WO2023025329A1 - 一种基于数字流域时空模型的污染排放确定方法和装置 - Google Patents
一种基于数字流域时空模型的污染排放确定方法和装置 Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 38
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 228
- 238000012544 monitoring process Methods 0.000 claims abstract description 223
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- 238000004062 sedimentation Methods 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims 2
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 14
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 7
- 229910052757 nitrogen Inorganic materials 0.000 description 7
- 229910052698 phosphorus Inorganic materials 0.000 description 7
- 239000011574 phosphorus Substances 0.000 description 7
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- 238000003911 water pollution Methods 0.000 description 4
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- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
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- 238000001514 detection method Methods 0.000 description 1
- 230000029087 digestion Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
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- G—PHYSICS
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- G06F30/20—Design optimisation, verification or simulation
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- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/08—Fluids
Definitions
- the present disclosure relates to the technical field of river water quality monitoring, in particular to a method and device for determining pollution discharge based on a digital watershed spatio-temporal model.
- the present disclosure provides a method and device for determining pollution discharge based on a digital watershed spatio-temporal model.
- the present disclosure provides a pollution discharge determination method based on a digital watershed space-time model.
- the river basin to be detected includes a plurality of sections, each section has a monitoring station, and each monitoring station is equipped with a water quality sensor.
- the water quality sensor Used to obtain water quality data; including:
- the monitored water quality data includes the initial point pollutant concentration and the cross-sectional pollutant concentration
- the monitored water quality data, the actual flow velocity, and the section distance are calculated multiple times with a one-dimensional steady-state model of the river to obtain the sum of the aerobic coefficient and the sedimentation coefficient of the pollutant;
- the calculated one-dimensional steady-state model of the river is established according to the sum of the aerobic coefficient and the sedimentation coefficient of the pollutant, the actual flow velocity, and the section distance.
- the method for determining pollution discharge based on a digital watershed spatio-temporal model further includes:
- the target pollutant is in the preset pollutant list, it is determined that there is pollution discharge between the current monitoring site and the previous monitoring site;
- the target pollutant is not in the preset pollutant list, it is determined that there is no pollution discharge between the current monitoring site and the previous monitoring site.
- the difference is less than or equal to the preset difference threshold, it is determined that there is no pollution discharge between the current monitoring site and the previous monitoring site.
- the method for determining pollution discharge based on a digital watershed spatio-temporal model further includes:
- the method for determining pollution discharge based on a digital watershed spatio-temporal model further includes:
- the pollutant discharge target area is determined according to the industry information.
- the disclosure provides a pollution discharge determination device based on a digital watershed spatio-temporal model.
- the river basin to be detected includes a plurality of sections, each section has a monitoring station, and each monitoring station is equipped with a water quality sensor.
- the water quality sensor Used to obtain water quality data; including:
- the first water quality acquisition module is used to acquire the current monitoring water quality data of the current monitoring station
- the second water quality acquisition module is used to acquire the actual water quality data of the previous monitoring station, calculate the actual water quality data through the calculated one-dimensional steady-state model of the river, and obtain the theoretical water quality data of the current monitoring station;
- the pollution discharge determination module is used to determine whether there is pollution discharge between the current monitoring site and the previous monitoring site according to the comparison result of the current monitoring water quality data and the theoretical water quality data.
- the present disclosure provides a computer storage medium, where the computer storage medium can store a program, and when the program is executed, some or all of the steps in each implementation manner of the communication method provided by the first aspect of the present disclosure can be implemented.
- an electronic device which is characterized in that it includes:
- memory for storing processor-executable instructions
- each section has a monitoring site, each of the monitoring sites is equipped with a water quality sensor, and the water quality sensor is used to obtain water quality data;
- the processor is configured to :
- the pollution discharge determination method based on the digital watershed space-time model of the present disclosure includes a plurality of sections in the river basin to be detected, each section has a monitoring station, and each monitoring station is equipped with a water quality sensor, and the water quality sensor is used to obtain water quality data.
- Obtain the current monitoring water quality data of the current monitoring station obtain the actual water quality data of the previous monitoring station, calculate the actual water quality data through the calculated one-dimensional steady-state model of the river, and obtain the theoretical water quality data of the current monitoring station.
- the comparison results of water quality data and theoretical water quality data determine whether there is pollution discharge between the current monitoring station and the previous monitoring station. Therefore, it is possible to judge the water pollution situation in real time, and effectively predict and trace the pollution situation.
- FIG. 1 is a schematic flowchart of a method for determining pollution discharge based on a digital watershed spatio-temporal model according to an embodiment of the present disclosure
- FIG. 2 is a schematic diagram of a digital watershed according to an embodiment of the present disclosure
- FIG. 3 is a schematic flowchart of a method for determining pollution discharge based on a digital watershed spatio-temporal model according to an embodiment of the present disclosure
- Fig. 4 is a schematic structural diagram of a pollution discharge determination device based on a digital watershed spatio-temporal model according to an embodiment of the present disclosure.
- the digital watershed in this disclosure refers to the comprehensive use of sensor technology, geographic information system and other technologies to collect information and digitally manage water bodies in the entire watershed, and build a comprehensive information platform for the entire watershed, so that relevant departments can timely grasp the water quality of the entire watershed. It can assist environmental protection inspectors to judge water pollution and effectively predict and trace pollution.
- the digital watershed can deduce the diffusion situation after the pollution occurs. Through the reverse deduction of the pollutant diffusion process, the pollution discharge can be checked, and the pollution status can be realized by using the geographic information system. Visual display and a series of related work.
- Fig. 1 is a schematic flowchart of a method for determining pollution discharge based on a digital watershed spatio-temporal model according to an embodiment of the present disclosure.
- the method includes:
- Step 101 obtaining the current monitoring water quality data of the current monitoring station.
- the river basin to be detected includes multiple sections, each section has a monitoring station, each monitoring station is equipped with a water quality sensor, and the water quality sensor is used to obtain water quality data.
- FIG. 2 it shows some sections 1 and 2 in the detection river basin, and each section has a monitoring station.
- the initial position of section 1 has a monitoring station A
- the last position has a monitoring station B.
- Monitoring stations A and B are equipped with water quality sensors to obtain water quality data.
- the initial position of section 2 has monitoring station B
- the last position has monitoring station C.
- Monitoring stations B and C are equipped with water quality sensors to obtain water quality data.
- the use of sensors is a common way to understand the water quality in the basin, but the cost of sensors is generally high. Understanding the water quality in a large watershed requires the deployment of relatively dense sensors, which will result in high monitoring and maintenance costs.
- a small number of water quality sensors are arranged in the watershed, and a one-dimensional steady-state model of the river is used to simulate the water quality of the whole watershed, and on this basis, early warning and source tracing judgments are made.
- the parameters of the one-dimensional steady-state model of the river are measured and calculated through real-time data.
- the type of the water quality sensor can be selected and set according to application requirements, so that the acquired water quality data can be one or more types, such as phosphorus concentration, nitrogen concentration, and the like.
- the currently monitored water quality data is the water quality data obtained by the water quality sensor of the current monitoring site.
- the current monitoring site may be any monitoring site, such as B or C in FIG. 2 , and the setting is specifically selected according to the application scenario.
- Step 102 obtaining the actual water quality data of the previous monitoring station, calculating the actual water quality data through the calculated one-dimensional steady-state model of the river, and obtaining the theoretical water quality data of the current monitoring station.
- the previous monitoring site can be understood as the river water of the previous monitoring site flows along the river to the current monitoring site, that is, the previous monitoring site is the upstream monitoring site of the current monitoring site, such as the current monitoring site in Figure 2 Site B, the previous monitoring site is C, the current monitoring site C, the previous monitoring site is B.
- the actual water quality data can be understood as the real water quality data of the previous monitoring station, such as the real phosphorus concentration and nitrogen concentration.
- the actual water quality data may be acquired by the water quality data acquired by the water quality sensor of the previous monitoring station.
- the actual water quality data is calculated through the calculated one-dimensional steady-state model of the river to obtain the theoretical water quality data of the current monitoring station.
- the actual flow velocity and section distance in the calculated one-dimensional steady-state model of the river can be adjusted in real time according to the needs, further improving the accuracy of judgment.
- the calculated one-dimensional steady-state model of the river is a mathematical model with the pollutant concentration at the initial point as the variable and the pollutant concentration at the end point as the calculation result.
- Other parameters in the model have been pre-calculated and obtained.
- the method of pre-calculating and obtaining parameters of the one-dimensional steady-state model of the river will be described in detail later, and will not be described in detail here.
- the water quality data includes phosphorus concentration, nitrogen concentration, etc.
- the concentration of multiple pollutants can be calculated at the same time, so as to obtain theoretical water quality data including the theoretical current What are the concentrations of phosphorus and nitrogen at the monitoring sites?
- Step 103 according to the comparison result of the current monitoring water quality data and the theoretical water quality data, determine whether there is pollution discharge between the current monitoring station and the previous monitoring station.
- the first example is to obtain the current pollution value of each pollutant from the current water quality data, obtain the theoretical pollution value of each pollutant from the theoretical water quality data, calculate the difference between the current pollution value and the theoretical pollution value, and obtain If the target pollutant whose difference is greater than the preset difference threshold, if the target pollutant is in the preset pollutant list, it is determined that there is pollution discharge between the current monitoring station and the previous monitoring station, and if the target pollutant is not in the preset pollutant list In the list, it is determined that there is no pollution discharge between the current monitoring site and the previous monitoring site.
- the pollution difference of each pollutant in the water quality data is calculated, and the pollution discharge is determined for which pollutants exceed the standard in advance.
- pollutants For example, phosphorus and nitrogen are included in the preset pollutant list, and the settings are selected according to application needs.
- the second example is to obtain the current pollution value of the target pollutant from the current water quality data, obtain the theoretical pollution value of the target pollutant from the theoretical water quality data, and calculate the difference between the current pollution value and the theoretical pollution value. If it is greater than the preset difference threshold, it is determined that there is pollution discharge between the current monitoring site and the previous monitoring site; if the difference is less than or equal to the preset difference threshold, then it is determined that the current monitoring site and the previous monitoring site There is no polluting discharge between them.
- the target pollutants are phosphorus and nitrogen, and only calculate the difference between phosphorus and nitrogen. If the difference is greater than the preset difference threshold, then determine the current monitoring There is pollution discharge between the station and the previous monitoring station, which further improves the calculation efficiency.
- the pollution discharge determination method based on the digital watershed space-time model of the present disclosure includes multiple sections in the river basin to be detected, each section has a monitoring station, and each monitoring station is equipped with a water quality sensor.
- To obtain water quality data obtain the current monitoring water quality data of the current monitoring station, obtain the actual water quality data of the previous monitoring station, calculate the actual water quality data through the calculated one-dimensional steady-state model of the river, and obtain the theoretical water quality of the current monitoring station According to the comparison results of the current monitoring water quality data and the theoretical water quality data, determine whether there is pollution discharge between the current monitoring station and the previous monitoring station. Therefore, it is possible to judge the water pollution situation in real time, and effectively predict and trace the pollution situation.
- the one-dimensional steady-state model of the river is pre-calculated, specifically described in conjunction with FIG. 3 , as shown in FIG. 3 , including :
- step 201 multiple flow velocities in each section are obtained, and the average value of the multiple flow velocities is calculated as the actual flow velocity of two adjacent sections.
- step 202 the monitored water quality data of each section and the cross-sectional distance between two adjacent sections are acquired; wherein, the monitored water quality data includes the initial point pollutant concentration and the cross-sectional pollutant concentration.
- step 203 the monitored water quality data, actual flow velocity, and cross-section distance are calculated multiple times using the one-dimensional steady-state model of the river to obtain the sum of the aerobic coefficient and the sedimentation coefficient of the pollutant.
- step 204 the calculated one-dimensional steady-state model of the river is established according to the sum of the aerobic coefficient and the sedimentation coefficient of the pollutant, the actual flow velocity, and the section distance.
- the one-dimensional steady-state model of the river is as follows:
- c is the pollutant concentration of the section
- the unit is mg/L
- c 0 is the pollutant concentration at the initial point
- the unit is mg/L
- c and c 0 are the monitoring water quality data
- K 1 is the aerobic coefficient
- the unit is 1 /d
- K 3 is the sedimentation coefficient of pollutants
- the unit is 1/d
- ⁇ is the actual flow velocity
- the unit is m/s
- x is the section distance (the distance from the initial calculation point to the downstream calculation section), and the unit is m.
- the applicable conditions of the one-dimensional steady-state model of the river meet the following conditions: (1) the fully mixed section of the river; (2) non-persistent pollutants; (3) the river is a constant flow; (4) continuous and stable discharge of wastewater.
- the comprehensive reduction coefficient K can be used to replace K 1 +K 3 in the above formula to predict the change of pollutant concentration along the course, and further improve the accuracy.
- the velocity meter is used to collect the flow velocity between the monitoring points.
- the velocity can be measured at multiple points in the section, and then the average value is calculated to represent the flow velocity level in the section, that is, the above actual
- the sum of the aerobic coefficient and the sedimentation coefficient of pollutants in each section can be obtained through the one-dimensional steady-state model of the river, that is, K 1 +K 3 .
- the initial water quality data of any target section is obtained, the initial water quality data is calculated through the calculated one-dimensional steady-state model of the river, the final water quality data of the target section is obtained, and the next section is obtained.
- the sum of the aerobic coefficient and the sedimentation coefficient of pollutants is adjusted until the final water quality data and the initial water quality data of the next section
- the error of the water quality data is within the preset threshold.
- the water quality data at the end of the section calculated by the one-dimensional steady-state model of the river is likely to be inconsistent with the monitored water quality data at the initial point of the next section.
- K 1 +K 3 Infinitely close to the real value, through the experience of historical data, the water quality data can be measured within a certain range to meet the accuracy. This enables digitization of the entire watershed.
- the target device is any device with computing capabilities, such as a PC (Personal Computer, personal computer), a mobile terminal, etc.
- the mobile terminal for example, can be a mobile phone, a tablet computer, a personal digital assistant, a wearable device, etc.
- the early warning information can include sections, monitoring sites, pollutant concentrations, time, etc., and the specific setting is selected according to the application scenario.
- the distance between the current monitoring site and the previous monitoring site is obtained, and if the distance is less than the preset distance threshold Next, obtain the industrial information within the distance, and determine the pollutant discharge target area according to the industrial information.
- the water quality data of each monitoring station can be used for preliminary research and judgment. If the pollutant concentration of the current monitoring station is much higher than that of the previous monitoring station, it can be concluded that the watershed There are sneak shots and omissions in the section.
- the theoretical value of the pollutant concentration of the current monitoring station can be calculated by using the one-dimensional steady-state model of the river through the pollutant concentration of the previous monitoring station. , comparing the theoretical value with the actual monitoring value at the current site, if the calculated theoretical value is much smaller than the actual monitoring value, there are good reasons to believe that there is a phenomenon of sneaking and leaking in this section. Moreover, an early warning can be given to a section whose calculated theoretical value is slightly smaller than the actual monitored value.
- the actual water quality data passing through the monitoring point B in Figure 2 can use the one-dimensional steady-state model of the river to deduce the water quality process at any time and location from B to C.
- the theoretical water quality data at C calculated by the one-dimensional steady-state model of the river is compared with the actual theoretical water quality data at C, if the calculated value is far lower than the actual value, it means that there is leakage of sewage between the BC sections , if the distance between BC and Segment is relatively short, it is possible to better target the surrounding areas of sneaky and leaky discharge enterprises based on the surrounding industrial conditions of this section, such as the production enterprises in this section, as well as the production time and product type, etc.
- the sensor data collection is real-time, and the real-time pollutant concentration can be obtained.
- the theoretical water quality data of each monitoring point in real time it is possible to compare each monitoring station in real time, lock the section of sneaky discharge and leaky discharge, and control Supervise and inspect the contaminated section to achieve the purpose of traceability.
- the learning of relevant parameters will be continuously optimized, and its accuracy will become higher and higher, which can effectively improve the inspection efficiency of sneaky and leaky discharges.
- Fig. 4 is a schematic structural diagram of a device for determining pollution discharge based on a digital watershed spatio-temporal model according to an embodiment of the present disclosure.
- the device can be implemented by software and/or hardware, and can generally be integrated in electronic equipment.
- the river basin to be detected includes multiple sections, each section has a monitoring station, each monitoring station is equipped with a water quality sensor, and the water quality sensor is used to obtain water quality data.
- the device includes:
- the first water quality acquisition module 401 is used to acquire the current monitoring water quality data of the current monitoring station.
- the second water quality acquisition module 402 is used to acquire the actual water quality data of the previous monitoring station, calculate the actual water quality data through the calculated one-dimensional steady-state model of the river, and obtain the theoretical water quality data of the current monitoring station.
- the pollution discharge determination module 403 is used to determine whether there is pollution discharge between the current monitoring station and the previous monitoring station according to the comparison result of the current monitoring water quality data and the theoretical water quality data.
- the device further includes: an acquisition flow velocity module, configured to acquire a plurality of flow velocities in each section, and calculate an average value of the plurality of flow velocities as the actual flow velocity of two adjacent sections ;
- Obtain water quality and distance module used to obtain the monitored water quality data of each section and the cross-sectional distance between two adjacent sections; wherein, the monitored water quality data includes the initial point pollutant concentration and cross-section pollutant concentration;
- the calculation module uses The monitoring water quality data, actual flow velocity, and cross-section distance are calculated multiple times with the one-dimensional steady-state model of the river to obtain the sum of the aerobic coefficient and the sedimentation coefficient of pollutants; a module is established to use the aerobic coefficient and the sedimentation coefficient of pollutants
- the calculated one-dimensional steady-state model of the river is established based on the sum of the subsidence coefficient, the actual flow velocity and the section distance.
- the device further includes: an acquisition calculation module, configured to acquire the initial water quality data of any target section, calculate the initial water quality data through the calculated one-dimensional steady-state model of the river, and acquire The final water quality data of the target section; the acquisition module is used to obtain the initial water quality data of the next section; the adjustment module is used to adjust the final water quality data and the initial water quality data of the next section when the final water quality data is inconsistent.
- the sum of the oxygen coefficient and the sedimentation coefficient of the pollutant until the error between the final water quality data and the initial water quality data of the next section is within the preset threshold.
- the pollution discharge determination module 403 is specifically configured to obtain the current pollution value of each pollutant from the current water quality data, and obtain the theoretical pollution value of each pollutant from the theoretical water quality data , calculate the difference between the current pollution value and the theoretical pollution value, obtain the target pollutant whose difference is greater than the preset difference threshold, and determine the difference between the current monitoring station and the previous monitoring station if the target pollutant is in the preset pollutant list If there is pollution discharge between the current monitoring station and the previous monitoring station, if the target pollutant is not in the preset pollutant list, it is determined that there is no pollution discharge between the current monitoring station and the previous monitoring station.
- the current pollution value of the target pollutant is obtained from the current water quality data
- the theoretical pollution value of the target pollutant is obtained from the theoretical water quality data
- the difference between the current pollution value and the theoretical pollution value is calculated value, if the difference is greater than the preset difference threshold, it is determined that there is pollution discharge between the current monitoring site and the previous monitoring site, and if the difference is less than or equal to the preset difference threshold, then it is determined that the current monitoring site There is no pollution discharge between the previous monitoring station.
- the device further includes: a generating and sending module configured to generate early warning information and send it to the target device when it is determined that there is pollution discharge between the current monitoring site and the previous monitoring site.
- a generating and sending module configured to generate early warning information and send it to the target device when it is determined that there is pollution discharge between the current monitoring site and the previous monitoring site.
- the device further includes: an analysis module, configured to obtain the current monitoring site and the previous monitoring site when it is determined that there is pollution discharge between the current monitoring site and the previous monitoring site. If the distance is less than the preset distance threshold, the industrial information within the distance is obtained, and the pollutant discharge target area is determined according to the industrial information.
- an analysis module configured to obtain the current monitoring site and the previous monitoring site when it is determined that there is pollution discharge between the current monitoring site and the previous monitoring site. If the distance is less than the preset distance threshold, the industrial information within the distance is obtained, and the pollutant discharge target area is determined according to the industrial information.
- the pollution discharge determination device based on the digital watershed space-time model of the present disclosure includes multiple sections in the river basin to be detected, each section has a monitoring station, and each monitoring station is equipped with a water quality sensor.
- To obtain water quality data obtain the current monitoring water quality data of the current monitoring station, obtain the actual water quality data of the previous monitoring station, calculate the actual water quality data through the calculated one-dimensional steady-state model of the river, and obtain the theoretical water quality of the current monitoring station According to the comparison results of the current monitoring water quality data and the theoretical water quality data, determine whether there is pollution discharge between the current monitoring station and the previous monitoring station. Therefore, it is possible to judge the water pollution situation in real time, and effectively predict and trace the pollution situation.
- An embodiment of the present disclosure also provides a computer storage medium, wherein the computer storage medium can store a program, and when the program is executed, the method for determining pollution discharge based on the digital watershed space-time model provided by the embodiment shown in Figures 1-3 can be realized Part or all of the steps in each implementation manner.
- An embodiment of the present disclosure also provides an electronic device, which includes:
- memory for storing processor-executable instructions
- the processor is configured as:
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Abstract
本公开涉及一种基于数字流域时空模型的污染排放确定方法和装置,涉及河流水质监测技术领域,其中,方法包括:待检测河流域中包括多个区段,每个区段具有监测站点,每个监测站点安装水质传感器,水质传感器用于获取水质数据,通过获取当前监测站点的当前监测水质数据;获取前一监测站点的实际水质数据,通过已计算的河流一维稳态模型对实际水质数据进行计算,获取当前监测站点的理论水质数据;根据当前监测水质数据和理论水质数据的比较结果,确定当前监测站点和前一监测站点之间是否存在污染排放。由此,能够实时对水质污染情况进行判断,有效对污染情况进行预测和溯源。
Description
本公开要求于2021年08月26日和2021年12月31日提交中国专利局、申请号为2021109908549和202111668234X、发明名称均为“一种基于数字流域时空模型的污染排放确定方法和装置”的中国专利申请的优先权,其全部内容通过引用结合在本公开中。
本公开涉及河流水质监测技术领域,尤其涉及一种基于数字流域时空模型的污染排放确定方法和装置。
水流域生态资源的有效治理问题是必须要解决与回应的重要问题。建立起数字流域,并且准确判断污染排放情况是流域治理现代化的重要举措。
发明内容
为了解决上述技术问题或者至少部分地解决上述技术问题,本公开提供了一种基于数字流域时空模型的污染排放确定方法和装置。
本公开提供了基于数字流域时空模型的污染排放确定方法,待检测河流域中包括多个区段,每个所述区段具有监测站点,每个所述监测站点安装水质传感器,所述水质传感器用于获取水质数据;包括:
获取当前监测站点的当前监测水质数据;
获取前一监测站点的实际水质数据,通过已计算的河流一维稳态模型对所述实际水质数据进行计算,获取所述当前监测站点的理论水质数据;
根据所述当前监测水质数据和所述理论水质数据的比较结果,确定所述当前监测站点和所述前一监测站点之间是否存在污染排放。
在本公开一个可选的实施例中,在所述通过已计算的河流一维稳态模型对所述实际水质数据进行计算之前,还包括:
获取每个所述区段内的多个流速,并计算所述多个流速的平均值作为相邻两个区段的实际流速;
获取每个所述区段的监测水质数据和相邻两个区段的断面距离;其中,所述监测水质数据包括初始点污染物浓度和断面污染物浓度;
将所述监测水质数据、所述实际流速、所述断面距离以河流一维稳态模型进行多次计算,获取好氧系数和污染物的沉降系数的和值;
根据所述好氧系数和污染物的沉降系数的和值、所述实际流速、所述断面距离建立所述已计算的河流一维稳态模型。
在本公开一个可选的实施例中,所述的基于数字流域时空模型的污染排放确定方法,还包括:
获取任一目标区段的初始水质数据,通过所述已计算的河流一维稳态模型对所述初始水质数据进行计算,获取所述目标区段的末位水质数据;
获取下一区段的初始水质数据;
在所述末位水质数据和所述下一区段的初始水质数据不一致的情况下,调整所述好氧系数和污染物的沉降系数的和值,直到所述末位水质数据和所述下一区段的初始水质数据的误差在预设阈值内。
在本公开一个可选的实施例中,所述根据所述当前监测水质数据和所述理论水质数据的比较结果,确定所述当前监测站点和所述前一监测站点之间是否存在污染排放,包括:
从所述当前水质数据中,获取每个污染物的当前污染值;
从所述理论水质数据中,获取每个污染物的理论污染值;
计算所述当前污染值和所述理论污染值的差值,获取所述差值大于预设差值阈值的目标污染物;
在所述目标污染物在预设污染物列表内,则确定所述当前监测站点和所述前一监测站点之间存在污染排放;
在所述目标污染物不在预设污染物列表内,则确定所述当前监测站点和所述前一监测站点之间不存在污染排放。
在本公开一个可选的实施例中,所述根据所述当前监测水质数据和所述理论水质数据的比较结果,确定所述当前监测站点和所述前一 监测站点之间是否存在污染排放,包括:
从所述当前水质数据中,获取目标污染物的当前污染值;
从所述理论水质数据中,获取所述目标污染物的理论污染值;
计算所述当前污染值和所述理论污染值的差值,在所述差值大于预设差值阈值的情况下,则确定所述当前监测站点和所述前一监测站点之间存在污染排放;
在所述差值小于等于预设差值阈值的情况下,则确定所述当前监测站点和所述前一监测站点之间不存在污染排放。
在本公开一个可选的实施例中,所述的基于数字流域时空模型的污染排放确定方法,还包括:
在确定所述当前监测站点和所述前一监测站点之间存在污染排放的情况下,生成预警信息并发送给目标设备。
在本公开一个可选的实施例中,所述的基于数字流域时空模型的污染排放确定方法,还包括:
在确定所述当前监测站点和所述前一监测站点之间存在污染排放的情况下,获取所述当前监测站点和所述前一监测站点之间距离;
在所述距离小于预设距离阈值的情况下,获取所述距离内的产业信息;
根据所述产业信息确定污染物排放目标区域。
本公开提供了基于数字流域时空模型的污染排放确定装置,待检测河流域中包括多个区段,每个所述区段具有监测站点,每个所述监测站点安装水质传感器,所述水质传感器用于获取水质数据;包括:
第一获取水质模块,用于获取当前监测站点的当前监测水质数据;
第二获取水质模块,用于获取前一监测站点的实际水质数据,通过已计算的河流一维稳态模型对所述实际水质数据进行计算,获取所述当前监测站点的理论水质数据;
污染排放确定模块,用于根据所述当前监测水质数据和所述理论水质数据的比较结果,确定所述当前监测站点和所述前一监测站点之间是否存在污染排放。
本公开提供一种计算机存储介质,其中,该计算机存储介质可存 储有程序,该程序执行时可实现本公开第一方面提供一种通信方法的各实现方式中的部分或全部步骤。
第五方面,还提供一种电子设备,其特征在于,包括:
处理器;
用于存储处理器可执行指令的存储器;
其中,待检测河流域中包括多个区段,每个所述区段具有监测站点,每个所述监测站点安装水质传感器,所述水质传感器用于获取水质数据;所述处理器被配置为:
获取当前监测站点的当前监测水质数据;
获取前一监测站点的实际水质数据,通过已计算的河流一维稳态模型对所述实际水质数据进行计算,获取所述当前监测站点的理论水质数据;
根据所述当前监测水质数据和所述理论水质数据的比较结果,确定所述当前监测站点和所述前一监测站点之间是否存在污染排放。
本公开实施例提供的上述技术方案与现有技术相比具有如下优点:
本公开的基于数字流域时空模型的污染排放确定方法,在待检测河流域中包括多个区段,每个区段具有监测站点,每个监测站点安装水质传感器,水质传感器用于获取水质数据,通过获取当前监测站点的当前监测水质数据,获取前一监测站点的实际水质数据,通过已计算的河流一维稳态模型对实际水质数据进行计算,获取当前监测站点的理论水质数据,根据当前监测水质数据和理论水质数据的比较结果,确定当前监测站点和前一监测站点之间是否存在污染排放。由此,能够实时对水质污染情况进行判断,有效对污染情况进行预测和溯源。
应当理解的是,以上的一般描述和后文的细节描述仅是示例性和解释性的,并不能限制本公开。
此处的附图被并入说明书中并构成本说明书的一部分,示出了符合本公开的实施例,并与说明书一起用于解释本公开的原理。
为了更清楚地说明本公开实施例或现有技术中的技术方案,下面 将对实施例或现有技术描述中所需要使用的附图作简单地介绍,显而易见地,对于本领域普通技术人员而言,在不付出创造性劳动性的前提下,还可以根据这些附图获得其他的附图。
图1为本公开实施例所述基于数字流域时空模型的污染排放确定方法的流程示意图;
图2为本公开实施例所述数字流域的示意图;
图3为本公开实施例所述基于数字流域时空模型的污染排放确定方法的流程示意图;
图4为本公开实施例所述一种基于数字流域时空模型的污染排放确定装置的结构示意图。
为使本公开实施例的目的、技术方案和优点更加清楚,下面将对本公开实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例是本公开的一部分实施例,而不是全部的实施例。基于本公开中的实施例,本领域普通技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例,都属于本公开保护的范围。
具体地,本公开的数字流域是指综合利用传感器技术、地理信息***等技术对全流域的水体进行信息采集和数字化管理,构建起全流域综合信息平台,使相关部门能够及时掌握全流域的水质情况,辅助环保督察,能够对水质污染情况进行判断,有效对污染情况进行预测和溯源。数字流域通过采集传感器数据,结合流域污染物扩散过程,能够推演污染产生后的扩散情况,通过对于污染物扩散过程的反向推演进而能够污染排放排查,并在利用地理信息***实现对于污染状况的可视化展示等等一系列相关工作。
图1为本公开实施例所述基于数字流域时空模型的污染排放确定方法的流程示意图。
如图1所示,该方法包括:
步骤101,获取当前监测站点的当前监测水质数据。
在本公开实施例中,待检测河流域中包括多个区段,每个区段具有监测站点,每个监测站点安装水质传感器,水质传感器用于获取水 质数据。
作为一种场景举例,如图2所示,显示检测河流域中的部分区段1和2,每个区段具有监测站点,比如区段1的初始位置具有监测站点A,末位具有监测站点B,监测站点A和B安装水质传感器用于获取水质数据,再比如区段2的初始位置具有监测站点B,末位具有监测站点C,监测站点B和C安装水质传感器用于获取水质数据。
其中,使用传感器是了解流域内水质状况的常用办法,但是传感器造价普遍较高。了解大流域内的水质状况需要布设较为密集的传感器,这样就会造成监测成本及维护成本高。本公开实施例在流域内布置少量的水质传感器,利用河流一维稳态模型对全流域的水质情况进行模拟,并在此基础之上进行预警和溯源的判断。其中河流一维稳态模型的参数通过实时数据进行测算。
在本公开实施例中,水质传感器的类型可以根据应用需要选择设置,从而获取的水质数据可以为一种或者多种,比如可以磷浓度、氮浓度等。
在本公开实施例中,当前监测水质数据为当前监测站点的水质传感器获取的水质数据。
在本公开实施例中,当前监测站点可以为任一监测站点,比如可以为图2中的B或者C等,具体根据应用场景选择设置。
步骤102,获取前一监测站点的实际水质数据,通过已计算的河流一维稳态模型对实际水质数据进行计算,获取当前监测站点的理论水质数据。
在本公开实施例中,前一监测站点可以理解为前一监测站点的河水顺着河水流向到当前监测站点,即前一监测站点为当前监测站点的上游监测站点,比如图2中的当前监测站点B,前一监测站点则为C,当前监测站点C,前一监测站点则为B。
在本公开实施例中,实际水质数据可以理解为前一监测站点真实的水质数据,比如真实的磷浓度为多少、氮浓度为多少等。
在本公开实施例中,实际水质数据获取的方式可以是前一监测站点的水质传感器获取的水质数据。
进一步地,通过已计算的河流一维稳态模型对实际水质数据进行计算,获取当前监测站点的理论水质数据。其中,已计算的河流一维稳态模型中的实际流速、断面距离可以根据需要实时调整,进一步提高判断准确性。
其中,已计算的河流一维稳态模型是一个以初始点污染物浓度为变量,结束点污染物浓度为计算结果的数学模型,模型中的其他参数已经预先计算获取。其中,河流一维稳态模型预先计算获取参数的方式后续详细描述,此处不再详述。
在本公开实施例中,可以理解的是,水质数据包括磷浓度、氮浓度等,为了提高判断的准确性,可以同时针对多个污染物的浓度进行计算,从而获取理论水质数据包括理论上当前监测站点的磷浓度、氮浓度等分别为多少。
步骤103,根据当前监测水质数据和理论水质数据的比较结果,确定当前监测站点和前一监测站点之间是否存在污染排放。
在本公开实施例中,根据当前监测水质数据和理论水质数据的比较结果,确定当前监测站点和前一监测站点之间是否存在污染排放的方式有很多种,举例说明如下。
第一种示例,从当前水质数据中,获取每个污染物的当前污染值,从理论水质数据中,获取每个污染物的理论污染值,计算当前污染值和理论污染值的差值,获取差值大于预设差值阈值的目标污染物,在目标污染物在预设污染物列表内,则确定当前监测站点和前一监测站点之间存在污染排放,在目标污染物不在预设污染物列表内,则确定当前监测站点和前一监测站点之间不存在污染排放。
具体地,水质数据中每个污染物的污染差值都计算,预先定好哪些污染超标则确定污染排放,比如预设污染物列表中包括磷和氮,具体根据应用需要选择设置。
第二种示例,从当前水质数据中,获取目标污染物的当前污染值,从理论水质数据中,获取目标污染物的理论污染值,计算当前污染值和理论污染值的差值,在差值大于预设差值阈值的情况下,则确定当前监测站点和前一监测站点之间存在污染排放,在差值小于等于预设 差值阈值的情况下,则确定当前监测站点和前一监测站点之间不存在污染排放。
具体地,先确定哪些污染物超标则确定污染排放,比如目标污染物为磷和氮,仅仅计算磷和氮对应的差值,在差值大于预设差值阈值的情况下,则确定当前监测站点和前一监测站点之间存在污染排放,进一步提高计算效率。
综上所述,本公开的基于数字流域时空模型的污染排放确定方法,在待检测河流域中包括多个区段,每个区段具有监测站点,每个监测站点安装水质传感器,水质传感器用于获取水质数据,通过获取当前监测站点的当前监测水质数据,获取前一监测站点的实际水质数据,通过已计算的河流一维稳态模型对实际水质数据进行计算,获取当前监测站点的理论水质数据,根据当前监测水质数据和理论水质数据的比较结果,确定当前监测站点和前一监测站点之间是否存在污染排放。由此,能够实时对水质污染情况进行判断,有效对污染情况进行预测和溯源。
在本公开一个可能实现方式中,在通过已计算的河流一维稳态模型对实际水质数据进行计算之前,预先计算河流一维稳态模型,具体结合图3描述,如图3所示,包括:
步骤201,获取每个区段内的多个流速,并计算多个流速的平均值作为相邻两个区段的实际流速。
步骤202,获取每个区段的监测水质数据和相邻两个区段的断面距离;其中,监测水质数据包括初始点污染物浓度和断面污染物浓度。
步骤203,将监测水质数据、实际流速、断面距离以河流一维稳态模型进行多次计算,获取好氧系数和污染物的沉降系数的和值。
步骤204,根据好氧系数和污染物的沉降系数的和值、实际流速、断面距离建立已计算的河流一维稳态模型。
具体地,河流一维稳态模型,如下:
其中,c为断面的污染物浓度,单位为mg/L,c
0为初始点污染物浓度,单位为mg/L,c和c
0为监测水质数据,K
1为好氧系数,单位为1/d,K
3为污染物的沉降系数,,单位为1/d,μ为实际流速,单位为m/s,x为断面距离(计算初始点到下游计算断面的距离),单位为m。
需要说明的是,河流一维稳态模型的适用条件满足以下条件(1)河流充分混合段;(2)非持久性污染物;(3)河流为恒定流动;(4)废水连续稳定排放。
需要说明的是,对于持续性污染物,在沉降作用明显的河流中,可以采用综合消减系数K替代上式中的K
1+K
3来预测污染物浓度沿程变化,进一步提高准确性。
具体地,利用流速仪采集各监测点位之间的流速,为保证区段内流速的准确性,可以对区段内多点进行测速,然后求取均值代表区段内的流速水平即上述实际流速,进行结合各监测点位测得的水质数据,通过河流一维稳态模型就可以获得各区段的好氧系数和污染物的沉降系数之和,即K
1+K
3。利用各区段监测初始水质数据、好氧系数和污染物的沉降系数之和、与初始点位之间的距离,就可以实现流域区段的数字化。
在本公开实施例中,获取任一目标区段的初始水质数据,通过已计算的河流一维稳态模型对初始水质数据进行计算,获取目标区段的末位水质数据,获取下一区段的初始水质数据,在末位水质数据和下一区段的初始水质数据不一致的情况下,调整好氧系数和污染物的沉降系数的和值,直到末位水质数据和下一区段的初始水质数据的误差在预设阈值内。
具体地,通过河流一维稳态模型测算的区段末位水质数据很有可能与下一区位的初始点的监测水质数据存在不一致的情况,可以通过不断的校验,可以使得K
1+K
3无限接近真实值,通过历史数据的经验可以使测算水质数据在一定范围能就能够满足准确性。这样就可以实现整个流域的数字化。
在本公开一个可能实现方式中,在确定当前监测站点和前一监测站点之间存在污染排放的情况下,生成预警信息并发送给目标设备。
其中,目标设备为任一具有计算能力的设备,例如可以为PC(Personal Computer,个人电脑)、移动终端等,移动终端例如可以为手机、平板电脑、个人数字助理、穿戴式设备等具有各种操作***、触摸屏和/或显示屏的硬件设备,具体根据应用场景选择设置。
其中,预警信息可以包括区段、监测站点、污染物浓度、时间等,具体根据应用场景选择设置。
在本公开一个可能实现方式中,在确定当前监测站点和前一监测站点之间存在污染排放的情况下,获取当前监测站点和前一监测站点之间距离,在距离小于预设距离阈值的情况下,获取距离内的产业信息,根据产业信息确定污染物排放目标区域。
具体地,利用各监测站点监测水质数据可以进行初步研判,在忽略水流过程中污染物自然消退的情况下,如果当前监测站点的污染物浓度远大于前一监测站点的污染物浓度,可以断定流域断面内存在偷拍漏排现象。
需要说明的是,当不能通过以上方式进行判断时,可以考虑污染物在随水流流动过程中的自然消解。经过长时间的学习,K
1+K
3已经能够准确的实现对于全流域的刻画,通过前一监测站点的污染物浓度利用河流一维稳态模型可以计算出当前监测站点的污染物浓度理论值,对比理论值与当前站点的实际监测值,如果计算的理论值远远小于实际监测值,有充分地理由相信,在该区段中存在偷排漏排现象。并且,可以对计算理论值稍微小于实际监测值的区段进行预警。
作为一种场景举例,继续以图2为例,图2中的通过监测点B的实际水质数据,利用河流一维稳态模型可以推演从B到C之间任何时间和位置的水质过程,将通过河流一维稳态模型计算的C处的理论水质数据与C处实际的理论水质数据相比之后,如果计算值远低于实际值,说明BC段之间存在着污水偷排漏排的情况,如果BC段距离较短,就可以结合该区段周边的产业情况比如该区段生产企业,以及生产时间和生产产品类型等等,更好地锁定周边偷排漏排企业的区域。
由此,传感器数据采集是实时的,可以获得实时的污染物浓度情况,通过实时计算各监测点的理论水质数据,就可以对各监测站点进 行实时对比,锁定偷排漏排的区段,对污染区段进行监督检查,达到溯源的目的。另外,随着监测站点布设的密度增大,相关参数的学习会不断得到优化,其准确度也将越来越高,可有效地提升对于偷排漏排现象的检查效率。
图4为本公开实施例所述一种基于数字流域时空模型的污染排放确定装置的结构示意图,该装置可由软件和/或硬件实现,一般可集成在电子设备中。如图4所示,待检测河流域中包括多个区段,每个区段具有监测站点,每个监测站点安装水质传感器,水质传感器用于获取水质数据,该装置包括:
第一获取水质模块401,用于获取当前监测站点的当前监测水质数据。
第二获取水质模块402,用于获取前一监测站点的实际水质数据,通过已计算的河流一维稳态模型对实际水质数据进行计算,获取当前监测站点的理论水质数据。
污染排放确定模块403,用于根据当前监测水质数据和理论水质数据的比较结果,确定当前监测站点和前一监测站点之间是否存在污染排放。
在本公开一个可选实施例中,该装置还包括:获取流速模块,用于获取每个区段内的多个流速,并计算多个流速的平均值作为相邻两个区段的实际流速;获取水质和距离模块,用于获取每个区段的监测水质数据和相邻两个区段的断面距离;其中,监测水质数据包括初始点污染物浓度和断面污染物浓度;计算模块,用于将监测水质数据、实际流速、断面距离以河流一维稳态模型进行多次计算,获取好氧系数和污染物的沉降系数的和值;建立模块,用于根据好氧系数和污染物的沉降系数的和值、实际流速、断面距离建立已计算的河流一维稳态模型。
在本公开一个可选实施例中,该装置还包括:获取计算模块,用于获取任一目标区段的初始水质数据,通过已计算的河流一维稳态模型对初始水质数据进行计算,获取目标区段的末位水质数据;获取模 块,用于获取下一区段的初始水质数据;调整模块,用于在末位水质数据和下一区段的初始水质数据不一致的情况下,调整好氧系数和污染物的沉降系数的和值,直到末位水质数据和下一区段的初始水质数据的误差在预设阈值内。
在本公开一个可选实施例中,污染排放确定模块403,具体用于从当前水质数据中,获取每个污染物的当前污染值,从理论水质数据中,获取每个污染物的理论污染值,计算当前污染值和理论污染值的差值,获取差值大于预设差值阈值的目标污染物,在目标污染物在预设污染物列表内,则确定当前监测站点和前一监测站点之间存在污染排放,在目标污染物不在预设污染物列表内,则确定当前监测站点和前一监测站点之间不存在污染排放。
在本公开一个可选实施例中,从当前水质数据中,获取目标污染物的当前污染值,从理论水质数据中,获取目标污染物的理论污染值,计算当前污染值和理论污染值的差值,在差值大于预设差值阈值的情况下,则确定当前监测站点和前一监测站点之间存在污染排放,在差值小于等于预设差值阈值的情况下,则确定当前监测站点和前一监测站点之间不存在污染排放。
在本公开一个可选实施例中,该装置还包括:生成发送模块,用于在确定当前监测站点和前一监测站点之间存在污染排放的情况下,生成预警信息并发送给目标设备。
在本公开一个可选实施例中,该装置还包括:分析模块,用于在确定当前监测站点和前一监测站点之间存在污染排放的情况下,获取当前监测站点和所述前一监测站点之间距离,在距离小于预设距离阈值的情况下,获取距离内的产业信息,根据产业信息确定污染物排放目标区域。
综上所述,本公开的基于数字流域时空模型的污染排放确定装置,在待检测河流域中包括多个区段,每个区段具有监测站点,每个监测站点安装水质传感器,水质传感器用于获取水质数据,通过获取当前监测站点的当前监测水质数据,获取前一监测站点的实际水质数据,通过已计算的河流一维稳态模型对实际水质数据进行计算,获取当前 监测站点的理论水质数据,根据当前监测水质数据和理论水质数据的比较结果,确定当前监测站点和前一监测站点之间是否存在污染排放。由此,能够实时对水质污染情况进行判断,有效对污染情况进行预测和溯源。
本公开实施例还提供一种计算机存储介质,其中,该计算机存储介质可存储有程序,该程序执行时可实现图1-图3所示实施例提供的基于数字流域时空模型的污染排放确定方法的各实现方式中的部分或全部步骤。
本公开实施例还提供了一种电子设备,该装置包括:
处理器;
用于存储处理器可执行指令的存储器;
其中,所述处理器被配置为:
获取当前监测站点的当前监测水质数据;
获取前一监测站点的实际水质数据,通过已计算的河流一维稳态模型对所述实际水质数据进行计算,获取所述当前监测站点的理论水质数据;
根据所述当前监测水质数据和所述理论水质数据的比较结果,确定所述当前监测站点和所述前一监测站点之间是否存在污染排放。
需要说明的是,在本文中,诸如“第一”和“第二”等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来,而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且,术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含,从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素,而且还包括没有明确列出的其他要素,或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下,由语句“包括一个……”限定的要素,并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
以上所述仅是本公开的具体实施方式,使本领域技术人员能够理解或实现本公开。对这些实施例的多种修改对本领域的技术人员来说将是显而易见的,本文中所定义的一般原理可以在不脱离本公开的精神或范围的情况下,在其它实施例中实现。因此,本公开将不会被限制于本文所示的这些实施例,而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。
Claims (10)
- 一种基于数字流域时空模型的污染排放确定方法,其特征在于,待检测河流域中包括多个区段,每个所述区段具有监测站点,每个所述监测站点安装水质传感器,所述水质传感器用于获取水质数据;包括:获取当前监测站点的当前监测水质数据;获取前一监测站点的实际水质数据,通过已计算的河流一维稳态模型对所述实际水质数据进行计算,获取所述当前监测站点的理论水质数据;根据所述当前监测水质数据和所述理论水质数据的比较结果,确定所述当前监测站点和所述前一监测站点之间是否存在污染排放。
- 根据权利要求1所述的基于数字流域时空模型的污染排放确定方法,其特征在于,在所述通过已计算的河流一维稳态模型对所述实际水质数据进行计算之前,还包括:获取每个所述区段内的多个流速,并计算所述多个流速的平均值作为相邻两个区段的实际流速;获取每个所述区段的监测水质数据和相邻两个区段的断面距离;其中,所述监测水质数据包括初始点污染物浓度和断面污染物浓度;将所述监测水质数据、所述实际流速、所述断面距离以河流一维稳态模型进行多次计算,获取好氧系数和污染物的沉降系数的和值;根据所述好氧系数和污染物的沉降系数的和值、所述实际流速、所述断面距离建立所述已计算的河流一维稳态模型。
- 根据权利要求2所述的基于数字流域时空模型的污染排放确定方法,其特征在于,还包括:获取任一目标区段的初始水质数据,通过所述已计算的河流一维稳态模型对所述初始水质数据进行计算,获取所述目标区段的末位水质数据;获取下一区段的初始水质数据;在所述末位水质数据和所述下一区段的初始水质数据不一致的情 况下,调整所述好氧系数和污染物的沉降系数的和值,直到所述末位水质数据和所述下一区段的初始水质数据的误差在预设阈值内。
- 根据权利要求1所述的基于数字流域时空模型的污染排放确定方法,其特征在于,所述根据所述当前监测水质数据和所述理论水质数据的比较结果,确定所述当前监测站点和所述前一监测站点之间是否存在污染排放,包括:从所述当前水质数据中,获取每个污染物的当前污染值;从所述理论水质数据中,获取每个污染物的理论污染值;计算所述当前污染值和所述理论污染值的差值,获取所述差值大于预设差值阈值的目标污染物;在所述目标污染物在预设污染物列表内,则确定所述当前监测站点和所述前一监测站点之间存在污染排放;在所述目标污染物不在预设污染物列表内,则确定所述当前监测站点和所述前一监测站点之间不存在污染排放。
- 根据权利要求1所述的基于数字流域时空模型的污染排放确定方法,其特征在于,所述根据所述当前监测水质数据和所述理论水质数据的比较结果,确定所述当前监测站点和所述前一监测站点之间是否存在污染排放,包括:从所述当前水质数据中,获取目标污染物的当前污染值;从所述理论水质数据中,获取所述目标污染物的理论污染值;计算所述当前污染值和所述理论污染值的差值,在所述差值大于预设差值阈值的情况下,则确定所述当前监测站点和所述前一监测站点之间存在污染排放;在所述差值小于等于预设差值阈值的情况下,则确定所述当前监测站点和所述前一监测站点之间不存在污染排放。
- 根据权利要求4或5所述的基于数字流域时空模型的污染排放确定方法,其特征在于,还包括:在确定所述当前监测站点和所述前一监测站点之间存在污染排放的情况下,生成预警信息并发送给目标设备。
- 根据权利要求4或5所述的基于数字流域时空模型的污染排放 确定方法,其特征在于,还包括:在确定所述当前监测站点和所述前一监测站点之间存在污染排放的情况下,获取所述当前监测站点和所述前一监测站点之间距离;在所述距离小于预设距离阈值的情况下,获取所述距离内的产业信息;根据所述产业信息确定污染物排放目标区域。
- 一种基于数字流域时空模型的污染排放确定装置,其特征在于,待检测河流域中包括多个区段,每个所述区段具有监测站点,每个所述监测站点安装水质传感器,所述水质传感器用于获取水质数据;包括:第一获取水质模块,用于获取当前监测站点的当前监测水质数据;第二获取水质模块,用于获取前一监测站点的实际水质数据,通过已计算的河流一维稳态模型对所述实际水质数据进行计算,获取所述当前监测站点的理论水质数据;污染排放确定模块,用于根据所述当前监测水质数据和所述理论水质数据的比较结果,确定所述当前监测站点和所述前一监测站点之间是否存在污染排放。
- 一种电子设备,其特征在于,所述电子设备包括:处理器;用于存储所述处理器可执行指令的存储器;所述处理器,用于从所述存储器中读取所述可执行指令,并执行所述指令以实现上述权利要求1-7中任一所述的基于数字流域时空模型的污染排放确定方法。
- 一种计算机可读存储介质,其特征在于,所述存储介质存储有计算机程序,所述计算机程序用于执行上述权利要求1-7中任一所述的基于数字流域时空模型的污染排放确定方法。
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Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101256664A (zh) * | 2007-02-27 | 2008-09-03 | 仁荷大学教产学协力团 | 使用gis对水污染负荷评估的决策支持***以及操作方法 |
CN104392100A (zh) * | 2014-10-29 | 2015-03-04 | 南京南瑞集团公司 | 基于水质在线监测***的污染源扩散预警方法 |
US20160202228A1 (en) * | 2015-01-08 | 2016-07-14 | International Business Machines Corporation | Water quality monitoring and early event detection |
CN113868347A (zh) * | 2021-08-26 | 2021-12-31 | 中国环境科学研究院 | 一种基于数字流域时空模型的污染排放确定方法和装置 |
Family Cites Families (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH11184839A (ja) * | 1997-12-25 | 1999-07-09 | Hitachi Ltd | 発散抑制型2次元河川シミュレーションシステム |
JP4390249B2 (ja) * | 2003-07-14 | 2009-12-24 | 五洋建設株式会社 | 水質環境の監視方法及び監視システム |
AU2020101392A4 (en) * | 2020-07-17 | 2020-08-20 | Beijing Normal University | Water quality prediction method and system based on water quality model |
CN112417721B (zh) * | 2020-11-11 | 2021-07-27 | 北京京航计算通讯研究所 | 基于二维有限元法扩散模型的水污染溯源方法及*** |
CN113128884B (zh) * | 2021-04-25 | 2023-09-01 | 南京大学 | 一种基于河网数学模型进行区域性水环境溯源的方法及*** |
-
2021
- 2021-08-26 CN CN202110990854.9A patent/CN113868347A/zh active Pending
- 2021-12-31 CN CN202111668234.XA patent/CN114254066A/zh active Pending
-
2022
- 2022-10-25 US US18/009,343 patent/US20240232456A1/en active Pending
- 2022-10-25 WO PCT/CN2022/127275 patent/WO2023025329A1/zh active Application Filing
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101256664A (zh) * | 2007-02-27 | 2008-09-03 | 仁荷大学教产学协力团 | 使用gis对水污染负荷评估的决策支持***以及操作方法 |
CN104392100A (zh) * | 2014-10-29 | 2015-03-04 | 南京南瑞集团公司 | 基于水质在线监测***的污染源扩散预警方法 |
US20160202228A1 (en) * | 2015-01-08 | 2016-07-14 | International Business Machines Corporation | Water quality monitoring and early event detection |
CN113868347A (zh) * | 2021-08-26 | 2021-12-31 | 中国环境科学研究院 | 一种基于数字流域时空模型的污染排放确定方法和装置 |
CN114254066A (zh) * | 2021-08-26 | 2022-03-29 | 中国环境科学研究院 | 一种基于数字流域时空模型的污染排放确定方法和装置 |
Non-Patent Citations (1)
Title |
---|
"Environmental Impact Assessment Technique and Method", 28 February 2019, ENVIRONMENTAL ENGINEERING EVALUATION CENTER OF MINISTRY OF ECOLOGY AND ENVIRONMENT, CN, ISBN: 978-7-5111-3905-4, article TAN MINQIANG: "Passage Chapter 6.3: Application of River Water Quality Model", pages: 262 - 267, XP009544043 * |
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116168771A (zh) * | 2023-04-26 | 2023-05-26 | 北京建工环境修复股份有限公司 | 一种基于物联网的河流中全氟化合物污染监测方法及*** |
CN116701885A (zh) * | 2023-08-04 | 2023-09-05 | 合肥工业大学 | 一种废水处理数据采集分析方法 |
CN116701885B (zh) * | 2023-08-04 | 2023-10-20 | 合肥工业大学 | 一种废水处理数据采集分析方法 |
CN117273221A (zh) * | 2023-09-21 | 2023-12-22 | 中节能天融科技有限公司 | 基于邻间断面特征分析的河段异常排放预警方法及装置 |
CN117273221B (zh) * | 2023-09-21 | 2024-06-07 | 中节能数字科技有限公司 | 基于邻间断面特征分析的河段异常排放预警方法及装置 |
CN117630309A (zh) * | 2023-10-17 | 2024-03-01 | 水利部珠江水利委员会水文局 | 一种河道水质智能监测方法及*** |
CN117630309B (zh) * | 2023-10-17 | 2024-05-28 | 水利部珠江水利委员会水文局 | 一种河道水质智能监测方法及*** |
CN117689273A (zh) * | 2024-02-02 | 2024-03-12 | 埃睿迪信息技术(北京)有限公司 | 一种信息处理方法、装置及设备 |
CN118094453A (zh) * | 2024-04-28 | 2024-05-28 | 山东维平信息安全测评技术有限公司 | 一种环保信息数据化管理监测*** |
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