Dynamic pollution source collecting and monitoring method and system

Abstract

The invention discloses a pollution source dynamic acquisition and monitoring method and system, relates to the technical field of pollution source dynamic acquisition and monitoring, and solves the problem that a scheme capable of sensing the pollution situation and autonomously optimizing the monitoring frequency is difficult to set in pharmaceutical production. And a dynamic threshold is difficult to set according to the dynamic influence of the environmental variable on the pollution diffusion rule. According to the method, the predicted pollution concentration of the current time period is obtained through the pre-evaluation model; determining the data acquisition frequency of the current time period according to the pollution concentration of the previous time period and the predicted pollution concentration of the current time period; acquiring the pollution concentration of each pollution source in the current time period based on the data acquisition frequency of the current time period; determining an alarm instruction of each pollution source based on the pollution concentration in the current time period, and giving an alarm based on the alarm instruction; according to the invention, environmental risk control can be changed from passive response to active prevention, and technical support is provided for green production and sustainable development of the pharmaceutical industry.

Description

Technical Field

[0001] The present invention belongs to the field of pharmaceutical safety supervision, relates to a pollution source dynamic collection and monitoring technology, and specifically provides a pollution source dynamic collection and monitoring method and system. Background Art

[0002] The pharmaceutical industry is highly concerned with environmental safety. Its production processes can generate various pollutants, such as chemicals and microorganisms. To ensure the environmental friendliness of the pharmaceutical process and the safety of workers, monitoring and collecting pollution sources is crucial. Traditional monitoring methods suffer from issues such as poor real-time performance and inflexible data collection. Therefore, developing a system and method that can dynamically collect and monitor pollution sources aims to improve monitoring accuracy and efficiency, safeguarding the environment and human health, and meeting the high standards of environmental protection and safe production in the modern pharmaceutical industry.

[0003] At present, most of the dynamic collection and monitoring methods and systems for pollution sources in pharmaceutical production find it difficult to set up a plan that can perceive the pollution situation and autonomously optimize the monitoring frequency. Pollution sources are only monitored at a fixed frequency, which may result in a small amount of data during high pollution or data processing costs during low pollution. At the same time, most of the dynamic collection and monitoring methods and systems for pollution sources find it difficult to set dynamic thresholds based on the dynamic impact of environmental variables on the pollution diffusion law, and it is difficult to reduce the misjudgment problem that may occur with traditional static thresholds under complex meteorological conditions.

[0004] Therefore, the present invention discloses a pollution source dynamic collection and monitoring method and system for solving the above technical problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a dynamic collection and monitoring method and system for pollution sources, which are used to solve the technical problems that it is difficult to set up a solution that can perceive the pollution situation and autonomously optimize the monitoring frequency in pharmaceutical production, and it is difficult to set dynamic thresholds based on the dynamic influence of environmental variables on the law of pollution diffusion. The present invention divides time periods to obtain planned production capacity and types of pollution sources, as well as the pollution concentrations of various types of pollution sources in the previous time period; obtains the expected pollution concentration in the current time period through a pre-assessment model; determines the data collection frequency of the current time period based on the pollution concentration in the previous time period and the expected pollution concentration in the current time period; obtains the pollution concentration of each pollution source in the current time period based on the data collection frequency of the current time period; determines the alarm instructions for each pollution source based on the pollution concentration in the current time period, and issues an alarm based on the alarm instructions to solve the above problems.

[0006] To achieve the above objectives, the first aspect of the present invention provides a method for dynamic collection and monitoring of pollution sources, comprising: Divide the time period and obtain the planned production capacity and types of pollution sources when the current time period arrives, as well as the pollution concentration of various types of pollution sources in the previous time period; Establish a pre-assessment model, input the planned production capacity of the current time period into the pre-assessment model to obtain the expected pollution concentration of the current time period; wherein the pre-assessment model is obtained through artificial intelligence model training; Determine the data collection frequency for the current time period based on the pollution concentration in the previous time period and the expected pollution concentration in the current time period; Obtain the pollution concentration of each pollution source in the current time period based on the data collection frequency of the current time period; The alarm instructions for each pollution source are determined based on the pollution concentration in the current time period, and an alarm is issued based on the alarm instructions.

[0007] Preferably, the time division includes: When the target time point of each day is reached, the working hours and non-working hours of the pharmaceutical factory on the corresponding day of the current day are obtained from the database, and the non-working hours are planned into several time periods according to the standard time length ZC. Among them, the target time point and the standard time length ZC are both manually set; Obtain the number of alarms (BC) in the previous n days, determine the flexible working hours (TC) based on the formula TC = ZC / exp(α × BC / ZBC), and plan working hours into several time periods based on the flexible working hours TC. Here, n is manually set, ZBC is the manually set standard number of alarms, and α is the manually set amplitude adjustment coefficient, which ranges from 0 to 1.

[0008] Preferably, obtaining the planned production capacity and the types of pollution sources, as well as the pollution concentrations of various types of pollution sources in the previous time period, includes: The planned production capacity of the current pharmaceutical factory in the current time period and the types of monitored pollution sources are extracted from the database, and several pollution concentrations recorded for various types of pollution sources in the previous time period are extracted in turn; among them, the types of pollution sources include antibiotics, hormones, heavy metals, acids and alkalis, and nitrogen present in wastewater, and toluene, dichloromethane, ethyl acetate, ammonia, and hydrogen sulfide present in exhaust gas.

[0009] Preferably, the pre-assessment model is obtained by training an artificial intelligence model, including: Extract the planned production capacity for each time period from historical data, as well as the actual production capacity for each time period and the pollution concentrations of various types of pollution sources obtained through monitoring; Integrate the planned capacity and actual capacity of each time period into several sets of training data; The planned production capacity for each time period, the actual production capacity for each time period and the pollution concentrations of various types of pollution sources obtained by monitoring are integrated into a number of test data; the artificial intelligence model is trained using the training data, and the trained artificial intelligence model is tested using the test data, and the artificial intelligence model is adjusted according to the test results; finally, a preliminary assessment model is obtained with the planned production capacity for the current time period as input and the expected pollution concentration for the current time period as output; among them, the artificial intelligence model includes a BP neural network model and an RBF neural network model.

[0010] Preferably, determining the data collection frequency of the current time period based on the pollution concentration of the previous time period and the estimated pollution concentration of the current time period includes: Extract several pollution concentrations recorded by various types of pollution sources in the previous time period , and the expected pollution concentration for the current time period ; Where i is the type number of the pollution source, and the value range of i is [1, n], and n is the maximum value of the pollution source type number; j is the number of several pollution concentrations of the pollution source with type number i, and the value range of j is [0, m], and m is the maximum value of the pollution concentration number; When the pollution concentration or the expected pollution concentration When the concentration exceeds the corresponding threshold, the data collection frequency of the pollution source numbered i in the current time period is set to the maximum value of the corresponding standard collection range; the standard collection range is obtained through experience, and the concentration threshold of the pollution source is set by temperature and wind speed; When the pollution concentration or the expected pollution concentration When there is no situation where the concentration exceeds the corresponding limit value, based on the pollution concentration or the expected pollution concentration Determine the data collection frequency of the pollution source numbered i in the current time period , the data collection frequency Satisfies the following formula: ; in, is the middle value of the standard collection range corresponding to the pollution source numbered i, is the manually set standard pollution concentration corresponding to the pollution source numbered i, and max() is the maximum value symbol; and are all manually set proportional adjustment coefficients, and , ; When the data collection frequency of the pollution source numbered i is when the data acquisition frequency of the pollution source numbered i is greater than the maximum value of the corresponding standard acquisition range, using the maximum value to update the data acquisition frequency ; when the data acquisition frequency of the pollution source numbered i is less than the minimum value of the corresponding standard acquisition range, using the minimum value to update the data acquisition frequency .

[0011] It should be noted that, the maximum value of the pollution concentration of the pollution source numbered i is taken.

[0012] Preferably, the concentration threshold of the pollution source is set by temperature and wind speed, including: when the pollution source is a non-volatile pollution source, the standard concentration threshold corresponding to the current pollution source is set as the concentration threshold of the current pollution source; when the pollution source is a volatile pollution source, the temperature WD and the wind speed FS in the current factory are obtained, and the concentration threshold of the current pollution source is determined based on the temperature WD and the wind speed FS ; the concentration threshold satisfies the following formula: ; wherein BWD is a standard temperature set by a human, and BS is a standard wind speed set by a human; is the standard concentration threshold corresponding to the pollution source numbered i; is an amplitude adjustment coefficient of the ln() function, and the value range of is [0, 1]; is an amplitude adjustment coefficient of the exp() function, and the value range of is [0, 2].

[0013] Preferably, the data acquisition frequency based on the current time period obtains the pollution concentration of each pollution source in the current time period, including: extracting the data acquisition frequency of the current time period, based on the data acquisition frequency acquiring the pollution concentration of the pollution source numbered i in the current time period.

[0014] Preferably, the alarm instruction of each pollution source based on the pollution concentration in the current time period includes: extracting the concentration threshold of each pollution source in the current time period in turn, and when the pollution concentration of a pollution source exceeds the corresponding concentration threshold, issuing an alarm instruction ; ​When the pollution concentration of no pollution source exceeds the corresponding concentration threshold, determine whether the pollution concentration of any pollution source exceeds 0.9 times the corresponding concentration threshold; if yes, issue an alarm instruction 2 ; No, issue alarm command three ; Among them, the alarm instruction includes an alarm instruction 、Alarm Command 2 and Alarm Command Three .

[0015] Preferably, issuing an alarm based on an alarm instruction includes: When the alarm command is alarm command 1 When the concentration of pollutant numbered i exceeds the standard, an alarm signal is sent to staff and management personnel through broadcasting and text messages, and the alarm light is set to red; When the alarm command is alarm command 2 When the concentration of pollutant numbered i is about to exceed the standard, an alarm signal is sent to staff and management personnel through broadcasting and text messages, and the alarm light is set to yellow; When the alarm command is alarm command three No operation is performed when

[0016] A second aspect of the present invention provides a pollution source dynamic collection and monitoring system, comprising: a concentration preset module, and a frequency setting module and a collection and monitoring module connected to the concentration preset module; The frequency setting module is used to divide the time period and obtain the planned production capacity and the type of pollution source when the current time period arrives, as well as the pollution concentration of various types of pollution sources in the previous time period; The concentration preset module is used to establish a pre-assessment model, input the planned production capacity of the current time period into the pre-assessment model to obtain the expected pollution concentration of the current time period; wherein the pre-assessment model is obtained by training an artificial intelligence model; The data collection and monitoring module is used to determine the data collection frequency of the current time period based on the pollution concentration of the previous time period and the expected pollution concentration of the current time period; obtain the pollution concentration of each pollution source in the current time period based on the data collection frequency of the current time period; determine the alarm instructions of each pollution source based on the pollution concentration in the current time period, and issue an alarm based on the alarm instruction.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1.The present application obtains the planned production capacity and the types of pollution sources when reaching the current time period by dividing the time period, and the pollution concentration of various types of pollution sources in the last time period; establishes a pre-evaluation model, inputs the planned production capacity of the current time period into the pre-evaluation model to obtain the predicted pollution concentration of the current time period; determines the data acquisition frequency of the current time period according to the pollution concentration of the last time period and the predicted pollution concentration of the current time period; obtains the pollution concentration of each pollution source in the current time period based on the data acquisition frequency of the current time period; determines the alarm instruction of each pollution source based on the pollution concentration in the current time period, and issues an alarm based on the alarm instruction, which solves the technical problems that it is difficult to set a scheme that can perceive the pollution situation and autonomously optimize the monitoring frequency in pharmaceutical production, and it is difficult to set a dynamic threshold according to the dynamic influence of environmental variables on the pollution diffusion law; the present application can change the environmental risk control from passive response to active prevention, and provides technical support for green production and sustainable development of the pharmaceutical industry.

[0018] 2.The present application significantly improves the intelligent level and resource utilization efficiency of the environmental monitoring system by introducing an innovative mechanism based on dynamic adjustment of data acquisition frequency according to pollution concentration, and by constructing a closed-loop feedback system that can perceive the pollution situation and autonomously optimize the monitoring strategy, the present application realizes fine management and control of various types of pollution sources by fusing historical pollution data and future prediction models; when the real-time concentration or predicted concentration of a certain pollution source breaks through the preset dynamic threshold, the system will immediately start the high-frequency acquisition mode; this response mechanism not only can capture the instantaneous fluctuation characteristics of the pollution concentration, and provide high-precision data support for pollution tracing and emergency disposal, but also effectively avoids the monitoring blind area caused by data missing by locking the acquisition frequency at the upper limit of the standard range; when the pollution concentration is in the safe interval, the system uses an adaptive algorithm in the form of an exponential function to dynamically adjust the acquisition frequency based on the statistical characteristics of historical data and the comprehensive evaluation of prediction values. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, a brief introduction will be given below to the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating any inventive labor.

[0020] Figure 1 The operation steps of the present application are shown in the figure; Figure 2 The operation steps of the present application for dividing time periods are shown in the figure; Figure 3 The system module of the present application is shown in the figure. DETAILED DESCRIPTION

[0021] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] See also Figure 1 The first embodiment of the present invention provides a method and system for dynamic collection and monitoring of pollution sources, including: Frequency setting module: used to divide time periods, obtain planned production capacity and types of pollution sources when the current time period arrives, as well as the pollution concentration of various types of pollution sources in the previous time period; Establish a pre-assessment model, input the planned production capacity of the current time period into the pre-assessment model to obtain the expected pollution concentration of the current time period; wherein the pre-assessment model is obtained through artificial intelligence model training; Determine the data collection frequency for the current time period based on the pollution concentration in the previous time period and the expected pollution concentration in the current time period; Obtain the pollution concentration of each pollution source in the current time period based on the data collection frequency of the current time period; The alarm instructions for each pollution source are determined based on the pollution concentration in the current time period, and an alarm is issued based on the alarm instructions.

[0023] See also Figure 2 , the time periods in the present invention include: When the target time point of each day is reached, the working hours and non-working hours of the pharmaceutical factory on the corresponding day of the current day are obtained from the database, and the non-working hours are planned into several time periods according to the standard time length ZC; the target time point and standard time length ZC are obtained through manual setting; Obtain the number of alarms (BC) in the previous n days, determine the flexible working hours (TC) based on the formula TC = ZC / exp(α × BC / ZBC), and plan working hours into several time periods based on the flexible working hours TC. Here, n is manually set, ZBC is the manually set standard number of alarms, and α is the manually set amplitude adjustment coefficient, which ranges from 0 to 1.

[0024] It should be noted that the target time point can be set to 0:00.

[0025] It should be noted that the standard duration ZC can be set to 10 minutes, and n can be set to 7.

[0026] It should be noted that the value of the alarm number BC is several times of n, which can be 0.2 times, 0.5 times, 1 times or 2 times of n.

[0027] It should be noted that α is the amplitude adjustment coefficient of the 1 / exp() function set manually, and α is the influence of the elastic time TC on the ratio of the number of alarms BC to the standard number of alarms ZBC. When other conditions remain unchanged, the larger α is, the greater the impact on the elastic time TC is, and the smaller α is, the smaller the impact on the elastic time TC is.

[0028] It should be noted that when non-working time is divided into several time periods according to the standard time period ZC, and working time is divided into several time periods according to the flexible time period TC, if there is non-working time or working time cannot be completely divided into several time periods, the last incomplete time period will be marked as one time period; For example: if the non-working hours are 13:30-14:35 and the standard time ZC is 10 minutes, then 13:30-14:35 will be divided into: 13.30-13:40, 13.40-13:50, 13.50-14:00, 14.00-14:10, 14.10-14:20, 14.20-14:30, 14.30-14:35.

[0029] For example, in this embodiment, the value of n is 3, the value of the number of alarms BC is 1 times n, the value of the standard duration ZC is 10 minutes, and the standard number of alarms ZBC is 5; Based on the formula TC = ZC / exp(α × BC / ZBC) = 10 / exp(0.1 × 3 / 5) = 9.4 min, the elastic time TC = 9.4 min is determined. In this embodiment, the value of α is 0.1. If the working hours are 9:00-11.30, then divide 9:00-11.30 into several time periods with 9.4 minutes as a time length.

[0030] The present invention obtains the planned production capacity and the types of pollution sources, as well as the pollution concentrations of various types of pollution sources in the previous time period, including: The planned production capacity of the current pharmaceutical factory in the current time period and the types of monitored pollution sources are extracted from the database, and several pollution concentrations recorded for various types of pollution sources in the previous time period are extracted in turn; among them, the types of pollution sources include antibiotics, hormones, heavy metals, acids and alkalis, and nitrogen present in wastewater, and toluene, dichloromethane, ethyl acetate, ammonia, and hydrogen sulfide present in exhaust gas.

[0031] The pre-assessment model in the present invention is obtained through artificial intelligence model training, including: Extract the planned production capacity for each time period from historical data, as well as the actual production capacity for each time period and the pollution concentrations of various types of pollution sources obtained through monitoring; Integrate the planned capacity and actual capacity of each time period into several sets of training data; The planned production capacity for each time period, the actual production capacity for each time period and the pollution concentrations of various types of pollution sources obtained by monitoring are integrated into a number of test data; the artificial intelligence model is trained using the training data, and the trained artificial intelligence model is tested using the test data, and the artificial intelligence model is adjusted according to the test results; finally, a preliminary assessment model is obtained with the planned production capacity for the current time period as input and the expected pollution concentration for the current time period as output; among them, the artificial intelligence model includes a BP neural network model and an RBF neural network model.

[0032] It should be noted that the greater the actual production capacity in each time period, the greater the probability that the current pharmaceutical factory will complete its planned production capacity, which means that when other conditions remain unchanged, the pollution concentration of the pollution source generated by the factory production in each time period will be greater.

[0033] Specifically, the trained artificial intelligence model is tested using test data, and the specific steps for adjusting the artificial intelligence model based on the test results are as follows: The planned production capacity for each time period in the test data and the actual production capacity for each time period are input into the trained artificial intelligence model to obtain the corresponding pollution concentrations of various types of pollution sources. The pollution concentrations of the corresponding various types of pollution sources are compared with the pollution concentrations of various types of pollution sources monitored in the test data. When the difference in pollution concentrations of various types of pollution sources is within the corresponding threshold value, which is obtained based on experience, there is no need to adjust the parameters, and the next set of test data is tested; if there is a pollution concentration that is not within the threshold value, the corresponding parameters are adjusted until the difference in pollution concentrations of various types of pollution sources is within the corresponding threshold value, and then the next set of test data is tested. When the number of test data in which the pollution concentrations of various types of pollution sources obtained from all test data are within the threshold value accounts for 90% or more of the total test data, a preliminary assessment model is obtained with the planned production capacity for the current time period as input and the expected pollution concentration for the current time period as output.

[0034] The present invention determines the data collection frequency of the current time period based on the pollution concentration of the previous time period and the expected pollution concentration of the current time period, including: Extract several pollution concentrations recorded by various types of pollution sources in the previous time period , and the expected pollution concentration for the current time period ; Where i is the type number of the pollution source, and the value range of i is [1, n], and n is the maximum value of the pollution source type number; j is the number of several pollution concentrations of the pollution source with type number i, and the value range of j is [0, m], and m is the maximum value of the pollution concentration number; When the pollution concentration or expected pollution concentration When the concentration exceeds the corresponding threshold, the data collection frequency of the pollution source numbered i in the current time period is set to the maximum value of the corresponding standard collection range; the standard collection range is obtained through experience, and the concentration threshold of the pollution source is set by temperature and wind speed; When the pollution concentration or expected pollution concentration When there is no situation where the concentration exceeds the corresponding limit value, based on the pollution concentration or expected pollution concentration Determine the data collection frequency of the pollution source numbered i in the current time period , the data collection frequency Satisfies the following formula: ; in, is the middle value of the standard collection range corresponding to the pollution source numbered i, is the manually set standard pollution concentration corresponding to the pollution source numbered i, and max() is the maximum value symbol; and are all manually set proportional adjustment coefficients, and , ; When the data collection frequency of the pollution source numbered i is When the value is greater than the maximum value of the corresponding standard acquisition range, the maximum value is used to collect data at the frequency. Make updates; When the data collection frequency of the pollution source numbered i is When the value is less than the minimum value of the corresponding standard acquisition range, the minimum value is used to collect data at the frequency. to update.

[0035] It is worth noting that the present invention significantly improves the intelligence level and resource utilization efficiency of the environmental monitoring system by introducing an innovative mechanism for dynamically adjusting the data collection frequency based on pollution concentration. The present invention constructs a closed-loop feedback system that can perceive the pollution situation and autonomously optimize the monitoring strategy. By integrating historical pollution data with future prediction models, it realizes refined control of various pollution sources. When the real-time concentration or predicted concentration of a pollution source exceeds the preset dynamic threshold, the system will immediately start the high-frequency collection mode. This response mechanism can not only capture the instantaneous fluctuation characteristics of pollutant concentrations, but also provide high-precision data support for pollution tracing and emergency disposal. At the same time, by locking the collection frequency at the upper limit of the standard range, it effectively avoids monitoring blind spots caused by data missing. When the pollution concentration is in a safe range, the system dynamically adjusts the collection frequency based on an adaptive algorithm in the form of an exponential function based on a comprehensive evaluation of the statistical characteristics of historical data and predicted values.

[0036] It is worth noting that the present invention introduces a dual parameter adjustment coefficient and The synergistic effect not only retains the sensitivity to the historical maximum concentration but also strengthens the ability to predict future trends. and The introduction of ensures that the collection strategies among different pollution sources are comparable and scalable. This dynamic balance mechanism effectively reduces the energy consumption of equipment operation and the cost of data storage and processing while ensuring the integrity of monitoring data. Especially in complex environments with large fluctuations in pollution concentration, the system can achieve precise allocation of monitoring resources through intelligent adjustment.

[0037] It is worth noting that the present invention establishes a nonlinear mapping relationship between pollution concentration and acquisition frequency, which enables the monitoring system to have stronger environmental adaptability and long-term operation stability, and can effectively cope with uncertain factors such as changes in meteorological conditions and migration of pollution sources, and ultimately form an intelligent monitoring network that can respond to pollution events in real time and has continuous optimization capabilities. This dynamic control mode not only improves the temporal and spatial resolution and analytical value of environmental data, but also provides a reliable data foundation for building a refined environmental management decision-making system. Its application prospects cover multiple fields such as air quality monitoring, water pollution early warning, and industrial emission supervision, and it has important practical significance for promoting the development of pollution monitoring technology towards intelligence and intensiveness.

[0038] It should be noted that The maximum value of several pollution concentrations of the pollution source numbered i is taken.

[0039] It should be noted that the acquisition frequency refers to the number of times data is collected per unit time, for example: 20 times per minute.

[0040] It should be noted that the proportional adjustment coefficient and proportional adjustment coefficient It is determined based on the diffusion characteristics of the pollution source. If the half-life of the pollution source is shorter, such as volatile organic compounds, the pollution concentration is expected to be higher. Corresponding proportional adjustment coefficient The larger the proportional adjustment coefficient According to the proportional adjustment coefficient The size of the setting; for example: the proportion adjustment coefficient of toluene The value can be 0.4, the proportional adjustment coefficient The value can be 0.6; the ratio adjustment coefficient of dichloromethane The value can be 0.3, the proportional adjustment coefficient The value can be 0.7.

[0041] The concentration threshold of the pollution source in the present invention is set by temperature and wind speed, including: When the pollution source is a non-volatile pollution source, the standard concentration limit value corresponding to the current pollution source is set as the concentration limit value of the current pollution source; When the pollution source is volatile, obtain the current temperature WD and wind speed FS in the factory, and determine the concentration threshold of the current pollution source based on the temperature WD and wind speed FS. ; The concentration limit Satisfies the following formula: ; Among them, BWD is the manually set standard temperature, and BS is the manually set standard wind speed; is the standard concentration limit corresponding to the pollution source numbered i; is the amplitude adjustment coefficient of the ln() function, and The value range of is [0,1]; is the amplitude adjustment coefficient of the exp() function, and The value range is [0,2].

[0042] It is worth noting that the present invention has constructed a multi-dimensional linkage pollution discrimination system by coupling the standard concentration limit of the pollution source with the temperature and wind speed. The core value of this step is to introduce the dynamic influence of environmental variables on the law of pollution diffusion, so that the pollution severity assessment has temporal and spatial adaptability and scientific accuracy, and can more truly reflect the actual environmental risks. Specifically, when the pollution source is of volatile type, the system uses the temperature WD and wind speed FS data collected in real time, combined with the preset standard parameters BWD and BS, and uses a nonlinear combination of logarithmic functions and exponential functions to construct a concentration limit calculation model that is strongly correlated with environmental conditions. This design not only takes into account the exponential effect of temperature changes on the volatilization rate of pollutants, but also realizes the dynamic simulation of the concentration distribution of pollutants in the atmosphere through the logarithmic adjustment of the diffusion capacity by wind speed. Parameters and As an adjustment coefficient, it further gives the model the ability to adapt to the characteristics of different pollution sources, such as The value range of [0,1] can control the impact of wind speed fluctuation on the concentration threshold, and The [0,2] range allows for differentiated configurations of temperature sensitivity. This design enables the model to maintain both mathematical stability and sufficient flexibility. Through this dynamic adjustment mechanism, the present invention can effectively solve the problem of misjudgment that may occur in traditional static thresholds under complex meteorological conditions.

[0043] It should be noted that temperature and wind speed will affect the volatility of pollution sources. The higher the temperature, the faster the pollutants evaporate, which will cause more harmful substances to exist in the room in a short period of time. As the temperature increases, the concentration limit of pollutants should be reduced. This will enable early warning and reduce the safety hazards caused by the increase in volatilization speed due to rising temperature. The wind speed will have a dissipating effect on the harmful substances brought by volatilization. The greater the wind speed, the better the dissipation effect. As the wind speed increases, the concentration limit of pollutants can be increased to avoid the waste of safety resources caused by false alarms.

[0044] It should be noted that volatile pollution sources and non-volatile pollution sources are obtained through artificial selection.

[0045] It should be noted that the current temperature WD and wind speed FS in the factory can be obtained through a temperature sensor and a wind speed sensor.

[0046] It should be noted that the standard temperature BWD may be set to 25° C., and the standard wind speed BS may be set to 0.5 m / s.

[0047] It should be noted that the standard concentration limit values ​​can be exemplified as follows: the standard concentration limit value of toluene can be 100 ppm, the standard concentration limit value of dichloromethane can be 50 ppm, the standard concentration limit value of ethyl acetate can be 400 ppm, the standard concentration limit value of ammonia can be 25 ppm, and the standard concentration limit value of hydrogen sulfide can be 10 ppm.

[0048] For example, taking toluene as an example: In this embodiment, the standard temperature BWD is 25°C, the standard wind speed BS is 0.5m / s, the standard concentration limit of toluene is 100ppm, the temperature WD in the current factory is 30°C, and the wind speed FS is 0.8m / s. The toluene concentration limit is determined based on the temperature WD and the wind speed FS. ; The concentration limit Satisfies the following formula: = In this embodiment, the amplitude adjustment coefficient The value is 0.5, the amplitude adjustment coefficient The value is 1.6.

[0049] The present invention obtains the pollution concentration of each pollution source in the current time period based on the data collection frequency of the current time period, including: Extract the data collection frequency for the current time period , based on data collection frequency Collect the pollution concentration of the corresponding numbered pollution source during the current time period.

[0050] The present invention determines the alarm instructions for each pollution source based on the pollution concentration in the current time period, including: Extract the concentration thresholds of each pollution source in the current time period in sequence. When the pollution concentration of a pollution source exceeds the corresponding concentration threshold, an alarm instruction is issued. ; When the pollution concentration of no pollution source exceeds the corresponding concentration threshold, determine whether the pollution concentration of any pollution source exceeds 0.9 times the corresponding concentration threshold; if yes, issue an alarm instruction 2 ; No, issue alarm command three ; Among them, the alarm instruction includes an alarm instruction 、Alarm Command 2 and Alarm Command Three .

[0051] The present invention issues an alarm based on an alarm instruction, including: When the alarm command is alarm command 1 When the concentration of pollutant numbered i exceeds the standard, an alarm signal is sent to staff and management personnel through broadcasting and text messages, and the alarm light is set to red; When the alarm command is alarm command 2 When the concentration of pollutant numbered i is about to exceed the standard, an alarm signal is sent to staff and management personnel through broadcasting and text messages, and the alarm light is set to yellow; When the alarm command is alarm command three No operation is performed when

[0052] See also Figure 3 , the second embodiment of the present invention provides a pollution source dynamic collection and monitoring system, including: a concentration preset module, and a frequency setting module, a collection and monitoring module and a database connected to the concentration preset module; Frequency setting module: used to divide time periods, obtain planned production capacity and types of pollution sources when the current time period arrives, as well as the pollution concentration of various types of pollution sources in the previous time period; Concentration preset module: used to establish a pre-assessment model, input the planned production capacity of the current time period into the pre-assessment model to obtain the expected pollution concentration of the current time period; the pre-assessment model is obtained through artificial intelligence model training; Data collection and monitoring module: used to determine the data collection frequency of the current time period based on the pollution concentration of the previous time period and the expected pollution concentration of the current time period; obtain the pollution concentration of each pollution source in the current time period based on the data collection frequency of the current time period; determine the alarm instructions for each pollution source based on the pollution concentration in the current time period, and issue an alarm based on the alarm instruction; The database is used for storing data.

[0053] Part of data in the above formula is calculated by removing dimension, and the formula is obtained by software simulation of a large amount of collected data to be closest to the real situation; the preset parameters and the preset threshold in the formula are set by the person skilled in the art according to the actual situation or obtained by a large amount of data simulation.

[0054] Working principle of the present application: For dividing time periods, obtaining the planned capacity and the types of pollution sources in the current time period when reaching the current time period, and the pollution concentrations of various types of pollution sources in the last time period; establishing a pre-evaluation model, inputting the planned capacity of the current time period into the pre-evaluation model to obtain the predicted pollution concentration in the current time period; determining the data acquisition frequency of the current time period according to the pollution concentration of the last time period and the predicted pollution concentration of the current time period; obtaining the pollution concentrations of various pollution sources in the current time period based on the data acquisition frequency of the current time period; determining the alarm instruction of each pollution source based on the pollution concentration in the current time period, and issuing an alarm based on the alarm instruction.

[0055] The above examples are only used to illustrate the technical method of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical method of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.

Claims

1. A pollution source dynamic collection and monitoring method and system, characterized in that: include: Divide the time period and obtain the planned production capacity and types of pollution sources when the current time period arrives, as well as the pollution concentration of various types of pollution sources in the previous time period; Establish a pre-assessment model, input the planned production capacity of the current time period into the pre-assessment model to obtain the expected pollution concentration of the current time period; wherein the pre-assessment model is obtained through artificial intelligence model training; Determine the data collection frequency for the current time period based on the pollution concentration in the previous time period and the expected pollution concentration in the current time period; Obtain the pollution concentration of each pollution source in the current time period based on the data collection frequency of the current time period; The alarm instructions for each pollution source are determined based on the pollution concentration in the current time period, and an alarm is issued based on the alarm instructions.

2. A pollution source dynamic collection and monitoring method and system according to claim 1, characterized in that: The time period division includes: When the target time point of each day is reached, the working hours and non-working hours of the pharmaceutical factory on the corresponding date of the current day are obtained from the database, and the non-working hours are planned into several time periods according to the standard time length ZC; Obtain the number of alarms (BC) in the previous n days, determine the flexible working hours (TC) based on the formula TC = ZC / exp(α × BC / ZBC), and plan working hours into several time periods based on the flexible working hours (TC). ZBC is the standard number of alarms, and α is the amplitude adjustment coefficient, which ranges from 0 to 1.

3. A pollution source dynamic collection and monitoring method and system according to claim 1, characterized in that: The acquisition of planned production capacity and types of pollution sources, as well as the pollution concentrations of various types of pollution sources in the previous time period, includes: The planned production capacity of the current pharmaceutical factory in the current time period and the types of monitored pollution sources are extracted from the database, and several pollution concentrations recorded for various types of pollution sources in the previous time period are extracted in turn; among them, the types of pollution sources include antibiotics, hormones, heavy metals, acids and alkalis, and nitrogen present in wastewater, and toluene, dichloromethane, ethyl acetate, ammonia, and hydrogen sulfide present in exhaust gas.

4. A pollution source dynamic collection and monitoring method and system according to claim 1, characterized in that: The pre-assessment model is obtained through artificial intelligence model training, including: Extract the planned production capacity for each time period from historical data, as well as the actual production capacity for each time period and the pollution concentrations of various types of pollution sources obtained through monitoring; Integrate the planned capacity and actual capacity of each time period into several sets of training data; The planned production capacity for each time period, the actual production capacity for each time period and the pollution concentrations of various types of pollution sources obtained by monitoring are integrated into a number of test data; the artificial intelligence model is trained using the training data, and the trained artificial intelligence model is tested using the test data, and the artificial intelligence model is adjusted according to the test results; finally, a preliminary assessment model is obtained with the planned production capacity for the current time period as input and the expected pollution concentration for the current time period as output; among them, the artificial intelligence model includes a BP neural network model and an RBF neural network model.

5. A pollution source dynamic collection and monitoring method and system according to claim 1, characterized in that: The step of determining the data collection frequency for the current time period based on the pollution concentration for the previous time period and the estimated pollution concentration for the current time period includes: Extract several pollution concentrations recorded by various types of pollution sources in the previous time period , and the expected pollution concentration for the current time period ; Where i is the type number of the pollution source, and the value range of i is [1, n], and n is the maximum value of the pollution source type number; j is the number of several pollution concentrations of the pollution source with type number i, and the value range of j is [0, m], and m is the maximum value of the pollution concentration number; When the pollution concentration or the expected pollution concentration When the concentration exceeds the corresponding threshold, the data collection frequency of the pollution source numbered i in the current time period is set to the maximum value of the corresponding standard collection range; When the pollution concentration or the expected pollution concentration When there is no situation where the concentration exceeds the corresponding limit value, based on the pollution concentration or the expected pollution concentration Determine the data collection frequency of the pollution source numbered i in the current time period , the data collection frequency Satisfies the following formula: ； in, is the middle value of the standard collection range corresponding to the pollution source numbered i, is the standard pollution concentration corresponding to the pollution source numbered i, and max() is the maximum value symbol; and are proportional adjustment coefficients, and , ; When the data collection frequency of the pollution source numbered i is When the value is greater than the maximum value of the corresponding standard acquisition range, the data acquisition frequency is adjusted using the maximum value. Make updates; When the data collection frequency of the pollution source numbered i is When the frequency is less than the minimum value of the corresponding standard acquisition range, the data acquisition frequency is adjusted according to the minimum value. to update.

6. A pollution source dynamic collection and monitoring method and system according to claim 5, characterized in that: The concentration thresholds for the pollution sources are set by temperature and wind speed, including: When the pollution source is a non-volatile pollution source, the standard concentration limit value corresponding to the current pollution source is set as the concentration limit value of the current pollution source; When the pollution source is a volatile pollution source, obtain the current temperature WD and wind speed FS in the factory, and determine the concentration threshold of the current pollution source based on the temperature WD and the wind speed FS. ; The concentration limit Satisfies the following formula: ； Among them, BWD is the standard temperature, BS is the standard wind speed; is the standard concentration limit corresponding to the pollution source numbered i; is the amplitude adjustment coefficient of the ln() function, and The value range of is [0,1]; is the amplitude adjustment coefficient of the exp() function, and The value range is [0,2].

7. A pollution source dynamic collection and monitoring method and system according to claim 1, characterized in that: The obtaining of the pollution concentration of each pollution source in the current time period based on the data collection frequency of the current time period includes: Extract the data collection frequency for the current time period , based on the data collection frequency Collect the pollution concentration of the corresponding numbered pollution source during the current time period.

8. A pollution source dynamic collection and monitoring method and system according to claim 5, characterized in that: The alarm instructions for each pollution source determined based on the pollution concentration in the current time period include: Extract the concentration thresholds of each pollution source in the current time period in sequence. When the pollution concentration of a pollution source exceeds the corresponding concentration threshold, an alarm instruction is issued. ; When the pollution concentration of no pollution source exceeds the corresponding concentration threshold, determine whether the pollution concentration of any pollution source exceeds 0.9 times the corresponding concentration threshold; if yes, issue an alarm instruction 2 ; No, issue alarm command three ; Among them, the alarm instruction includes an alarm instruction 、Alarm Command 2 and Alarm Command Three .

9. A pollution source dynamic collection and monitoring method and system according to claim 8, characterized in that: The issuing of an alarm based on the alarm instruction comprises: When the alarm command is alarm command 1 When the concentration of pollutant numbered i exceeds the standard, an alarm signal is sent to staff and management personnel through broadcasting and text messages, and the alarm light is set to red; When the alarm command is alarm command 2 When the concentration of pollutant numbered i is about to exceed the standard, an alarm signal is sent to staff and management personnel through broadcasting and text messages, and the alarm light is set to yellow; When the alarm command is alarm command three No operation is performed when 10. A pollution source dynamic collection and monitoring system, used to run a pollution source dynamic collection and monitoring method according to any one of claims 1 to 9, characterized in that: include: A concentration preset module, and a frequency setting module and an acquisition monitoring module connected to the concentration preset module; The frequency setting module is used to divide the time period and obtain the planned production capacity and the type of pollution source when the current time period arrives, as well as the pollution concentration of various types of pollution sources in the previous time period; The concentration preset module is used to establish a pre-assessment model, input the planned production capacity of the current time period into the pre-assessment model to obtain the expected pollution concentration of the current time period; wherein the pre-assessment model is obtained by training an artificial intelligence model; The data collection and monitoring module is used to determine the data collection frequency of the current time period based on the pollution concentration of the previous time period and the expected pollution concentration of the current time period; obtain the pollution concentration of each pollution source in the current time period based on the data collection frequency of the current time period; determine the alarm instructions of each pollution source based on the pollution concentration in the current time period, and issue an alarm based on the alarm instruction.

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