CN117964011A - Method and device for determining chlorine adding flow, electronic equipment and storage medium - Google Patents
Method and device for determining chlorine adding flow, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN117964011A CN117964011A CN202410170790.1A CN202410170790A CN117964011A CN 117964011 A CN117964011 A CN 117964011A CN 202410170790 A CN202410170790 A CN 202410170790A CN 117964011 A CN117964011 A CN 117964011A
- Authority
- CN
- China
- Prior art keywords
- flow
- target
- determining
- preset
- historical
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000000460 chlorine Substances 0.000 title claims abstract description 240
- 229910052801 chlorine Inorganic materials 0.000 title claims abstract description 240
- ZAMOUSCENKQFHK-UHFFFAOYSA-N Chlorine atom Chemical compound [Cl] ZAMOUSCENKQFHK-UHFFFAOYSA-N 0.000 title claims abstract description 210
- 238000000034 method Methods 0.000 title claims abstract description 55
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 200
- 238000005660 chlorination reaction Methods 0.000 claims abstract description 126
- 238000005070 sampling Methods 0.000 claims abstract description 123
- 230000001502 supplementing effect Effects 0.000 claims abstract description 19
- 238000012952 Resampling Methods 0.000 claims abstract description 14
- 238000004590 computer program Methods 0.000 claims description 16
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000004907 flux Effects 0.000 claims description 2
- 239000005708 Sodium hypochlorite Substances 0.000 abstract description 11
- SUKJFIGYRHOWBL-UHFFFAOYSA-N sodium hypochlorite Chemical compound [Na+].Cl[O-] SUKJFIGYRHOWBL-UHFFFAOYSA-N 0.000 abstract description 11
- 238000012544 monitoring process Methods 0.000 description 12
- 238000004891 communication Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
- 230000008901 benefit Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 239000008399 tap water Substances 0.000 description 3
- 235000020679 tap water Nutrition 0.000 description 3
- 238000013461 design Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000004576 sand Substances 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A20/00—Water conservation; Efficient water supply; Efficient water use
- Y02A20/152—Water filtration
Landscapes
- Treatment Of Water By Oxidation Or Reduction (AREA)
Abstract
The invention discloses a chlorine adding flow determining method, a chlorine adding flow determining device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a historical flow and a historical sectional area corresponding to a water inlet main pipe; determining a current sampling frequency based on the historical flow, the historical cross-sectional area and a preset acquisition frequency; resampling data based on the current sampling frequency to determine a current sampling data set; determining a target pre-chlorination flow corresponding to the residual chlorine concentration of the target clean water tank based on the current sampling data set and a preset residual chlorine concentration prediction model of the clean water tank; determining a target post-chlorination flow corresponding to the target residual chlorine concentration of the factory water based on the current sampling data set, a preset factory residual chlorine concentration prediction model and the target pre-chlorination flow; and determining the target chlorine supplementing flow based on the target pre-chlorine adding flow, the target post-chlorine adding flow, the preset pre-chlorine adding flow and the preset post-chlorine adding flow, so as to accurately determine the chlorine adding amount of the water plant at each stage, control the sodium hypochlorite adding precision and save the adding cost.
Description
Technical Field
The present invention relates to the field of computing technologies, and in particular, to a method and apparatus for determining a chlorine adding flow, an electronic device, and a storage medium.
Background
With the development of computer technology, more and more intelligent models are being applied to production and life. For example, a mechanism model or a data model is utilized to simulate a chlorine adding system of a water works, so as to realize the pre-estimated sodium hypochlorite adding amount.
At present, when the model is utilized to estimate the sodium hypochlorite throwing amount in each stage, the modeling capability and the sensor precision requirements are very high, and the current monitored water of the residual chlorine monitoring point of the clean water tank cannot be accurately known when the residual chlorine monitoring point of the factory water is reached due to the difference of water flow rates in a water works. For example, the time for reaching the monitoring point of residual chlorine in the water leaving factory is advanced due to the fact that the water flow rate is too high, the time for reaching the monitoring point of residual chlorine in the water leaving factory is delayed due to the fact that the water flow rate is too low, at the moment, accurate monitoring data cannot be obtained based on the time difference between two monitoring points corresponding to a certain water flow rate, and therefore the chlorine adding flow rate cannot be accurately determined, and the sodium hypochlorite adding amount cannot be accurately estimated.
Disclosure of Invention
The invention provides a chlorine adding flow determining method, a chlorine adding flow determining device, electronic equipment and a storage medium, which can accurately determine the chlorine adding amount of a water plant at each stage, thereby controlling the adding precision of sodium hypochlorite and saving the adding cost.
According to an aspect of the present invention, there is provided a chlorine flow rate determining method, including:
acquiring a historical flow and a historical sectional area corresponding to a water inlet main pipe in a target water plant in a historical time period;
determining a current sampling frequency based on the historical flow, the historical cross-section area and a preset acquisition frequency corresponding to a preset time index;
Performing data resampling from a data buffer based on the current sampling frequency, and determining a current sampling data set corresponding to the current sampling frequency; the data buffer is used for storing historical data acquired in a preset time index based on a preset acquisition frequency;
Determining a target pre-chlorination flow corresponding to the residual chlorine concentration of the target clean water tank based on the current sampling data set and a preset residual chlorine concentration prediction model of the clean water tank;
determining a target post-chlorination flow corresponding to the target residual chlorine concentration of the factory water based on the current sampling data set, a preset factory residual chlorine concentration prediction model and the target pre-chlorination flow;
and determining the target chlorine supplementing flow corresponding to the target water plant based on the target pre-chlorine adding flow, the target post-chlorine adding flow, the preset pre-chlorine adding flow and the preset post-chlorine adding flow.
According to another aspect of the present invention, there is provided a chlorine flow rate determination apparatus comprising:
the historical data acquisition module is used for acquiring the historical flow and the historical sectional area corresponding to the water inlet header pipe in the target water plant in the historical time period;
The current sampling frequency determining module is used for determining the current sampling frequency based on the historical flow, the historical cross-section area and the preset acquisition frequency corresponding to the preset time index;
the current sampling data set determining module is used for carrying out data resampling from the data buffer based on the current sampling frequency and determining a current sampling data set corresponding to the current sampling frequency; the data buffer is used for storing historical data acquired in a preset time index based on a preset acquisition frequency;
The target pre-chlorination flow determining module is used for determining the target pre-chlorination flow corresponding to the residual chlorine concentration of the target clean water tank based on the current sampling data set and a preset residual chlorine concentration prediction model of the clean water tank;
The target post-chlorination flow rate determining module is used for determining target post-chlorination flow rates corresponding to the residual chlorine concentration of the factory water based on the current sampling data set, a preset factory water residual chlorine concentration prediction model and the target pre-chlorination flow rates;
and the target chlorine supplementing flow determining module is used for determining the target chlorine supplementing flow corresponding to the target water plant based on the target pre-chlorine adding flow, the target post-chlorine adding flow, the preset pre-chlorine adding flow and the preset post-chlorine adding flow.
According to another aspect of the present invention, there is provided an electronic apparatus including:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of determining a chlorinating flux as described in any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the method for determining a chlorine flow according to any embodiment of the present invention.
According to the technical scheme, the historical flow and the historical sectional area corresponding to the water inlet header pipe in the target water plant in the historical time period are obtained; determining a current sampling frequency based on the historical flow, the historical cross-section area and a preset acquisition frequency corresponding to a preset time index; performing data resampling from a data buffer based on the current sampling frequency, and determining a current sampling data set corresponding to the current sampling frequency; the data buffer is used for storing historical data acquired based on a preset acquisition frequency in a preset time index, so that a current sampling data set with the same sampling frequency as a data set used for modeling is obtained, the problem that a model cannot be fitted due to inconsistent backtracking windows caused by different system time lags in the model is solved, and the accuracy and generalization of a prediction model are improved; determining a target pre-chlorination flow corresponding to the residual chlorine concentration of the target clean water tank based on the current sampling data set and a preset residual chlorine concentration prediction model of the clean water tank; determining a target post-chlorination flow corresponding to the target residual chlorine concentration of the factory water based on the current sampling data set, a preset factory residual chlorine concentration prediction model and the target pre-chlorination flow; and determining the target chlorine supplementing flow corresponding to the target water plant based on the target pre-chlorine adding flow, the target post-chlorine adding flow, the preset pre-chlorine adding flow and the preset post-chlorine adding flow, so that the chlorine adding amount of the water plant at each stage can be accurately determined, the control stability of residual chlorine of a factory is improved, the sodium hypochlorite adding precision is controlled, and the adding cost is saved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a chlorine adding flow determination method according to a first embodiment of the present invention;
Fig. 2 is a flowchart of a chlorine adding flow determination method according to a second embodiment of the present invention;
fig. 3 is a schematic structural view of a chlorine adding flow rate determining device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device for implementing the chlorine adding flow determining method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," "target," "initial," and the like in the description and claims of the present invention and in the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a method for determining a chlorine adding flow rate according to an embodiment of the present invention, where the method may be performed by a chlorine adding flow rate determining device, and the chlorine adding flow rate determining device may be implemented in hardware and/or software, and the chlorine adding flow rate determining device may be configured in an electronic device. As shown in fig. 1, the method includes:
s110, acquiring the historical flow and the historical sectional area corresponding to the water inlet header pipe in the target water plant in the historical time.
The historical time period may be a time period before the preset current time. For example, the historical time period may be, but is not limited to, the last 60 minutes. The target water plant may be a tap water plant equipped with a tap water chlorination system. The source water may refer to water to be treated. The water inlet pipe of the source water is a water inlet main pipe. The historical flow may refer to average flow information of the intake manifold over a historical period of time. The cross-sectional area may refer to the cross-sectional area of the intake manifold.
Specifically, the historical flow and the historical sectional area corresponding to the water inlet header pipe in the target water plant in the historical time period are obtained from the data buffer. The data buffer may be a first-in first-out buffer.
The standard design scheme of the tap water chlorination system is three-time addition and two-time detection, namely three sodium hypochlorite addition points of adding chlorine before setting (before entering a sand filter tank), adding chlorine after setting (before entering a clean water tank) and adding chlorine after setting (leaving a factory) and two residual chlorine monitoring points of a clean water tank residual chlorine monitoring point and a leaving factory water residual chlorine monitoring point.
S120, determining the current sampling frequency based on the historical flow, the historical cross-sectional area and the preset acquisition frequency corresponding to the preset time index.
The preset time index may refer to a time index reconstructed based on the intake manifold flow rate. The preset time index is related to the intake manifold flow rate of the target water plant. The current sampling frequency may refer to the sampling frequency at which data resampling is performed.
Specifically, dividing the historical flow by the historical sectional area, determining the average flow velocity of the water inlet manifold in the historical time, dividing the average flow velocity by the preset acquisition frequency, and determining the current sampling frequency corresponding to the input data of the model.
S130, carrying out data resampling from a data buffer based on the current sampling frequency, and determining a current sampling data set corresponding to the current sampling frequency; the data buffer is used for storing historical data acquired in a preset time index based on a preset acquisition frequency.
The current sampling data set comprises various data influencing the residual chlorine concentration in water. Specifically, historical data in the data buffer can be copied and the copied historical data is subjected to data resampling based on the current sampling frequency, so that a current sampling data set corresponding to the current sampling frequency is determined, and the current sampling data set with the same sampling frequency as the data set used for modeling is obtained, so that the problem that a model cannot be fitted due to inconsistent backtracking windows caused by different system time lags in the model is solved, and the accuracy and generalization of a prediction model are improved.
And S140, determining the target pre-chlorination flow corresponding to the residual chlorine concentration of the target clean water tank based on the current sampling data set and a preset residual chlorine concentration prediction model of the clean water tank.
The pre-set clear water pond residual chlorine concentration prediction model can be a chlorine concentration prediction model pre-established based on a long-short-term memory model (long-short term memory, LSTM). The method comprises the steps of presetting a clear water tank residual chlorine concentration prediction model for predicting the residual chlorine concentration of the clear water tank. The target residual chlorine concentration in the clean water tank can be the optimal residual chlorine concentration in the clean water tank preset based on business requirements.
Specifically, the current sampling data set is brought into a preset clear water tank residual chlorine concentration prediction model to predict the residual chlorine concentration of the clear water tank, and the target pre-chlorination flow is determined according to the principle that the prediction result is closest to the target clear water tank residual chlorine concentration.
S150, determining a target post-chlorination flow corresponding to the target residual chlorine concentration of the factory water based on the current sampling data set, a preset factory water residual chlorine concentration prediction model and the target pre-chlorination flow.
The preset factory water residual chlorine concentration prediction model can be a chlorine concentration prediction model which is built in advance based on an LSTM model. And presetting a factory water residual chlorine concentration prediction model for predicting residual chlorine concentration in a factory water collecting well. The target leaving water residual chlorine concentration may be an optimal residual chlorine concentration in a leaving water collection well preset based on business requirements.
Specifically, the current sampling data set and the target pre-chlorination flow rate are brought into a preset factory water residual chlorine concentration prediction model to predict the factory water residual chlorine concentration, and the target post-chlorination flow rate is determined according to the principle that the prediction result is closest to the target factory water residual chlorine concentration.
S160, determining a target chlorine supplementing flow corresponding to the target water plant based on the target pre-chlorine adding flow, the target post-chlorine adding flow, the preset pre-chlorine adding flow and the preset post-chlorine adding flow.
The preset pre-chlorination flow rate may refer to an optimal pre-chlorination flow rate preset based on the service requirement. For example, the preset pre-chlorination flow rate may refer to the pre-chlorination flow rate required to reach the target clean water tank residual chlorine concentration. The preset post-chlorination flow rate may refer to an optimal post-chlorination flow rate preset based on the service requirement. For example, the preset post-chlorination flow rate may refer to the post-chlorination flow rate required to achieve the target factory water residual chlorine concentration.
Specifically, a first weighted flow difference value between the target pre-chlorination flow and the preset pre-chlorination flow is determined, a second weighted flow difference value between the target post-chlorination flow and the preset post-chlorination flow is determined, the first weighted flow difference value and the second weighted flow difference value are input into a flow PID controller to perform chlorine supplementing flow calculation, and the target chlorine supplementing flow corresponding to the target water plant is determined.
According to the technical scheme, the historical flow and the historical sectional area corresponding to the water inlet header pipe in the target water plant in the historical time period are obtained; determining a current sampling frequency based on a historical flow, a historical cross-sectional area and a preset acquisition frequency corresponding to a preset time index; carrying out data resampling from the data buffer based on the current sampling frequency, and determining a current sampling data set corresponding to the current sampling frequency; the data buffer is used for storing historical data acquired based on a preset acquisition frequency in a preset time index, so that a current sampling data set with the same sampling frequency as a data set used for modeling is obtained, the problem that a model cannot be fitted due to inconsistent backtracking windows caused by different system time lags in the model is solved, and the accuracy and generalization of a prediction model are improved; determining a target pre-chlorination flow corresponding to the residual chlorine concentration of the target clean water tank based on the current sampling data set and a preset residual chlorine concentration prediction model of the clean water tank; determining a target post-chlorination flow corresponding to the target residual chlorine concentration of the factory water based on the current sampling data set, a preset factory residual chlorine concentration prediction model and the target pre-chlorination flow; and determining the target chlorine supplementing flow corresponding to the target water plant based on the target pre-chlorine adding flow, the target post-chlorine adding flow, the preset pre-chlorine adding flow and the preset post-chlorine adding flow, so that the chlorine adding amount of the water plant in each stage can be accurately determined, the control stability of residual chlorine from a factory is improved, the sodium hypochlorite adding precision is controlled, and the adding cost is saved.
Based on the above technical solution, S120 may include: dividing the historical flow and the historical sectional area to determine the average flow velocity corresponding to the water inlet manifold in the historical time period; rounding the average flow velocity to obtain a rounded flow velocity; and dividing the preset acquisition frequency corresponding to the preset time index by the rounding flow rate to determine the current sampling frequency.
The rounding flow rate may be a flow rate obtained by rounding down the average flow rate by a pointer. The rounding flow rate is 10 at maximum and 1 at minimum. This has the advantage that the rounding flow rate used to calculate the current sampling frequency can be guaranteed to be within an effective range.
Based on the above technical solution, S130 may include: determining a current sampling period based on the current sampling frequency, and taking the current sampling period as a current time unit; and backtracking and collecting a current water quality data set, a current water quantity data set and a current meteorological data set corresponding to a preset number of current time units from the current moment to the historical moment in the data buffer based on the current sampling frequency.
Wherein, current water quality data set includes: at least one of turbidity, conductivity, pH value and water temperature; the current water quantity data set comprises the flow of a water inlet main pipe; the current weather data set includes: gas temperature and/or light. The current sampling period may refer to the time interval between each sample of data. The current time unit may refer to a time unit corresponding to a unit time. For example, 1 time unit corresponding to 3 minutes. Backtracking is required using the current time unit. For example, the backtracking length is 20 time units for 60 minutes.
Based on the technical scheme, the method further comprises the following steps: acquiring a cross-sectional area corresponding to a water inlet main pipe and an instantaneous flow corresponding to the water inlet main pipe at each historical acquisition moment in an original time index; determining the instantaneous flow rate corresponding to the water inlet main pipe based on the cross-sectional area and the instantaneous flow rate, rounding the instantaneous flow rate, and determining the rounded instantaneous flow rate; and setting the counting time length corresponding to the current historical acquisition time based on the rounded instantaneous flow velocity, and superposing the counting time length corresponding to each historical acquisition time on the basis of the first historical acquisition time to determine a preset time index.
The original time index may be a time index established in world time. The first historical collection time may refer to a historical collection time corresponding to the first historical data. The count duration is related to the instantaneous flow rate at the time of the historical data acquisition.
For example, the instantaneous flow rate of the water inlet manifold Q In(m3/h) is obtained), and the cross-sectional area of the water inlet manifold of the target water plant C s(m2), and the instantaneous flow rate of the water inlet manifold is calculated as V In(m/s)＝QIn/(Cs x 3600). The instantaneous flow velocity V In is rounded and recorded asDefinition/>The minimum value of (2) is 1 and the maximum value is 10. According to/>Setting the counting length of the historical acquisition data as/>And calculating corresponding counting length for each historical acquisition data in the whole historical data set, setting a clock starting point corresponding to the first historical acquisition time as 2020.01.0100:00:00, longitudinally accumulating the counting length corresponding to each historical acquisition data, and constructing a reformed preset time index. Based on the reorganized preset time index, the historical acquisition data can be combined or interpolated according to the sampling frequency of 3 minutes to obtain a new data set. The newly constructed preset time index is reformed based on intake manifold flow rate as compared to the original time index of the dataset. When the flow rate of the water inlet main pipe is larger, the time flow rate in the preset time index is slower, so that the same number of time units are traced back, in the time period with the larger flow rate, the time range of the tracing is shorter, and in the time period with the smaller flow rate, the time range of the tracing is larger, and the time range of the tracing is consistent with objective reality.
Example two
Fig. 2 is a flowchart of a method for determining a chlorine adding flow according to a second embodiment of the present invention, and the process of determining a chlorine adding flow before a target is described in detail on the basis of the foregoing embodiment. Wherein the explanation of the same or corresponding terms as those of the above embodiments is not repeated herein. As shown in fig. 2, the method includes:
S210, acquiring the historical flow and the historical sectional area corresponding to the water inlet header pipe in the target water plant in the historical time.
S220, determining the current sampling frequency based on the historical flow, the historical cross-sectional area and the preset acquisition frequency corresponding to the preset time index.
S230, resampling data from a data buffer based on the current sampling frequency, and determining a current sampling data set corresponding to the current sampling frequency; the data buffer is used for storing historical data acquired in a preset time index based on a preset acquisition frequency.
S240, inputting the current sampling data set into a preset clear water tank residual chlorine concentration prediction model, intercepting a data block required for prediction based on the current sampling data set in the preset clear water tank residual chlorine concentration prediction model, and predicting the pre-chlorination flow based on the data block to determine the candidate pre-chlorination flow corresponding to the target clear water tank residual chlorine concentration.
The data block may refer to data within a period of time required for performing pre-chlorination flow prediction. The data block may be data corresponding to a partial time period within a predetermined time index range.
And S250, if the candidate pre-chlorination flow rate is detected to be in the available flow rate range, determining the candidate pre-chlorination flow rate as a target pre-chlorination flow rate corresponding to the residual chlorine concentration of the target clean water tank.
The available flow range may be a flow range corresponding to the pre-chlorination flow that can be achieved in the target water plant. The arrangement has the advantages of being suitable for hardware facilities of different water plants and determining accurate target pre-chlorination flow.
And S260, if the candidate pre-chlorination flow rate is detected not to be in the available flow rate range, determining the pre-chlorination flow rate closest to the candidate pre-chlorination flow rate in the available flow rate range as a target pre-chlorination flow rate corresponding to the residual chlorine concentration of the target clean water tank.
The method has the advantages that the method can adapt to hardware facilities of different water plants, and the achievable target pre-chlorination flow is determined on the basis of the target pre-chlorination flow which can be achieved by the target water plants.
S270, determining a target post-chlorination flow corresponding to the target residual chlorine concentration of the factory water based on the current sampling data set, the preset factory water residual chlorine concentration prediction model and the target pre-chlorination flow.
S280, determining the target chlorine supplementing flow corresponding to the target water plant based on the target pre-chlorine adding flow, the target post-chlorine adding flow, the preset pre-chlorine adding flow and the preset post-chlorine adding flow.
According to the technical scheme, the current sampling data set is input into the preset clear water tank residual chlorine concentration prediction model, a data block required by prediction is intercepted and predicted in the preset clear water tank residual chlorine concentration prediction model based on the current sampling data set, and the pre-chlorination flow prediction is performed based on the data block, so that the candidate pre-chlorination flow corresponding to the target clear water tank residual chlorine concentration is determined; if the candidate pre-chlorination flow rate is detected to be in the available flow rate range, determining the candidate pre-chlorination flow rate as a target pre-chlorination flow rate corresponding to the residual chlorine concentration of the target clean water tank; if the candidate pre-chlorination flow is detected not to be in the available flow range, the pre-chlorination flow closest to the candidate pre-chlorination flow in the available flow range is determined to be the target pre-chlorination flow corresponding to the residual chlorine concentration of the target clean water tank, so that the determined target pre-chlorination flow is ensured to be realized by the target water plant, the accuracy and generalization of a residual chlorine concentration prediction model in a chlorination system of the water plant are further effectively improved, the control stability of the residual chlorine concentration is further improved, and the sodium hypochlorite adding cost is reduced.
It should be noted that, after adding chlorine before adding sodium hypochlorite in the source water treatment process, the source water needs to sequentially pass through a conveying pipeline, a sand filter tank, a carbon filter tank and reach the residual chlorine monitoring point of the clean water tank, which usually takes 30 minutes to 90 minutes, and the specific reaching time depends on the flow rate of the main pipe of the factory. Meanwhile, under different total pipe flow rates, the water inlet speed and the water outlet speed of the clean water tank can also be obviously different. Because the time difference between the different historical acquired data needs to be considered in the modeling process, and because of the difference of the flow rates, the time difference is not fixed, but can be reduced along with the increase of the flow rate of the water inlet main pipe and can be increased along with the reduction of the flow rate of the main pipe. Therefore, the influence caused by different water inlet header flow rates needs to be considered in the residual chlorine concentration prediction model.
Based on the above technical solution, S270 may include: and inputting the current sampling data set and the target pre-chlorination flow into a preset factory residual chlorine concentration prediction model, and intercepting a data block which is required for prediction and corresponds to a data block of the preset clean water tank residual chlorine concentration prediction model in the preset factory residual chlorine concentration prediction model based on the current sampling data set. It is understood that the two data blocks correspond to historical sampled data corresponding to water entering through the intake manifold at the same time. Predicting post-chlorination flow based on the intercepted data block, and determining candidate post-chlorination flow corresponding to the residual chlorine concentration of the target factory water; if the candidate post-chlorination flow rate is detected to be in the available flow rate range, determining the candidate post-chlorination flow rate as a target post-chlorination flow rate corresponding to the residual chlorine concentration of the target factory water; and if the candidate post-chlorination flow rate is detected not to be in the available flow rate range, determining the post-chlorination flow rate closest to the candidate post-chlorination flow rate in the available flow rate range as a target post-chlorination flow rate corresponding to the residual chlorine concentration of the target factory water.
Based on the above technical solution, S280 may include: determining a first chlorine flow difference value based on the target pre-chlorine flow and the preset pre-chlorine flow; determining a second chlorine flow difference value based on the target post-chlorine flow and the preset post-chlorine flow; and performing closed-loop control calculation based on the first chlorine flow difference value and the second chlorine flow difference value, and determining the target chlorine supplementing flow corresponding to the target water plant.
The method has the advantage that the target chlorine supplementing flow corresponding to the target water plant can be determined through the difference value between the chlorine adding flows.
After determining the target pre-chlorination flow rate and the target post-chlorination flow rate, the method further includes: and inputting the difference value between the residual chlorine monitoring value of the leaving water and the residual chlorine concentration of the leaving water, which are monitored by the residual chlorine monitoring point of the leaving water, into a concentration PID controller for calculation, and determining the target chlorine supplementing flow corresponding to the target water plant.
The following is an embodiment of a chlorination flow rate determining device provided by an embodiment of the present disclosure, which belongs to the same inventive concept as the chlorination flow rate determining method of the foregoing embodiments, and details of the embodiment of the chlorination flow rate determining device, which are not described in detail, may refer to the embodiment of the foregoing chlorination flow rate determining method.
Example III
Fig. 3 is a schematic structural diagram of a chlorine adding flow rate determining device according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes: a historical data acquisition module 310, a current sampling frequency determination module 320, a current sampling data set determination module 330, a target pre-chlorination flow determination module 340, a target post-chlorination flow determination module 350, and a target make-up chlorine flow determination module 360.
The historical data acquisition module 310 is configured to acquire a historical flow and a historical sectional area corresponding to a water inlet manifold in a target water plant in a historical time period; the current sampling frequency determining module 320 is configured to determine a current sampling frequency based on a historical flow, a historical cross-section area, and a preset sampling frequency corresponding to a preset time index; the current sampling data set determining module 330 is configured to perform data resampling from the data buffer based on the current sampling frequency, and determine a current sampling data set corresponding to the current sampling frequency; the data buffer is used for storing historical data acquired in a preset time index based on a preset acquisition frequency; the target pre-chlorination flow determining module 340 is configured to determine a target pre-chlorination flow corresponding to a residual chlorine concentration of the target clean water tank based on the current sampling data set and a preset residual chlorine concentration prediction model of the clean water tank; the target post-chlorination flow rate determining module 350 is configured to determine a target post-chlorination flow rate corresponding to the target residual chlorine concentration of the factory water based on the current sampling data set, the preset factory residual chlorine concentration prediction model and the target pre-chlorination flow rate; the target chlorine replenishment flow rate determining module 360 is configured to determine a target chlorine replenishment flow rate corresponding to the target water plant based on the target pre-chlorine replenishment flow rate, the target post-chlorine replenishment flow rate, the preset pre-chlorine replenishment flow rate, and the preset post-chlorine replenishment flow rate.
According to the technical scheme, the historical flow and the historical sectional area corresponding to the water inlet header pipe in the target water plant in the historical time period are obtained; determining a current sampling frequency based on a historical flow, a historical cross-sectional area and a preset acquisition frequency corresponding to a preset time index; carrying out data resampling from the data buffer based on the current sampling frequency, and determining a current sampling data set corresponding to the current sampling frequency; the data buffer is used for storing historical data acquired based on a preset acquisition frequency in a preset time index, so that a current sampling data set with the same sampling frequency as a data set used for modeling is obtained, the problem that a model cannot be fitted due to inconsistent backtracking windows caused by different system time lags in the model is solved, and the accuracy and generalization of a prediction model are improved; determining a target pre-chlorination flow corresponding to the residual chlorine concentration of the target clean water tank based on the current sampling data set and a preset residual chlorine concentration prediction model of the clean water tank; determining a target post-chlorination flow corresponding to the target residual chlorine concentration of the factory water based on the current sampling data set, a preset factory residual chlorine concentration prediction model and the target pre-chlorination flow; and determining the target chlorine supplementing flow corresponding to the target water plant based on the target pre-chlorine adding flow, the target post-chlorine adding flow, the preset pre-chlorine adding flow and the preset post-chlorine adding flow, so that the chlorine adding amount of the water plant in each stage can be accurately determined, the control stability of residual chlorine from a factory is improved, the sodium hypochlorite adding precision is controlled, and the adding cost is saved.
Optionally, the current sampling frequency determining module 320 is specifically configured to: dividing the historical flow and the historical sectional area to determine the average flow velocity corresponding to the water inlet manifold in the historical time period;
Rounding the average flow velocity to obtain a rounded flow velocity; and dividing the preset acquisition frequency corresponding to the preset time index by the rounding flow rate to determine the current sampling frequency.
Optionally, the current sampling data set determining module 330 is specifically configured to: determining a current sampling period based on the current sampling frequency, and taking the current sampling period as a current time unit; and backtracking and collecting a current water quality data set, a current water quantity data set and a current meteorological data set corresponding to a preset number of current time units from the current moment to the historical moment in the data buffer based on the current sampling frequency.
Optionally, the current water quality data set includes: at least one of turbidity, conductivity, pH value and water temperature; the current water quantity data set comprises the flow of a water inlet main pipe; the current weather data set includes: gas temperature and/or light.
Optionally, the target pre-chlorination flow determination module 340 is specifically configured to: inputting the current sampling data set into a preset clear water tank residual chlorine concentration prediction model, intercepting a data block required for prediction based on the current sampling data set in the preset clear water tank residual chlorine concentration prediction model, and predicting the pre-chlorination flow based on the data block to determine candidate pre-chlorination flow corresponding to the target clear water tank residual chlorine concentration; if the candidate pre-chlorination flow rate is detected to be in the available flow rate range, determining the candidate pre-chlorination flow rate as a target pre-chlorination flow rate corresponding to the residual chlorine concentration of the target clean water tank; and if the candidate pre-chlorination flow rate is detected not to be in the available flow rate range, determining the pre-chlorination flow rate closest to the candidate pre-chlorination flow rate in the available flow rate range as a target pre-chlorination flow rate corresponding to the residual chlorine concentration of the target clean water tank.
Optionally, the target chlorine make-up flow determination module 360 is specifically configured to: determining a first chlorine flow difference value based on the target pre-chlorine flow and the preset pre-chlorine flow; determining a second chlorine flow difference value based on the target post-chlorine flow and the preset post-chlorine flow; and performing closed-loop control calculation based on the first chlorine flow difference value and the second chlorine flow difference value, and determining the target chlorine supplementing flow corresponding to the target water plant.
Optionally, the apparatus further comprises:
The instantaneous flow acquisition module is used for acquiring the corresponding cross-sectional area of the water inlet manifold and the corresponding instantaneous flow of the water inlet manifold at each historical acquisition moment in the original time index;
The rounding instantaneous flow rate determining module is used for determining the instantaneous flow rate corresponding to the water inlet manifold based on the cross-sectional area and the instantaneous flow rate, rounding the instantaneous flow rate and determining the rounding instantaneous flow rate;
The preset time index determining module is used for setting the counting time length corresponding to the current historical acquisition time based on the rounded instantaneous flow velocity, and superposing the counting time length corresponding to each historical acquisition time on the basis of the first historical acquisition time to determine the preset time index.
The chlorination flow rate determining device provided by the embodiment of the invention can execute the chlorination flow rate determining method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the chlorination flow rate determining method.
It should be noted that, in the above embodiment of determining the chlorine adding flow, each included unit and module are only divided according to the functional logic, but not limited to the above division, so long as the corresponding function can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Example IV
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the chlorine flow determination method.
In some embodiments, the chlorine flow determination method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the chlorination flow determination method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the chlorination flow determination method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. The chlorine adding flow determining method is characterized by comprising the following steps:
acquiring a historical flow and a historical sectional area corresponding to a water inlet main pipe in a target water plant in a historical time period;
determining a current sampling frequency based on the historical flow, the historical cross-section area and a preset acquisition frequency corresponding to a preset time index;
Performing data resampling from a data buffer based on the current sampling frequency, and determining a current sampling data set corresponding to the current sampling frequency; the data buffer is used for storing historical data acquired in a preset time index based on a preset acquisition frequency;
Determining a target pre-chlorination flow corresponding to the residual chlorine concentration of the target clean water tank based on the current sampling data set and a preset residual chlorine concentration prediction model of the clean water tank;
determining a target post-chlorination flow corresponding to the target residual chlorine concentration of the factory water based on the current sampling data set, a preset factory residual chlorine concentration prediction model and the target pre-chlorination flow;
and determining the target chlorine supplementing flow corresponding to the target water plant based on the target pre-chlorine adding flow, the target post-chlorine adding flow, the preset pre-chlorine adding flow and the preset post-chlorine adding flow.
2. The method of claim 1, wherein the determining the current sampling frequency based on the historical flow, the historical cross-sectional area, and a preset acquisition frequency corresponding to a preset time index comprises:
dividing the historical flow and the historical sectional area to determine the average flow velocity corresponding to the water inlet manifold in the historical time period;
Rounding the average flow velocity to obtain a rounding flow velocity;
and dividing the preset acquisition frequency corresponding to the preset time index with the rounding flow rate to determine the current sampling frequency.
3. The method of claim 1, wherein the determining the current sample data set corresponding to the current sampling frequency based on the data resampling of the current sampling frequency from the data buffer comprises:
Determining a current sampling period based on the current sampling frequency, and taking the current sampling period as a current time unit;
And backtracking and collecting a current water quality data set, a current water quantity data set and a current meteorological data set corresponding to a preset number of current time units from the current moment to the historical moment in a data buffer based on the current sampling frequency.
4. A method according to claim 3, wherein the current water quality data set comprises: at least one of turbidity, conductivity, pH value and water temperature; the current water quantity data set comprises the flow of a water inlet main pipe; the current weather data set includes: gas temperature and/or light.
5. The method of claim 1, wherein determining a target pre-chlorination flow rate corresponding to a target clean water tank residual chlorine concentration based on the current sampled data set and a preset clean water tank residual chlorine concentration prediction model comprises:
Inputting the current sampling data set into a preset clear water tank residual chlorine concentration prediction model, intercepting a data block required for prediction based on the current sampling data set in the preset clear water tank residual chlorine concentration prediction model, and predicting the pre-chlorination flow based on the data block to determine candidate pre-chlorination flow corresponding to the target clear water tank residual chlorine concentration;
If the candidate pre-chlorination flow rate is detected to be in the available flow rate range, determining the candidate pre-chlorination flow rate as a target pre-chlorination flow rate corresponding to the residual chlorine concentration of the target clean water tank;
and if the candidate pre-chlorination flow rate is detected not to be in the available flow rate range, determining the pre-chlorination flow rate closest to the candidate pre-chlorination flow rate in the available flow rate range as a target pre-chlorination flow rate corresponding to the residual chlorine concentration of the target clean water tank.
6. The method of claim 1, wherein determining the target chlorine make-up flow corresponding to the target water plant based on the target pre-chlorine flow, the target post-chlorine flow, a preset pre-chlorine flow, and a preset post-chlorine flow comprises:
Determining a first chlorine flow difference value based on the target pre-chlorination flow and a preset pre-chlorination flow;
Determining a second chlorine flow difference value based on the target post-chlorine flow and a preset post-chlorine flow;
And performing closed-loop control calculation based on the first chlorine flow difference value and the second chlorine flow difference value, and determining a target chlorine supplementing flow corresponding to a target water plant.
7. The method according to claim 1, wherein the method further comprises:
Acquiring a cross-sectional area corresponding to a water inlet main pipe and an instantaneous flow corresponding to the water inlet main pipe at each historical acquisition moment in an original time index;
Determining an instantaneous flow rate corresponding to the water inlet header pipe based on the cross-sectional area and the instantaneous flow rate, rounding the instantaneous flow rate, and determining a rounded instantaneous flow rate;
And setting the counting time length corresponding to the current historical acquisition time based on the rounding instantaneous flow rate, and superposing the counting time length corresponding to each historical acquisition time on the basis of the first historical acquisition time to determine a preset time index.
8. A chlorine flow rate determination apparatus comprising:
the historical data acquisition module is used for acquiring the historical flow and the historical sectional area corresponding to the water inlet header pipe in the target water plant in the historical time period;
The current sampling frequency determining module is used for determining the current sampling frequency based on the historical flow, the historical cross-section area and the preset acquisition frequency corresponding to the preset time index;
the current sampling data set determining module is used for carrying out data resampling from the data buffer based on the current sampling frequency and determining a current sampling data set corresponding to the current sampling frequency; the data buffer is used for storing historical data acquired in a preset time index based on a preset acquisition frequency;
The target pre-chlorination flow determining module is used for determining the target pre-chlorination flow corresponding to the residual chlorine concentration of the target clean water tank based on the current sampling data set and a preset residual chlorine concentration prediction model of the clean water tank;
The target post-chlorination flow rate determining module is used for determining target post-chlorination flow rates corresponding to the residual chlorine concentration of the factory water based on the current sampling data set, a preset factory water residual chlorine concentration prediction model and the target pre-chlorination flow rates;
and the target chlorine supplementing flow determining module is used for determining the target chlorine supplementing flow corresponding to the target water plant based on the target pre-chlorine adding flow, the target post-chlorine adding flow, the preset pre-chlorine adding flow and the preset post-chlorine adding flow.
9. An electronic device, the electronic device comprising:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the chlorination flow determination method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the method of determining a chlorinating flux as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410170790.1A CN117964011A (en) | 2024-02-06 | 2024-02-06 | Method and device for determining chlorine adding flow, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410170790.1A CN117964011A (en) | 2024-02-06 | 2024-02-06 | Method and device for determining chlorine adding flow, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117964011A true CN117964011A (en) | 2024-05-03 |
Family
ID=90855229
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410170790.1A Pending CN117964011A (en) | 2024-02-06 | 2024-02-06 | Method and device for determining chlorine adding flow, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117964011A (en) |
-
2024
- 2024-02-06 CN CN202410170790.1A patent/CN117964011A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110837933A (en) | Leakage identification method, device, equipment and storage medium based on neural network | |
| CN116307215A (en) | Load prediction method, device, equipment and storage medium of power system | |
| CN114500339A (en) | Node bandwidth monitoring method and device, electronic equipment and storage medium | |
| CN116697497B (en) | Method, device, equipment and storage medium for controlling ventilation air conditioning system | |
| CN117540163A (en) | Pump performance curve generation method, model construction and training method and device | |
| CN113962425A (en) | Heating data generation method, device, equipment and computer storage medium | |
| CN117964011A (en) | Method and device for determining chlorine adding flow, electronic equipment and storage medium | |
| CN115168154B (en) | Abnormal log detection method, device and equipment based on dynamic baseline | |
| CN114511225A (en) | Section identification method, device, equipment and storage medium | |
| CN115169731A (en) | Smart campus energy consumption prediction method, device, equipment and medium | |
| CN114692987A (en) | Time sequence data analysis method, device, equipment and storage medium | |
| CN114118562A (en) | Flow prediction method, model training method and device and electronic equipment | |
| CN117131315B (en) | Out-of-tolerance electric energy meter determining method and medium based on solving multi-element quadratic function extremum | |
| CN115006921B (en) | Method, device, equipment and medium for predicting service life of air filter | |
| CN115373449B (en) | Data processing method, device, equipment and storage medium | |
| CN116804690A (en) | Transformer fault detection method and device, electronic equipment and storage medium | |
| CN115658826A (en) | Method, device and equipment for determining track stop point and storage medium | |
| CN118057390A (en) | Method, device, equipment and medium for determining numerical simulation fitting precision of oil reservoir | |
| CN117131353A (en) | Method and device for determining out-of-tolerance electric energy meter, electronic equipment and storage medium | |
| CN117150325A (en) | Processing method, device, equipment and medium for abnormal residual error data | |
| CN118154026A (en) | Method, device, equipment and medium for determining fuel reliability index | |
| CN117688499A (en) | Multi-index anomaly detection method and device, electronic equipment and storage medium | |
| CN117252444A (en) | Power distribution network planning method and device, electronic equipment and storage medium | |
| CN118100168A (en) | Training method of distributed photovoltaic power prediction model, distributed photovoltaic power prediction method, device, electronic equipment and storage medium | |
| CN116147204A (en) | Water consumption reminding method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination |