Water quality monitoring method for biological medicine processing wastewater

Abstract

The invention relates to the technical field of water quality monitoring, in particular to a water quality monitoring method for biological medicine processing wastewater. The confluence disturbance characterization vector of the wastewater sample collection pool is determined by drawing the confluence characterization vector of each upstream collection pipeline, and whether the water sample monitoring of the wastewater sample collection pool has monitoring hysteresis or not is judged according to the monitoring result disturbance degree. Determining a water sample monitoring lag type according to the comparison condition of the confluence disturbance characterization vectors at a plurality of moments, determining collection point locations of wastewater sample collection in the wastewater sample collection pool according to the water sample monitoring lag type, and finally respectively introducing the wastewater samples at the plurality of collection point locations into a COD water quality analyzer to obtain a water quality monitoring result. According to the method, the water flow introduction characteristics are analyzed, so that the sampling point position is dynamically optimized, and the accuracy and timeliness of a water quality monitoring result are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water quality monitoring, in particular to a water quality monitoring method for biological and pharmaceutical processing wastewater. BACKGROUND

[0002] In the biological and pharmaceutical processing industry, the production process produces wastewater with complex components, including antibiotics, bioactive substances, macromolecular organic matter, residual solvents and other characteristic pollutants. The content of these pollutants is related to the evaluation of wastewater treatment effect, so accurate water quality monitoring of biological and pharmaceutical processing wastewater is crucial. Traditional water quality monitoring methods rely on fixed point sampling, but biological and pharmaceutical processing wastewater from multiple upstream pipelines into the collection tank is disturbed, which may affect the timeliness and accuracy of the monitoring results.

[0003] For example, Chinese patent publication No. CN117990874A discloses a wastewater monitoring method and system, which first acquires initial sewage pool structure parameters, pollutant type information and sewage pool flow information; then establishes a pollution model of the sewage pool according to hydrodynamics and pollutant diffusion dynamics; the model includes a pollutant concentration change model and a prediction model, which can reflect the distribution and concentration change of pollutants in the sewage pool in real time; according to the structure parameters of the sewage pool and the pollution model, the key point position and depth of sampling, as well as the sampling frequency are determined, so as to effectively collect samples; after sampling, the samples are detected, and the data is entered into the database, providing a reference for subsequent monitoring and processing.

[0004] The existing technology also has the following problems: The existing technology does not consider that the wastewater from each link in the biological and pharmaceutical industry is easy to form local vortex when introduced into the wastewater pool through different pipelines, causing local accumulation of macromolecular components. The existing technology cannot dynamically optimize the sampling point according to the analysis result of the water flow introduction characteristics, affecting the accuracy and timeliness of the water quality monitoring result. SUMMARY

[0005] Therefore, the present application provides a water quality monitoring method for biological and pharmaceutical processing wastewater to overcome the problem that the existing technology does not consider that the wastewater from each link in the biological and pharmaceutical industry is easy to form local vortex when introduced into the wastewater pool through different pipelines, causing local accumulation of macromolecular components, and cannot dynamically optimize the sampling point according to the analysis result of the water flow introduction characteristics.

[0006] To achieve the above purpose, the present application provides a water quality monitoring method for biological and pharmaceutical processing wastewater, comprising: acquiring the water flow velocity and direction of each upstream collection pipeline of the wastewater sample collection tank of the processing wastewater by a plurality of intelligent monitoring sensors; A confluence characteristic vector of each upstream collection pipeline is drawn according to the water flow velocity and the water flow direction, a confluence disturbance characteristic vector of the wastewater sample collection pool is determined based on the confluence characteristic vector, a monitoring result disturbance degree is determined based on the confluence disturbance characteristic vector, and whether there is monitoring lag in water sample monitoring is determined. In response to the determination result that there is monitoring lag, confluence disturbance characteristic vectors at several time instants within a preset monitoring period are obtained, and a water sample monitoring lag type is determined based on a comparison of the confluence disturbance characteristic vectors at the several time instants. A collection point of wastewater sample collection is determined in the wastewater sample collection pool according to the water sample monitoring lag type, including determining the collection point of the wastewater sample collection pool according to a vector direction of the confluence disturbance characteristic vectors at the several time instants, or determining the collection point of wastewater sample collection according to the confluence characteristic vectors of each upstream collection pipeline in each time window. The sample introduction time window is determined according to the water flow velocity of each upstream collection pipeline. Wastewater samples at the several collection points are introduced into a COD water quality analyzer respectively to determine water quality components.

[0007] Further, the vector direction of the confluence characteristic vector is the water flow direction of confluence in the upstream collection pipeline, and the vector length of the confluence characteristic vector is the water flow velocity of confluence in the upstream collection pipeline.

[0008] Further, the process of determining the confluence disturbance characteristic vector of the wastewater sample collection pool includes: determining the confluence characteristic vector corresponding to each upstream collection pipeline; adding the several confluence characteristic vectors to obtain a vector, which is determined as the confluence disturbance characteristic vector; Each confluence characteristic vector takes the center reference of the wastewater sample collection pool as the vector starting point.

[0009] Further, the process of determining whether there is monitoring lag in water sample monitoring includes: obtaining a vector length characteristic parameter of the confluence disturbance characteristic vector, determining the vector length of the vector length characteristic parameter as the monitoring result disturbance degree; comparing the monitoring result disturbance degree with a preset monitoring result disturbance degree threshold; If the monitoring result disturbance degree is less than or equal to the monitoring result disturbance degree threshold, it is determined that there is monitoring lag in water sample monitoring.

[0010] Further, the process of determining the comparison of the confluence disturbance characteristic vectors at the several time instants includes: determining a vector included angle between two confluence disturbance characteristic vectors at adjacent time instants in a time sequence dimension within a preset monitoring period; Calculate the standard deviation of the angles of several vectors.

[0011] Further, the process of determining the water sample monitoring lag type based on the standard deviation of the vector angles comprises: If the standard deviation of the vector angles is less than or equal to a preset standard deviation reference value, the water sample monitoring lag type is determined to be a steady-state lag type; If the standard deviation of the vector angles is greater than the preset standard deviation reference value, the water sample monitoring lag type is determined to be a non-steady-state lag type.

[0012] Further, the process of determining the collection point of the wastewater sample collection comprises: If the water sample monitoring lag type is a steady-state lag type, the collection point of the wastewater sample collection pool is determined according to the vector direction of the confluence disturbance representation vector at several time points; If the water sample monitoring lag type is a non-steady-state lag type, the collection point of the wastewater sample collection is determined according to the confluence representation vector of each upstream collection pipeline in each time window.

[0013] Further, the process of determining the collection point of the wastewater sample collection pool according to the vector direction of the confluence disturbance representation vector at several time points comprises: The vector obtained by adding the confluence disturbance representation vectors at several time points is determined as the confluence aggregation representation vector; The intersection point of the vector direction extension line of the confluence aggregation representation vector and the edge of the wastewater sample collection pool is determined as the collection point.

[0014] Further, the process of determining the sample introduction time window of each upstream collection pipeline according to the flow velocity comprises: Determine the flow velocity of each upstream collection pipeline; The time window for wastewater sample introduction of a first type of upstream collection pipeline is placed in a first sample introduction time window, and the time window for wastewater sample introduction of a second type of upstream collection pipeline is placed in a second sample introduction time window, and there is no intersection between the time intervals of the first sample introduction time window and the second sample introduction time window. Wherein, the first type of upstream collection pipeline is the upstream collection pipeline corresponding to the maximum flow velocity, and the second type of upstream collection pipeline includes the upstream collection pipelines excluding the upstream collection pipeline corresponding to the maximum flow velocity.

[0015] Further, the process of determining the collection point of the wastewater sample collection according to the confluence representation vector of each upstream collection pipeline in each time window comprises: determine a first vector obtained by adding the confluence characteristic vectors corresponding to the first type of upstream collection pipes in the first sample introduction time window, and determine a second vector obtained by adding the confluence characteristic vectors corresponding to the second type of upstream collection pipes in the second sample introduction time window; determine the intersection point of the vector direction extension line of the first vector and the edge of the wastewater sample collection pool and the intersection point of the vector direction extension line of the second vector and the edge of the wastewater sample collection pool as the collection point positions, respectively.

[0016] Compared with the prior art, the beneficial effects of the present application are that the present application obtains the flow velocity and flow direction of each upstream collection pipe through an intelligent monitoring sensor, determines the confluence disturbance characteristic vector of the wastewater sample collection pool by drawing the confluence characteristic vectors of each upstream collection pipe, determines whether the water sample monitoring of the wastewater sample collection pool has monitoring hysteresis according to the monitoring result disturbance degree, determines the water sample monitoring hysteresis type through the comparison of the confluence disturbance characteristic vectors at several time points, determines the collection point positions of the wastewater sample collection in the wastewater sample collection pool through the water sample monitoring hysteresis type, and finally introduces the wastewater samples at the several collection point positions into the COD water quality analyzer to obtain the water quality monitoring result. The present application realizes dynamic optimization of the sampling point position by analyzing the water flow introduction characteristics, improves the accuracy and timeliness of the water quality monitoring result.

[0017] Further, the present application adds the confluence characteristic vectors of each upstream pipe to obtain the confluence disturbance characteristic vector, and incorporates the flow characteristics of each upstream pipe into a unified spatial coordinate system, so that the result of vector addition can truly reflect the superposition effect of multiple water flows in the confluence pool. When water flows of different directions and speeds converge, the vector addition process is equivalent to simulating the actual interaction of water flows in the pool. This quantitative result is directly related to the actual water flow state such as the local eddy current and the main flow direction that may be formed in the pool. The length of the confluence disturbance characteristic vector corresponds to the monitoring result disturbance degree, and the vector direction implies the overall trend of the water flow. Through vector superposition, the complex mutual influence of multiple pipe confluences can be comprehensively captured.

[0018] Further, the present application calculates the included angle and standard deviation of the confluence disturbance characteristic vectors at adjacent time points within the preset monitoring period, and distinguishes the steady-state and non-steady-state hysteresis types based thereon. It can be understood that the vector included angle reflects the difference in the confluence disturbance direction at different time points, and the standard deviation quantifies the dispersion degree of this difference. A small standard deviation indicates that the direction change of the water flow disturbance at each time point is stable, i.e., the overall flow direction of the eddy current region is relatively fixed. A large standard deviation indicates that the water flow disturbance direction fluctuates dramatically, and the eddy current region is in unstable dynamic change. By constructing the recognition mechanism of the hysteresis type, the scene adaptation capability is improved to meet the monitoring needs under different actual working conditions.

[0019] Further, the present application is aimed at the confluence performance type of the steady-state lag type, the confluence aggregation characteristic vector is obtained by adding the confluence disturbance characteristic vectors of several time points, and the intersection point of the direction of the confluence aggregation characteristic vector and the edge of the confluence pool is taken as the collection point, the core feature of the steady-state lag type is that the overall direction of water flow disturbance is stable, at this time, the position of the local vortex area and the pollution accumulation trend are relatively fixed, the confluence aggregation characteristic vector is equivalent to weighted aggregation of the stable flow trend by superimposing the flow direction features of multiple time points, the direction of the confluence aggregation characteristic vector can accurately reflect the main flow direction of the water flow in the confluence pool, the interaction between the water flow in this direction and the vortex area is the most frequent, and the intersection point of the direction of the confluence aggregation characteristic vector and the edge of the confluence pool is taken as the collection point, so that the core area of the macromolecular component set can be covered to the maximum extent, thereby improving the reliability of the water quality monitoring result, realizing dynamic optimization of the sampling point, and improving the accuracy and timeliness of the water quality monitoring result.

[0020] Further, the determination of the first vector and the second vector in the present application corresponds to the water flow features of respective time windows: the first vector is the superposition of the confluence characteristics of a type of pipeline in the non-disturbance state, and the direction of the first vector truly reflects the dominant direction of the strong water flow impact; the second vector is the confluence result of the second type of pipeline in the weak disturbance environment, and the direction stability is improved. The intersection point of the extension lines of the two vectors and the edge of the confluence pool is taken as the collection point, so that the sampling point is accurately corresponding to the accumulation area of the macromolecular component in this period, and the problem that the sampling result of a single point in the non-steady-state water flow is not timely is effectively avoided. Since the two types of time windows have no intersection, the corresponding collection points also reflect the water quality in different periods, realize dynamic optimization of the sampling point, and improve the accuracy and timeliness of the water quality monitoring result. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 A water quality monitoring method step diagram for biological pharmaceutical processing wastewater of an embodiment of the present application; Figure 2 A logic flow diagram for determining whether water sample monitoring of a wastewater sample collection pool has monitoring lag; Figure 3 A logic flow diagram for determining a water sample monitoring lag type; Figure 4 A schematic diagram for determining a collection point when the water sample monitoring lag type is a steady-state lag type; In the figure, 1 is a wastewater sample collection pool, 2 is a confluence aggregation characteristic vector, 3 is a vector direction extension line, and 4 is an intersection point. DETAILED DESCRIPTION

[0022] In order to make the purpose and advantages of the present application clearer and more apparent, the present application is further described below in combination with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0023] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art will understand that the embodiments are only used to explain the technical principles of the present application, and are not intended to limit the protection scope of the present application.

[0024] It should be noted that in the description of the present application, the terms indicating the direction or positional relationship of "upper", "lower", "inner", "outer" and the like are based on the direction or positional relationship shown in the drawings, which is only for the convenience of description, and does not indicate or imply that the device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0025] Please refer to Figure 1 As shown in the figure, it is a step diagram of the water quality monitoring method of the biological pharmaceutical processing wastewater of the embodiment of the present application, the water quality monitoring method of the biological pharmaceutical processing wastewater of the present application comprises: Step S100, the water flow velocity and the water flow direction of each upstream collection pipeline of the wastewater sample collection pool 1 are acquired by a plurality of intelligent monitoring sensors respectively; Specifically, in the implementation of the present application, the water flow velocity in the upstream collection pipeline can be acquired by a flow rate sensor, and the flow rate sensor can be an ultrasonic flow meter, which is a commonly used sensor in the art and will not be described here.

[0026] Specifically, in the implementation of the present application, the water flow direction can be acquired by a water flow direction sensor, and the sensitive element built-in the water flow direction sensor senses the flow direction of the water flow and outputs a corresponding signal, which is a commonly used sensor in the art and will not be described here.

[0027] Step S200, a confluence representation vector of each upstream collection pipeline is drawn according to the water flow velocity and the water flow direction, a confluence disturbance representation vector of the wastewater sample collection pool 1 is determined based on the confluence representation vector, and a monitoring result disturbance degree is determined based on the confluence disturbance representation vector to determine whether there is monitoring hysteresis in water sample monitoring; Step S300, in response to the determination result that there is monitoring hysteresis, the confluence disturbance representation vectors at a plurality of time points within a preset monitoring period are acquired, and the water sample monitoring hysteresis type is determined based on the comparison of the confluence disturbance representation vectors at the plurality of time points; Specifically, the duration of the preset monitoring period can be set by those skilled in the art according to the monitoring accuracy requirement, the greater the monitoring accuracy requirement, the longer the duration of the preset monitoring period needs to be, but the algorithm calculation amount brought by it is also greater, in the implementation of the present application, the duration of the preset monitoring period can be [3, 10] min, preferably, the duration of the preset monitoring period is 5 min.

[0028] Step S400, determining the collection point of the wastewater sample collection in the wastewater sample collection pool 1 according to the water sample monitoring lag type, including determining the collection point of the wastewater sample collection pool 1 according to the vector direction of the confluence disturbance representation vector at several time points, or determining the collection point of the wastewater sample collection according to the confluence representation vector of each upstream collection pipeline in each time window. Wherein, the sample introduction time window is determined according to the water flow velocity of each upstream collection pipeline. Step S500, introducing the wastewater samples at several collection points into the COD water quality analyzer respectively to determine the water quality composition.

[0029] Specifically, COD is a key indicator for measuring the degree of organic pollution in water bodies. For biological and pharmaceutical processing wastewater, the pollutants such as antibiotics, biological macromolecules and organic solvents contained therein will directly affect the COD value. Therefore, the pollution degree of the wastewater sample can be effectively reflected by the COD water quality analyzer. The COD water quality analyzer is an instrument for determining the chemical oxygen demand of water quality, which is widely used in the water quality monitoring of industrial wastewater, and will not be described here.

[0030] Specifically, in the implementation of the present application, the wastewater at different collection points can be collected by controlling the position of the sampling pump at the edge of the wastewater sample collection pool, and the collected wastewater can be pumped to the COD water quality analyzer through the pipeline. The sampling pump receives a position signal containing the moving distance along the length and width directions of the wastewater sample collection pool to determine the moving direction and distance. The sampling pump can also be manually controlled to move to the collection point by the coordinate information of the determined collection point, which will not be described here.

[0031] Specifically, in the production process of biological and pharmaceutical industries and chemical industry, different production links will produce wastewater with different compositions. These wastewaters are usually transported through independent upstream pipelines and finally flow into the wastewater sample collection pool for temporary storage. The wastewater sample collection pool is set up on the one hand to integrate scattered wastewater sources, and on the other hand to mix the wastewater from different process links to provide relatively stable water conditions for subsequent biochemical treatment, advanced purification and other processes. It is this multi-pipeline confluence characteristic that causes complex disturbance of the water flow in the pool due to the differences in flow rate and flow direction of each pipeline, which brings lag challenges to the timeliness and accuracy of the water quality monitoring results.

[0032] Specifically, the vector direction of the confluence representation vector is the direction of the water flow in the upstream collection pipeline, and the length of the confluence representation vector is the water flow velocity in the upstream collection pipeline.

[0033] Specifically, the application draws the confluence representation vector, so as to intuitively reflect the flow direction of each upstream pipeline wastewater and the strength of the water flow by the length of the vector, and the significance lies in that the quantitative analysis of the wastewater sample collection pool connected to the complex pipeline network is realized, so that the spatial characteristics such as the inflow angle and impact direction of different pipeline wastewater are visualized.

[0034] Specifically, the process of determining the confluence disturbance representation vector of the wastewater sample collection pool comprises: determining the confluence representation vector corresponding to each upstream collection pipeline; determining the vector obtained by adding the confluence representation vectors as the confluence disturbance representation vector; wherein each confluence representation vector takes the center reference of the wastewater sample collection pool as the starting point of the vector.

[0035] Specifically, the vector addition calculation is performed based on the parallelogram law in the implementation of the application.

[0036] Specifically, the confluence disturbance representation vector is obtained by adding the confluence representation vectors of each upstream pipeline, and the flow characteristics of each upstream pipeline are included in the unified spatial coordinate system, so that the result of vector addition can truly reflect the superposition effect of multiple water flows in the confluence pool. When water flows of different directions and speeds converge, the vector addition process is equivalent to simulating the actual interaction of water flows in the pool. This quantitative result is directly related to the actual water flow state such as the local vortex and the main flow direction that may be formed in the pool. The length of the confluence disturbance representation vector directly corresponds to the disturbance degree of the monitoring result, and the vector direction implies the overall trend of the water flow. Through vector superposition, the complex mutual influence of multi-pipeline confluence can be comprehensively captured.

[0037] Specifically, please refer to Figure 2 The logic flow chart for determining whether the water sample monitoring of the wastewater sample collection pool has monitoring hysteresis is shown in the figure, and the process of determining whether the water sample monitoring has monitoring hysteresis comprises: obtaining the vector length characteristic parameter of the confluence disturbance representation vector, and determining the vector length of the vector length characteristic parameter as the disturbance degree of the monitoring result; comparing the disturbance degree of the monitoring result with a preset disturbance degree threshold of the monitoring result; if the disturbance degree of the monitoring result is less than or equal to the disturbance degree threshold of the monitoring result, it is determined that the water sample monitoring has monitoring hysteresis, and if the disturbance degree of the monitoring result is greater than the disturbance degree threshold of the monitoring result, it is determined that the water sample monitoring does not have monitoring hysteresis.

[0038] Specifically, the preset monitoring result disturbance degree threshold value can be calculated by collecting the flow velocity, flow direction and other related data in the wastewater sample pool historical data, and the monitoring result historical disturbance degree can be calculated, and the monitoring result disturbance degree threshold value = monitoring result historical disturbance degree x disturbance degree threshold value factor, wherein the value range of the disturbance degree threshold value factor is [1.2, 1.5], and here a value of the disturbance degree threshold value factor is provided, and the disturbance degree threshold value factor can be 1.25.

[0039] Specifically, the length of the confluence disturbance representation vector is obtained as the monitoring result disturbance degree, and the length of the confluence disturbance representation vector is compared with the preset threshold value to determine whether the water sample monitoring exists hysteresis, and the length of the confluence disturbance representation vector essentially reflects the overall disturbance intensity of the interaction of the multiple upstream water flows in the confluence pool, and the shorter the vector length, the more significant the hedging and offsetting effect between the water flows, and the higher the possibility of forming local vortex, and those skilled in the art can understand that the vortex is an important reason for causing the accumulation of macromolecular pollutants and causing monitoring hysteresis, and the accurate analysis of the water flow introduction characteristics is realized. Specifically, the process of determining the comparison of the confluence disturbance representation vectors at the plurality of time points includes: determining the vector included angle between the two confluence disturbance representation vectors at the adjacent time points in the time sequence dimension within the preset monitoring period; calculating the standard deviation of the plurality of vector included angles.

[0040] Specifically, the time interval between the adjacent time points within the preset monitoring period is set by those skilled in the art, and if the duration of the preset monitoring period is 5 min, the time interval between the adjacent time points can be set to 20 s.

[0041] Specifically, please refer to Figure 3 The length of the confluence disturbance representation vector is obtained as the monitoring result disturbance degree, and the length of the confluence disturbance representation vector is compared with the preset threshold value to determine whether the water sample monitoring exists hysteresis, and the length of the confluence disturbance representation vector essentially reflects the overall disturbance intensity of the interaction of the multiple upstream water flows in the confluence pool, and the shorter the vector length, the more significant the hedging and offsetting effect between the water flows, and the higher the possibility of forming local vortex, and those skilled in the art can understand that the vortex is an important reason for causing the accumulation of macromolecular pollutants and causing monitoring hysteresis, and the accurate analysis of the water flow introduction characteristics is realized. If the standard deviation of the vector included angle is less than or equal to the preset standard deviation reference value, the water sample monitoring hysteresis type is determined to be a steady-state hysteresis type; If the standard deviation of the vector included angle is greater than the preset standard deviation reference value, the water sample monitoring hysteresis type is determined to be a non-steady-state hysteresis type.

[0042] Specifically, the preset standard deviation reference value is determined by calculating the vector included angle standard deviation average value in the historical monitoring data, and according to the calculation of the historical monitoring data, the value range of the standard deviation reference value can be set to [5°, 10°], and preferably, the value of the standard deviation reference value is 8°.

[0043] Specifically, the application can distinguish between steady-state and non-steady-state lag types by calculating the included angle and standard deviation of the adjacent time confluence disturbance representation vector within a preset monitoring period, it can be understood that the vector included angle reflects the difference in the direction of water flow disturbance at different times, and the standard deviation quantifies the discrete degree of the difference, a small standard deviation indicates that the direction of water flow disturbance at each time is stable, that is, the overall flow direction of the vortex region is relatively fixed, a large standard deviation indicates that the direction of water flow disturbance fluctuates violently, and the vortex region is in unstable dynamic change, the recognition mechanism of the lag type is constructed to improve the scene adaptation capability and meet the monitoring needs under different actual working conditions.

[0044] Specifically, the process of determining the collection point of the wastewater sample collection includes: If the water sample monitoring lag type is a steady-state lag type, the collection point of the wastewater sample collection is determined according to the vector direction of the confluence disturbance representation vector at several times; If the water sample monitoring lag type is a non-steady-state lag type, the collection point of the wastewater sample collection is determined according to the confluence representation vector of each upstream collection pipeline in each time window.

[0045] Specifically, please refer to Figure 4 As shown in the figure, it is a schematic diagram of determining the collection point of the wastewater sample collection under the condition that the water sample monitoring lag type is a steady-state lag type, the process of determining the collection point of the wastewater sample collection according to the vector direction of the confluence disturbance representation vector at several times includes: The vector obtained by adding the confluence disturbance representation vectors at several times is determined as the confluence aggregation representation vector 2; The intersection point 4 of the vector direction extension line 3 of the confluence aggregation representation vector 2 and the edge of the wastewater sample collection pool is determined as the collection point.

[0046] It can be understood that, for the confluence performance type of the steady-state lag type, the confluence aggregation representation vector is obtained by adding the confluence disturbance representation vectors at several times, and the intersection point of its direction and the edge of the confluence pool is used as the collection point, the core feature of the steady-state lag type is that the overall direction of water flow disturbance is stable, at this time, the position of the local vortex region and the pollution accumulation trend are relatively fixed, the confluence aggregation representation vector is equivalent to weighted aggregation of the stable flow trend by superimposing the water flow direction features at several times, the direction can accurately reflect the main flow direction of the water flow in the confluence pool, the interaction between the water flow and the vortex region in this direction is the most frequent, the collection point is set at the intersection point of the direction and the edge of the pool, which can maximize the coverage of the core area of macromolecular component aggregation, thereby improving the reliability of the water quality monitoring result, realizing dynamic optimization of the sampling point, and improving the accuracy and timeliness of the water quality monitoring result.

[0047] Specifically, the process of determining the sample introduction time window of each upstream collection pipeline according to the water flow velocity comprises: determining the water flow velocity of each upstream collection pipeline; placing the time window for wastewater sample introduction of a type of upstream collection pipeline in a first sample introduction time window; and placing the time window for wastewater sample introduction of a second type of upstream collection pipeline in a second sample introduction time window, wherein the first sample introduction time window and the second sample introduction time window do not have a time interval intersection; wherein the type of upstream collection pipeline is the upstream collection pipeline corresponding to the maximum water flow velocity, and the second type of upstream collection pipeline comprises the upstream collection pipelines excluding the upstream collection pipeline corresponding to the maximum water flow velocity.

[0048] For example, the wastewater treatment system matched with a biopharmaceutical production workshop is provided with five upstream collection pipelines (P1, P2, P3, P4, and P5). After data acquisition and calculation, the flow velocities of the pipelines are as follows: P1 is 3.2 m / s, P2 is 1.8 m / s, P3 is 2.5 m / s, P4 is 1.2 m / s, and P5 is 0.9 m / s. According to the water flow velocity, the type of upstream collection pipeline is determined as P1, the second type of upstream collection pipeline is determined as P2, P3, P4, and P5, the first sample introduction time window is assigned to P1, the duration of the first sample introduction time window can be 5 min, the second sample introduction time window is assigned to P2, P3, P4, and P5, the duration of the second sample introduction time window can be 5 min, a 2-minute interval is set between the first sample introduction time window and the second sample introduction time window for pipeline flushing and sample pipeline emptying, and an on-off valve controlled by an electromagnetic valve is installed at the outlet of each upstream collection pipeline, which is automatically controlled by a microcontroller according to the time window.

[0049] Specifically, the present application separately places the type of pipeline with the maximum water flow velocity in the first sample introduction time window, which is equivalent to separating the most dominant disturbance factor from the complex multi-pipeline collection system. In the non-steady-state lag type, the water flow of the high-flow-rate pipeline is often the main reason for the frequent change of the overall disturbance direction due to the strong impact force. By setting the second sample introduction time window, the water flow disturbance in the second window excludes the influence of the original severe fluctuation.

[0050] It can be understood that the separate monitoring of a type of pipeline in the first sample introduction time window can accurately capture the migration characteristics of the macromolecular components in the high flow state, and since it is not disturbed by other pipelines, the confluence characterization vector can truly reflect the direction of the water flow. In the second sample introduction time window, the disturbance direction of the water flow is significantly reduced by excluding the interference of the maximum flow rate, and the stability of the collection point determined by vector superposition is higher, which can more accurately target the area where the macromolecular components are prone to accumulate in this period, so that the characteristics of the pollutants corresponding to the two types of pipelines can be clearly captured. Specifically, the process of determining the collection point of the wastewater sample according to the confluence characterization vector of each upstream confluence pipeline in each time window comprises: determining a first vector obtained by adding the confluence characterization vectors corresponding to the first type of upstream confluence pipeline in the first sample introduction time window, and determining a second vector obtained by adding the confluence characterization vectors corresponding to the second type of upstream confluence pipeline in the second sample introduction time window; determining the intersection point of the vector direction extension line of the first vector and the edge of the wastewater sample collection pool and the intersection point of the vector direction extension line of the second vector and the edge of the wastewater sample collection pool as the collection point, respectively.

[0051] Specifically, the determination of the first vector and the second vector in the present application corresponds to the water flow characteristics of each time window: the first vector is the superposition of the confluence characterization of the first type of pipeline in the undisturbed state, and its direction truly reflects the dominant direction of the strong water flow impact; the second vector is the confluence result of the second type of pipeline in the weak disturbance environment, and the direction stability is improved. The intersection point of the extension lines of the two vectors and the edge of the confluence pool is set as the collection point, so that the sampling point is accurately corresponding to the accumulation area of the macromolecular components in this period, effectively avoiding the problem that the sampling result of a single point in the non-steady state water flow is not timely. Since the two types of time windows have no intersection, the corresponding collection points also reflect the water quality in different periods, realize dynamic optimization of the sampling point, and improve the accuracy and timeliness of the water quality monitoring result.

[0052] The embodiment also provides a computer readable storage medium having computer program code stored therein, which, when executed on a computer, causes the computer to execute the above-mentioned related method steps to realize the water quality monitoring method of the biological and pharmaceutical processing wastewater provided by the above-mentioned embodiment.

[0053] So far, the technical solutions of the present application have been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after the changes or replacements will fall within the protection scope of the present application.

[0054] The above merely provides the preferred embodiments of the present application, but is not intended to limit the present application. The present application can have various modifications and changes for those skilled in the art. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall fall into the scope of protection of the present application.

Claims

1. A method for monitoring the quality of water in biopharmaceutical processing wastewater, characterized by, The method comprises the following steps: obtaining the flow velocity and direction of each upstream collection pipeline of the wastewater sample collection pool through a plurality of intelligent monitoring sensors; drawing a confluence representation vector of each upstream collection pipeline according to the flow velocity and direction, determining a confluence disturbance representation vector of the wastewater sample collection pool based on the confluence representation vector, determining a monitoring result disturbance degree based on the confluence disturbance representation vector, and determining whether the water sample monitoring has monitoring lag; in response to the determination result that there is monitoring lag, obtaining the confluence disturbance representation vectors at a plurality of time points within a preset monitoring period, and determining the water sample monitoring lag type based on the comparison of the confluence disturbance representation vectors at the plurality of time points; determining the collection point of the wastewater sample collection pool according to the water sample monitoring lag type, including determining the collection point of the wastewater sample collection pool according to the vector direction of the confluence disturbance representation vector at a plurality of time points, or determining the collection point of the wastewater sample collection pool according to the confluence representation vector of each upstream collection pipeline within each time window; wherein the sample introduction time window is determined according to the flow velocity of each upstream collection pipeline; directing the wastewater samples at a plurality of collection points into a COD water quality analyzer for water quality composition determination.

2. The method of water quality monitoring of bio-pharmaceutical processing wastewater as claimed in claim 1, wherein, The vector direction of the confluence representation vector is the flow direction of the confluence in the upstream collection pipeline, and the vector length of the confluence representation vector is the flow velocity of the confluence in the upstream collection pipeline.

3. The method of water quality monitoring of bio-pharmaceutical processing wastewater as claimed in claim 2, wherein, The process of determining the confluence disturbance representation vector of the wastewater sample collection pool comprises: determining the confluence representation vector corresponding to each upstream collection pipeline; adding the plurality of confluence representation vectors to obtain a vector, which is determined as the confluence disturbance representation vector; wherein each confluence representation vector takes the center reference of the wastewater sample collection pool as the starting point of the vector.

4. The method of water quality monitoring of bio-pharmaceutical processing wastewater as claimed in claim 3, wherein, The process of determining whether the water sample monitoring has monitoring lag comprises: obtaining a vector length characteristic parameter of the confluence disturbance representation vector, determining the vector length of the vector length characteristic parameter as the monitoring result disturbance degree; comparing the monitoring result disturbance degree with a preset monitoring result disturbance degree threshold value; if the monitoring result disturbance degree is less than or equal to the monitoring result disturbance degree threshold value, it is determined that the water sample monitoring has monitoring lag.

5. The method of monitoring the quality of water of bio-pharmaceutical processing wastewater according to claim 1, wherein, The process of determining the comparison of the confluence disturbance representation vectors at a plurality of time points comprises: determining the vector included angle between two confluence disturbance representation vectors at adjacent time points in the time sequence dimension within a preset monitoring period; calculating the standard deviation of a plurality of vector included angles.

6. The method of water quality monitoring of bio-pharmaceutical processing wastewater as claimed in claim 5, wherein, The process of determining the water sample monitoring lag type based on the standard deviation of the vector included angle comprises: if the standard deviation of the vector included angle is less than or equal to a preset standard deviation reference value, it is determined that the water sample monitoring lag type is a steady-state lag type; if the standard deviation of the vector included angle is greater than the preset standard deviation reference value, it is determined that the water sample monitoring lag type is a non-steady-state lag type.

7. The method of water quality monitoring of bio-pharmaceutical processing wastewater as claimed in claim 6, wherein, The process of determining the collection point of the wastewater sample collection pool comprises: if the water sample monitoring lag type is a steady-state lag type, determining the collection point of the wastewater sample collection pool according to the vector direction of the confluence disturbance representation vector at a plurality of time points. If the water sample monitoring lag type is a non-steady-state lag type, the collection point of the wastewater sample collection is determined according to the confluence representation vector of each upstream collection pipeline in each time window.

8. The method of water quality monitoring of bio-pharmaceutical processing wastewater as claimed in claim 7, wherein, The process of determining the collection point of the wastewater sample collection pool according to the vector direction of the confluence disturbance representation vector at several time points includes: The vector obtained by adding the confluence disturbance representation vectors at several time points is determined as the confluence aggregation representation vector; The intersection point of the vector direction extension line of the confluence aggregation representation vector and the edge of the wastewater sample collection pool is determined as the collection point.

9. The method of water quality monitoring of bio-pharmaceutical processing wastewater as claimed in claim 7, wherein, The process of determining the sample introduction time window of each upstream collection pipeline according to the water flow velocity includes: Determine the water flow velocity of each upstream collection pipeline; The time window of a type of upstream collection pipeline for wastewater sample introduction is placed in the first sample introduction time window, and the time window of a type of upstream collection pipeline for wastewater sample introduction is placed in the second sample introduction time window, and the time interval of the first sample introduction time window and the second sample introduction time window does not intersect. The first type of upstream collection pipeline is the upstream collection pipeline corresponding to the maximum water flow velocity, and the second type of upstream collection pipeline includes the upstream collection pipeline excluding the upstream collection pipeline corresponding to the maximum water flow velocity.

10. The method of water quality monitoring of bio-pharmaceutical processing wastewater as claimed in claim 9, wherein, The process of determining the collection point of the wastewater sample collection according to the confluence representation vector of each upstream collection pipeline in each time window includes: Determine the first vector obtained by adding the confluence representation vectors corresponding to the first type of upstream collection pipeline in the first sample introduction time window, and determine the second vector obtained by adding the confluence representation vectors corresponding to the second type of upstream collection pipeline in the second sample introduction time window; The intersection point of the vector direction extension line of the first vector and the edge of the wastewater sample collection pool and the intersection point of the vector direction extension line of the second vector and the edge of the wastewater sample collection pool are respectively determined as the collection point.

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