CN113836475B - Intelligent sewage treatment method and system based on ecological environment restoration - Google Patents
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Abstract
The invention discloses an intelligent sewage treatment method based on ecological environment restoration, which comprises the steps of obtaining effective sub-areas by partitioning a target area, constructing a sensor network in the effective sub-areas, obtaining sewage data, preprocessing the sewage data, obtaining a sewage data set, calculating a pollution threat value according to the sewage data set, obtaining a pollution risk area, and storing position information of the pollution risk area into a database or sending the position information to a terminal of an administrator. The invention realizes continuous detection of the water body, automatically starts the sewage treatment device and pushes the pollution early warning information.
Description
Technical Field
The invention relates to the technical field of sewage treatment, in particular to an intelligent sewage treatment method and system based on ecological environment restoration.
Background
At present, surface water pollution is not negligible, the water quality of a plurality of main rivers and key lakes exceeds 3 types of water quality, perennial or periodic black ugly phenomenon occurs in water bodies in a plurality of areas, the problem of treating sewage in ecological environment gradually becomes key work, and a complete set of technologies of controlling emission reduction, hydrodynamic recovery, water conservancy regulation, ecological restoration and comprehensive treatment are developed into the polluted water body treatment technology by utilizing gradually mature technologies and practical experience from the control of water body quality targets.
In the prior art, for example, the method for treating black and odorous river sewage is disclosed as CN107188318B, which is a simple method for treating black and odorous river sewage, and the problems of low efficiency, low pertinence and the like are caused when a water body is treated by physical means such as a grid, a filter and the like. How to efficiently detect and automatically control water quality is the research direction of those skilled in the art.
Disclosure of Invention
The invention aims to provide an intelligent sewage treatment method based on ecological environment restoration, which aims to solve one or more technical problems in the prior art and provide at least one beneficial choice or creation condition.
In order to achieve the technical purpose, the technical scheme of the invention is as follows:
an intelligent sewage treatment method based on ecological environment restoration comprises the following steps:
step 1, obtaining effective subregions by partitioning a target region, and constructing a sensor network in the effective subregions to obtain sewage data;
step 2, preprocessing sewage data to obtain a sewage data set;
step 3, calculating a pollution threat value according to the sewage data set to obtain a pollution risk area;
and 4, storing the position information of the pollution risk area into a database or sending the position information to a terminal of an administrator.
Further, the method comprises a step 5 of repairing the sewage treatment device of the area for controlling the pollution risk.
Further, in step 1, the target area is partitioned to obtain an effective sub-area, a sensor network is constructed in the effective sub-area, and the sub-step of obtaining sewage data is as follows:
dividing a target area into N sub-areas, specifically:
selecting a point outside a target area as an origin, establishing a coordinate system, taking the east-west direction as an X axis and the south-north direction as a Y axis, wherein the target area is completely positioned in a first quadrant of a coordinate axis, and the target area is an area ready for collecting sewage data;
setting separation lines parallel or perpendicular to an X axis or a Y axis by taking an original point of a coordinate system as a division reference point, dividing a target area into sub-areas through the separation lines by taking a step M as a distance between the separation lines, wherein the area of each sub-area is M;
all the divided sub-areas cover the whole target area, part of the sub-areas are not contained in the target area, and the situation that the sub-areas do not cover the target area completely does not exist;
numbering the sub-regions, and setting the sub-region set as A;
traversing the sub-regions, judging whether each sub-region is completely contained in the target region, if the current sub-region is not completely contained in the target region, calculating the area of the current sub-region occupied by the target region in the current sub-region, and putting an area set SA, wherein the set SA = { SA1, SA2, SA3, … … and SAz }, and the size of the set SA is z;
calculating a reference condition of the effective area:
wherein SAi is the ith element of the set SA and represents the area occupied by the target area in the ith sub-area, SAi is less than M, SAF is the reference condition, MAX (SA) is the largest element in the set SA, and MIN (SA) is the smallest element in the set SA;
sequentially judging the relationship between SAi and SAF, i belongs to [1, z ], and if the selected SAi is not greater than the SAF, removing the current sub-region from the set A;
the sub-regions in the set A are effective sub-regions;
arranging a composite sensor in each effective subregion, wherein all the composite sensors form a sensor network, and the composite sensors can obtain sewage data, and the sewage data comprises a pH value, Chemical Oxygen Demand (COD), temperature and ammonia nitrogen concentration; the transmission mode of each composite sensor is wireless transmission;
the compound sensor acquires the sewage data at interval INT 0.
Preferably, the composite sensor of each sub-area is further provided with a sewage treatment device, wherein one sewage treatment device is selected, and the sewage treatment device can be one or more of a sewage treatment device disclosed in the application with the publication number of CN112978850A, a submerged floating type river sewage treatment device disclosed in the application with the publication number of CN107253771A and a sewage stink treatment device disclosed in the application with the publication number of CN 112058014A.
Further, in step 2, the sewage data is preprocessed, and the substep of obtaining the sewage data set is as follows:
the sub-regions in the step 2 refer to the effective sub-regions in the set A in the step 1;
each pH value in the wastewater data was corrected for pH at standard conditions:
pH1=pH'-((T-T0)×(pH0-pH')×τ)，
wherein, pH1 is the corrected pH value, pH 'is the pH value in the sewage data, T is the temperature of the sub-region corresponding to the current pH' in the sewage data, the unit is C, T0 is the reference temperature, T0 is 25 ℃, pH0 is the reference pH value, pH0 is 7, tau is the pH correction coefficient, and the unit is C-1；
Repairing abnormal data in the sewage data;
and forming a sewage data set by the repaired sewage data, wherein the sewage data set comprises the sewage data of the first three sampling periods at the current moment.
The electrode of the pH meter can be a single battery with high internal resistance, the output voltage range of the electrode is-420 mV to +430mV and is in a linear relation with the measured pH, the voltage output changes by about 60mV when the pH value changes by 1, the pH is related to the temperature, the measured pH value needs to be converted into the pH value under the standard state, and the value of tau is 0.003 in one embodiment.
Further, in step 3, a pollution threat value is calculated according to the sewage data set, and the sub-step of obtaining the pollution risk area is as follows:
the sub-regions in step 3 refer to the effective sub-regions in the set A in step 1;
step 3.1, acquiring sewage data of a sewage data set;
step 3.2, calculating the pH standard coefficient pHf：
pHf=exp(|pH1-pH0|/pH0)，
In the formula, pHfThe pH value is a pH standard coefficient, the pH value 1 is a corrected pH value at the latest moment in the sewage data, and the pH value 0 is a reference pH value;
the pH value of 0 is 6.5-7.5, depending on the actual conditions of the current water body, and in a preferred embodiment, the pH value of 0 is 6.5;
step 3.3, calculating the pollution index:
WF=(c(NH3-NH4)/c0(NH3-NH4))×pHf+(COD/COD0)×0.22，
wherein WF is the contamination index, pHfFor the standard pH coefficient, c (NH3-NH4) is the ammonia nitrogen concentration in the sewage data, c0(NH3-NH4) is the ammonia nitrogen concentration threshold, COD is the acquired chemical oxygen demand, COD0Is a reference concentration of chemical oxygen demand;
in one example, c0(NH3-NH4) is 120mg/L, COD0Is 100 mg/L;
step 3.4, calculating a pollution threat value:
THREAT=0.5×WF+0.3×WF-1+0.2×WF-2，
in the formula, THREAT is the pollution THREAT value of the current subregion, WF is the pollution index of the sewage data sampled at the latest time from the current moment, and WF-1Pollution index, WF, of sewage data obtained at a time immediately preceding the most recent sampling time at the present time-2The pollution index of the sewage data obtained at the first two moments of the latest sampling moment at the current moment, if WF-1And/or WF-2Absence, then WF-1And/or WF-2Taking a value of 0;
step 3.5, obtaining the pollution THREAT value of each sub-area, sorting the pollution THREAT values of all the sub-areas in a descending order to obtain sub-areas with the pollution THREAT values larger than a threshold value, putting the sub-areas into a set THS, THS = { THREAT1, THREAT2, … …, THREATr }, skipping to step 3.6, if no area with the pollution THREAT value larger than the threshold value exists, taking an area with the maximum pollution THREAT value, and re-executing the step 3.5 after a time interval T0, wherein r is the number of sub-areas in the set THS;
step 3.6, sequentially traversing the sub-areas in the THS, if the linear distance of the composite sensor in the two sub-areas is smaller than D2, obtaining the coordinates of the composite sensor in the two sub-areas as (x1, y1), (x2, y2), constructing a boundary point, and setting a pollution risk area, specifically:
step 3.6.1, a calculation method of the boundary point 1: (MIN (x1, x2) + ABS (x2-x1), MAX (y1, y2)), method of calculating boundary point 2: (MIN (x1, x2), MIN (y1, y2)), where MIN (x1, x2) denotes taking the smaller of x1 and x2, MIN (y1, y2) denotes taking the smaller of y1 and y2, MAX (y1, y2) denotes taking the larger of y1 and y2, ABS (x2-x1) denotes taking the absolute of (x2-x 1);
step 3.6.2, constructing a closed region PA consisting of a boundary point 1, a boundary point 2, a (x1, y1) and (x2, y2), acquiring sub-regions which are covered by the closed region PA and do not belong to the two sub-regions selected in the step 3.6 to form a sub-region PA 'set, judging whether the proportion covered by the closed region PA in each sub-region PA' set is greater than a first threshold, and if the proportion covered by the closed region PA in the selected sub-region is greater than the first threshold, setting the currently selected sub-region as a region with a pollution risk;
in a preferred embodiment, the first threshold is 0.4M; wherein D2 is the average distance value between all sub-regions and the geometric center point of the target region;
and 3.6.3, repeating the step 3.6 until all sub-regions in the set THS are traversed, and identifying all regions with pollution risks.
Further, in step 3, the substep of repairing the abnormal data in the sewage data is as follows:
for data L0 to be evaluated (i.e., the value of any parameter in the sewage data), the eight composite sensors closest to the source composite sensor of L0 are taken as the center, the data sampled by the eight composite sensors at the latest time from the current time is taken as a set P 'u = { P'1, P '2, … …, P' k }, the data sampled by the eight composite sensors at the latest time at an interval T0 is taken as a set Pu = { P1, P2, … …, Pk }, and k = 8:
calculating the estimated error value LR and the estimated average value Lavg of the data L0 to be evaluated:
wherein Pj is the jth value of the set Pu, P ' j is the jth value of the set P ' u, dj is the planar distance from the source composite sensor of L0 to the source composite sensor of the selected P ' j, delta is the diffusion coefficient, if (Lavg-LR) is less than or equal to L0 and less than or equal to (Lavg + LR), L0 is the normal value, otherwise, L0 is judged as the abnormal data, and the normal value of the first two time intervals T0 of the source composite sensor of L0 is used for replacing the abnormal value L0;
and completing abnormal data repair.
Further, in step 4, the sub-step of storing the location information of the area with the risk of contamination into a database or sending the location information to the terminal of the administrator is:
the sub-regions in the step 4 refer to the effective sub-regions in the set A in the step 1;
and sending early warning information to the subarea with the pollution risk, wherein the early warning information comprises the coordinates of the subarea with the pollution risk and the pollution occurrence time.
Further, the center coordinates of the area at risk of contamination are selected as the coordinates of the area at risk of contamination.
An intelligent sewage treatment system based on ecological environment restoration, the system comprises:
the device comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor executes the computer program to run an intelligent sewage treatment method based on ecological environment restoration in the following units of the system:
the sensor network comprises a composite sensor in a plurality of sub-areas and is used for acquiring data of the composite sensor;
the composite sensor is used for acquiring various water indexes of a detected water area in real time and comprises the following modules: the pH meter, the COD detector and the data transmission module are used for sending data obtained by the sensor network;
the data receiving module is used for receiving sensor data sent by a data transmission module of a sensor Network and transmitting the sensor data to a data processing module, the data transmission module communicates with the data receiving module through a wireless technology, the wireless technology is a Low-Power Wide-Area Network (LPWAN) technology, and the LPWAN technology comprises one or more of the following technologies: NB-IoT, LTE-M, Weightless, HaLow, LoRa, Sigfox, RPMA, Neul, BLE;
the data processing module comprises a server, a computer, a computing workstation, a hardware firewall and a router and is used for processing the sensor data from the data receiving module and outputting early warning information;
the sewage treatment control module: acquiring early warning information of a data processing module, and controlling a sewage treatment device, wherein the sewage treatment device can release chemicals in water to treat pollutants in sewage;
the data early warning module: and the early warning module is used for sending out early warning according to the early warning information from the data processing module.
Compared with the prior art, the invention has the following beneficial technical effects:
the water body is continuously detected, the sewage treatment device is automatically started, and pollution early warning information is pushed.
Drawings
The foregoing and other features of the present invention will become more apparent to those skilled in the art from the following detailed description of the embodiments taken in conjunction with the accompanying drawings, in which like reference characters designate the same or similar elements, and in which it is apparent that the drawings described below are merely exemplary of the invention and that other drawings may be derived therefrom without the inventive faculty, to those skilled in the art, and in which:
FIG. 1 is a flow chart of an intelligent sewage treatment method based on ecological environment restoration provided by the invention;
fig. 2 is a block diagram of an intelligent sewage treatment system based on ecological environment remediation, according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. The specific embodiments described herein are merely illustrative of the invention and are not intended to be limiting.
It is also to be understood that the following examples are illustrative of the present invention and are not to be construed as limiting the scope of the invention, and that certain insubstantial modifications and adaptations of the invention by those skilled in the art in light of the foregoing description are intended to be included within the scope of the invention.
The following exemplarily illustrates an intelligent sewage treatment method based on ecological environment restoration provided by the invention.
Fig. 1 is a flow chart of an intelligent sewage treatment method based on ecological environment restoration, and the intelligent sewage treatment method based on ecological environment restoration according to an embodiment of the present invention is described below with reference to fig. 1, and the method includes the following steps:
step 1, obtaining effective subregions by partitioning a target region, and constructing a sensor network in the effective subregions to obtain sewage data;
step 2, preprocessing sewage data to obtain a sewage data set;
step 3, calculating a pollution threat value according to the sewage data set to obtain a pollution risk area;
and 4, storing the position information of the pollution risk area into a database or sending the position information to a terminal of an administrator.
Further, in step 1, the target area is partitioned to obtain an effective sub-area, a sensor network is constructed in the effective sub-area, and the sub-step of obtaining sewage data is as follows:
dividing a target area into N sub-areas, specifically:
selecting a point outside a target area as an origin, establishing a coordinate system, taking the east-west direction as an X axis and the south-north direction as a Y axis, and positioning all the target area in a first quadrant of a coordinate axis;
setting separation lines parallel or perpendicular to an X axis or a Y axis by taking an original point of a coordinate system as a division reference point, dividing a target area into sub-areas through the separation lines by taking a step M as a distance between the separation lines, wherein the area of each sub-area is M;
all the divided sub-areas cover the whole target area, part of the sub-areas are not contained in the target area, and the situation that the sub-areas do not cover the target area completely does not exist;
numbering the sub-regions, and setting the sub-region set as A;
traversing the sub-regions, judging whether each sub-region is completely contained in the target region, if the current sub-region is not completely contained in the target region, calculating the area of the current sub-region occupied by the target region in the current sub-region, and putting an area set SA, wherein the set SA = { SA1, SA2, SA3, … … and SAz }, and the size of the set SA is z;
calculating a reference condition of the effective area:
wherein SAi is the ith element of the set SA and represents the area occupied by the target area in the ith sub-area, SAi is less than M, SAF is the reference condition, MAX (SA) is the largest element in the set SA, and MIN (SA) is the smallest element in the set SA;
sequentially judging the relationship between SAi and SAF, i belongs to [1, z ], and if the selected SAi is not greater than the SAF, removing the current sub-region from the set A;
the sub-regions in the set A are effective sub-regions;
arranging a composite sensor in each effective subregion, wherein all the composite sensors form a sensor network, and the composite sensors can obtain sewage data, and the sewage data comprises a pH value, Chemical Oxygen Demand (COD), temperature and ammonia nitrogen concentration; the transmission mode of each composite sensor is wireless transmission;
the compound sensor acquires the sewage data at interval INT 0.
Preferably, the composite sensor of each sub-area is further provided with a sewage treatment device, wherein one sewage treatment device is selected, and the sewage treatment device can be one or more of a sewage treatment device disclosed in the application with the publication number of CN112978850A, a submerged floating type river sewage treatment device disclosed in the application with the publication number of CN107253771A and a sewage stink treatment device disclosed in the application with the publication number of CN 112058014A.
Further, in step 2, the sewage data is preprocessed, and the substep of obtaining the sewage data set is as follows:
the sub-regions in the step 2 refer to the effective sub-regions in the set A in the step 1;
each pH value in the wastewater data was corrected for pH at standard conditions:
pH1=pH'-((T-T0)×(pH0-pH')×τ)，
wherein, pH1 is the corrected pH value, pH 'is the pH value in the sewage data, T is the temperature of the sub-region corresponding to the current pH' in the sewage data, the unit is C, T0 is the reference temperature, T0 is 25 ℃, pH0 is the reference pH value, pH0 is 7, tau is the pH correction coefficient, and the unit is C-1；
Repairing abnormal data in the sewage data;
the pH correction coefficient is a parameter of the sensor, comes from a correction process, and is used for correcting the accumulated error of the sensor, and the value is 0.96.
And forming a sewage data set by the repaired sewage data, wherein the sewage data set comprises the sewage data of the first three sampling periods at the current moment.
The electrode of the pH meter can be a single battery with high internal resistance, the output voltage range of the electrode is-420 mV to +430mV and is in a linear relation with the measured pH, the voltage output changes by about 60mV when the pH value changes by 1, the pH is related to the temperature, the measured pH value needs to be converted into the pH value under the standard state, and the value of tau is 0.003 in one embodiment.
Further, in step 3, a pollution threat value is calculated according to the sewage data set, and the sub-step of obtaining the pollution risk area is as follows:
the sub-regions in step 3 refer to the effective sub-regions in the set A in step 1;
step 3.1, acquiring sewage data of a sewage data set;
step 3.2, calculating the pH standard coefficient pHf：
pHf=exp(|pH1-pH0|/pH0)，
In the formula, pHfThe pH value is a pH standard coefficient, the pH value 1 is a corrected pH value at the latest moment in the sewage data, the pH value 0 is a preset normal pH value of the current water body, and exp is an exponential function;
the pH value of 0 is 6.5-7.5, depending on the actual conditions of the current water body, and in a preferred embodiment, the pH value of 0 is 6.5;
step 3.3, calculating the pollution index:
WF=(c(NH3-NH4)/c0(NH3-NH4))×pHf+(COD/COD0)×0.22，
wherein WF is the contamination index, pHfFor the standard pH coefficient, c (NH3-NH4) is the ammonia nitrogen concentration in the sewage data, c0(NH3-NH4) is the ammonia nitrogen concentration threshold, COD is the acquired chemical oxygen demand, COD0Is a reference concentration of chemical oxygen demand;
in one example, c0(NH3-NH4) is 120mg/L, COD0Is 100 mg/L;
step 3.4, calculating a pollution threat value:
THREAT=0.5×WF+0.3×WF-1+0.2×WF-2，
in the formula, THREAT is the pollution THREAT value of the current subregion, WF is the pollution index of the sewage data sampled at the latest time from the current moment, and WF-1Pollution index, WF, of sewage data obtained at a time immediately preceding the most recent sampling time at the present time-2The pollution index of the sewage data obtained at the first two moments of the latest sampling moment at the current moment, if WF-1And/or WF-2Absence, then WF-1And/or WF-2Taking a value of 0;
3.5, obtaining the pollution THREAT values of each sub-area, sorting the pollution THREAT values of all the sub-areas in a descending order to obtain sub-areas with the pollution THREAT values larger than a threshold value, putting the sub-areas into a set THS, THS = { THREAT1, THREAT2, … …, THREATr }, skipping to the step 3.6, if no area with the pollution THREAT value larger than the threshold value exists, taking an area with the maximum pollution THREAT value, re-executing the step 3.5 after a time interval T0, wherein r is the size of the set THS, and the threshold value is the average value of the pollution THREAT values of all the sub-areas;
step 3.6, sequentially traversing the sub-areas in the THS, if the linear distance of the composite sensor in the two sub-areas is smaller than D2, obtaining the coordinates of the composite sensor in the two sub-areas as (x1, y1), (x2, y2), constructing a boundary point, and setting a pollution risk area, specifically:
step 3.6.1, a calculation method of the boundary point 1: (MIN (x1, x2) + ABS (x2-x1), MAX (y1, y2)), method of calculating boundary point 2: (MIN (x1, x2), MIN (y1, y2)), where MIN (x1, x2) denotes taking the smaller of x1 and x2, MIN (y1, y2) denotes taking the smaller of y1 and y2, MAX (y1, y2) denotes taking the larger of y1 and y2, ABS (x2-x1) denotes taking the absolute of (x2-x 1);
step 3.6.2, constructing a closed region PA consisting of a boundary point 1, a boundary point 2, a (x1, y1) and (x2, y2), acquiring sub-regions which are covered by the closed region PA and do not belong to the two sub-regions selected in the step 3.6 to form a sub-region PA 'set, judging whether the proportion covered by the closed region PA in each sub-region PA' set is greater than a first threshold, and if the proportion covered by the closed region PA in the selected sub-region is greater than the first threshold, setting the currently selected sub-region as a region with a pollution risk;
in a preferred embodiment, the first threshold is 0.4M;
step 3.6.3, repeat step 3.6 until all sub-regions in the set THS have been traversed.
Further, in step 3, the substep of repairing the abnormal data in the sewage data is as follows:
for data L0 to be evaluated (i.e., a value of any parameter in sewage data, such as ammonia nitrogen concentration, ammonia nitrogen concentration threshold, or chemical oxygen demand), taking an original composite sensor of L0 as a center and eight composite sensors closest to the original composite sensor, taking data of the eight composite sensors sampled at the latest time from the current time as a set P 'u = { P'1, P '2, … …, P' k }, taking data of the eight composite sensors sampled at the latest time interval T0 as a set Pu = { P1, P2, … …, Pk }, where k = 8:
calculating the estimated error value LR and the estimated average value Lavg of the data L0 to be evaluated:
wherein Pj is the jth value of the set Pu, P ' j is the jth value of the set P ' u, dj is the planar distance from the source composite sensor of L0 to the source composite sensor of the selected P ' j, delta is the diffusion coefficient, if (Lavg-LR) is less than or equal to L0 and less than or equal to (Lavg + LR), L0 is the normal value, otherwise, L0 is judged as the abnormal data, and the normal value of the first two time intervals T0 of the source composite sensor of L0 is used for replacing the abnormal value L0; wherein, delta is [0.2,1 ];
and completing abnormal data repair.
Further, in step 4, the sub-step of storing the location information of the area with the risk of contamination into a database or sending the location information to the terminal of the administrator is:
the sub-regions in the step 4 refer to the effective sub-regions in the set A in the step 1;
and sending early warning information to the subarea with the pollution risk, wherein the early warning information comprises the coordinates of the subarea with the pollution risk and the pollution occurrence time.
An intelligent sewage treatment system based on ecological environment restoration, the system comprises:
FIG. 2 is a block diagram schematically illustrating an intelligent sewage treatment system based on ecological environment remediation, according to an embodiment of the present invention;
a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in the units of the following system:
the sensor network comprises a composite sensor in a plurality of sub-areas and is used for acquiring data of the composite sensor;
the composite sensor is used for acquiring various water indexes of a detected water area in real time and comprises the following modules: the pH meter, the COD detector and the data transmission module are used for sending data obtained by the sensor network;
the data receiving module is used for receiving sensor data sent by a data transmission module of a sensor Network and transmitting the sensor data to a data processing module, the data transmission module communicates with the data receiving module through a wireless technology, the wireless technology is a Low-Power Wide-Area Network (LPWAN) technology, and the LPWAN technology comprises one or more of the following technologies: NB-IoT, LTE-M, Weightless, HaLow, LoRa, Sigfox, RPMA, Neul, BLE;
the data processing module comprises a server, a computer, a computing workstation, a hardware firewall and a router and is used for processing the sensor data from the data receiving module and outputting early warning information;
the sewage treatment control module: acquiring early warning information of a data processing module, and controlling a sewage treatment device, wherein the sewage treatment device can release chemicals in water to treat pollutants in sewage;
the data early warning module: and the early warning module is used for sending out early warning according to the early warning information from the data processing module.
The sewage intelligent treatment system based on ecological environment restoration can be operated in computing equipment such as desktop computers, notebooks, palm computers and cloud servers. The sewage intelligent treatment system based on ecological environment restoration can be operated by a system comprising, but not limited to, a processor and a memory. It will be understood by those skilled in the art that the example is merely an example of an intelligent sewage treatment system based on ecological environment remediation and does not constitute a limitation of an intelligent sewage treatment system based on ecological environment remediation and may include more or less components than the other, or some components in combination, or different components, for example, the intelligent sewage treatment system based on ecological environment remediation may further include input and output devices, network access devices, buses, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. The general processor can be a microprocessor or the processor can be any conventional processor and the like, the processor is a control center of the ecological environment restoration-based intelligent sewage treatment system operation system, and various interfaces and lines are utilized to connect all parts of the whole ecological environment restoration-based intelligent sewage treatment system operable system.
The memory can be used for storing the computer programs and/or modules, and the processor realizes various functions of the intelligent sewage treatment system based on ecological environment restoration by running or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Although the present invention has been described in considerable detail and with reference to certain illustrated embodiments, it is not intended to be limited to any such details or embodiments or any particular embodiment, so as to effectively encompass the intended scope of the invention. Furthermore, the foregoing describes the invention in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial modifications of the invention, not presently foreseen, may nonetheless represent equivalent modifications thereto.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (4)
1. An intelligent sewage treatment method based on ecological environment restoration is characterized by comprising the following steps:
step 1, obtaining effective subregions by partitioning a target region, and constructing a sensor network in the effective subregions to obtain sewage data;
step 2, preprocessing sewage data to obtain a sewage data set;
step 3, calculating a pollution threat value according to the sewage data set to obtain a pollution risk area;
step 4, storing the position information of the pollution risk area into a database or sending the position information to a terminal of an administrator;
the method comprises the following steps of 1, obtaining effective sub-areas by partitioning a target area, constructing a sensor network in the effective sub-areas, and obtaining sewage data, wherein the sub-steps of: dividing a target area into N sub-areas, specifically:
selecting a point outside a target area as an origin, establishing a coordinate system, taking the east-west direction as an X axis and the south-north direction as a Y axis, wherein the target area is completely positioned in a first quadrant of a coordinate axis, and the target area is an area ready for collecting sewage data;
setting separation lines parallel or perpendicular to an X axis or a Y axis by taking an original point of a coordinate system as a division reference point, dividing a target area into sub-areas through the separation lines by taking a step M as a distance between the separation lines, wherein the area of each sub-area is M;
all the divided sub-areas cover the whole target area, part of the sub-areas are not contained in the target area, and the situation that the sub-areas do not cover the target area completely does not exist;
numbering the sub-regions, and setting the sub-region set as A;
traversing the sub-regions, judging whether each sub-region is completely contained in the target region, if the current sub-region is not completely contained in the target region, calculating the area of the current sub-region occupied by the target region in the current sub-region, and putting an area set SA, wherein the set SA = { SA1, SA2, SA3, … … and SAz }, and the size of the set SA is z;
calculating a reference condition of the effective area:
wherein SAi is the ith element of the set SA and represents the area occupied by the target area part in the ith sub-area, SAi < M, SAF is the reference condition, MAX (SA) is the largest element in the set SA, MIN (SA) is the smallest element in the set SA;
sequentially judging the relationship between SAi and SAF, i belongs to [1, z ], and if the selected SAi is not greater than the SAF, removing the selected SAi from the set A;
after the removing operation is carried out, the sub-regions in the set A are effective sub-regions;
deploying a composite sensor in each effective subregion, wherein all the composite sensors form a sensor network, and the composite sensors can obtain sewage data, and the sewage data comprises a pH value, a Chemical Oxygen Demand (COD), a temperature and a nitrogen concentration; the transmission mode of each composite sensor is wireless transmission;
the compound sensor obtains sewage data at interval INT 0;
calculating a pollution threat value according to the sewage data set in the step 3, wherein the substep of obtaining the pollution risk area is as follows:
the sub-regions in step 3 refer to the effective sub-regions in the set A in step 1;
step 3.1, acquiring sewage data of a sewage data set;
step 3.2, calculating the pH standard coefficient pHf：
pHf=exp(|pH1-pH0|/pH0)，
In the formula, pHfThe pH value is a pH standard coefficient, the pH value 1 is a corrected pH value at the latest moment in the sewage data, the pH value 0 is a reference pH value, and exp is an exponential function;
step 3.3, calculating the pollution index:
WF=(c(NH3-NH4)/c0(NH3-NH4))×pHf+(COD/COD0)×0.22，
wherein WF is the contamination index, pHfFor the standard pH coefficient, c (NH3-NH4) is the ammonia nitrogen concentration in the sewage data, c0(NH3-NH4) is the ammonia nitrogen concentration threshold, COD is the acquired chemical oxygen demand, COD0Is a reference concentration of chemical oxygen demand;
step 3.4, calculating a pollution threat value:
THREAT=0.5×WF+0.3×WF-1+0.2×WF-2，
in the formula, THREAT is the pollution THREAT value of the current subregion, WF is the pollution index of the sewage data sampled at the latest time from the current moment, and WF-1Pollution index, WF, of sewage data obtained at a time immediately preceding the most recent sampling time at the present time-2The pollution index of the sewage data obtained at the first two moments of the latest sampling moment at the current moment, if WF-1And/or WF-2Absence, then WF-1And/or WF-2Taking a value of 0;
step 3.5, obtaining the pollution THREAT value of each sub-area, sorting the pollution THREAT values of all the sub-areas in a descending order to obtain sub-areas with the pollution THREAT values larger than a threshold value, putting the sub-areas into a set THS, THS = { THREAT1, THREAT2, … …, THREATr }, skipping to step 3.6, if no area with the pollution THREAT value larger than the threshold value exists, taking an area with the maximum pollution THREAT value, and re-executing the step 3.5 after a time interval T0, wherein r is the number of sub-areas in the set THS;
step 3.6, sequentially traversing the sub-areas in the THS, if the linear distance of the composite sensor in the two sub-areas is smaller than D2, obtaining the coordinates of the composite sensor in the two sub-areas as (x1, y1), (x2, y2), constructing a boundary point, and setting a pollution risk area, specifically:
step 3.6.1, a calculation method of the boundary point 1: (MIN (x1, x2) + ABS (x2-x1), MAX (y1, y2)), method of calculating boundary point 2: (MIN (x1, x2), MIN (y1, y2)), where MIN (x1, x2) denotes taking the smaller of x1 and x2, MIN (y1, y2) denotes taking the smaller of y1 and y2, MAX (y1, y2) denotes taking the larger of y1 and y2, ABS (x2-x1) denotes taking the absolute of (x2-x 1);
step 3.6.2, constructing a closed region PA consisting of a boundary point 1, a boundary point 2 and (x1, y1), (x2, y2), acquiring sub-regions which are covered by the closed region PA and do not belong to the two sub-regions selected in the step 3.6 to form a sub-region PA 'set, judging whether the proportion covered by the closed region PA in each sub-region PA' set is greater than a first threshold, and if the proportion covered by the closed region PA in the selected sub-region is greater than the first threshold, setting the currently selected sub-region as a region with a pollution risk, wherein D2 is the average distance value between all sub-regions and the geometric center point of the target region;
and 3.6.3, repeating the step 3.6 until all sub-regions in the set THS are traversed, and identifying all regions with pollution risks.
2. The intelligent sewage treatment method based on ecological environment restoration according to claim 1, wherein in step 2, the sewage data is preprocessed, and the substep of obtaining the sewage data set is as follows:
the sub-regions in the step 2 refer to the effective sub-regions in the set A in the step 1;
each pH value in the wastewater data was corrected for pH at standard conditions:
pH1=pH'-((T-T0)×(pH0-pH')×τ)，
wherein, pH1 is the corrected pH value, pH 'is the pH value in the sewage data, T is the temperature of the sub-region corresponding to the current pH' in the sewage data, the unit is C, T0 is the reference temperature, T0 is 25 ℃, pH0 is the reference pH value, pH0 is 7, tau is the pH correction coefficient, and the unit is C-1(ii) a Repairing abnormal data in the sewage data;
and forming a sewage data set by the repaired sewage data, wherein the sewage data set comprises the sewage data of the first three sampling periods at the current moment.
3. The intelligent sewage treatment method based on ecological environment restoration according to claim 1, wherein in step 4, the sub-steps of storing the location information of the pollution risk area into a database or sending the location information to a terminal of an administrator are as follows:
the sub-regions in the step 4 refer to the effective sub-regions in the set A in the step 1;
and sending early warning information to the subarea with the pollution risk, wherein the early warning information comprises the coordinates of the subarea with the pollution risk and the pollution occurrence time.
4. The intelligent sewage treatment method based on ecological environment restoration according to claim 2, wherein the substep of restoring the abnormal data in the sewage data is as follows:
for data L0 to be evaluated, taking the source composite sensor of L0 as the center and eight composite sensors closest to the source composite sensor, taking the data of the eight composite sensors sampled most recently from the current time as a set P 'u = { P'1, P '2, … …, P' k }, taking the data of the eight composite sensors sampled most recently one time before T0 as a set Pu = { P1, P2, … …, Pk }, where k = 8:
calculating the estimated error value LR and the estimated average value Lavg of the data L0 to be evaluated:
wherein Pj is the jth value of the set Pu, P ' j is the jth value of the set P ' u, dj is the planar distance from the source composite sensor of L0 to the source composite sensor of the selected P ' j, delta is the diffusion coefficient, if (Lavg-LR) is less than or equal to L0 and less than or equal to (Lavg + LR), L0 is the normal value, otherwise, L0 is judged as the abnormal data, and the normal value of the previous two time intervals T0 of the source composite sensor of L0 is used for replacing the abnormal value L0;
and completing abnormal data repair.
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