Target resource processing method and system based on artificial intelligence

Abstract

The invention relates to the technical field of target resource treatment, in particular to a target resource treatment method and system based on artificial intelligence, and the method comprises the steps: collecting the waste gas concentrations of an inlet and an outlet of waste gas purification equipment connected with a cracking furnace, and calculating the purification efficiency of each waste gas; based on the purification efficiency of each waste gas, a purification low-efficiency proportion is obtained, and the waste gas purification efficiency in the operation state of the cracking furnace is evaluated through the purification low-efficiency proportion; based on the purification efficiency of each waste gas, obtaining a waste gas purification performance value, and evaluating the waste gas purification performance of the cracking furnace in the running state through the waste gas purification performance value; when the high waste gas purification efficiency signal or the excellent waste gas purification performance signal is not obtained, the power of the fan unit is controlled; the power of the follow-up fan unit is regulated and controlled, so that the fan unit is reduced on the basis of the original power, and the waste gas purification efficiency is improved by reducing the waste gas conveying efficiency and prolonging the duration of the purification reaction with the waste gas purification equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of target resource processing, and in particular to a target resource processing method and system based on artificial intelligence. Background Art

[0002] The processing of target resources by petrochemical equipment is a systematic project covering raw material conversion, energy utilization, by-product recovery and waste resource utilization; Regarding the waste resource utilization system, in the existing technology, petrochemical equipment does generate a variety of waste gases during the production and processing process. If these waste gases are directly discharged without treatment, they will cause harm. During the purification process, the purification capacity and efficiency of the waste gas processed by the waste resource utilization cannot be accurately evaluated. It is even more impossible to make corresponding automated control that meets the requirements based on the evaluation results to match the efficiency and performance of the petrochemical equipment in processing the target resources. Summary of the invention

[0003] The purpose of the present invention is to provide a target resource processing method and system based on artificial intelligence. The technical problem solved by the present invention is: during the purification process, it is not possible to accurately evaluate the purification capacity and efficiency of the waste gas from waste resource treatment, and it is even more impossible to make corresponding automated control that meets the requirements based on the evaluation results.

[0004] The purpose of the present invention can be achieved through the following technical solutions: A target resource processing method based on artificial intelligence, comprising: Step 1: Collect the waste gas concentration at the inlet and outlet of the waste gas purification equipment connected to the cracking furnace, and calculate the purification efficiency of each waste gas; Step 2: based on the purification efficiency of each exhaust gas, the purification inefficiency ratio is obtained, and the exhaust gas purification efficiency under the operation state of the cracking furnace is evaluated by the purification inefficiency ratio; Step 3: based on the purification efficiency of each exhaust gas, obtain the exhaust gas purification performance value, and evaluate the exhaust gas purification performance under the operation state of the cracking furnace through the exhaust gas purification performance value; Step 4: When no exhaust gas purification efficiency high signal or exhaust gas purification performance excellent signal is obtained, the power of the fan unit is controlled.

[0005] As a further technical solution of the present invention: the calculation formula of the purification efficiency of each exhaust gas is as follows: Where i represents the type of exhaust gas, n i Expressed as the purification efficiency of the i-th exhaust gas, C1 i Expressed as the concentration of the i-th exhaust gas at the inlet of the exhaust gas purification equipment, C2 iIt is expressed as the concentration of the i-th exhaust gas at the outlet of the exhaust gas purification equipment.

[0006] As a further technical solution of the present invention: the purification inefficiency ratio is obtained by summing the inefficiency type ratio and the inefficiency time ratio.

[0007] As a further technical solution of the present invention: the process of obtaining the inefficient species ratio is as follows: Set the evaluation cycle, obtain the purification efficiency of each exhaust gas in real time, and construct the time series of each exhaust gas purification efficiency in chronological order; Obtain each exhaust gas purification efficiency time series, perform mean processing on all elements in each exhaust gas purification efficiency time series, and obtain the mean value of each exhaust gas purification efficiency; If the mean value of each exhaust gas purification efficiency is less than the exhaust gas purification efficiency threshold, the type of exhaust gas is marked as an abnormally purified exhaust gas type; The number of types of abnormally purified exhaust gas is compared with the number of types of all exhaust gas to obtain the ratio of inefficient types; The process of obtaining the inefficient duration ratio is as follows: Calculate the average of all types of exhaust gas purification efficiencies at the same time to obtain an average exhaust gas purification efficiency time series, obtain the average exhaust gas purification efficiencies at different times in the average exhaust gas purification efficiency time series, and compare the exhaust gas purification efficiency thresholds; If the average exhaust gas purification efficiency is less than the exhaust gas purification efficiency threshold, the moment is marked as an abnormal purification moment; The number of abnormal purification moments and the number of time series are obtained and the ratio is calculated to obtain the inefficient duration ratio.

[0008] As a further technical solution of the present invention: the process of evaluating the exhaust gas purification efficiency under the operation state of the cracking furnace is: If the purification inefficiency ratio is greater than or equal to the purification inefficiency ratio threshold, a low exhaust gas purification efficiency signal is generated; If the purification inefficiency ratio is less than the purification inefficiency ratio threshold, a high exhaust gas purification efficiency signal is generated.

[0009] As a further technical solution of the present invention: the exhaust gas purification performance value is obtained by calculating the sum of the first exhaust gas purification analysis value and the second exhaust gas purification analysis value.

[0010] As a further technical solution of the present invention: the specific process of obtaining the first analysis value of exhaust gas purification is as follows: Obtain each exhaust gas purification efficiency time series, perform mean processing on all elements in each exhaust gas purification efficiency time series, and obtain the mean value of each exhaust gas purification efficiency; Perform variance processing on the mean values ​​of all types of exhaust gas purification efficiencies to obtain the first analysis value of exhaust gas purification; The specific process of obtaining the second analysis value of exhaust gas purification is as follows: Obtain each exhaust gas purification efficiency time series, perform variance processing on all elements in each exhaust gas purification efficiency time series, and obtain the variance of each exhaust gas purification efficiency; The variance of each exhaust gas purification efficiency of all types is processed by averaging to obtain the second analysis value of exhaust gas purification.

[0011] As a further technical solution of the present invention: if the exhaust gas purification performance value is greater than or equal to the exhaust gas purification performance threshold, an exhaust gas purification performance difference signal is generated; If the exhaust gas purification performance value is less than the exhaust gas purification performance threshold, an exhaust gas purification performance good signal is generated.

[0012] As a further technical solution of the present invention: obtaining the mean value of the purification efficiency of each exhaust gas, extracting the exhaust gas type with the smallest mean value of the purification efficiency, marking it as the first type of analysis exhaust gas, and obtaining the exhaust gas purification efficiency time series corresponding to the first type of analysis exhaust gas; For each exhaust gas purification efficiency variance, extract the exhaust gas type with the largest exhaust gas purification efficiency variance, mark it as the second type of analysis exhaust gas, and obtain the exhaust gas purification efficiency time series corresponding to the second type of analysis exhaust gas; All elements in the exhaust gas purification efficiency time series corresponding to the first type of exhaust gas analyzed are processed by average value to obtain the first exhaust gas purification efficiency average value. All elements in the exhaust gas purification efficiency time series corresponding to the second type of exhaust gas analyzed are processed by average value to obtain the second exhaust gas purification efficiency average value. The control power of the fan is calculated by the formula. The specific formula is as follows: Among them, PT represents the control power of the fan, PY represents the pre-set power of the exhaust fan unit during the evaluation period, jn1 represents the mean efficiency of the first type of exhaust gas purification, jn2 represents the mean efficiency of the second type of exhaust gas purification, and ny represents the exhaust gas purification efficiency threshold.

[0013] A target resource processing system based on artificial intelligence, the target resource processing system comprising: Collection module: collects the exhaust gas concentration at the inlet and outlet of the exhaust gas purification equipment, and calculates the purification efficiency of each exhaust gas; Efficiency evaluation module: Based on the purification efficiency of each exhaust gas, the purification inefficiency ratio is obtained, and the exhaust gas purification efficiency under the operation state of the cracking furnace is evaluated through the purification inefficiency ratio; Performance evaluation module: based on the purification efficiency of each exhaust gas, the exhaust gas purification performance value is obtained, and the exhaust gas purification performance under the operation state of the cracking furnace is evaluated through the exhaust gas purification performance value; Control module: When no exhaust gas purification efficiency high signal or exhaust gas purification performance excellent signal is obtained, the power of the exhaust fan unit is controlled.

[0014] Beneficial effects of the present invention: (1) The present invention collects the waste gas concentration at the inlet and outlet of the waste gas purification equipment; based on the purification efficiency of each waste gas, the purification inefficiency ratio is obtained, and the waste gas purification efficiency under the laboratory operation state is evaluated by the purification inefficiency ratio; based on the purification efficiency of each waste gas, the waste gas purification performance value is obtained, and the waste gas purification performance under the operation state of the cracking furnace is evaluated by the waste gas purification performance value; the present invention analyzes and processes the waste gas purification efficiency under different types and different time conditions, so as to achieve accurate evaluation of both the waste gas purification efficiency and performance under the operation state of the cracking furnace; (2) The present invention controls the power of the fan group when a signal of low exhaust gas purification efficiency and a signal of poor exhaust gas purification performance are generated. The present invention conducts a comprehensive analysis by determining the type of the worst purification effect and the most unstable purification sequence at present. Based on the degree of purification difference between the two, the power of the subsequent fan group is adjusted to reduce the power of the fan group on the basis of the original power. By reducing the exhaust gas transmission efficiency, the purification reaction time between the exhaust gas purification equipment and the exhaust gas purification equipment is increased, thereby improving the exhaust gas purification efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 It is a flowchart of a target resource processing method based on artificial intelligence provided in Embodiment 1 of the present invention; Figure 2 This is a system block diagram of an artificial intelligence-based target resource processing method provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0018] Embodiment 1 like Figure 1As shown, an embodiment of the present invention provides a method for processing target resources based on artificial intelligence. The embodiment of the present invention can be applied to the situation where petrochemical equipment processes target resources. Specifically, petrochemical equipment will generate a variety of waste gases during the production and processing of target equipment. If these waste gases are directly discharged without treatment, they will cause harm to the environment. For example, an ethylene cracking furnace: the concentration of non-methane total hydrocarbons (NMHC) emitted is about 2000 mg / m³, containing active ingredients such as olefins and alkynes. Therefore, it is very important to use artificial intelligence technology to purify the waste gas produced by target resources; This embodiment provides a method for processing target resources based on artificial intelligence, which specifically includes the following steps: Step 1: Collect the waste gas concentration at the inlet and outlet of the waste gas purification equipment connected to the cracking furnace, and calculate the purification efficiency of each waste gas; Among them, the exhaust gas concentration includes the concentration of olefins, alkynes, etc.; In some embodiments, a fan unit is provided in the cracking furnace to send the waste gas generated during the operation of the cracking furnace to the waste gas purification equipment, and a corresponding gas detector is provided at the inlet and outlet of the waste gas purification equipment to collect the type and concentration of the waste gas before and after purification; The purification efficiency of each exhaust gas is calculated through the formula. The specific formula is as follows: Where i represents the type of exhaust gas, n i Expressed as the purification efficiency of the i-th exhaust gas, C1 i Expressed as the concentration of the i-th exhaust gas at the inlet of the exhaust gas purification equipment, C2 i It is expressed as the concentration of the i-th exhaust gas at the outlet of the exhaust gas purification equipment; Exemplarily, a corresponding gas detector is provided at the inlet and outlet of the exhaust gas purification equipment to collect the concentration of olefins before and after purification; According to the above purification efficiency n i The calculation formula of olefin purification efficiency is obtained; Step 2: based on the purification efficiency of each exhaust gas, the purification inefficiency ratio is obtained, and the exhaust gas purification efficiency under the operation state of the cracking furnace is evaluated by the purification inefficiency ratio; In some implementation schemes, an evaluation period is set. The duration of the evaluation period can be set by those skilled in the art according to actual needs. The evaluation period can be 5 minutes, 10 minutes, etc.; Obtain the purification efficiency of each exhaust gas in real time, and construct the time series of each exhaust gas purification efficiency in chronological order (n i1 , n i2 , ..., n ij ), j represents the time series, n ij It represents the purification efficiency of the i-th exhaust gas at the j-th moment; Obtain each exhaust gas purification efficiency time series, perform mean processing on all elements in each exhaust gas purification efficiency time series, obtain the mean of each exhaust gas purification efficiency, and compare it with the exhaust gas purification efficiency threshold; If the mean value of each type of exhaust gas purification efficiency is greater than or equal to the exhaust gas purification efficiency threshold, it means that the exhaust gas of this type meets the process requirements after purification, and the type of exhaust gas is marked as a normal purified exhaust gas type; If the mean value of each type of exhaust gas purification efficiency is less than the exhaust gas purification efficiency threshold, it means that the exhaust gas of this type does not meet the process requirements after purification, and the type of exhaust gas is marked as abnormal purification exhaust gas type; It should be added that the exhaust gas purification efficiency threshold refers to the minimum treatment efficiency that needs to be achieved, and the exhaust gas purification efficiency threshold can be set by the technicians according to the industry standards; The number of types of abnormally purified exhaust gas is compared with the number of types of all exhaust gas to obtain the ratio of inefficient types; Get the time series of each exhaust gas purification efficiency (n i1 , n i2 , ..., n ij ), calculate the average of all types of exhaust gas purification efficiencies at the same time, obtain an average exhaust gas purification efficiency time series, obtain the average exhaust gas purification efficiencies at different times in the average exhaust gas purification efficiency time series, and compare the exhaust gas purification efficiency thresholds; If the average exhaust gas purification efficiency is greater than or equal to the exhaust gas purification efficiency threshold, it means that the exhaust gas at that moment meets the process requirements after purification, and the moment is marked as a normal purification moment; If the average exhaust gas purification efficiency is less than the exhaust gas purification efficiency threshold, it means that the exhaust gas after purification at that moment does not meet the process requirements, and the moment is marked as an abnormal purification moment; The number of abnormal purification moments and the number of time series are obtained and the ratio is calculated to obtain the inefficient duration ratio; The inefficient type ratio and the inefficient duration ratio are summed to obtain the purification inefficiency ratio. When the purification inefficiency ratio is obtained, the purification inefficiency ratio is compared with the purification inefficiency ratio threshold; If the purification inefficiency ratio is greater than or equal to the purification inefficiency ratio threshold, it means that the exhaust gas purification efficiency is low under the operation state of the cracking furnace, and a low exhaust gas purification efficiency signal is generated; If the proportion of low purification efficiency is less than the threshold of low purification efficiency, it means that the exhaust gas purification efficiency is relatively high under the operation state of the cracking furnace, and a high exhaust gas purification efficiency signal is generated; It should be explained that the purification inefficiency ratio reflects the purification inefficiency ratio obtained by analyzing the exhaust gas purification equipment from the perspective of type and time during the evaluation period. The purification inefficiency ratio can determine the current purification efficiency more comprehensively. Step 3: based on the purification efficiency of each exhaust gas, obtain the exhaust gas purification performance value, and evaluate the exhaust gas purification performance under the operation state of the cracking furnace through the exhaust gas purification performance value; In some implementation schemes, an evaluation period is set, and adaptability and stability analysis is performed according to each exhaust gas purification efficiency time series analysis to obtain a first exhaust gas purification analysis value and a second exhaust gas purification analysis value; The first exhaust gas purification analysis value and the second exhaust gas purification analysis value are summed to obtain an exhaust gas purification performance value; First, specifically, the specific process of obtaining the first analysis value of exhaust gas purification is as follows: Obtain each exhaust gas purification efficiency time series, perform mean processing on all elements in each exhaust gas purification efficiency time series, and obtain the mean value of each exhaust gas purification efficiency; Perform variance processing on the mean values ​​of all types of exhaust gas purification efficiencies to obtain the first analysis value of exhaust gas purification; The first exhaust gas purification analysis value is used to evaluate whether there is a large difference in the purification efficiency of all types of exhaust gases provided by the exhaust gas purification equipment; Second, specifically, the specific process of obtaining the second analysis value of exhaust gas purification is as follows: Obtain each exhaust gas purification efficiency time series, perform variance processing on all elements in each exhaust gas purification efficiency time series, and obtain the variance of each exhaust gas purification efficiency; The variance of each exhaust gas purification efficiency of all types is processed by averaging to obtain the second analysis value of exhaust gas purification; The second exhaust gas purification analysis value is used to evaluate whether the exhaust gas purification of the installed exhaust gas purification equipment has a large fluctuation during the evaluation period; obtaining an exhaust gas purification performance value, and comparing the exhaust gas purification performance value with an exhaust gas purification performance threshold; If the exhaust gas purification performance value is greater than or equal to the exhaust gas purification performance threshold, an exhaust gas purification performance poor signal is generated; If the exhaust gas purification performance value is less than the exhaust gas purification performance threshold, an exhaust gas purification performance good signal is generated; It should be explained that: the signal of poor exhaust gas purification performance indicates that during the evaluation period, the efficiency and performance of the exhaust gas purification equipment installed in purifying the exhaust gas generated during the operation of the cracking furnace are not stable and comprehensive enough; the signal of excellent exhaust gas purification performance indicates that during the evaluation period, the efficiency and performance of the exhaust gas purification equipment installed in purifying the exhaust gas generated during the operation of the cracking furnace are stable and comprehensive; The technical solution of the embodiment of the present invention is as follows: the exhaust gas concentration at the inlet and outlet of the exhaust gas purification equipment is collected; based on the purification efficiency of each exhaust gas, the purification inefficiency ratio is obtained, and the exhaust gas purification efficiency under the laboratory operation state is evaluated through the purification inefficiency ratio; based on the purification efficiency of each exhaust gas, the exhaust gas purification performance value is obtained, and the exhaust gas purification performance under the operation state of the cracking furnace is evaluated through the exhaust gas purification performance value; the present invention realizes accurate evaluation of both the exhaust gas purification efficiency and performance under the operation state of the cracking furnace by analyzing and processing the exhaust gas purification efficiency of different types and under different time conditions.

[0019] Embodiment 2 like Figure 1 As shown, an artificial intelligence-based target resource processing method provided by an embodiment of the present invention further includes the following steps: Step 4: When no exhaust gas purification efficiency high signal or exhaust gas purification performance excellent signal is obtained, the power of the fan unit is controlled; In some embodiments, when no exhaust gas purification efficiency high signal or exhaust gas purification performance excellent signal is obtained; the mean value of each exhaust gas purification efficiency is obtained, the exhaust gas type with the smallest mean purification efficiency is extracted, and it is marked as the first type of analysis exhaust gas, and the exhaust gas purification efficiency time series corresponding to the first type of analysis exhaust gas is obtained; For each exhaust gas purification efficiency variance, extract the exhaust gas type with the largest exhaust gas purification efficiency variance, mark it as the second type of analysis exhaust gas, and obtain the exhaust gas purification efficiency time series corresponding to the second type of analysis exhaust gas; All elements in the exhaust gas purification efficiency time series corresponding to the first type of exhaust gas analyzed are processed by average value to obtain the first exhaust gas purification efficiency average value. All elements in the exhaust gas purification efficiency time series corresponding to the second type of exhaust gas analyzed are processed by average value to obtain the second exhaust gas purification efficiency average value. The control power of the fan unit is calculated by the formula. The specific formula is as follows: Among them, PT represents the control power of the fan group, PY represents the pre-set power of the fan group during the evaluation period, jn1 represents the mean value of the first type of exhaust gas purification efficiency, jn2 represents the mean value of the second type of exhaust gas purification efficiency, and ny represents the exhaust gas purification efficiency threshold; The technical solution of the embodiment of the present invention: when there is a signal of low exhaust gas purification efficiency and a signal of poor exhaust gas purification performance, the power of the fan group is controlled. The present invention conducts a comprehensive analysis by determining the type with the worst current purification effect and the most unstable purification sequence, and controls the power of subsequent fan groups based on the degree of difference in purification between the two, so that the power of the fan group is reduced on the basis of the original power. By reducing the exhaust gas transmission efficiency, the purification reaction time between the exhaust gas purification equipment and the exhaust gas purification equipment is increased, thereby improving the exhaust gas purification efficiency.

[0020] Embodiment 3 like Figure 2 As shown, an artificial intelligence-based target resource processing system provided by an embodiment of the present invention includes the following modules: Collection module: collects the exhaust gas concentration at the inlet and outlet of the cracking furnace and calculates the purification efficiency of each exhaust gas; In this embodiment, an exhaust fan unit is provided in the cracking furnace to send the waste gas generated during the operation of the cracking furnace to the waste gas purification device. A corresponding gas detector is provided at the inlet and outlet of the waste gas purification device to collect the type and concentration of the waste gas before and after purification. Efficiency evaluation module: Based on the purification efficiency of each exhaust gas, the purification inefficiency ratio is obtained, and the exhaust gas purification efficiency under the operation state of the cracking furnace is evaluated through the purification inefficiency ratio; In this embodiment, an evaluation cycle is set to obtain the purification efficiency of each exhaust gas in real time, and a time series of the purification efficiency of each exhaust gas is constructed in chronological order (n i1 , n i2 , ..., n ij ), j represents the time series, n ij It represents the purification efficiency of the i-th exhaust gas at the j-th moment; Obtain each exhaust gas purification efficiency time series, perform mean processing on all elements in each exhaust gas purification efficiency time series, obtain the mean of each exhaust gas purification efficiency, and compare it with the exhaust gas purification efficiency threshold; If the mean value of each type of exhaust gas purification efficiency is greater than or equal to the exhaust gas purification efficiency threshold, it means that the exhaust gas of this type meets the process requirements after purification, and the type of exhaust gas is marked as a normal purified exhaust gas type; If the mean value of each type of exhaust gas purification efficiency is less than the exhaust gas purification efficiency threshold, it means that the exhaust gas of this type does not meet the process requirements after purification, and the type of exhaust gas is marked as abnormal purification exhaust gas type; The number of types of abnormally purified exhaust gas is compared with the number of types of all exhaust gas to obtain the ratio of inefficient types; Get the time series of each exhaust gas purification efficiency (n i1 , n i2 , ..., n ij ), calculate the average of all types of exhaust gas purification efficiencies at the same time, obtain an average exhaust gas purification efficiency time series, obtain the average exhaust gas purification efficiencies at different times in the average exhaust gas purification efficiency time series, and compare the exhaust gas purification efficiency thresholds; If the average exhaust gas purification efficiency is greater than or equal to the exhaust gas purification efficiency threshold, it means that the exhaust gas at that moment meets the process requirements after purification, and the moment is marked as a normal purification moment; If the average exhaust gas purification efficiency is less than the exhaust gas purification efficiency threshold, it means that the exhaust gas after purification at that moment does not meet the process requirements, and the moment is marked as an abnormal purification moment; The number of abnormal purification moments and the number of time series are obtained and the ratio is calculated to obtain the inefficient duration ratio; The inefficient type ratio and the inefficient duration ratio are summed to obtain the purification inefficiency ratio. When the purification inefficiency ratio is obtained, the purification inefficiency ratio is compared with the purification inefficiency ratio threshold; If the purification inefficiency ratio is greater than or equal to the purification inefficiency ratio threshold, it means that the exhaust gas purification efficiency is low under the laboratory operation state, and a low exhaust gas purification efficiency signal is generated; If the proportion of low-efficiency purification is less than the threshold of low-efficiency purification, it means that the exhaust gas purification efficiency is relatively high under the laboratory operation state, and a high exhaust gas purification efficiency signal is generated; Performance evaluation module: Based on the purification efficiency of each exhaust gas, the exhaust gas purification performance value is obtained, and the exhaust gas purification performance under the laboratory operation state is evaluated through the exhaust gas purification performance value; In this embodiment, an evaluation period is set, and adaptability and stability analysis is performed according to each exhaust gas purification efficiency time series analysis to obtain the first exhaust gas purification analysis value and the second exhaust gas purification analysis value; The first exhaust gas purification analysis value and the second exhaust gas purification analysis value are summed to obtain an exhaust gas purification performance value; First, specifically, the specific process of obtaining the first analysis value of exhaust gas purification is as follows: Obtain each exhaust gas purification efficiency time series, perform mean processing on all elements in each exhaust gas purification efficiency time series, and obtain the mean value of each exhaust gas purification efficiency; Perform variance processing on the mean values ​​of all types of exhaust gas purification efficiencies to obtain the first analysis value of exhaust gas purification; Second, specifically, the specific process of obtaining the second analysis value of exhaust gas purification is as follows: Obtain each exhaust gas purification efficiency time series, perform variance processing on all elements in each exhaust gas purification efficiency time series, and obtain the variance of each exhaust gas purification efficiency; The variance of each exhaust gas purification efficiency of all types is processed by averaging to obtain the second analysis value of exhaust gas purification; obtaining an exhaust gas purification performance value, and comparing the exhaust gas purification performance value with an exhaust gas purification performance threshold; If the exhaust gas purification performance value is greater than or equal to the exhaust gas purification performance threshold, an exhaust gas purification performance poor signal is generated; If the exhaust gas purification performance value is less than the exhaust gas purification performance threshold, an exhaust gas purification performance good signal is generated; Control module: When no signal of high exhaust gas purification efficiency or excellent exhaust gas purification performance is obtained, the power of the fan unit is controlled; In this embodiment, when no exhaust gas purification efficiency high signal or exhaust gas purification performance excellent signal is obtained; the mean value of each exhaust gas purification efficiency is obtained, the exhaust gas type with the smallest mean purification efficiency is extracted, and it is marked as the first type of analysis exhaust gas, and the exhaust gas purification efficiency time series corresponding to the first type of analysis exhaust gas is obtained; For each exhaust gas purification efficiency variance, extract the exhaust gas type with the largest exhaust gas purification efficiency variance, mark it as the second type of analysis exhaust gas, and obtain the exhaust gas purification efficiency time series corresponding to the second type of analysis exhaust gas; All elements in the exhaust gas purification efficiency time series corresponding to the first type of exhaust gas analyzed are processed by average value to obtain the first exhaust gas purification efficiency average value. All elements in the exhaust gas purification efficiency time series corresponding to the second type of exhaust gas analyzed are processed by average value to obtain the second exhaust gas purification efficiency average value. The control power of the fan unit is calculated by the formula. The specific formula is as follows: Among them, PT represents the control power of the fan group, PY represents the pre-set power of the fan group during the evaluation period, jn1 represents the mean efficiency of the first type of exhaust gas purification, jn2 represents the mean efficiency of the second type of exhaust gas purification, and ny represents the exhaust gas purification efficiency threshold.

[0021] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A target resource processing method based on artificial intelligence, characterized in that: include: Step 1: Collect the waste gas concentration at the inlet and outlet of the waste gas purification equipment connected to the cracking furnace, and calculate the purification efficiency of each waste gas; Step 2: based on the purification efficiency of each exhaust gas, the purification inefficiency ratio is obtained, and the exhaust gas purification efficiency under the operation state of the cracking furnace is evaluated by the purification inefficiency ratio; Step 3: based on the purification efficiency of each exhaust gas, obtain the exhaust gas purification performance value, and evaluate the exhaust gas purification performance under the operation state of the cracking furnace through the exhaust gas purification performance value; Step 4: When no exhaust gas purification efficiency high signal or exhaust gas purification performance excellent signal is obtained, the power of the fan unit is controlled.

2. According to the method of claim 1, the method is characterized in that: The calculation formula for the purification efficiency of each exhaust gas is as follows: Where i represents the type of exhaust gas, n i Expressed as the purification efficiency of the i-th exhaust gas, C1 i Expressed as the concentration of the i-th exhaust gas at the inlet of the exhaust gas purification equipment, C2 i It is expressed as the concentration of the i-th exhaust gas at the outlet of the exhaust gas purification equipment.

3. The method for processing target resources based on artificial intelligence according to claim 1, characterized in that: The proportion of inefficient purification is obtained by summing the ratio of inefficient types and the ratio of inefficient duration.

4. The method for processing target resources based on artificial intelligence according to claim 3 is characterized in that: The process of obtaining the inefficient species ratio is as follows: Set the evaluation cycle, obtain the purification efficiency of each exhaust gas in real time, and construct the time series of each exhaust gas purification efficiency in chronological order; Obtain each exhaust gas purification efficiency time series, perform mean processing on all elements in each exhaust gas purification efficiency time series, and obtain the mean value of each exhaust gas purification efficiency; If the mean value of each exhaust gas purification efficiency is less than the exhaust gas purification efficiency threshold, the type of exhaust gas is marked as an abnormally purified exhaust gas type; The number of types of abnormally purified exhaust gas is compared with the number of types of all exhaust gas to obtain the ratio of inefficient types; The process of obtaining the inefficient duration ratio is as follows: Calculate the average of all types of exhaust gas purification efficiencies at the same time to obtain an average exhaust gas purification efficiency time series, obtain the average exhaust gas purification efficiencies at different times in the average exhaust gas purification efficiency time series, and compare the exhaust gas purification efficiency thresholds; If the average exhaust gas purification efficiency is less than the exhaust gas purification efficiency threshold, the moment is marked as an abnormal purification moment; The number of abnormal purification moments and the number of time series are obtained and the ratio is calculated to obtain the inefficient duration ratio.

5. The method for processing target resources based on artificial intelligence according to claim 1, characterized in that: The process of evaluating the exhaust gas purification efficiency under the operation state of the cracking furnace is as follows: If the purification inefficiency ratio is greater than or equal to the purification inefficiency ratio threshold, a low exhaust gas purification efficiency signal is generated; If the purification inefficiency ratio is less than the purification inefficiency ratio threshold, a high exhaust gas purification efficiency signal is generated.

6. The method for processing target resources based on artificial intelligence according to claim 1, characterized in that: The exhaust gas purification performance value is obtained by calculating the sum of the exhaust gas purification first analysis value and the exhaust gas purification second analysis value.

7. The method for processing target resources based on artificial intelligence according to claim 6 is characterized in that: The specific process of obtaining the first analysis value of exhaust gas purification is as follows: Obtain each exhaust gas purification efficiency time series, perform mean processing on all elements in each exhaust gas purification efficiency time series, and obtain the mean value of each exhaust gas purification efficiency; Perform variance processing on the mean values ​​of all types of exhaust gas purification efficiencies to obtain the first analysis value of exhaust gas purification; The specific process of obtaining the second analysis value of exhaust gas purification is as follows: Obtain each exhaust gas purification efficiency time series, perform variance processing on all elements in each exhaust gas purification efficiency time series, and obtain the variance of each exhaust gas purification efficiency; The variance of each exhaust gas purification efficiency of all types is processed by averaging to obtain the second analysis value of exhaust gas purification.

8. The method for processing target resources based on artificial intelligence according to claim 1, characterized in that: If the exhaust gas purification performance value is greater than or equal to the exhaust gas purification performance threshold, an exhaust gas purification performance poor signal is generated; If the exhaust gas purification performance value is less than the exhaust gas purification performance threshold, an exhaust gas purification performance good signal is generated.

9. The method for processing target resources based on artificial intelligence according to claim 1, characterized in that: Obtain the mean value of the purification efficiency of each exhaust gas, extract the exhaust gas type with the smallest mean value of the purification efficiency, mark it as the first type of analysis exhaust gas, and obtain the exhaust gas purification efficiency time series corresponding to the first type of analysis exhaust gas; For each exhaust gas purification efficiency variance, extract the exhaust gas type with the largest exhaust gas purification efficiency variance, mark it as the second type of analysis exhaust gas, and obtain the exhaust gas purification efficiency time series corresponding to the second type of analysis exhaust gas; All elements in the exhaust gas purification efficiency time series corresponding to the first type of exhaust gas analyzed are processed by average value to obtain the first exhaust gas purification efficiency average value. All elements in the exhaust gas purification efficiency time series corresponding to the second type of exhaust gas analyzed are processed by average value to obtain the second exhaust gas purification efficiency average value. The control power of the fan is calculated by the formula. The specific formula is as follows: Among them, PT represents the control power of the fan, PY represents the pre-set power of the exhaust fan unit during the evaluation period, jn1 represents the mean efficiency of the first type of exhaust gas purification, jn2 represents the mean efficiency of the second type of exhaust gas purification, and ny represents the exhaust gas purification efficiency threshold.

10. A target resource processing system based on artificial intelligence, characterized in that: The system is used to execute the method described in any one of claims 1 to 9 above, and the target resource processing system includes: Collection module: collects the exhaust gas concentration at the inlet and outlet of the exhaust gas purification equipment and calculates the purification efficiency of each exhaust gas; Efficiency evaluation module: Based on the purification efficiency of each exhaust gas, the purification inefficiency ratio is obtained, and the exhaust gas purification efficiency under the operation state of the cracking furnace is evaluated through the purification inefficiency ratio; Performance evaluation module: based on the purification efficiency of each exhaust gas, the exhaust gas purification performance value is obtained, and the exhaust gas purification performance under the operation state of the cracking furnace is evaluated through the exhaust gas purification performance value; Control module: When no exhaust gas purification efficiency high signal or exhaust gas purification performance excellent signal is obtained, the power of the exhaust fan unit is controlled.

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