A bioretention purification method and device for rainwater collection and treatment in a port terminal and a readable storage medium

Abstract

The application discloses a kind of biological retention purification method, device and readable storage medium for port rainwater collection and processing, comprising: first, through rainwater collection pipe, the rainwater of port yard stockyard drainage ditch that is initially filtered by grid is collected, then depth purification is carried out using biological filter with multiple layers of structure such as vegetation layer and water distribution layer, and the filtered rainwater is stored in target water collecting pool. Then, the water quality of the water collecting pool is evaluated at a predetermined time interval, and if the biological retention purification is passed, a reclaimed water reuse task is configured for it. This method realizes port rainwater purification and reuse, and improves water resource utilization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of port management, in particular to a biological retention purification method and device for collecting and processing rainwater at a port terminal, and a readable storage medium. BACKGROUND

[0002] In the operation of a port terminal, rainwater runoff carries pollutants, and if it is directly discharged, it will cause water pollution. The traditional rainwater treatment method has the problems of low efficiency and inability to treat rainwater in different areas. The existing technology cannot effectively collect and treat rainwater with different pollution levels in different functional areas of the port, and does not fully consider rainwater reuse. The present application aims to provide an efficient biological retention purification method for collecting and processing rainwater at a port terminal, to realize classified collection, deep purification and reasonable reuse of rainwater, and to solve the problems of rainwater pollution and water resource utilization in the port area. SUMMARY

[0003] The present application aims to provide a biological retention purification method and device for collecting and processing rainwater at a port terminal, and a readable storage medium.

[0004] In a first aspect, the present application provides a biological retention purification method for collecting and processing rainwater at a port terminal, comprising:

[0005] Collecting rainwater from the drainage ditch of the port yard through the rainwater collection pipe after preliminary filtration by the grid;

[0006] Deeply purifying the rainwater through a biological filter, and storing the deeply purified rainwater in a target water collection tank through a water collection pipe; the biological filter comprises a vegetation layer, a water distribution layer, a microbial filler layer, a supporting layer and a percolation layer;

[0007] Evaluating the water quality of the target water collection tank at a preset time interval to obtain a water quality evaluation result;

[0008] If the water quality evaluation result indicates that the biological retention purification is successful, configuring a reclaimed water reuse task for the target water collection tank.

[0009] In one possible implementation, the cross section of the biological filter is circular arc shaped;

[0010] The vegetation layer is located on the side of the biological filter close to the ground, and is composed of a predetermined port plant species; the vegetation layer is used to filter and intercept the substances in the runoff rainwater that have not been filtered by the grid;

[0011] The water distribution layer is located on the side of the vegetation layer away from the ground, and is composed of quartz sand filling; the water distribution layer is used to filter and control the uniform infiltration of rainwater;

[0012] The microbial filler layer is located on the side of the water distribution layer away from the vegetation layer, and is composed of activated carbon particles inoculated with a composite microbial membrane combination.

[0013] The supporting layer is located on the side of the microbial filler layer away from the water distribution layer, and is composed of gravel filling, and is used to support the vegetation layer, the water distribution layer, and the microbial filler layer.

[0014] The percolation layer is located on the side of the supporting layer away from the microbial filler layer, and is composed of zeolite filling, and is used to filter and adsorb pollutants.

[0015] In one possible implementation, the water quality of the target water collection pool is evaluated at a preset time interval to obtain a water quality evaluation result, which includes:

[0016] The feature extraction operation is performed on the to-be-evaluated water quality sample in the to-be-evaluated water quality sample set at a preset time interval to obtain a water quality feature set, wherein the to-be-evaluated water quality sample set includes a plurality of to-be-evaluated water quality samples for indicating different monitoring points of the target water collection pool, and the water quality feature set includes a plurality of water quality features arranged in a preset monitoring point order.

[0017] According to the monitoring point position information represented by the to-be-evaluated water quality sample in the to-be-evaluated water quality sample set, the target water quality feature and the target sub-zone evaluation vector corresponding to the target water quality feature are determined from the water quality feature set.

[0018] The target water quality feature is taken as a feature reference parameter and a feature weight parameter respectively, and the target sub-zone evaluation vector is taken as an evaluation condition parameter, and water quality correlation mapping processing is performed to obtain the sub-zone water quality sub-feature.

[0019] According to a plurality of sub-zone water quality sub-features, a comprehensive water quality feature is obtained, wherein the evaluation vector includes a plurality of evaluation unit parameters, and the evaluation unit parameters are used to indicate the water collection pool sub-zone characteristics of the target water collection pool.

[0020] The comprehensive water quality feature is subjected to pollution type determination to obtain a basic pollution category probability.

[0021] The comprehensive water quality feature is subjected to pollution sub-zone determination to obtain a basic pollution sub-zone identifier.

[0022] According to the basic pollution category probability and the basic pollution sub-zone identifier, the basic water quality evaluation result is obtained.

[0023] According to the basic water quality evaluation result, evaluation unit parameters in the evaluation vector are removed to obtain an optimized evaluation vector;

[0024] According to the optimized evaluation vector and the water quality feature set, a water quality evaluation result is obtained.

[0025] In a possible implementation, the basic water quality evaluation result includes a plurality of basic pollution category probabilities and a plurality of basic pollution zoning identifiers in a mapping relationship with the plurality of basic pollution category probabilities, and the plurality of basic pollution zoning identifiers are in a mapping relationship with a plurality of evaluation unit parameters in the evaluation vector;

[0026] The removing, according to the basic water quality evaluation result, of the evaluation unit parameters in the evaluation vector to obtain the optimized evaluation vector includes:

[0027] For each of the basic pollution zoning identifiers, a to-be-removed evaluation unit parameter corresponding to the basic pollution zoning identifier is determined from the evaluation vector;

[0028] According to a basic pollution category probability corresponding to the basic pollution zoning identifier and a pollution determination threshold, a parameter elimination determination of the to-be-removed evaluation unit parameter is determined.

[0029] According to a plurality of parameter elimination determinations, the evaluation unit parameters in the evaluation vector are removed to obtain the optimized evaluation vector.

[0030] In a possible implementation, the basic water quality evaluation result includes a plurality of basic pollution category probabilities and a plurality of basic pollution zoning identifiers in a mapping relationship with the plurality of basic pollution zoning identifiers, and the plurality of basic pollution zoning identifiers are in a mapping relationship with a plurality of evaluation unit parameters in the evaluation vector;

[0031] The removing, according to the basic water quality evaluation result, of the evaluation unit parameters in the evaluation vector to obtain the optimized evaluation vector includes:

[0032] A to-be-removed zoning evaluation sub-vector corresponding to a preset monitoring area in the target catchment pool is determined from the evaluation vector;

[0033] According to the plurality of basic pollution zoning identifiers, a specified number of target basic pollution category probabilities corresponding to the preset monitoring area are determined from the plurality of basic pollution category probabilities;

[0034] According to basic pollution zoning identifiers of the specified number of target basic pollution category probabilities, respectively, evaluation unit parameters in the to-be-removed zoning evaluation sub-vector are removed to obtain the optimized zoning evaluation sub-vector.

[0035] According to the plurality of the optimized partition evaluation sub-vectors, an optimized evaluation vector is obtained.

[0036] In a possible implementation, the water quality evaluation result is obtained according to the optimized evaluation vector and the set of water quality features, including:

[0037] According to the optimized evaluation vector and the set of water quality features, a target comprehensive water quality feature is obtained.

[0038] The target comprehensive water quality feature is subjected to pollution type determination to obtain a pollution category probability.

[0039] The target comprehensive water quality feature is subjected to pollution partition determination to obtain a pollution partition identifier.

[0040] The water quality evaluation result is obtained according to the pollution category probability and the pollution partition identifier.

[0041] In a possible implementation, the target comprehensive water quality feature is obtained according to the optimized evaluation vector and the set of water quality features, including:

[0042] The optimized evaluation vector is subjected to pollution feature interaction processing to obtain a target evaluation vector.

[0043] The target evaluation vector and the set of water quality features are subjected to water quality correlation analysis to obtain the target comprehensive water quality feature.

[0044] The target comprehensive water quality feature is obtained according to the optimized evaluation vector and the set of water quality features, and further including:

[0045] In a case where it is determined that the current period is less than the preset period number, a previous period target evaluation vector is subjected to pollution feature interaction processing to obtain a current period target evaluation vector.

[0046] The current period target evaluation vector and a previous period comprehensive water quality feature are subjected to water quality correlation analysis to obtain a current period comprehensive water quality feature.

[0047] In a case where it is determined that the current period is equal to the preset period number, the target comprehensive water quality feature is determined according to the current period comprehensive water quality feature.

[0048] In a possible implementation, the method further includes:

[0049] The pollution feature interaction processing is performed on the basic evaluation vector to obtain the evaluation vector, wherein the basic evaluation vector comprises a plurality of dynamic adjustable evaluation unit parameters, and the dynamic adjustable evaluation unit parameters are obtained by training a historical water quality sample set and a pollution level label corresponding to the historical water quality sample set.

[0050] In a second aspect, an embodiment of the present application provides a bioretention purification device for collecting and processing rainwater in a port terminal, comprising:

[0051] The purification module is configured to collect rainwater from the drainage ditch of the port yard through the rainwater collection pipe after the rainwater is preliminarily filtered by the grid, to perform deep purification on the rainwater by the biological filter tank, and to store the rainwater after the deep purification into the target water collection tank through the water collection pipe; the biological filter tank comprises a vegetation layer, a water distribution layer, a microbial filler layer, a supporting layer, and a percolation layer.

[0052] The evaluation module is configured to perform water quality evaluation on the target water collection tank at a preset time interval to obtain a water quality evaluation result, and to configure a reclaimed water reuse task for the target water collection tank when the water quality evaluation result indicates that the bioretention purification is passed.

[0053] In a third aspect, an embodiment of the present application provides a readable storage medium, which comprises a computer program, and the computer program controls a computer device where the readable storage medium is located to execute the method of the first aspect when the computer program is run.

[0054] Compared with the prior art, the present application has the following beneficial effects: by using the bioretention purification method, device and readable storage medium for collecting and processing rainwater in a port terminal, rainwater from the drainage ditch of the port yard after preliminary filtration by the grid is collected through the rainwater collection pipe, and then deep purification is performed on the rainwater by the biological filter tank with a multi-layer structure comprising a vegetation layer, a water distribution layer, etc., and the filtered rainwater is stored in the target water collection tank. Then, water quality evaluation is performed on the water collection tank at a preset time interval, and a reclaimed water reuse task is configured for the target water collection tank when the bioretention purification is passed. The method realizes rainwater purification and reuse in the port terminal, and improves the utilization rate of water resources. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0056] Figure 1 The step flowchart of the bioretention purification method for collecting and processing rainwater in a port terminal provided by the embodiments of the present application is shown in the following figure:

[0057] Figure 2 A biological retention purification system framework schematic diagram for port wharf rainwater collection and treatment is provided for the embodiment of the present application.

[0058] Figure 3 A biological retention purification device structure schematic diagram for port wharf rainwater collection and treatment is provided for the embodiment of the present application.

[0059] Figure 4 A computer device structure schematic diagram is provided for the embodiment of the present application. DETAILED DESCRIPTION

[0060] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments but not all of the present application. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0061] The specific embodiments of the present application will be described in detail below in connection with the drawings.

[0062] To solve the technical problems in the foregoing background art, Figure 1 A biological retention purification method for port wharf rainwater collection and treatment is provided for the embodiment of the present application, and the biological retention purification method for port wharf rainwater collection and treatment will be described in detail below.

[0063] Step S201, collecting rainwater of a port area stockyard drainage ditch that has been preliminarily filtered through a grid through a rainwater collection pipe;

[0064] Step S202, performing deep purification on the rainwater through a biological filter tank, and storing the deep-purified rainwater into a target water collection tank through a water collection pipe; the biological filter tank includes a vegetation layer, a water distribution layer, a microorganism filler layer, a supporting layer and a percolation layer;

[0065] Step S203, performing water quality evaluation on the target water collection tank at a preset time interval to obtain a water quality evaluation result;

[0066] Step S204, configuring a reclaimed water reuse task for the target water collection tank in a case where the water quality evaluation result represents that the biological retention purification passes.

[0067] In the embodiment of the present application, for example, the server first carries out detailed partitioning of the port area, divides the port area into different regions such as a living area, a yard area, a loading and unloading area, a transfer area, and the like according to functions, and further divides the yard area into a container yard area, a piece of miscellaneous cargo yard area, a dry bulk cargo yard area, and the like according to cargo types. The rainwater and sewage generated in different partitions are collected in a classified manner. For example, the rainwater and sewage generated in the living area is relatively clean, and the main pollutants are impurities in domestic sewage. The server controls rainwater collection facilities around the living area, and guides the rainwater and sewage in the area to specific collection pipelines, which are independent of the collection pipelines in other areas, so as to ensure that the rainwater and sewage in the living area is not mixed with the rainwater and sewage in other areas.

[0068] For the yard area, the rainwater and sewage in different types of yard areas differ greatly in pollution degree and pollutant types. For example, in the dry bulk cargo yard area, a large amount of dust, scattered objects, and possible heavy metal pollutants are carried by the rainwater and sewage during rainfall because a large amount of ores and coal are stored. The server controls the pre-set drainage ditches around the yard area, collects only the rainwater and sewage in the area, and transports them to a grid for preliminary filtration through independent pipelines.

[0069] The rainwater and sewage in the loading and unloading area may contain oil stains and mechanical wear debris generated during the loading and unloading process. The server collects the rainwater and sewage in the loading and unloading area through special rainwater and sewage collection devices arranged in the area, and guides them into special collection pipelines for subsequent processing.

[0070] The rainwater and sewage in the transfer area also have different characteristics. The server collects the rainwater and sewage in a classified manner according to the characteristics of the transferred cargo, and guides them into corresponding collection pipelines. Through the partitioning of the port area and the classified collection of the rainwater and sewage according to the partitions, the rainwater and sewage with different pollution degrees and types can be processed more targetedly, and the efficiency and purification effect of rainwater collection and processing are improved.

[0071] Please refer to Figure 2 , Figure 2 The server starts the grid device after monitoring the water flow signal. The grid intercepts large suspended solids and floating objects such as coal blocks, ore particles, and sundries, and prevents them from entering the subsequent processing program.

[0072] The rainwater filtered by the grid enters the rainwater collection pipe along the established channel. The server precisely controls the connection between the rainwater collection pipe and the yard to ensure that the filtered rainwater smoothly enters the collection pipe. These rainwater collection pipes are responsible for transporting the rainwater filtered by the grid from the drainage ditches of the yards to the biological filter tank.

[0073] For example, after a rainstorm in a large coal dry bulk cargo yard area, the water flow in the drainage ditch is turbulent, and the rainwater mixed with coal dust and small particles of coal quickly converges. The grid starts to intercept larger coal particles, and the fine coal dust and rainwater enter the rainwater collection pipe. The server monitors the rainwater flow and impurity conditions in real time to ensure the normal operation of the collection pipe and prevent blockage.

[0074] Structure and function of biological filter:

[0075] Rainwater transported through the rainwater collection pipe flows into the biological filter. The biological filter relies on the reconstruction of the original port drainage channel or vegetation landscape path, with a circular arc-shaped overall section to reduce rainwater runoff erosion. The top width is designed according to the size of the port road or the original landscape, and the bottom width is controlled at 0.5-1.5m. The slope gradient is determined in combination with the soil properties of the port area and the length of the original drainage channel, generally not exceeding 1:3.

[0076] Purification process of vegetation layer:

[0077] The uppermost layer of the biological filter is the vegetation layer. The server selects suitable vegetation according to the type of the port area (inland river or sea port). For example, in a sea port area, salt-tolerant herbaceous plants may be selected for the vegetation layer. Rainwater flowing into the biological filter first contacts the vegetation layer, and the vegetation stems and leaves filter and intercept small particles such as coal, mineral dust, and other small particles in the runoff rainwater. For example, in a coal yard area, the vegetation layer plants' roots adsorb coal dust, reducing the coal dust content in the rainwater, while slowing down the rainwater flow, which facilitates rainwater infiltration into the underlying soil layer.

[0078] Filtering and uniform infiltration of water distribution layer:

[0079] Rainwater passes through the vegetation layer to reach the water distribution layer. The water distribution layer is located below the vegetation layer and is filled with 2.0-5.0mm quartz sand, with a thickness of about 100-200mm. The quartz sand further filters the remaining small impurities in the rainwater, while achieving uniform infiltration of the rainwater, ensuring that the rainwater is evenly distributed to the underlying microbial filler layer, ensuring the consistency of the biological filter's purification effect.

[0080] Depth purification of microbial filler layer:

[0081] After passing through the water distribution layer, the rainwater enters the microbial filler layer. This layer is composed of activated carbon particles inoculated with a composite microbial membrane, with a thickness of about 20-50mm. The server inoculates the composite microbial membrane with corresponding functional microorganisms or activated sludge from the original sewage treatment plant according to the characteristics of the rainwater and sewage in different zones of the port area. For example, in the dry bulk cargo area, denitrifying bacteria and phosphorus-accumulating bacteria are inoculated to deal with high concentrations of total nitrogen, total phosphorus, organic matter, heavy metals, and other pollutants in the rainwater and sewage. These microorganisms decompose and transform pollutants in the rainwater on the surface of activated carbon particles, such as denitrifying bacteria that convert nitrate to nitrogen gas to reduce total nitrogen content and phosphorus-accumulating bacteria that absorb phosphorus elements to remove phosphorus, significantly reducing the concentration of pollutants in the rainwater.

[0082] Supporting role of supporting layer:

[0083] Below the microbial filler layer is a supporting layer filled with 50-100mm gravel, about 100-500mm thick. The supporting layer supports the upper layers and prevents them from collapsing under the pressure of rainwater, while the gravel voids provide a channel for rainwater to flow and ensure that it penetrates downward smoothly.

[0084] Final purification and collection of percolation layer:

[0085] Below the supporting layer is a percolation layer composed of 10-20mm zeolite, with a layer thickness of 100-300mm. The microporous structure of the zeolite further filters and adsorbs residual pollutants in the rainwater, improving the quality of the rainwater. The server monitors the purification effect of the percolation layer to ensure that the treated rainwater meets the standards. The treated rainwater is connected to the water collection pipe at the bottom of the percolation layer, which is a pvc pipe with a trapezoidal cross-section and an inclined bottom. The upper section is porous and used for percolation of treated rainwater. These water collection pipes collect and transport the purified rainwater to the target water storage tank.

[0086] For example, in the biological filter corresponding to the ore dry bulk storage area, the rainwater transported by the grid and rainwater collection pipe is successively treated by each layer. The vegetation layer intercepts ore dust, the water distribution layer evenly distributes the water flow and filters impurities, the microbial filler layer removes heavy metals and organic matter, the supporting layer stabilizes the structure, and the percolation layer adsorbs residual pollutants. Finally, the clear rainwater flows into the target water storage tank through the water collection pipe, completing the deep purification and collection.

[0087] The target water storage tank stores rainwater that has been deeply purified by the biological filter, and the server performs water quality evaluation at preset time intervals.

[0088] The preset time interval is set according to the actual situation of the port terminal, such as every 24 hours or every 48 hours. When the preset time is reached, the server starts the water quality evaluation program and controls the water quality detection equipment to sample the rainwater in the target water storage tank. The detection equipment collects representative water samples from different positions in the water storage tank.

[0089] The water samples are transported to the water quality analysis unit, and the server cooperates with the analysis unit. The analysis unit detects indicators such as pH value, chemical oxygen demand (COD), biochemical oxygen demand (BOD), total nitrogen, total phosphorus, and heavy metal content. For example, to detect the chemical oxygen demand (COD), the analysis unit measures the amount of oxidizing agent consumed by reducing substances in the water sample to evaluate the organic matter content; to detect the heavy metal content, atomic absorption spectrometry and other technologies are used to measure the concentration of elements such as lead, mercury, and cadmium.

[0090] After detection is completed, the analysis unit transmits data to the server, the server compares the detection data with preset water quality standards, the standards are formulated according to different uses of rainwater recycling in the port terminal, for example, the water quality standard is relatively low for ground sprinkling and dust reduction, the water standard is relatively high for toilet flushing in the living area and landscape water use. The server comprehensively analyzes various indexes to obtain water quality evaluation results, and judges whether the rainwater meets the recycling standard.

[0091] When the water quality evaluation results obtained by the server show that the biological retention purification passes, the server configures a recycled water recycling task for the target water collecting pool.

[0092] The server arranges recycled water for ground sprinkling and dust reduction according to the dust condition in the port area and weather data. In the port terminal stockyard area, loading and unloading area and other areas prone to dust, when the weather is dry, the wind is strong and the goods are frequently loaded and unloaded, the server controls the sprinkling system to be turned on, and the recycled water in the target water collecting pool is uniformly sprayed on the ground through the nozzle, adsorbs and settles the dust, and improves the air quality and working environment in the port area.

[0093] The server introduces the recycled water in the target water collecting pool into the toilet pipeline system in the living area through the intelligent control system. When residents use the toilet to flush, the recycled water flows out from the pipeline to complete the flushing, realizes the recycling of water resources, reduces the dependence on fresh water resources, and reduces the pressure of sewage treatment in the living area.

[0094] In the landscape area of the port terminal, such as the garden, artificial lake and the like, the server controls the supply of recycled water according to the water demand of the landscape area. In summer, the landscape plants need a large amount of water, the server controls the irrigation system to deliver the recycled water to irrigate the plants in the garden; at the same time, the artificial lake is supplemented with recycled water through the pipeline to maintain the water level and water quality of the lake, and create a beautiful landscape environment.

[0095] For example, in summer, the dust in the stockyard area of the port terminal is large, the server starts the ground sprinkling task to suppress the dust; the residents in the living area normally use the recycled water to flush the toilet; the recycled water is used in the landscape area to irrigate the flowers, and the artificial lake is supplemented with the recycled water, realizing the efficient recycling of water resources and guaranteeing the sustainable development of the port terminal.

[0096] In summary, through the precise control and scientific management of the server on each step of the biological retention purification method for collecting and treating rainwater in the port terminal, efficient collection, deep purification and reasonable recycling of rainwater are realized, which effectively supports the sustainable development of the port terminal.

[0097] In the embodiment of the present application, the cross section of the biological filter tank is circular arc;

[0098] The vegetation layer is located on one side of the biological filter tank close to the ground, the vegetation layer is composed of preset plant species in the port area, and the vegetation layer is used to filter and intercept the substances carried by the grid in the runoff rainwater which are not filtered.

[0099] The water distribution layer is located on the side of the vegetation layer away from the ground, and is filled with quartz sand, and the water distribution layer is used for filtering and controlling uniform infiltration of rainwater.

[0100] The microbial filler layer is located on the side of the water distribution layer away from the vegetation layer, and is composed of activated carbon particles inoculated with a composite microbial membrane combination, and the microbial filler layer is inoculated with corresponding functional microorganisms based on the characteristics of rainwater and sewage in different zones of the port.

[0101] The supporting layer is located on the side of the microbial filler layer away from the water distribution layer, and is filled with gravel, and the supporting layer is used to support the vegetation layer, the water distribution layer, and the microbial filler layer.

[0102] The percolation layer is located on the side of the supporting layer away from the microbial filler layer, and is filled with zeolite, and the percolation layer is used for filtering and adsorbing pollutants.

[0103] In the embodiment of the present application, for example, in a large comprehensive port, the server is responsible for controlling the operation of the biological filter. The cross section of the biological filter is arc-shaped, which can effectively reduce the scouring of rainwater runoff.

[0104] The biological filter is close to the ground on one side of the vegetation layer, and the server selects the preset plant species of the port according to the conditions of the port area, such as inland river or sea port, to build the vegetation layer. For example, in a sea port, salt-tolerant plants are selected. When it rains, rainwater carrying various pollutants flows into the biological filter from the yard after being preliminarily filtered by the grid. The vegetation layer plays a role in filtering and intercepting substances such as coal dust, fine mineral particles and other substances that cannot be filtered by the grid.

[0105] Below the vegetation layer is the water distribution layer, which is filled with quartz sand. When the rainwater passes through the vegetation layer and enters the water distribution layer, the quartz sand further filters the rainwater and removes finer impurities. At the same time, the water distribution layer controls the uniform infiltration of rainwater. For example, in the biological filter corresponding to the dry bulk cargo yard, when a large amount of rainwater flows in, the water distribution layer ensures that the rainwater is evenly distributed to the lower layer, avoiding the concentration of local water flow, and ensuring the consistency of the purification effect.

[0106] Further down is the microbial filler layer, which is composed of activated carbon particles inoculated with a composite microbial membrane combination. The server inoculates corresponding functional microorganisms in this layer according to the characteristics of rainwater and sewage in different zones of the port. For example, in the biological filter corresponding to the loading and unloading area, due to the large amount of oil in the rainwater and sewage, microorganisms that can degrade oil pollution are inoculated. Microorganisms on the surface of activated carbon particles decompose and transform pollutants in rainwater, deep purifying water quality.

[0107] The supporting layer is filled with gravel and bears the weight of the vegetation layer, the water distribution layer and the microbial filler layer above it, ensuring the stability of the biofilter structure. Under the long-term rainwater erosion and pressure of each layer, the supporting layer remains stable, ensuring the normal operation of the entire filter.

[0108] The bottommost is the percolation layer filled with zeolite. The rainwater treated by the previous layers flows into the percolation layer, and the zeolite, with its rich microporous structure, performs the final filtration and adsorption of the residual pollutants in the rainwater, further improving the water quality, so that the rainwater flowing out of the biofilter reaches higher standards, laying a foundation for subsequent entry into the target catchment pool and reuse.

[0109] In the embodiment of the present application, the water quality evaluation of the target catchment pool at preset time intervals to obtain the water quality evaluation result can be implemented by the following examples.

[0110] Perform feature extraction operations on the to-be-evaluated water quality samples in the to-be-evaluated water quality sample set at preset time intervals to obtain a water quality feature set, wherein the to-be-evaluated water quality sample set includes a plurality of to-be-evaluated water quality samples for indicating different monitoring points of the target catchment pool, and the water quality feature set includes a plurality of water quality features arranged in a preset monitoring point order;

[0111] Perform water quality correlation analysis on the water quality feature set and the evaluation vector to obtain a comprehensive water quality feature, wherein the evaluation vector includes a plurality of evaluation unit parameters, and the evaluation unit parameters are used to indicate the catchment pool partition characteristics of the target catchment pool;

[0112] Perform water quality evaluation on the comprehensive water quality feature to obtain a basic water quality evaluation result;

[0113] According to the basic water quality evaluation result, remove the evaluation unit parameters in the evaluation vector to obtain an optimized evaluation vector;

[0114] According to the optimized evaluation vector and the water quality feature set, obtain a water quality evaluation result.

[0115] In the embodiment of the present application, the server starts the water quality evaluation work at preset time intervals. It controls the sampling device to collect a plurality of to-be-evaluated water quality samples from different monitoring points of the target catchment pool. These monitoring points are distributed at different positions of the catchment pool and cover key areas such as the inlet, the middle part and the outlet, forming a to-be-evaluated water quality sample set. Subsequently, the server performs feature extraction operations on each to-be-evaluated water quality sample in the set. For example, for the target catchment pool into which the rainwater after treatment in the dry bulk cargo area flows, the dust content and heavy metal content in the sample are detected; for the sample related to the loading and unloading area, the oil content is more concerned. After extraction, a water quality feature set arranged in a preset monitoring point order is obtained, which includes a plurality of water quality features.

[0116] Then, the server performs water quality correlation analysis on the water quality feature set and the evaluation vector. The evaluation vector contains multiple evaluation unit parameters, which are set according to different partition features of the target water collection pool. For example, different partitions correspond to different functions, some are used to store preliminary purified water, and some are close to the reuse end. The server analyzes the relationship between the water quality features and the evaluation unit parameters of each partition, for example, if a monitoring point is close to the edge of the water collection pool, its water quality features interact with the evaluation unit parameters of the corresponding edge partition in the correlation analysis, and finally the comprehensive water quality features are integrated.

[0117] The server performs water quality evaluation on the comprehensive water quality features. It determines whether the comprehensive water quality features meet the standards according to the pre-set water quality standards, such as pH value range, upper limit of pollutant concentration, etc., to obtain the basic water quality evaluation result. Assuming that the basic water quality evaluation result shows that the water quality in some areas does not meet the standard in some aspects, the server will remove the evaluation unit parameters in the evaluation vector according to this result. For example, if it is found that the water quality in a corner of the water collection pool is abnormal due to special reasons, the evaluation unit parameter corresponding to the corner may be removed to optimize the evaluation vector.

[0118] Finally, the server uses the optimized evaluation vector and the water quality feature set to perform comprehensive analysis again. It reconsiders the relationship between each water quality feature and the parameters in the optimized evaluation vector, and thus obtains a more accurate water quality evaluation result. This result comprehensively reflects the water quality status of the target water collection pool, providing a reliable basis for subsequent reuse of reclaimed water.

[0119] In the embodiment of the application, the water quality correlation analysis of the water quality feature set and the evaluation vector to obtain the comprehensive water quality feature can be implemented through the following examples.

[0120] According to the monitoring point position information represented by the water quality sample to be evaluated in the water quality sample set to be evaluated, the target water quality feature and the target partition evaluation sub-vector corresponding to the target water quality feature are determined from the water quality feature set;

[0121] Perform water quality correlation analysis on the target water quality feature and the target partition evaluation sub-vector to obtain a partition water quality sub-feature;

[0122] According to a plurality of partition water quality sub-features, the comprehensive water quality feature is obtained.

[0123] In the embodiments of the present application, the server performs work according to the position information of the monitoring points represented by each of the water quality samples in the set of water quality samples to be evaluated. For example, in the target water collection pool, the monitoring points at different positions correspond to different functional areas, such as the monitoring point near the inflow end of rainwater in the container yard area, the monitoring point near the reuse end in the residential area, etc. The server determines, from the set of water quality characteristics, for each monitoring point position, the target water quality characteristic corresponding thereto, such as the monitoring point near the inflow end of rainwater in the container yard area, which may focus on the solid suspended substance content, the residue of specific pollutants, etc. At the same time, the server determines the target sub-zone evaluation vector corresponding to the target water quality characteristic, which contains the evaluation unit parameters related to the sub-zone where the monitoring point is located, such as the special requirements of the sub-zone on water quality, the past water quality data characteristics, etc.

[0124] Subsequently, the server performs water quality correlation analysis on the determined target water quality characteristic and target sub-zone evaluation vector. Taking a monitoring point near the dry bulk cargo yard area as an example, the dust content in the target water quality characteristic is relatively high, and the filtering requirements and acceptable range of dust in the target sub-zone evaluation vector are clear. The server analyzes the relationship between the dust content and these requirements and ranges, while considering the mutual influence of other related water quality indicators and evaluation parameters, thereby obtaining the sub-zone water quality sub-characteristic of the sub-zone, which comprehensively reflects the water quality characteristics of the sub-zone where the monitoring point is located.

[0125] The server performs the above operation on all monitoring points to obtain a plurality of sub-zone water quality sub-characteristics. Then, the server comprehensively considers these sub-zone water quality sub-characteristics. For example, the sub-zone water quality sub-characteristics near different functional areas are integrated according to certain weights. The sub-zone water quality sub-characteristic near the monitoring point near the source of rainwater may have a relatively large weight, and the sub-zone water quality sub-characteristic near the monitoring point near the reuse end may have a relatively small weight. In this way, the server finally obtains the comprehensive water quality characteristic, which comprehensively and meticulously reflects the overall water quality condition of the target water collection pool, thereby providing a solid data foundation for subsequent water quality evaluation and reuse decision-making.

[0126] In the embodiments of the present application, the water quality correlation analysis on the target water quality characteristic and the target sub-zone evaluation vector to obtain the sub-zone water quality sub-characteristic can be implemented through the following examples.

[0127] The target water quality characteristic is taken as a characteristic reference parameter and a characteristic weight parameter, respectively, and the target sub-zone evaluation vector is taken as an evaluation condition parameter, and water quality correlation mapping processing is performed to obtain the sub-zone water quality sub-characteristic.

[0128] In the embodiment of the present application, the server collects water quality samples to be evaluated from different monitoring points, determines target water quality characteristics and corresponding target sub-evaluation vectors of the partition. Taking the monitoring point of the target water collection pool corresponding to the port loading and unloading area as an example, the target water quality characteristics can be the oil content in the rainwater, the size and concentration of cargo debris particles, etc. The target sub-evaluation vector of the partition includes acceptable standards of the loading and unloading area for oil and debris in the rainwater, the expected purification degree of the treatment process, and other evaluation condition parameters.

[0129] Next, the server starts the water quality correlation mapping process. It takes the target water quality characteristics as characteristic reference parameters and characteristic weight parameters, respectively. For example, for the oil content, on the one hand, it is taken as a characteristic reference parameter, representing the actual oil content level in the current rainwater, which is the basic data for judging the water quality condition; on the other hand, it is taken as a characteristic weight parameter, because the loading and unloading area is more sensitive to oil, and the oil content has a greater impact on the overall water quality, so it is given a higher weight.

[0130] Then, the server takes the target sub-evaluation vector of the partition as evaluation condition parameters. For example, the loading and unloading area stipulates that the oil content in the rainwater must be below a certain threshold, and there are corresponding standards for the size and concentration of cargo debris particles, which constitute the evaluation condition parameters. The server compares and analyzes the characteristic reference parameters (actual oil content, etc.) with the evaluation condition parameters (stipulated oil threshold, etc.), while considering the characteristic weight parameters (high weight of oil). If the actual oil content is close to or exceeds the threshold, and because of its high weight, the impact on the partition water quality is greater under comprehensive consideration.

[0131] Through such water quality correlation mapping process, the server comprehensively and carefully analyzes the relationship between the target water quality characteristics and the target sub-evaluation vector of the partition, and finally obtains the sub-feature of the partition water quality of the loading and unloading area where the monitoring point is located. This sub-feature of the partition water quality comprehensively reflects the relationship between various pollutants in the rainwater of the region and the evaluation standards, and accurately reflects the water quality condition of the region, providing key data support for subsequent formation of comprehensive water quality characteristics and overall water quality evaluation.

[0132] In the embodiment of the present application, the basic water quality evaluation result includes a plurality of basic pollution category probabilities and a plurality of basic pollution partition identifiers in a mapping relationship with the plurality of basic pollution category probabilities, and the plurality of basic pollution partition identifiers are in a mapping relationship with a plurality of evaluation unit parameters in the evaluation vector;

[0133] The evaluation unit parameters in the evaluation vector are removed according to the basic water quality evaluation result to obtain an optimized evaluation vector, which can be implemented by the following example.

[0134] For each of the basic pollution partition identifier, determine the to-be-removed evaluation unit parameter corresponding to the basic pollution partition identifier from the evaluation vector;

[0135] According to the basic pollution category probability corresponding to the basic pollution partition identifier and the pollution determination threshold, determine the parameter elimination determination of the to-be-removed evaluation unit parameter;

[0136] According to a plurality of parameter elimination determinations, remove the evaluation unit parameter in the evaluation vector to obtain the optimized evaluation vector.

[0137] In the embodiment of the present application, an exemplary target water collection pool of a certain port terminal is taken as an example. The basic water quality evaluation result shows that, under the basic pollution partition identifier corresponding to the dry bulk cargo yard area, the coal dust pollution category probability is 80%, and under the basic pollution partition identifier corresponding to the loading and unloading area, the oil pollution category probability is 70%, and the like.

[0138] Next, the server removes the evaluation unit parameter in the evaluation vector according to the basic water quality evaluation result to obtain the optimized evaluation vector.

[0139] For each basic pollution partition identifier, the server determines the to-be-removed evaluation unit parameter corresponding thereto from the evaluation vector. For example, for the basic pollution partition identifier corresponding to the dry bulk cargo yard area, the to-be-removed evaluation unit parameter corresponding thereto in the evaluation vector can include parameters related to coal dust treatment, such as the expected purification rate of the coal dust in the area water collection pool, the treatment capacity of a specific filter layer for coal dust, and the like.

[0140] Then, the server determines the parameter elimination determination of the to-be-removed evaluation unit parameter according to the basic pollution category probability corresponding to the basic pollution partition identifier and the pollution determination threshold. Assuming that the pollution determination threshold is set to 60%, for the dry bulk cargo yard area, the basic pollution category probability is 80%, which is higher than the threshold, indicating that the coal dust pollution is relatively serious, and the to-be-removed evaluation unit parameter corresponding thereto can be determined to be eliminated because the current treatment parameter cannot effectively control the pollution; and for a certain partition, the basic pollution category probability is 50%, which is lower than the threshold, and the to-be-removed evaluation unit parameter corresponding thereto can be determined to be not eliminated.

[0141] The server removes the evaluation unit parameter in the evaluation vector according to a plurality of parameter elimination determinations. For example, according to the determination results of each partition, the evaluation unit parameter determined to be eliminated in the evaluation vector is removed, and finally the optimized evaluation vector is obtained. The optimized evaluation vector is more suitable for the actual water quality of the current target water collection pool, lays a foundation for obtaining more accurate water quality evaluation results by combining the water quality feature set in the future, and helps to make more scientific decisions on whether rainwater is suitable for reuse and how to further optimize the treatment process.

[0142] In the embodiments of the present application, the basic water quality evaluation result includes a plurality of basic pollution category probabilities and a plurality of basic pollution zoning identifiers in a mapping relationship with the plurality of basic pollution zoning identifiers, and the plurality of basic pollution zoning identifiers are in a mapping relationship with a plurality of evaluation unit parameters in the evaluation vector.

[0143] The evaluation unit parameters in the evaluation vector are removed according to the basic water quality evaluation result to obtain an optimized evaluation vector, which can be implemented by the following examples.

[0144] The evaluation unit parameters in the evaluation vector are removed according to the basic water quality evaluation result to obtain an optimized evaluation vector, which can be implemented by the following examples.

[0145] According to the plurality of basic pollution zoning identifiers, a specified number of target basic pollution category probabilities corresponding to the preset monitoring area are determined from the plurality of basic pollution category probabilities.

[0146] According to the specified number of target basic pollution category probabilities, the evaluation unit parameters in the to-be-removed zoning evaluation sub-vector are removed according to the basic pollution zoning identifiers of the specified number of target basic pollution category probabilities to obtain the optimized zoning evaluation sub-vector.

[0147] According to a plurality of the optimized zoning evaluation sub-vectors, the optimized evaluation vector is obtained.

[0148] In the embodiments of the present application, for example, in the rainwater collection and treatment system of a certain port terminal, the server undertakes the key task of water quality evaluation and related parameter optimization. The basic water quality evaluation result includes a plurality of basic pollution category probabilities and a plurality of basic pollution zoning identifiers mapped thereto, and the plurality of basic pollution zoning identifiers are in a mapping relationship with a plurality of evaluation unit parameters in the evaluation vector.

[0149] The server first determines a to-be-removed zoning evaluation sub-vector corresponding to a preset monitoring area in the target catchment pool from the evaluation vector. Assuming that the target catchment pool is divided into a plurality of preset monitoring areas, such as A area near the dry bulk cargo yard area, B area near the loading and unloading area, etc. Taking the A area as an example, the server identifies a series of evaluation unit parameters related to the A area in the evaluation vector to form the to-be-removed zoning evaluation sub-vector, which may involve the treatment expectation of the rainwater flowing into the target catchment pool from the dry bulk cargo yard area, the purification standard, etc.

[0150] Then, according to the basis pollution partition identification of the plurality of target basis pollution category probabilities corresponding to the specified pollution level number, the server removes the evaluation unit parameters in the to-be-removed partition evaluation sub-vector, thereby obtaining an optimized partition evaluation sub-vector. For example, the basis pollution partition identification corresponding to the coal dust pollution clearly shows the processing parameters related to the A region. If the pollution probability shows that the processing effect is not good, the server removes the evaluation unit parameters in the to-be-removed partition evaluation sub-vector that are related to the coal dust processing effect that is not ideal, such as unreasonable filter layer setting, too low adsorbent dosage standard, and the like, thereby forming the optimized partition evaluation sub-vector.

[0151] Then, according to the basis pollution partition identification of the plurality of target basis pollution category probabilities corresponding to the specified pollution level number, the server removes the evaluation unit parameters in the to-be-removed partition evaluation sub-vector, thereby obtaining an optimized partition evaluation sub-vector. For example, the basis pollution partition identification corresponding to the coal dust pollution clearly shows the processing parameters related to the A region. If the pollution probability shows that the processing effect is not good, the server removes the evaluation unit parameters in the to-be-removed partition evaluation sub-vector that are related to the coal dust processing effect that is not ideal, such as unreasonable filter layer setting, too low adsorbent dosage standard, and the like, thereby forming the optimized partition evaluation sub-vector.

[0152] The server performs the above operation on each preset monitoring region to obtain a plurality of optimized partition evaluation sub-vectors. Finally, the optimized partition evaluation sub-vectors are integrated to obtain an optimized evaluation vector. The optimized evaluation vector can more accurately reflect the actual water quality of each region of the target water collection pool, provide strong support for more accurate water quality evaluation combined with the water quality feature set in the subsequent process, and help the port to more scientifically plan the rainwater reuse and processing strategy.

[0153] In the embodiment of the application, the water quality evaluation result can be obtained according to the optimized evaluation vector and the water quality feature set by the following example.

[0154] According to the optimized evaluation vector and the water quality feature set, a target comprehensive water quality feature is obtained.

[0155] The target comprehensive water quality feature is subjected to water quality evaluation to obtain the water quality evaluation result.

[0156] In an embodiment of the present application, in the port rainwater collection and treatment system, the server derives the target comprehensive water quality feature according to the optimized evaluation vector and the water quality feature set. Taking a certain port as an example, it is assumed that the optimized evaluation vector is adjusted according to the water quality requirements and treatment expectations of different areas (such as living areas, loading and unloading areas, and storage areas). The water quality feature set includes various water quality features analyzed from samples collected from different monitoring points of the target water collection pool, such as the microbial content of the living area monitoring point, the oil pollution concentration of the loading and unloading area monitoring point, and the dust particle size of the storage area monitoring point.

[0157] The server correlates the optimized evaluation vector and the water quality feature set. For example, for the loading and unloading area, the optimized evaluation vector emphasizes strict standards for oil pollution treatment, and the server focuses on the oil pollution concentration related features of the loading and unloading area monitoring point in the water quality feature set. It comprehensively analyzes the relationship between the oil pollution concentration and the acceptable range, the expected effect of the treatment process, and other parameters in the evaluation vector, while combining other related water quality features, such as the content of other chemical substances that may exist with oil pollution. Through this comprehensive and detailed analysis, the target comprehensive water quality feature reflecting the overall water quality of the target water collection pool is integrated. This target comprehensive water quality feature is no longer a simple list of water quality features of individual monitoring points, but a comprehensive reflection of different regional functional requirements, treatment expectations, and the mutual influence of various water quality features.

[0158] The server performs water quality evaluation on the target comprehensive water quality feature, thereby obtaining the final water quality evaluation result. The server compares the target comprehensive water quality feature with preset water quality standards. These standards cover a series of indicators from pH to various pollutant concentrations. For example, for the standard of port recycled rainwater used for landscape water, the server checks whether each indicator in the target comprehensive water quality feature meets the standard. If all indicators are within the standard range, it is determined that the water quality meets the standard; if some indicators exceed the standard, such as the oil pollution concentration in the loading and unloading area being higher than the maximum value allowed for landscape water, the server will clearly indicate the exceeding items and the degree. The final water quality evaluation result will provide key basis for whether the port can safely and effectively recycle the rainwater, and also provide important reference for further optimizing the rainwater treatment process.

[0159] In an embodiment of the present application, the target comprehensive water quality feature can be obtained by executing the following examples.

[0160] performing pollution feature interaction processing on the optimized evaluation vector to obtain a target evaluation vector; and

[0161] performing water quality correlation analysis on the target evaluation vector and the water quality feature set to obtain the target comprehensive water quality feature.

[0162] In an embodiment of the present application, the server performs pollution feature interaction processing on the optimized evaluation vector to generate a target evaluation vector. For example, in a certain port terminal, the optimized evaluation vector contains evaluation unit parameters for different areas (such as container yard area, bulk cargo yard area, loading and unloading area, etc.), which reflect the processing requirements and expectations of each area for different pollution. The server analyzes the mutual influence between the pollution features of each area. Assuming that the main pollution of the bulk cargo yard area is coal dust, and the loading and unloading area may generate oil pollution due to cargo loading and unloading, and the loading and unloading area is close to the bulk cargo yard area, coal dust may mix with oil pollution. The server will consider this potential pollution mixing situation and adjust the evaluation unit parameters corresponding to the bulk cargo yard area and the loading and unloading area in the evaluation vector. For example, when adjusting the oil pollution treatment parameter of the loading and unloading area, considering the influence of the mixed coal dust on the oil pollution treatment process, the consideration factor of mixed pollution treatment effect is increased, so as to obtain a target evaluation vector that can better reflect the actual pollution interaction situation.

[0163] The server performs water quality correlation analysis on the target evaluation vector and the water quality feature set to obtain a target comprehensive water quality feature. The water quality feature set is obtained by collecting samples from different monitoring points of the target catchment, and contains various water quality features of each area. For example, the water quality features of the monitoring points in the container yard area may include the content of chemical substances left by cargo and the concentration of fine particles carried by rainwater. The server correlates each parameter in the target evaluation vector with the corresponding feature in the water quality feature set. For example, the parameter in the target evaluation vector for the treatment of chemical substances left by cargo in the container yard area is compared and analyzed with the content of chemical substances left by cargo at the monitoring point in this area in the water quality feature set. The gap between the actual content of the chemical substance and the treatment expectation in the evaluation vector is analyzed, while considering the influence of other related water quality features, such as the adsorption or reaction of fine particles on chemical substances. Through comprehensive and detailed correlation analysis of all related features in the target evaluation vector and the water quality feature set, the target comprehensive water quality feature reflecting the actual water quality situation of the entire target catchment and the mutual relationship of various pollution factors is obtained. This target comprehensive water quality feature comprehensively considers the pollution interaction of different areas and the actual water quality, providing a solid data foundation for subsequent accurate water quality evaluation.

[0164] In an embodiment of the present application, the target comprehensive water quality feature can be obtained by executing the following example.

[0165] In a case where it is determined that the current period is less than the preset number of periods, performing pollution feature interaction processing on the previous period target evaluation vector to obtain a current period target evaluation vector;

[0166] perform water quality correlation analysis on the current period target evaluation vector and the previous period comprehensive water quality feature to obtain a current period comprehensive water quality feature; and

[0167] In a case where it is determined that the current period is equal to the preset period number, the target comprehensive water quality feature is determined according to the current period comprehensive water quality feature.

[0168] In an embodiment of the present application, it is assumed that a port terminal sets a water quality evaluation period. The server first determines whether the current period is less than the preset period number. For example, the preset period number is 5, and the server determines that the current period is the third period, that is, the condition that the current period is less than the preset period number is met. At this time, the server performs pollution feature interaction processing on the target evaluation vector of the previous period (the second period). In the target evaluation vector of the previous period, evaluation parameters of various pollution features for different regions (such as a living area, a yard area, and a loading and unloading area) are included. The server further analyzes the mutual influence between these pollution features according to the actual situation. For example, dust from the yard area may spread to the living area along with air flow, affecting the air quality of the living area and further affecting the pollutant composition in rainwater. The server comprehensively considers these factors, adjusts the evaluation parameters of the relevant regions in the target evaluation vector of the previous period, and obtains the target evaluation vector of the current period.

[0169] Then, the server performs water quality correlation analysis on the target evaluation vector of the current period and the comprehensive water quality feature of the previous period (the second period). The comprehensive water quality feature of the previous period is a comprehensive reflection of the overall water quality condition of the target collection pool at that time, which covers the water quality features of each monitoring point and the mutual relationship of pollution features in different regions. The server compares and analyzes the evaluation parameters in the target evaluation vector of the current period with each index in the comprehensive water quality feature of the previous period in detail. For example, for the loading and unloading area, the requirement for oil pollution treatment effect in the target evaluation vector of the current period may be adjusted. The server checks the actual content, distribution, and interaction with other pollutants of oil pollution in the loading and unloading area in the comprehensive water quality feature of the previous period, analyzes the difference between the current requirement and the actual water quality, and obtains the current period comprehensive water quality feature.

[0170] When the server determines that the current period is equal to the preset period number (for example, the current period is the 5th period), the target comprehensive water quality feature is determined according to the current period comprehensive water quality feature. After analysis and adjustment of multiple periods, the current period comprehensive water quality feature has fully considered the changes of the pollution characteristics of each area of the port terminal in different periods and the mutual influence therebetween. The server determines the current period comprehensive water quality feature as the target comprehensive water quality feature, and the target comprehensive water quality feature comprehensively and accurately reflects the overall water quality condition of the target water collection pool in the preset period, provides a key basis for subsequent water quality evaluation, and helps the port terminal to scientifically decide the rainwater recycling and treatment scheme.

[0171] In the embodiment of the present application, the water quality evaluation on the target comprehensive water quality feature to obtain the water quality evaluation result can be implemented through the following examples.

[0172] The pollution type determination on the target comprehensive water quality feature to obtain a pollution category probability;

[0173] The pollution zoning determination on the target comprehensive water quality feature to obtain a pollution zoning identifier; and

[0174] The water quality evaluation result is obtained according to the pollution category probability and the pollution zoning identifier.

[0175] In the embodiment of the present application, for example, the server performs pollution type determination on the target comprehensive water quality feature, thereby obtaining a pollution category probability. It is assumed that the target comprehensive water quality feature contains various water quality data obtained from different area monitoring points of the target water collection pool, which covers various information such as pH value, chemical oxygen demand, various heavy metal content, oil pollution concentration and the like. The server performs deep analysis on these data according to a preset algorithm and database. For example, for the target water collection pool of a certain port terminal, the server finds through analysis that the water quality data of the monitoring point near the loading and unloading area is abnormally prominent in oil pollution related indicators. After comparison with a large amount of historical data and standard pollution type characteristics, the server determines that the probability of oil pollution in this area is 80%, and at the same time, since the cargo loading and unloading process may be accompanied by generation of other pollutants, it is determined that the probability of trace heavy metal pollution is 20%. In this way, the server obtains the pollution category probability corresponding to different pollution types.

[0176] The server determines a pollution partition according to the water quality features of the target set of water pools, and obtains a pollution partition identifier. The target set of water pools corresponds to different functional areas of the port terminal, such as a residential area, a storage area, and a loading and unloading area. The server determines the pollution partition according to the association between the monitoring points in the water quality features and the different functional areas, and the common pollution type characteristics of different areas. For example, according to the position information and the water quality data features of the monitoring points, the server confirms that the monitoring points in the dry bulk cargo storage area are mainly polluted by coal dust, which is consistent with the common pollution type in this area, and therefore marks this area as a “dry bulk cargo storage area-dust pollution area”, which is a pollution partition identifier. Through analysis of all the monitoring points, the server determines the identifiers of the different pollution areas.

[0177] The server obtains a water quality evaluation result according to the obtained pollution type probability and the pollution partition identifier. For example, after determining that the loading and unloading area is a high-probability oil pollution area, the server combines the pollution type probability and the corresponding pollution partition identifier. If the port has clear water quality standards and treatment requirements for different areas and different pollution types, and the oil pollution probability of the loading and unloading area exceeds 60%, the server determines that the water quality of the loading and unloading area does not meet the ideal standard and needs to be treated with enhanced oil pollution treatment. According to this standard, the server determines that the water quality of the loading and unloading area does not meet the ideal standard and needs to be treated with enhanced oil pollution treatment according to the determined oil pollution probability of 80% of the loading and unloading area. Through similar analysis of all the pollution partition identifiers and the corresponding pollution type probabilities, the server finally forms a comprehensive and accurate water quality evaluation result, which provides a key basis for the port terminal to take targeted rainwater treatment measures and reasonably reuse rainwater.

[0178] In the embodiment of the present application, the water quality evaluation on the comprehensive water quality features to obtain a basic water quality evaluation result can be implemented through the following examples.

[0179] The pollution type determination on the comprehensive water quality features obtains a basic pollution type probability;

[0180] The pollution partition determination on the comprehensive water quality features obtains a basic pollution partition identifier;

[0181] The basic water quality evaluation result is obtained according to the basic pollution type probability and the basic pollution partition identifier.

[0182] In an embodiment of the present application, the server determines the pollution type based on the comprehensive water quality characteristics to obtain the basic pollution category probability. Taking a large comprehensive port as an example, the comprehensive water quality characteristic data is derived from the analysis of samples collected at different positions and different time periods of the target water collection pool, and contains a large number of water quality parameters, such as the content of different chemical substances and microbial indicators. The server uses built-in algorithms and professional databases for in-depth analysis. For example, at a monitoring point near the liquid chemical loading and unloading area, the water quality data shows that the concentration of certain specific chemical substances is abnormal. The server compares these data with the standard characteristics of various pollution types, and after complex calculation, it is determined that the probability of chemical pollution in this area is 75%, and because other impurities may be introduced during loading and unloading operations, the probability of existence of micro-particle pollution is 25%, thereby determining the basic pollution category probability.

[0183] The server determines the pollution zoning based on the comprehensive water quality characteristics to obtain the basic pollution zoning identifier. The target water collection pool of the port is associated with multiple functional areas, such as residential areas, container yard areas, dry bulk cargo yard areas, loading and unloading areas, etc. The server divides the pollution area according to the geographical position of each monitoring point and the pollution source and characteristics reflected by the water quality characteristics. For example, at a monitoring point near the residential area, the water quality characteristics mainly reflect the related pollutants of domestic sewage, and the server accordingly marks this area as "residential area-domestic sewage pollution area", which is a basic pollution zoning identifier. Through the analysis of each monitoring point one by one, the server determines all the basic pollution zoning identifiers.

[0184] The server generates the basic water quality evaluation result based on the obtained basic pollution category probability and basic pollution zoning identifier. For example, in the dry bulk cargo yard area, the server determines that the probability of coal dust pollution in this area is 80%, corresponding to the basic pollution zoning identifier of "dry bulk cargo yard area-coal dust pollution area". If the port has set a clear standard for this type of pollution in this area, such as starting an additional dust reduction and purification process when the probability of coal dust pollution exceeds 60%. The server determines the basic water quality evaluation result that the water quality in this area needs to be treated by additional dust reduction and purification according to this standard and the determination of the dry bulk cargo yard area. Through detailed analysis of each basic pollution zoning identifier and its corresponding basic pollution category probability, the server comprehensively and accurately generates the basic water quality evaluation result, providing important data support for subsequent optimization of the evaluation vector and obtaining the final water quality evaluation result.

[0185] In an embodiment of the present application, the following implementation is also provided.

[0186] The basic evaluation vector is subjected to pollution characteristic interaction processing to obtain the evaluation vector, wherein the basic evaluation vector includes a plurality of dynamic adjustable evaluation unit parameters, and the dynamic adjustable evaluation unit parameters are obtained by training a historical water quality sample set and a pollution level label corresponding to the historical water quality sample set.

[0187] In an embodiment of the present application, the server first obtains a basic evaluation vector, which contains a plurality of dynamically adjustable evaluation unit parameters. These parameters are not fixed, but are obtained by training on a historical water quality sample set and the corresponding pollution level labels. For example, a port terminal has long accumulated a large number of rainwater sample data from various regions such as residential areas, yard areas, loading and unloading areas under different seasons and different weather conditions, which constitute a historical water quality sample set. At the same time, the staff labeled the corresponding pollution level for each sample according to the actual pollution situation, such as light pollution, moderate pollution, severe pollution, etc.

[0188] The server uses these data for training. In the training process, it analyzes the relationship between various water quality indicators and pollution levels in the historical water quality sample set. For example, in the historical samples of the loading and unloading area, it is found that the oil content and the amount of cargo debris show a certain correlation with the pollution level. When the oil content and the amount of cargo debris are both high, they often correspond to a higher pollution level. Through the learning of a large amount of similar data, the server determines the initial values of the various dynamically adjustable evaluation unit parameters, which can preliminarily reflect the relationship between water quality and pollution level in different regions.

[0189] The server performs pollution feature interaction processing on the basic evaluation vector to obtain the final evaluation vector. In the actual operation of the port terminal, the pollution features of different regions are not isolated, but interact with each other. For example, the coal dust in the dry bulk yard area may be blown by the airflow to the nearby loading and unloading area and mixed with the oil pollution there, thereby changing the pollution characteristics. The server considers this interaction of pollution features and adjusts the dynamically adjustable evaluation unit parameters in the basic evaluation vector. For the parameters corresponding to the dry bulk yard area and the loading and unloading area, the server will recompute and adjust the parameters according to the possible pollution interaction between the two. For example, the weight of the related parameter that considers the treatment effect of mixed pollutants is increased, so that the evaluation vector can more accurately reflect the actual pollution situation. After such processing, the server obtains the evaluation vector, which fully considers the interaction of the pollution features of each region of the port terminal and the rules of the historical water quality data, and provides a more practical basis for subsequent water quality correlation analysis and water quality evaluation, which helps to more accurately evaluate the water quality of the target water collection tank and provides strong support for the decision-making of rainwater treatment and reuse in the port terminal.

[0190] Please refer to Figure 3 , Figure 3 A biological retention purification device 110 for port terminal rainwater collection and treatment is provided in an embodiment of the present application, comprising:

[0191] The purification module 1101 is used for collecting rainwater of the port yard drainage ditch which is preliminarily filtered through the grid through the rainwater collection pipe; the rainwater is deeply purified through the biological filter tank, and the deeply purified rainwater is stored into the target water collecting tank through the water collecting pipe; the biological filter tank comprises a vegetation layer, a water distribution layer, a microbial filler layer, a supporting layer and a percolation layer.

[0192] The evaluation module 1102 is used for evaluating the water quality of the target water collecting tank at a preset time interval to obtain a water quality evaluation result; and when the water quality evaluation result represents that the biological retention purification is passed, the target water collecting tank is configured with a reclaimed water reuse task.

[0193] It should be noted that the implementation principle of the biological retention purification device 110 for port rainwater collection and treatment can refer to the implementation principle of the biological retention purification method for port rainwater collection and treatment, which will not be repeated here. It should be understood that the division of each module of the above device is only a logical function division, and all or part of the actual implementation can be integrated into one physical entity, or can be physically separated. And these modules can all be in the form of software called by the processing element; all can be in the form of hardware; some modules can be in the form of software called by the processing element, and some modules can be in the form of hardware. For example, the biological retention purification device 110 for port rainwater collection and treatment can be a separate processing element, or can be integrated into a chip of the above device, in addition, it can also be in the form of program code stored in the memory of the above device, and the above biological retention purification device 110 for port rainwater collection and treatment is called and executed by a processing element of the above device. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together, or can be independently implemented. The processing element described herein can be an integrated circuit with signal processing capability. In the implementation process, each step of the above method or each module can be completed by the integrated logic circuit of hardware or the instruction of software in the processing element.

[0194] For example, the above modules can be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs), etc. For another example, when a certain module above is implemented in the form of a processing element scheduling code, the processing element can be a general purpose processor, such as a central processing unit (CPU) or other processor that can invoke code. For another example, the modules can be integrated together to implement a system-on-a-chip (SOC).

[0195] The embodiment of the present application provides a computer device 100, which comprises a processor and a nonvolatile memory storing computer instructions, and the computer device 100 executes the aforementioned biological retention purification device 110 for port wharf rainwater collection and treatment when the computer instructions are executed by the processor. As shown in the figure, Figure 4 Figure 4 The embodiment of the present application provides a structural block diagram of the computer device 100. The computer device 100 comprises the biological retention purification device 110 for port wharf rainwater collection and treatment, a memory 111, a processor 112 and a communication unit 113.

[0196] In order to realize the transmission or interaction of data, the memory 111, the processor 112 and the communication unit 113 are directly or indirectly electrically connected with each other. For example, the electrical connection between the elements can be realized by one or more communication buses or signal lines. The biological retention purification device 110 for port wharf rainwater collection and treatment comprises at least one software function module stored in the memory 111 in the form of software or firmware or solidified in the operating system (OS) of the computer device 100. The processor 112 is used to execute the biological retention purification device 110 for port wharf rainwater collection and treatment stored in the memory 111, such as the software function module and the computer program included in the biological retention purification device 110 for port wharf rainwater collection and treatment.

[0197] The embodiment of the present application provides a readable storage medium, which comprises a computer program, and the computer program controls the computer device where the readable storage medium is located to execute the aforementioned biological retention purification device 110 for port wharf rainwater collection and treatment when running. ​

[0198] The foregoing description is made with reference to particular embodiments for the purpose of illustration only. The description is not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Many modifications and variations are possible in light of the above teachings. The described embodiments were chosen in order to best illustrate the principles of the disclosure and its practical application to thereby enable others skilled in the art to best utilize the disclosure, along with various embodiments and with various modifications as are suited to the particular situations.

Claims

1. A biological retention and purification method for rainwater collection and treatment in port terminals, characterized in that, include: Rainwater from the port yard drainage ditch, which has undergone preliminary filtration by a bar screen, is collected through rainwater collection pipes. Rainwater is deeply purified through a biological filter, and the purified rainwater is stored in a target collection tank through a collection pipe; the biological filter includes a vegetation layer, a water separation layer, a microbial packing layer, a support layer, and an infiltration layer. The water quality of the target water collection tank is assessed at preset time intervals to obtain water quality assessment results. If the water quality assessment results indicate that bioretention purification has been achieved, a reclaimed water reuse task will be configured for the target collection tank. The step of conducting water quality assessments on the target collection tank at preset time intervals to obtain water quality assessment results includes: At preset time intervals, feature extraction operations are performed on the water quality samples to be evaluated in the water quality sample set to be evaluated to obtain a water quality feature set. The water quality sample set to be evaluated includes multiple water quality samples to be evaluated that indicate different monitoring points of the target collection pool. The water quality feature set includes multiple water quality features arranged in a preset monitoring point order. Based on the monitoring point location information represented by the water quality samples to be evaluated in the set of water quality samples to be evaluated, the target water quality feature and the target zoning evaluation sub-vector corresponding to the target water quality feature are determined from the set of water quality features. The target water quality features are used as feature benchmark parameters and feature weight parameters, respectively, and the target partition evaluation sub-vector is used as evaluation condition parameters. Water quality correlation mapping is performed to obtain the partition water quality sub-features. Based on multiple water quality sub-features of the respective zones, a comprehensive water quality feature is obtained. The target zone evaluation sub-vector includes multiple evaluation unit parameters, which are used to indicate the zone features of the target water collection pool. The pollution type is determined based on the comprehensive water quality characteristics to obtain the probability of basic pollution category; The pollution zoning is determined based on the comprehensive water quality characteristics to obtain basic pollution zoning identifiers; The basic water quality assessment results are obtained based on the basic pollution category probabilities and the basic pollution zoning identifiers. Based on the basic water quality assessment results, the assessment unit parameters in the target zoning assessment sub-vector are removed to obtain the optimized target zoning assessment sub-vector. The water quality assessment result is obtained based on the optimized target partition evaluation sub-vector and the water quality feature set.

2. The method according to claim 1, characterized in that, The cross-section of the biological filter is arc-shaped; The vegetation layer is located on the side of the biological filter near the ground. The vegetation layer is composed of a preset background plant species of the port area. The vegetation layer is used to filter and intercept substances carried by the runoff rainwater that are not filtered by the grid. The water-separating layer is located on the side of the vegetation layer away from the ground. The water-separating layer is filled with quartz sand and is used to filter and control the uniform infiltration of rainwater. The microbial packing layer is located on the side of the water separation layer away from the vegetation layer. The microbial packing layer is composed of activated carbon granules inoculated with a composite microbial membrane. The microbial packing layer is inoculated with corresponding functional microorganisms based on the characteristics of rainwater and sewage in different zones of the port area. The support layer is located on the side of the microbial filler layer away from the water distribution layer. The support layer is filled with gravel and is used to support the vegetation layer, the water distribution layer, and the microbial filler layer. The percolation layer is located on the side of the support layer away from the microbial packing layer. The percolation layer is filled with zeolite and is used to filter and adsorb pollutants.

3. The method according to claim 1, characterized in that, The basic water quality assessment results include multiple basic pollution category probabilities and multiple basic pollution zone identifiers that are mapped to the multiple basic pollution category probabilities. The multiple basic pollution zone identifiers are mapped to multiple assessment unit parameters in the target zone assessment sub-vector. The step of removing assessment unit parameters from the target zoning assessment sub-vector based on the basic water quality assessment results to obtain an optimized target zoning assessment sub-vector includes: For each of the basic pollution zone identifiers, the parameters of the evaluation unit to be removed corresponding to the basic pollution zone identifier are determined from the target zone evaluation sub-vector; Based on the basic pollution category probability and pollution determination threshold corresponding to the basic pollution zoning identifier, the parameter removal determination of the evaluation unit parameter to be removed is determined; Based on the elimination criteria of multiple parameters, the evaluation unit parameters in the target partition evaluation sub-vector are removed to obtain the optimized target partition evaluation sub-vector.

4. The method according to claim 1, characterized in that, The basic water quality assessment results include multiple basic pollution category probabilities and multiple basic pollution zoning identifiers that are mapped to the multiple basic pollution zoning identifiers. The multiple basic pollution zoning identifiers are mapped to multiple assessment unit parameters in the target zoning assessment sub-vector. The step of removing assessment unit parameters from the target zoning assessment sub-vector based on the basic water quality assessment results to obtain an optimized target zoning assessment sub-vector includes: Determine the partition evaluation sub-vector to be removed from the target partition evaluation sub-vector, which corresponds to the preset monitoring area in the target water collection tank; Based on the multiple basic pollution zone identifiers, determine the number of target basic pollution category probabilities corresponding to the preset monitoring area from the multiple basic pollution category probabilities; Based on the basic pollution partition identifier of each of the specified number of target basic pollution category probabilities at the specified pollution level, the evaluation unit parameters in the partition evaluation sub-vector to be removed are removed to obtain the optimized partition evaluation sub-vector. The optimized target partition evaluation subvector is obtained based on multiple optimized partition evaluation subvectors.

5. The method according to claim 1, characterized in that, The step of obtaining water quality assessment results based on the optimized target partition evaluation sub-vector and the water quality feature set includes: Based on the optimized target partition evaluation sub-vector and the water quality feature set, the target comprehensive water quality characteristics are obtained; The pollution type is determined based on the comprehensive water quality characteristics of the target, and the probability of pollution category is obtained; Pollution zoning is determined based on the comprehensive water quality characteristics of the target, and pollution zoning identifiers are obtained; The water quality assessment results are obtained based on the pollution category probability and the pollution zone identifier.

6. The method according to claim 5, characterized in that, The step of obtaining the target comprehensive water quality characteristics based on the optimized target partition evaluation sub-vector and the water quality feature set includes: The optimized target partition evaluation sub-vector is subjected to contamination feature interaction processing to obtain the target partition evaluation sub-vector; A water quality correlation analysis is performed on the target zoning evaluation sub-vector and the water quality feature set to obtain the target comprehensive water quality characteristics. The step of obtaining the target comprehensive water quality characteristics based on the optimized target partition evaluation sub-vector and the water quality feature set further includes: If the current period is determined to be less than the preset number of periods, the target partition evaluation subvector of the previous period is subjected to pollution feature interaction processing to obtain the target partition evaluation subvector of the current period. A water quality correlation analysis is performed between the current period target partition evaluation sub-vector and the previous period comprehensive water quality characteristics to obtain the current period comprehensive water quality characteristics. If the current period is determined to be equal to the preset number of periods, the target comprehensive water quality characteristics are determined based on the comprehensive water quality characteristics of the current period.

7. The method according to claim 1, characterized in that, Also includes: The basic target zoning evaluation sub-vector is subjected to pollution feature interaction processing to obtain the target zoning evaluation sub-vector. The basic target zoning evaluation sub-vector includes multiple dynamically adjustable evaluation unit parameters, which are obtained by training through historical water quality sample sets and pollution level labels corresponding to the historical water quality sample sets.

8. A readable storage medium, characterized in that, The readable storage medium includes a computer program, which, when executed, controls the computer device on which the readable storage medium is located to perform the method described in any one of claims 1-7.

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