Water quantity control method and device based on water quantity prediction, equipment and storage medium

Abstract

The application relates to the technical field of water quantity control, in particular to a water quantity control method and device based on water quantity prediction, equipment and a storage medium, wherein the water quantity control method based on water quantity prediction comprises the following steps: acquiring actual measurement data of a regulating pool, conveying pump detection data and sewage plant flow data; calculating the actual measurement data of the regulating pool, the conveying pump detection data and the sewage plant flow data respectively to obtain regulating pool prediction results, conveying pump prediction results and sewage plant prediction results; respectively calibrating the regulating pool prediction results, the conveying pump prediction results and the sewage plant prediction results to obtain calibration results; if the calibration results pass the calibration, inputting the regulating pool prediction results, the conveying pump prediction results and the sewage plant prediction results into a preset mathematical prediction model to obtain regulating pool residual overflow time prediction results, and generating a water inflow control instruction according to the regulating pool residual overflow time prediction results. The application has the effect of improving the precision of water inflow quantity control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water quantity control, and in particular to a water quantity control method and device based on water quantity prediction, equipment and a storage medium. BACKGROUND

[0002] In the field of industrial wastewater treatment, the regulating pool, as one of the key facilities, plays an important role in balancing the inflow and subsequent treatment capacity. With the continuous expansion of modern industrial enterprises and the increasingly stringent environmental protection requirements, how to efficiently manage the water quantity in the regulating pool has become the core link to ensure the stable operation of the wastewater treatment system. Traditionally, manual observation of the liquid level of the regulating pool combined with the experience of the operator to make production control is a common practice. Although this method is simple and easy to implement, it is not up to the task when faced with complex and variable actual working conditions.

[0003] In order to cope with the above challenges, the industry usually adopts various means in order to improve the accuracy of water quantity monitoring and the level of automation. For example, various types of sensors are installed to collect data in real time, such as liquid level meters to monitor the change in liquid level; deploy flow meters to record the inflow and outflow velocity information; use specially designed calculation tools to integrate these raw materials to obtain more accurate results. In addition, some prediction models based on historical statistical data are also used to help plan for possible situations in the short term.

[0004] The various technical means widely used at present still have obvious shortcomings, especially in quickly and accurately predicting upcoming water quantity fluctuations. This limitation makes it difficult for on-site technical personnel to make reasonable decisions in a timely manner, thereby increasing the risk of accidents such as overflow of wastewater due to overload of the regulating pool. Therefore, there is still room for improvement. SUMMARY

[0005] In order to improve the accuracy of incoming water quantity control, the present application provides a water quantity control method and device based on water quantity prediction, equipment and a storage medium.

[0006] The above invention of the present application is achieved by the following technical solutions:

[0007] A water quantity control method and device based on water quantity prediction, the water quantity control method and device based on water quantity prediction comprises:

[0008] Obtaining regulating pool measured data, conveying pump detection data and sewage plant flow data;

[0009] According to the regulating pool measured data, the conveying pump detection data and the sewage plant flow data, respectively, the regulating pool prediction result, the conveying pump prediction result and the sewage plant prediction result are obtained by calculation;

[0010] calibrate the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result respectively to obtain calibration results;

[0011] If the calibration result is calibration pass, input the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result into a preset mathematical prediction model to obtain a regulating pool remaining overflow time prediction result, and generate an inflow control instruction according to the regulating pool remaining overflow time prediction result.

[0012] By adopting the above technical solution, accurate prediction and efficient control of the inflow water quantity of the sewage treatment plant can be realized. First, in terms of obtaining the measured data of the regulating pool, the actual status of the current regulating pool can be accurately mastered by comprehensively monitoring the liquid level change in the regulating pool, the opening time length of the lifting pump and the inflow of sludge filtrate. These data not only reflect the real-time inflow quantity, but also provide key basic support for subsequent prediction. Secondly, in the delivery pump detection data part, the flow per unit time of the delivery pump and the corresponding delivery time are mainly concerned. Since different production activities of enterprises may cause changes in delivery behavior, the data collection at this link is crucial. It is directly related to the effective estimation of the total amount of wastewater delivered by each enterprise to the sewage treatment plant within a certain period. Furthermore, sewage plant flow data is also indispensable. This part covers the overall flow statistics of the sewage treatment facility receiving water from multiple sources. This includes but is not limited to industrial wastewater and other types of drainage records generated by various enterprises during daily operations. By integrating these macro-level information resources, a more complete and systematic cognitive framework can be formed, thereby better serving the overall regulation strategy formulation. The prediction processing for the regulating pool determines the total inflow quantity of wastewater into the sewage treatment plant. This step fully considers the maximum load level that can be accepted under the capacity limitation condition of the regulating pool, and combines historical experience values to correct and adjust to improve accuracy. For the prediction operation related to the delivery pump, the proportion of different types of sources is refined to allocate priority and arrange work arrangements. Finally, it involves a unique algorithm designed based on the characteristics of the internal operation mechanism of the sewage plant. After completing the independent calculation in the above three directions, cross-validation and audit procedures are still needed. When all the above-mentioned tests meet the expected standards, they can be formally sent to the pre-built advanced mathematical prediction model to further explore potential regularity characteristics. Once the specific value of the regulating pool remaining overflow time is successfully obtained, appropriate response decisions can be made according to the pre-planned emergency plan level division rules.

[0013] The application can be further configured in a preferred example as follows: the adjustment tank measured data, the delivery pump detection data and the sewage plant flow data are respectively calculated to obtain the adjustment tank prediction result, the delivery pump prediction result and the sewage plant prediction result, specifically including:

[0014] The adjustment tank measured data is input into the following formula to calculate the adjustment tank prediction result:

[0015] Wherein, Q 第一总进 is the adjustment tank prediction result of the total influent quantity of wastewater entering the sewage treatment plant, T 提 is the lifting pump opening duration, A 调 is the adjustment tank area, H 束 is the liquid level of the adjustment tank at the end of the period, H 始 is the liquid level of the adjustment tank at the beginning of the period, Q 滤液 is the sludge filtrate quantity entering the adjustment tank, T 周 is the predicted or measured period time;

[0016] The delivery pump detection data is input into the following formula to calculate the delivery pump prediction result:

[0017]

[0018] Q 第二总进 =∑Q’ 进 , t / h, wherein Q 第二总进 is the delivery pump prediction result of the total influent quantity of wastewater entering the sewage treatment plant, Q’ 进 is the water quantity of a certain type of wastewater discharged by a certain sewage plant, Q 输 is the flow rate of the delivery pump per unit time, T 输 is the measured delivery duration of the delivery pump;

[0019] The sewage plant flow data is input into the following formula to calculate the sewage plant prediction result:

[0020] W 废 =ζ×W 总 -W 生 , wherein W 废 is the total wastewater quantity input into the sewage treatment plant on the day, ζ is the billing coefficient, W 总 is the cumulative water quantity displayed by the total water meter of the enterprise on the day, W 生 is the cumulative water quantity displayed by the domestic water meter of the enterprise on the day.

[0021] By adopting the above technical solution, the inflow water quantity prediction result of the sewage treatment plant can be accurately calculated. Specifically, by applying the measured data of the adjusting pool, the actual inflow quantity based on the internal changes of the adjusting pool can be accurately obtained, ensuring high sensitivity to water quantity fluctuations in the short term; by using the detection data of the delivery pump, not only the specific wastewater quantity discharged by individual enterprises is considered, but also the global delivery total quantity estimation is obtained through accumulation operation, thereby enhancing the overall and comprehensive prediction; at the same time, with the help of the sewage plant flow data, combined with the daily water use and drainage characteristics of the enterprises, the data source of the prediction model is further corrected and supplemented, so that the three prediction results formed finally are more reliable. Under the joint action of these measures, the inflow water quantity prediction accuracy is significantly improved, providing solid data support for subsequent production adjustment. Among them, the measured data processing part of the adjusting pool effectively improves the short-term prediction accuracy; the delivery pump related calculation increases the consideration dimension of the discharge behavior of different enterprises; and the sewage plant flow data analysis strengthens the long-term trend grasping ability.

[0022] The application can be further configured in a preferred example: the adjusting pool prediction result, the delivery pump prediction result and the sewage plant prediction result are calibrated respectively to obtain a calibration result, specifically including:

[0023] The adjusting pool prediction result is calibrated by the following formula to obtain a first calibration result:

[0024] Q 流累 = Q 提 × T 提 , wherein Q 流累 is the first calibration result of the cumulative flow of the individual period read by the flow meter after the booster pump, Q 提 is the unit time flow of the booster pump, T 提 is the opening time length of the booster pump;

[0025] The Q 第二总进 is compared with the Q 第一总进 , and a second calibration result is obtained according to the comparison result;

[0026] The sewage plant prediction result is compared with the Q 第二总进 and the Q 第一总进 , and a third calibration result is obtained according to the comparison result;

[0027] The calibration result is generated according to the first calibration result, the second calibration result and the third calibration result.

[0028] By adopting the above technical solutions, the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result can be accurately calibrated. The accuracy of the cumulative flow measurement is ensured by using the product of the lifting pump flow and the opening time length to calibrate the regulating pool prediction result. The matching degree of the regulating pool prediction result and the delivery pump prediction result is further verified by comparing the delivery pump prediction result with the regulating pool prediction result, so that a more reliable second calibration result is obtained. Meanwhile, the sewage plant prediction result is compared with the other two prediction results, and the three calibration methods are comprehensively compared, so that the reliability and stability of the overall prediction system are finally improved. Under the joint action of these measures, the working precision and reliability of the entire water quantity prediction control system are effectively improved.

[0029] In a preferred example, the application can be further configured to: input the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result into a preset mathematical prediction model to obtain a regulating pool remaining overflow time prediction result, specifically including:

[0030] Input the regulating pool prediction result into the following formula for total inflow quantity method prediction:

[0031]

[0032] wherein, T 第一剩余 refers to the total inflow quantity prediction result;

[0033] Input the delivery pump prediction result into the following formula for inflow trend method prediction:

[0034] wherein, T 第二剩余 refers to the inflow trend prediction result;

[0035] Input the sewage plant prediction result into the following formula for delivery time length prediction method prediction:

[0036] V' 测输 = Q 输 × t 输 ;

[0037] V 测输 =∑V' 测输 ;

[0038] wherein, V' 测输 refers to the wastewater quantity of a certain type of wastewater discharged into the sewage plant by the model prediction within a prediction period, t 输 refers to the predicted delivery time length, V 测输 refers to the wastewater quantity of a certain type of wastewater discharged into the sewage plant by the model prediction within a prediction period within the scope of the sewage treatment plant, T 第三剩余 refers to the delivery time length prediction method prediction result;

[0039] generating the regulating pool residual overflow time prediction result T according to the total water inflow prediction result, the water inflow trend prediction result and the conveying duration prediction result 剩余 .

[0040] By adopting the technical scheme, the three different prediction methods of total water inflow method, water inflow trend method and conveying duration prediction method are comprehensively used to make overall and accurate prediction of the regulating pool residual overflow time. Specifically, the regulating pool prediction result is used for total water inflow method prediction, the potential overflow risk can be accurately evaluated based on the actual treatment capacity of the regulating pool and the difference of water inflow; the conveying pump prediction result is used for water inflow trend method prediction, the change trend of wastewater inflow can be dynamically tracked, and abnormal fluctuation can be captured in time; and the sewage plant prediction result is used for conveying duration prediction method prediction, the wastewater discharge law of each enterprise in different time periods and its influence on future water inflow can be fully considered. The overall regulating pool residual overflow time prediction result obtained by integrating the three prediction results not only improves the prediction accuracy, but also provides a scientific basis for formulating a reasonable water inflow control strategy, thereby effectively preventing overflow of the regulating pool and ensuring stable and efficient operation of the sewage treatment process. The effect embodied in the corresponding single claim is to provide a data processing mechanism integrating multiple prediction methods, which significantly enhances the adaptability and accuracy of the prediction system.

[0041] In a preferred example, the application can be further configured to: generating the water inflow control instruction according to the regulating pool residual overflow time prediction result, specifically comprising:

[0042] acquiring a preset emergency duration judgment interval, comparing the T 剩余 with the emergency duration judgment interval;

[0043] generating the water inflow control instruction according to the comparison result.

[0044] By adopting the technical scheme, accurate prediction of the regulating pool residual overflow time of the sewage treatment plant can be realized, and the water inflow control instruction can be quickly generated according to the prediction result. This method not only improves the ability to respond to sudden changes in water inflow, but also effectively prevents overflow of the regulating pool caused by excessive water inflow, thereby ensuring stable operation and efficient management of the entire sewage treatment system. In addition, by setting and comparing different emergency duration intervals, the control system is more intelligent and flexible, and can adapt to various complex working conditions.

[0045] The second invention purpose of the application is achieved by the following technical scheme:

[0046] A water inflow control device based on water inflow prediction, comprising:

[0047] a basic data acquisition module configured to acquire actual data of a regulating pool, detection data of a delivery pump, and flow data of a sewage plant;

[0048] a total water volume prediction module configured to calculate, respectively, a regulating pool prediction result, a delivery pump prediction result, and a sewage plant prediction result according to the actual data of the regulating pool, the detection data of the delivery pump, and the flow data of the sewage plant;

[0049] a data calibration module configured to calibrate, respectively, the regulating pool prediction result, the delivery pump prediction result, and the sewage plant prediction result to obtain a calibration result;

[0050] a prediction control module configured to, if the calibration result is a calibration pass, input the regulating pool prediction result, the delivery pump prediction result, and the sewage plant prediction result into a preset mathematical prediction model to obtain a regulating pool remaining overflow time prediction result, and generate an inflow control instruction according to the regulating pool remaining overflow time prediction result.

[0051] By adopting the above technical solutions, accurate prediction and efficient control of the inflow water quantity of the sewage treatment plant can be realized. First, in terms of obtaining the measured data of the adjusting pool, the actual status of the adjusting pool can be accurately mastered by comprehensively monitoring the liquid level change in the adjusting pool, the opening time length of the lifting pump, and the inflow of sludge filtrate. These data not only reflect the real-time inflow quantity, but also provide key basic support for subsequent prediction. Second, in the delivery pump detection data part, the flow per unit time of the delivery pump and the corresponding delivery time length are mainly concerned. Since different production activities of enterprises may lead to changes in delivery behavior, data collection at this link is crucial. It is directly related to the effective estimation of the total amount of wastewater delivered by each enterprise to the sewage treatment plant within a specific period. Moreover, sewage plant flow data is also indispensable. This part covers the overall flow statistics of the sewage treatment facility receiving water from multiple sources, including but not limited to industrial wastewater and other types of drainage records generated by various enterprises during daily operations. By integrating these macro-level information resources, a more complete and systematic cognitive framework can be formed, thereby better serving the overall regulation strategy formulation. The prediction process for the adjusting pool determines the total inflow quantity of wastewater into the sewage treatment plant. This step fully considers the maximum load level that can be accepted under the capacity limitation condition of the adjusting pool, and combines historical experience values for correction and adjustment to improve accuracy. For the prediction operation related to the delivery pump, the proportion of different types of sources is determined, and then the management measures are refined, priority is set, and work arrangements are made. Finally, it involves a unique algorithm designed based on the characteristics of the internal operation mechanism of the sewage plant. After completing the independent calculation in the above three directions, cross-validation and audit procedures are still needed. When all the above-mentioned tests meet the expected standards, they can be formally sent to the pre-built advanced mathematical prediction model for further exploration of potential regularity characteristics. Once the specific value of the remaining overflow time of the adjusting pool is successfully obtained, appropriate response decisions can be quickly made according to the pre-planned emergency plan level division rules.

[0052] The above-mentioned third purpose of the present application is achieved through the following technical solutions:

[0053] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above-mentioned water quantity control method based on water quantity prediction when executing the computer program.

[0054] The above-mentioned fourth purpose of the present application is achieved through the following technical solutions:

[0055] A computer readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the water quantity prediction-based water quantity control method.

[0056] In summary, the present application includes at least one of the following beneficial technical effects:

[0057] 1. By integrating the measured data of the regulating pool, the detection data of the delivery pump, and the flow data of the sewage plant for multi-dimensional calculation and calibration, the accuracy of the water quantity prediction is significantly improved, solving the deviation problem caused by a single data source in traditional methods; 2. The mathematical prediction model is introduced to comprehensively predict the remaining overflow time of the regulating pool, which can provide early warning in abnormal water conditions and effectively avoid the production risks caused by the overflow of the regulating pool;

[0058] 3. Combining multiple prediction results to generate water control instructions, realizing automatic emergency processing and improving the stability and response speed of the sewage treatment system. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 is a flowchart of the water quantity prediction-based water quantity control method in an embodiment of the present application;

[0060] Figure 2 is a principle block diagram of the water quantity prediction-based water quantity control system in an embodiment of the present application;

[0061] Figure 3 is a device schematic diagram in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The present application will be further described in detail below with reference to the accompanying drawings.

[0063] In an embodiment, as shown in Figure 1 the present application discloses a water quantity prediction-based water quantity control method, which specifically includes the following steps:

[0064] S10: Obtain the measured data of the regulating pool, the detection data of the delivery pump, and the flow data of the sewage plant.

[0065] Specifically, by comprehensively collecting the measured data of the regulating pool, the detection data of the delivery pump, and the flow data of the sewage plant, the authenticity and diversity of the data sources are ensured, which is the basic guarantee of the entire control system. This multi-dimensional data collection method covers the key information from the source to the intermediate transportation and finally to the receiving link, thereby laying a solid foundation for subsequent accurate calculation.

[0066] S20: Calculate the regulating pool prediction result, the delivery pump prediction result, and the sewage plant prediction result according to the measured data of the regulating pool, the detection data of the delivery pump, and the flow data of the sewage plant, respectively.

[0067] Specifically, the acquired regulating pool measured data, the delivery pump detection data and the sewage plant flow data are analyzed in depth to obtain the corresponding regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result. The core of this stage is to use advanced algorithm strategies to analyze the internal relationship between different types of input data and their dynamic change rules. For example, for the regulating pool, considering its high internal environmental complexity and large interference from various external factors, it is necessary to comprehensively consider multiple key parameters such as liquid level fluctuation amplitude, inflow and outflow rate difference and the like; for the delivery pump, it is focused on whether there is a deviation phenomenon between its actual working efficiency and design standard; as for the sewage plant as a whole, more attention is paid to the overall planning ability training under the macro-control perspective.

[0068] S30: The regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result are calibrated respectively to obtain a calibration result.

[0069] Specifically, after obtaining the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result, numerical comparison is performed therebetween, if the comparison result exceeds the preset value, corresponding correction is performed, if it meets the expectation, it is determined that the calibration is passed.

[0070] S40: If the calibration result is calibration passed, the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result are input into a preset mathematical prediction model to obtain a regulating pool remaining overflow time prediction result, and a water inflow control instruction is generated according to the regulating pool remaining overflow time prediction result.

[0071] Specifically, if the calibration is passed, the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result are input into a preset mathematical prediction model, the corresponding prediction results are calculated respectively, and after processing these prediction results, the regulating pool remaining overflow time prediction result is obtained. Further, according to the obtained regulating pool remaining overflow time prediction result, the corresponding water inflow control instruction is triggered.

[0072] In an embodiment, in step S20, the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result are obtained respectively according to the regulating pool measured data, the delivery pump detection data and the sewage plant flow data, specifically including:

[0073] S21: The regulating pool measured data is input into the following formula to obtain the regulating pool prediction result:

[0074] Wherein, Q 第一总进 is the regulating pool prediction result of the total inflow of wastewater into the sewage treatment plant, T 提is the length of the opening of the lifting pump, A 调 is the area of the adjusting tank, H 束 is the liquid level of the adjusting tank at the end of the cycle, H 始 is the liquid level of the adjusting tank at the beginning of the cycle, Q 滤液 is the amount of sludge filtrate entering the adjusting tank, T 周 is the predicted or measured cycle time;

[0075] S22: input the delivery pump detection data into the following formula to calculate the delivery pump prediction result:

[0076]

[0077] Q 第二总进 =∑Q’ 进 , t / h, wherein Q 第二总进 is the delivery pump prediction result of the total influent amount of wastewater entering the wastewater treatment plant, Q’ 进 is the amount of a certain type of wastewater discharged by a certain wastewater treatment plant, Q 输 is the flow rate per unit time of the delivery pump, T 输 is the measured delivery time of the delivery pump;

[0078] S23: input the wastewater plant flow data into the following formula to calculate the wastewater plant prediction result:

[0079] W 废 =ζ×W 总 -W 生 , wherein W 废 is the total wastewater amount entering the wastewater treatment plant on the day, ζ is the billing coefficient, W 总 is the cumulative water amount displayed by the total water meter of the enterprise on the day, W 生 is the cumulative water amount displayed by the domestic water meter of the enterprise on the day.

[0080] Specifically, during wastewater treatment, by obtaining the opening length of the lifting pump, the area of the adjusting tank, the liquid level change of the adjusting tank, etc. Data, and through the measured data method, the delivery pump theory method and the tap water meter prediction method, i.e. the formula in steps S21-S23, the current total influent amount is predicted, so as to calculate different prediction results according to different algorithms, i.e. the adjusting tank prediction result, the delivery pump prediction result and the wastewater plant prediction result.

[0081] In this embodiment, accurate prediction of the water quantity of the sewage treatment plant and effective water control can be realized. Specifically, on the basis of obtaining the measured data of the regulating pool, the detection data of the delivery pump and the flow data of the sewage plant, different calculation formulas are used to obtain the prediction results of three different sources, i.e. the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result. This multi-source data fusion method significantly improves the prediction accuracy and ensures the accuracy and reliability of the subsequent control measures.

[0082] Firstly, for the regulating pool part, through the application of a specific formula (Q 第一总进 = (A 调 × (H 束 -H 始 )+Q 滤液 ×T 周 ) / T 提 ), the total inflow of wastewater into the sewage treatment plant can be accurately calculated as the regulating pool prediction result. Each variable here is crucial: the pump opening time (T 提 ) reflects the changes in treatment capacity during the entire period; the regulating pool area (A 调 ), the regulating pool liquid level at the end and beginning of the period (H 束 , H 始 ), and the sludge filtrate quantity (Q 滤液 ) together depict the specific picture of the dynamic changes of the liquid inside the regulating pool; plus the prediction or measurement period time (T 周 ), the final value obtained not only has real-time nature but also has certain foresight.

[0083] For the delivery pump, two steps are used to complete its prediction process. The first step uses the formula Q' 进 = Q 输 *T to obtain the water quantity of a certain type of wastewater discharged by a single enterprise, and then accumulates and sums up the total delivery pump prediction result Q 第二总进 =∑Q' 进 . The importance of this step lies in its consideration of the differences and complexities between different enterprises and various categories of wastewater. Because each enterprise has different production characteristics, produces a variety of pollutants and the quantity is uncertain, only by making detailed considerations at the individual level and summarizing can a more realistic overall evaluation standard be obtained.

[0084] As for the monitoring system of the sewage plant itself, a relatively macro method is used to estimate the total quantity - by using the water meter reading and subtracting the domestic water consumption after appropriate conversion to estimate the direct contribution part of industrial activities (W 废 =ζ×W 总 -W生 Here, a particularly important correction factor, the billing coefficient ζ, is introduced to balance the risk of deviation between theoretical calculations and practical operations. Since there may be some fluctuations in the original statistical data due to aging of metering tools and other reasons in reality, it is particularly important to set such a flexible and adjustable factor. At the same time, it should be noted that the total water resource consumption status and the proportion of other non-target use expenditures should be distinguished to avoid interfering with the effective degree of the final conclusion.

[0085] In an embodiment, in step S30, the conditioning tank prediction result, the delivery pump prediction result and the sewage plant prediction result are calibrated respectively to obtain calibration results, specifically including:

[0086] S31: The conditioning tank prediction result is calibrated by the following formula to obtain a first calibration result:

[0087] Q 流累 = Q 提 × T 提 , wherein Q 流累 is the first calibration result of the cumulative flow of the individual period read by the flow meter after the booster pump, Q 提 is the flow rate per unit time of the booster pump, and T 提 is the opening time length of the booster pump.

[0088] S32: Q 第二总进 is compared with Q 第一总进 , and a second calibration result is obtained according to the comparison result.

[0089] S33: The sewage plant prediction result is compared with Q 第二总进 and Q 第一总进 , and a third calibration result is obtained according to the comparison result.

[0090] S34: The calibration result is generated according to the first calibration result, the second calibration result and the third calibration result.

[0091] Specifically, after the conditioning tank prediction result, the delivery pump prediction result and the sewage plant prediction result are calculated, in order to verify the accuracy of the prediction result, corresponding calibration is needed to obtain the first calibration result, the second calibration result and the third calibration result. When the three calibration results are within the error range, any one prediction result can be used, or the average of the three prediction results is used to generate the calibration result.

[0092] If the first calibration result, the second calibration result and / or the third calibration result are outside the error range, the equipment corresponding to the first calibration result, the second calibration result and / or the third calibration result outside the error range is checked to reduce the error rate.

[0093] In the embodiment, effective calibration of the adjustment tank prediction result, the delivery pump prediction result and the sewage plant prediction result can be realized. Firstly, for the adjustment tank prediction result, the relationship between the unit time flow of the lifting pump and the opening time length of the lifting pump is used for calculation to obtain the first calibration result of the individual cycle cumulative flow. In this process, the data read by the flow meter after the lifting pump is accurately quantified to ensure the accuracy of the subsequent calibration link. Next, on the basis of the first calibration result, the delivery pump prediction result (Q 第二总进) ) calculated based on the delivery pump detection data is compared and analyzed with the adjustment tank prediction result (Q 第一总进 ), so as to form the second calibration result. This mutual verification method not only improves the consistency between the prediction results, but also guarantees the reliability of the entire system from different dimensions.

[0094] Subsequently, in order to more comprehensively consider the influencing factors of each prediction source, the sewage plant prediction result also needs to be introduced into the final overall calibration process. Specifically, the sewage plant prediction result needs to be compared and audited with the previously established standards, i.e., Q 第二总进 and Q 第一总进 , so as to determine the third calibration result. This step aims to comprehensively consider the indirect influence of the internal water consumption of the enterprise on the external environment and incorporate it into the overall monitoring system, thereby improving the ability to predict future development trends.

[0095] Finally, the first calibration result, the second calibration result and the third calibration result jointly generate the calibration conclusion under the final unified coordination. Such a design idea reflects a rigorous and scientific attitude throughout, and fully demonstrates the strong adaptability and precision of the present application when facing complex and variable actual application scenarios. In the entire operation process, each stage is closely connected and supports each other, maintaining relative independence while complementing each other, forming a complete and rigorous technical support chain. This makes it possible to timely, accurately and efficiently deal with problems in daily operation and maintenance or emergency response in special situations, greatly enhancing the stability and security of the system. In addition, this method can also help enterprises better understand and master the characteristics of their own resource consumption, providing strong support for optimizing management strategies, and has high practical value and social benefits.

[0096] In an embodiment, in step S40, the adjustment tank prediction result, the delivery pump prediction result and the sewage plant prediction result are input into a preset mathematical prediction model to obtain the adjustment tank remaining overflow time prediction result, specifically including:

[0097] S41: input the adjustment tank prediction result into the following formula for total inflow amount method prediction:

[0098]

[0099] wherein T 第一剩余 refers to the total inflow prediction result;

[0100] S42: input the delivery pump prediction result into the following formula for the water inflow trend method prediction:

[0101] wherein T 第二剩余 refers to the water inflow trend prediction result;

[0102] S43: input the sewage plant prediction result into the following formula for the delivery duration prediction method prediction:

[0103] V' 测输 = Q 输 x t 输 ;

[0104] V 测输 =∑V' 测输 ;

[0105] wherein V' 测输 refers to the amount of wastewater of a certain type predicted by the model to be discharged into the sewage plant by a certain sewage plant within the prediction period, t 输 refers to the predicted delivery duration, V 测输 refers to the amount of wastewater of a certain type predicted by the model to be discharged into the sewage plant by all enterprises within the scope of the sewage plant within the prediction period, T 第三剩余 refers to the delivery duration prediction result;

[0106] S44: generate the adjusted pool remaining overflow time prediction result T 剩余 according to the total inflow prediction result, the water inflow trend prediction result and the delivery duration prediction result.

[0107] Specifically, accurate prediction of the remaining overflow time of the sewage treatment plant adjusting pool can be achieved. Specifically, first, the total inflow method is used to calculate the prediction result of the adjusting pool, that is, the measured data of the adjusting pool is substituted into a specific formula, and a predicted overflow duration T 第一剩余 based on the current adjusting pool condition can be obtained. This process takes into account the amount of wastewater treated within the period and the change of the liquid level in the adjusting pool, thereby accurately reflecting the relationship between the actual capacity of the adjusting pool and the current water inflow rate.

[0108] Secondly, in terms of delivery pumps, the delivery pump detection data is used to further refine the prediction model. With the help of the water inflow trend method, by analyzing the actual delivery duration and the corresponding flow in different time periods, an effective estimate value T 第二剩余This step focuses on the impact of dynamic changes in enterprise production rhythm and product types on the enterprise drainage mode, ensuring that the prediction is more realistic and forward-looking.

[0109] Furthermore, from the overall perspective of the sewage plant, the data of domestic water, industrial water, and various special wastewater from each enterprise are integrated to build a more comprehensive mathematical prediction system. For each type of wastewater from each enterprise, not only is the instantaneous discharge intensity considered, but also the long-term cumulative effect is emphasized, based on which the total discharge V of all enterprises within the prediction period in the entire sewage plant is predicted. 测输 And the potential overflow risk of the regulation tank caused by enterprise drainage, i.e., T 第三剩余 , is estimated.

[0110] Finally, the results obtained from the three different prediction paths are combined and verified to generate the final prediction result of the regulation tank remaining overflow time T 剩余 . This design makes full use of information resources from multiple sources, overcomes the limitations of a single method, improves prediction accuracy and timeliness, and provides solid and reliable data support for subsequent development of scientific and reasonable emergency measures. At the same time, this method can also help identify where there are greater uncertainties or larger deviations, and then target to strengthen monitoring or improve the performance of related facilities and equipment to reduce the likelihood of accidents. In summary, this integrated multi-dimensional prediction strategy greatly improves the efficiency and quality of service of the sewage treatment system, ensuring environmental safety and social stability.

[0111] In an embodiment, in step S40, the inflow control instruction is generated according to the prediction result of the regulation tank remaining overflow time, specifically including:

[0112] S45: Obtain a preset emergency duration judgment interval, and compare T 剩余 with the emergency duration judgment interval;

[0113] S46: Generate an inflow control instruction according to the comparison result.

[0114] Specifically, when the predicted regulation tank remaining overflow time T 剩余 is less than a threshold value, the inflow control instruction is started, for example:

[0115] First-level warning (T 剩余 <2h): immediately notify the production department to reduce production or switch processes;

[0116] Second-level warning (2h≤T 剩余 <4h): adjust the operation parameters of the sewage treatment equipment;

[0117] Third-level warning (T 剩余≥ 4h): continuously monitor and optimize the dispatch plan.

[0118] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0119] In an embodiment, a water inflow control device based on water inflow prediction is provided, which corresponds to the water inflow control method based on water inflow prediction in the above embodiment. As shown in the figure, the water inflow control device based on water inflow prediction comprises a basic data acquisition module, a total water inflow prediction module, a data calibration module and a prediction control module. The functions of each module are described in detail as follows: Figure 2

[0120] The basic data acquisition module is used to acquire the measured data of the regulating pool, the detection data of the delivery pump and the flow data of the sewage plant.

[0121] The total water inflow prediction module is used to calculate the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result respectively according to the measured data of the regulating pool, the detection data of the delivery pump and the flow data of the sewage plant.

[0122] The data calibration module is used to calibrate the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result respectively to obtain the calibration result.

[0123] The prediction control module is used to input the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result into a preset mathematical prediction model to obtain the regulating pool remaining overflow time prediction result if the calibration result is calibration passed, and to generate the inflow control instruction according to the regulating pool remaining overflow time prediction result.

[0124] Optionally, the total water inflow prediction module comprises:

[0125] The first prediction sub-module is used to input the measured data of the regulating pool into the following formula to calculate the regulating pool prediction result: Wherein, Q 第一总进 is the regulating pool prediction result of the total inflow of wastewater into the sewage treatment plant, T 提 is the on time of the lifting pump, A 调 is the area of the regulating pool, H 束 is the liquid level of the regulating pool at the end of the period, H 始 is the liquid level of the regulating pool at the beginning of the period, Q 滤液 is the amount of sludge filtrate entering the regulating pool, T 周 is the prediction or measured period time.

[0126] ​The second prediction submodule is configured to input the delivery pump detection data into the following formula to calculate a delivery pump prediction result:

[0127] Q 第二总进 =∑Q' 进 , t / h, wherein Q 第二总进 represents the delivery pump prediction result of the total influent quantity of wastewater entering the wastewater treatment plant, Q' 进 represents the quantity of a certain type of wastewater discharged by a certain wastewater plant, Q 输 represents the flow rate of the delivery pump per unit time, and T 输 represents the measured delivery time length of the delivery pump.

[0128] The third prediction submodule is configured to input the wastewater plant flow data into the following formula to calculate a wastewater plant prediction result: W 废 =ζ×W 总 -W 生 , wherein W 废 represents the total wastewater quantity entering the wastewater treatment plant on the day, ζ represents a billing coefficient, W 总 represents the cumulative water quantity displayed by the total water meter of the enterprise on the day, and W 生 represents the cumulative water quantity displayed by the domestic water meter of the enterprise on the day.

[0129] Optionally, the data calibration module comprises:

[0130] The first calibration submodule is configured to calibrate the regulation tank prediction result by the following formula to obtain a first calibration result: Q 流累 =Q 提 ×T 提 , wherein Q 流累 represents the first calibration result of the cumulative flow rate of the flow meter after the booster pump in the current period, Q 提 represents the flow rate of the booster pump per unit time, and T 提 represents the on time length of the booster pump.

[0131] The second calibration submodule is configured to compare Q 第二总进 and Q 第一总进 , and obtain a second calibration result according to the comparison result.

[0132] The third calibration submodule is configured to compare the wastewater plant prediction result with Q 第二总进 and Q 第一总进 , and obtain a third calibration result according to the comparison result.

[0133] The calibration generation submodule is configured to generate a calibration result according to the first calibration result, the second calibration result, and the third calibration result.

[0134] Optionally, the prediction control module comprises:

[0135] The first time prediction submodule is configured to input the adjusted pool prediction result into the following formula to perform total inflow amount method prediction:

[0136]

[0137] wherein, T 第一剩余 refers to the total inflow amount prediction result;

[0138] The second time prediction submodule is configured to input the delivery pump prediction result into the following formula to perform incoming water trend method prediction: wherein, T 第二剩余 refers to the incoming water trend prediction result;

[0139] The third time prediction submodule is configured to input the sewage plant prediction result into the following formula to perform delivery time length prediction method prediction: V’ 测输 = Q 输 × t 输 ;

[0140] V 测输 =∑V’ 测输 ;

[0141] wherein, V’ 测输 refers to the amount of wastewater of a certain type predicted by the model to be discharged into the sewage plant by a certain sewage plant within a prediction period, t 输 refers to the predicted delivery time length, V 测输 refers to the amount of wastewater of a certain type predicted by the model to be discharged into the sewage plant by all enterprises within the scope of the sewage treatment plant within a prediction period, T 第三剩余 refers to the delivery time length prediction method prediction result;

[0142] The prediction result integration submodule is configured to generate the adjusted pool remaining overflow time prediction result T 剩余 according to the total inflow amount prediction result, the incoming water trend prediction result, and the delivery time length prediction result.

[0143] Optionally, the prediction control module comprises:

[0144] The threshold comparison submodule is configured to obtain a preset emergency time length judgment interval, and compare T 剩余 with the emergency time length judgment interval;

[0145] The control corresponding submodule is configured to generate an incoming water control instruction according to the comparison result.

[0146] The specific limitation of the water inflow control device based on water inflow prediction can refer to the limitation of the water inflow control method based on water inflow prediction, which is not repeated here. Each module in the water inflow control device based on water inflow prediction can be realized by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so that the processor calls and executes the operation corresponding to each module.

[0147] In one embodiment, a computer device, which can be a server, is provided, and an internal structure diagram thereof can be as shown in Figure 3 The computer device includes a processor, a memory, a network interface and a database connected by a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement a water inflow control method based on water inflow prediction.

[0148] In one embodiment, a computer device is provided, including a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program:

[0149] Obtaining the measured data of the regulating pool, the detection data of the delivery pump and the flow data of the sewage plant;

[0150] According to the measured data of the regulating pool, the detection data of the delivery pump and the flow data of the sewage plant, the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result are obtained by calculation respectively;

[0151] The regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result are calibrated respectively to obtain the calibration result;

[0152] If the calibration result is calibration passed, the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result are input into the preset mathematical prediction model to obtain the regulating pool remaining overflow time prediction result, and the water inflow control instruction is generated according to the regulating pool remaining overflow time prediction result.

[0153] In one embodiment, a computer readable storage medium is provided, and a computer program is stored thereon, and the computer program is executed by the processor to implement the following steps:

[0154] Obtaining measured data of the regulating pool, detection data of the delivery pump and flow data of the sewage plant;

[0155] According to the measured data of the regulating pool, the detection data of the delivery pump and the flow data of the sewage plant, respectively, the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result are obtained;

[0156] The regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result are calibrated respectively to obtain the calibration result;

[0157] If the calibration result is calibration passed, the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result are input into a preset mathematical prediction model to obtain the regulating pool residual overflow time prediction result, and the inflow control instruction is generated according to the regulating pool residual overflow time prediction result.

[0158] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, the processes of the above-mentioned embodiments can be included. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM) and the like.

[0159] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is exemplified. In actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0160] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A water quantity prediction-based water quantity control method, characterized by, The water inflow quantity control method based on water quantity prediction comprises the following steps: Obtaining the measured data of the regulating pool, the detection data of the delivery pump and the flow data of the sewage plant; According to the measured data of the regulating pool, the detection data of the delivery pump and the flow data of the sewage plant, the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result are obtained by calculation, specifically comprising the following steps: The measured data of the regulating pool is input into the following formula to obtain the regulating pool prediction result: Q 第一总进 = , t / h, wherein Q 第一总进 is the predicted result of the regulating tank for the total inflow of wastewater into the wastewater treatment plant, T 提 is the length of time the lift pump is on, A 调 is the area of the regulating tank, H 束 is the liquid level of the regulating tank at the end of the cycle, H 始 is the liquid level of the regulating tank at the beginning of the cycle, Q 滤液 is the amount of sludge filtrate into the regulating tank, T 周 is the predicted or measured cycle time; The detection data of the delivery pump is input into the following formula to obtain the delivery pump prediction result: Q' 进 = , t / h; Q 第二总进 =∑Q’ 进 , t / h, wherein Q 第二总进 refers to the predicted result of the delivery pump of the total influent quantity of wastewater entering the wastewater treatment plant, Q 进 refers to the water quantity of a certain type of wastewater discharged by a certain wastewater treatment plant, Q 输 refers to the flow rate of the delivery pump per unit time, and T 输 refers to the measured delivery duration of the delivery pump. The flow data of the sewage plant is input into the following formula to obtain the sewage plant prediction result: W 废 = ζ x W 总 -W 生 , wherein W 废 represents the total wastewater amount inputted into the wastewater treatment plant on the day, ζ represents the billing coefficient, W 总 represents the cumulative water amount displayed by the total water meter of the enterprise on the day, and W 生 represents the cumulative water amount displayed by the domestic water meter of the enterprise on the day. The regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result are calibrated respectively to obtain the calibration result; If the calibration result is calibration passed, the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result are input into the preset mathematical prediction model to obtain the regulating pool remaining overflow time prediction result, and the water inflow control instruction is generated according to the regulating pool remaining overflow time prediction result.

2. The water amount prediction-based water amount control method according to claim 1, characterized by, The calibration result is obtained by calibrating the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result respectively, specifically comprising the following steps: The regulating pool prediction result is calibrated by the following formula to obtain the first calibration result: Q 流累 =Q 提 ×T 提 , wherein Q 流累 represents a first calibration result of the cumulative flow of an individual cycle read by the flow meter after the booster pump, Q 提 represents the flow per unit time of the booster pump, and T 提 represents the length of time when the booster pump is turned on. The Q 第二总进 is compared with the Q 第一总进 , and a second calibration result is obtained according to the comparison result; The sewage plant prediction result is compared with the Q 第二总进 The Q 第一总进 A third calibration result is obtained according to the comparison result. The calibration result is generated according to the first calibration result, the second calibration result and the third calibration result.

3. The water amount prediction-based water amount control method according to claim 1, characterized by, The regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result are input into the preset mathematical prediction model to obtain the regulating pool remaining overflow time prediction result, specifically comprising the following steps: The regulating pool prediction result is input into the following formula to obtain the total water inflow quantity prediction: T 第一剩余 = , h; Q' 提= , t / h, where T 第一剩余 refers to the total water inflow prediction result; The delivery pump prediction result is input into the following formula to obtain the water inflow trend prediction: T 第二剩余 = , h, where T 第二剩余 is the forecast of incoming water trends; The sewage plant prediction result is input into the following formula to obtain the delivery time length prediction: V’ 测输 = ； V 测输 =∑V’ 测输 ; T 第三剩余 = , h, wherein V' 测输 is the amount of wastewater of the type predicted by the model to be discharged into the wastewater treatment plant by a certain wastewater treatment plant within the prediction period, t 输 is the prediction delivery time, V 测输 is the amount of wastewater of the type predicted by the model to be discharged into the wastewater treatment plant by all enterprises within the scope of the wastewater treatment plant within the prediction period, T 第三剩余 is the prediction result of the delivery time prediction method; The remaining overflow time prediction result T of the regulating pool is generated according to the total inflow prediction result, the incoming water trend prediction result and the delivery time length prediction result 剩余 .

4. The water amount prediction-based water amount control method according to claim 3, characterized by, The water inflow control instruction is generated according to the comparison result. obtaining a preset emergency duration judgment interval, comparing the T 剩余 with the emergency duration judgment interval; The water inflow quantity control device based on water quantity prediction comprises the following steps:

5. A water amount prediction-based water amount control device characterized by comprising: The basic data acquisition module is used to obtain the measured data of the regulating pool, the detection data of the delivery pump and the flow data of the sewage plant; The total water quantity prediction module is used to calculate the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result according to the measured data of the regulating pool, the detection data of the delivery pump and the flow data of the sewage plant, and the total water quantity prediction module comprises the following steps: The first prediction submodule is used to input the measured data of the regulating pool into the following formula to obtain the regulating pool prediction result: The second prediction submodule is used to input the detection data of the delivery pump into the following formula to obtain the delivery pump prediction result: Q 第一总进 = , t / h, wherein Q 第一总进 is the predicted result of the conditioning tank for the total influent quantity of wastewater entering the wastewater treatment plant, T 提 is the length of time the lift pump is on, A 调 is the conditioning tank area, H 束 is the conditioning tank level at the end of the period, H 始 is the conditioning tank level at the beginning of the period, Q 滤液 is the quantity of sludge filtrate entering the conditioning tank, T 周 is the predicted or measured period time; The third prediction submodule is used to input the flow data of the sewage plant into the following formula to obtain the sewage plant prediction result: Q' 进 = , t / h; Q 第二总进 =∑Q’ 进 , t / h, wherein Q 第二总进 refers to the predicted result of the delivery pump of the total influent quantity of wastewater entering the wastewater treatment plant, Q 进 refers to the water quantity of a certain kind of wastewater discharged by a certain wastewater treatment plant, Q 输 refers to the flow rate of the delivery pump per unit time, and T 输 refers to the measured delivery time length of the delivery pump. The data calibration module is used to calibrate the regulating pool prediction result, the delivery pump prediction result and the sewage plant prediction result respectively to obtain the calibration result; W 废 = ζ x W 总 -W 生 , wherein W 废 represents the total wastewater amount inputted into the wastewater treatment plant on the day, ζ represents the billing coefficient, W 总 represents the cumulative water amount displayed by the total water meter of the enterprise on the day, and W 生 represents the cumulative water amount displayed by the domestic water meter of the enterprise on the day. ​ The prediction control module is configured to input the prediction result of the regulating pool, the prediction result of the delivery pump and the prediction result of the sewage plant into a preset mathematical prediction model if the calibration result is calibration pass, to obtain a prediction result of a remaining overflow time of the regulating pool, and to generate an inflow control instruction according to the prediction result of the remaining overflow time of the regulating pool.

6. The water quantity prediction-based water quantity control apparatus according to claim 5, wherein The data calibration module comprises: A first calibration submodule is configured to calibrate the prediction result of the regulating pool by the following formula to obtain a first calibration result: Q 流累 =Q 提 ×T 提 , wherein Q 流累 represents a first calibration result of the cumulative flow of an individual cycle read by the flow meter after the booster pump, Q 提 represents the flow per unit time of the booster pump, and T 提 represents the length of time when the booster pump is turned on. The second calibration sub-module is configured to compare the Q 第二总进 with the Q 第一总进 and obtain a second calibration result according to a comparison result. a third calibration submodule configured to compare the sewage plant prediction result with the Q 第二总进 with the Q 第一总进 and obtain a third calibration result according to a comparison result. A calibration generation submodule is configured to generate the calibration result according to the first calibration result, the second calibration result and the third calibration result.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the inflow water quantity control method based on water quantity prediction according to any one of claims 1 to 4.

8. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 7. The computer program is executed by the processor to implement the steps of the inflow water quantity control method based on water quantity prediction according to any one of claims 1 to 4.

Images