can be included in the device / apparatus / system described in the above embodiments, or can exist independently without being assembled into the device / apparatus / system. The computer readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the application.

[0219] According to the embodiments of the application, the computer readable storage medium can be a non-volatile computer readable storage medium, which can include, but is not limited to, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any appropriate combination thereof. In this application, a computer readable storage medium can be any tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. For example, in the embodiments of the application, the computer readable storage medium can include one or more of the ROM 702 and / or the RAM 703 described above, and / or one or more memory devices other than the ROM 702 and the RAM 703.

[0220] The embodiments of the application also include a computer program product, which includes a computer program containing program codes for executing the methods shown in the flowcharts. When the computer program product is run in a computer system, the program codes are used to make the computer system implement the methods provided by the embodiments of the application.

[0221] The above functions defined in the system / apparatus of the embodiments of the application are performed when the computer program is executed by the processor 701. According to the embodiments of the application, the system, apparatus, module, unit, etc. described above can be implemented by computer program modules.

[0222] In one embodiment, the computer program can rely on a tangible storage medium such as an optical storage device, a magnetic storage device, etc. In another embodiment, the computer program can also be transmitted, distributed, and downloaded in the form of a signal on a network medium, and be downloaded and installed through the communication part 709, and / or installed from the detachable medium 711. The program codes contained in the computer program can be transmitted by any appropriate network medium, including but not limited to wireless, wired, etc., or any appropriate combination thereof.

[0223] In such embodiments, the computer program can be downloaded and installed from the network via the communication section 709, and / or installed from the removable media 711. When the computer program is executed by the processor 701, the above-described functions defined in the system of the embodiments of the present application are executed. The system, device, apparatus, module, unit, etc. described above can be realized by the computer program modules according to the embodiments of the present application.

[0224] According to the embodiments of the present application, the program code for executing the computer program provided by the embodiments of the present application can be written in any combination of one or more programming languages, and specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming language, and / or assembly / machine language. The programming language includes, but is not limited to, such as Java, C++, python, "C" language or similar programming language. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case involving a remote computing device, the remote computing device can be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, connected to the Internet through an Internet service provider).

[0225] The flowcharts and block diagrams in the drawings illustrate the possible architectures, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, a segment, or a portion of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks noted in succession can in fact be executed substantially concurrently or in the reverse order, depending on the functionality involved. It should also be noted that each block in the flowcharts or block diagrams, and combinations of blocks in the flowcharts or block diagrams, can be implemented by special-purpose hardware-based systems that perform the specified functions or operations, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0226] Those skilled in the art can understand that the features described in various embodiments of the present application can be combined and / or integrated in various combinations and / or integrations, even if such combinations or integrations are not explicitly described in the present application. In particular, the features described in various embodiments of the present application can be combined and / or integrated in various combinations and / or integrations without departing from the spirit and teachings of the present application. All such combinations and / or integrations fall within the scope of the present application.

[0227] The embodiments of the application have been described. However, these embodiments are merely for illustration and are not intended to limit the scope of the application. Although each embodiment is described above separately, this does not mean that the measures in each embodiment cannot be used advantageously in combination. Various alternatives and modifications to the embodiments described herein will be apparent to those skilled in the art in view of the foregoing without departing from the scope of the application.

Claims

1. A method for assessing and optimizing the resilience function of ecological infrastructure to cope with contamination during flood season, characterized in that, The method comprises: acquiring multi-modal monitoring data of ecological infrastructure in a to-be-tested region in a target period, wherein the target period comprises a flood season of the to-be-tested region, and the multi-modal monitoring data comprises multi-modal time series data of the to-be-tested region at a plurality of sampling time points in the target period; calculating, based on the multi-modal monitoring data, a pollution risk of each of a plurality of to-be-tested sub-regions of the to-be-tested region, wherein the pollution risk represents an accumulated amount of pollutants in a water body entering the to-be-tested sub-region after the pollutants are reduced by ecological infrastructure in the to-be-tested sub-region; determining, based on a plurality of the multi-modal time series data, a functional state of the ecological infrastructure in the to-be-tested region at each of a plurality of the sampling time points; performing correlation analysis on the to-be-tested region based on the multi-modal monitoring data, the pollution risk of each of a plurality of the to-be-tested sub-regions, and the functional state of the ecological infrastructure in the to-be-tested region at each of a plurality of the sampling time points, to obtain a correlation analysis result, wherein the correlation analysis result is used to represent monitoring data in the multi-modal monitoring data that has the strongest correlation with the pollution risk and / or the functional state; and constructing a resilience evaluation index system for the ecological infrastructure based on the correlation analysis result; determining, based on the resilience evaluation index system, whether a resilience level of the ecological infrastructure indicates that the ecological infrastructure has a functional shortcoming, wherein the functional shortcoming comprises a to-be-tested sub-region with a pollution risk higher than a risk threshold, and a to-be-evaluated time period with an update effect indicating that the resilience of the ecological infrastructure is not improved by updating, the to-be-tested sub-region being obtained by splitting and analyzing the to-be-tested region in a spatial dimension, and the to-be-evaluated time period being obtained by splitting and analyzing the to-be-tested region in a time dimension; for the spatial dimension, in a case where it is determined that there is a to-be-tested sub-region with a pollution risk higher than a risk threshold, determining the to-be-tested sub-region with the pollution risk higher than the risk threshold as a to-be-optimized region; determining, according to a correlation analysis result of the to-be-optimized region, an optimization target for the ecological infrastructure in the to-be-optimized region; for the time dimension, in a case where it is determined that there is a to-be-evaluated time period with an update effect indicating that the resilience of the ecological infrastructure is not improved by updating, determining the to-be-evaluated time period as a to-be-optimized time period; and determining, according to a correlation analysis result of the to-be-optimized time period, an optimization target for the ecological infrastructure in the to-be-optimized region.

2. The method of claim 1, wherein, The method comprises: determining, based on the multi-modal monitoring data, a pollution intensity of each of a plurality of the to-be-tested sub-regions and a pollution control rate of each of a plurality of the to-be-tested sub-regions in the target period; determining, based on the pollution intensity of each of a plurality of the to-be-tested sub-regions, a pollution degree of each of a plurality of the to-be-tested sub-regions; and Determine a pollution risk of each of the sub-regions to be tested based on the pollution control rates and the pollution levels.

3. The method of claim 2, wherein, The pollution intensity is determined by one of the following methods: Determine areas corresponding to each of the underlying surface types in the sub-region to be tested based on the underlying surface types at each position in the sub-region to be tested; Determine an area proportion of the areas corresponding to each of the underlying surface types based on a total area of the sub-region to be tested and the areas corresponding to each of the underlying surface types; and determine the pollution intensity based on the area proportion and an average runoff concentration corresponding to each of the underlying surface types. Or Determine a pollution input in a target water body based on monitoring data of the target water body in the multi-modal monitoring data, wherein the target water body represents a water body for containing precipitation flowing through the sub-region to be tested; and determine the pollution intensity based on the pollution input.

4. The method of claim 3, wherein, The pollution input is determined by one of the following methods: Determine a pollution accumulation in the target water body based on the monitoring data of the target water body; Determine a wash-off coefficient, a wash-off index of the sub-region to be tested, and a unit-area runoff of the target water body based on the multi-modal monitoring data; Determine a pollution runoff input based on the wash-off coefficient, the wash-off index, the unit-area runoff, and the pollution accumulation; Determine the pollution input based on a pollution runoff input of the sub-region to be tested and a pollution life input of the sub-region to be tested; Determine a pollution concentration of each of a plurality of pollution types, a degradation coefficient of each of the plurality of pollution types, and a water volume of the target water body based on the multi-modal monitoring data; and Determine the pollution input based on the pollution concentrations, the degradation coefficients, and the water volume.

5. The method of claim 2, wherein, The determination of the pollution levels of the sub-regions to be tested based on the pollution intensities of the sub-regions to be tested comprises: Determine a maximum first pollution intensity and a minimum second pollution intensity from the pollution intensities of the sub-regions to be tested; and Normalize the pollution intensities of the sub-regions to be tested based on the first pollution intensity and the second pollution intensity to obtain the pollution levels of the sub-regions to be tested.

6. The method of claim 1, wherein The determination of the functional states of the ecological infrastructure in the sub-region to be tested at the sampling time points based on the multi-modal time-series data comprises: Analyze each of the sampling time points in sequence according to a time sequence relationship between the sampling time points to determine a pollution content of each of a plurality of pollution types contained in a water body corresponding to the sub-region to be tested at the sampling time point; Determine a water quality index of the water body corresponding to the sub-region to be tested at the sampling time points based on preset weights of the pollution types and the pollution contents of the pollution types; and Determine the functional states of the ecological infrastructure in the sub-region to be tested at the sampling time points based on the water quality indices.

7. The method of claim 6, wherein, The method further comprises: in response to an update of the ecological infrastructure in the to-be-tested region, determining a to-be-evaluated time period of the update, the to-be-evaluated time period being determined according to the adjacent preceding sampling time point and the sampling time point; and based on a function state determined according to the adjacent preceding sampling time point and the sampling time point corresponding to the to-be-evaluated time period, determining an update effect of the update. The method further comprises: based on the resilience evaluation index system, determining whether the resilience level of the ecological infrastructure indicates that the ecological infrastructure has a function short board, comprising:

8. The method of claim 7, wherein, based on the resilience evaluation index system, quantifying the resilience level of the ecological infrastructure under different conditions, wherein the resilience level indicates the ability of the ecological infrastructure to cope with pollution caused by the target time period; and based on the resilience level and a preset resilience threshold range of the ecological infrastructure, determining whether the resilience level of the ecological infrastructure indicates that the ecological infrastructure has a function short board. The method further comprises:

9. The method of claim 8, wherein, based on the multi-modal monitoring data, the pollution risk of each of the to-be-tested sub-regions, and the function state of the ecological infrastructure in the to-be-tested region at each of the sampling time points, performing correlation analysis on the to-be-tested region to obtain a correlation analysis result, comprising: taking the multi-modal monitoring data as a plurality of independent variables, taking the pollution risk of each of the to-be-tested sub-regions and the function state of the ecological infrastructure in the to-be-tested region at each of the sampling time points as a plurality of dependent variables, and constructing a correlation model between the independent variables and the dependent variables; using a correlation analysis method, performing correlation analysis on the correlation model to obtain an association strength relationship between the independent variables and the dependent variables; and 10. The method of claim 1, wherein, based on the association strength relationship, determining the correlation analysis result. ​ ​ ​

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