Industrial gas sulfur content detection method and device based on big data

Abstract

The invention relates to the technical field of industrial gas sulfur content detection, and particularly discloses an industrial gas sulfur content detection method and device based on big data, and the method comprises the steps of actual sulfur content confirmation, sulfur content abnormity analysis, data acquisition frequency confirmation and production stage problem feedback. According to the method, the sulfur content of the industrial gas is acquired by utilizing various types of sensors, the data acquisition frequency corresponding to each monitoring time period is determined in combination with the actual sulfur content corresponding to each monitoring time period in each production stage in a target factory on each historical monitoring day, and the aging information corresponding to each production device is combined to determine the sulfur content of the industrial gas. Whether the sulfur content correspondingly generated in each production stage has a problem is evaluated, all possible ranges of the sulfur content in the industrial gas are covered as far as possible, data accuracy limitation is avoided, the reliability of industrial gas sulfur content collection is improved, waste of a large amount of manpower, material resources and time resources is avoided, and the economic benefit is increased. And an enterprise is helped to make correct production decisions and quality control measures.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial gas sulfur content detection, and relates to an industrial gas sulfur content detection method and device based on big data. Background Art

[0002] In industrial gas detection, accurate measurement of sulfur content is of great significance for ensuring product quality, environmental protection and safe production. Traditional detection methods often rely on expensive analytical instruments and are complicated to operate, making it difficult to meet the needs of modern industry for efficient, accurate and real-time monitoring. Therefore, the development of an industrial gas sulfur content detection system based on big data has broad application prospects and important practical value.

[0003] For example, the Chinese patent publication number CN114236036A discloses a detection system and method for sulfur content in industrial coal gas, including: a combustion chamber for combustion treatment of industrial coal gas and air; a detection device for detecting the content of sulfur dioxide and oxygen in the residual gas after combustion in the combustion chamber, the detection device being connected to the combustion chamber; a gas pipeline for treating the industrial coal gas and transporting it to the combustion chamber, the gas pipeline being connected to the combustion chamber; an air pipeline for treating the air and transporting it to the combustion chamber, the air pipeline being connected to the combustion chamber. The industrial coal gas combustion is directly connected to the detection device for use, and the organic sulfur and inorganic sulfur that are difficult to detect and are easily absorbed in the industrial coal gas are all converted into sulfur dioxide, and then the detection is performed, so as to directly, continuously, stably, accurately and in real time detect the change of total sulfur in the coal gas.

[0004] The above existing technologies still have the following problems: 1. Failure to use multiple types of sensors to collect the sulfur content of industrial gases may not be able to fully cover all possible ranges of sulfur content in industrial gases, resulting in limited data accuracy and reduced reliability of industrial gas sulfur content collection.

[0005] 2. The data collection frequency corresponding to the monitoring period is not confirmed in combination with the abnormal sulfur content corresponding to the monitoring period. If the data collection frequency is too high during the period when the sulfur content is stable or changes little, it will waste a lot of manpower, material and time resources. On the contrary, if the data collection frequency is too low during the period when the sulfur content changes greatly, it may form a monitoring blind spot, resulting in the inability to timely discover and deal with potential environmental problems.

[0006] 3. The impact of the aging information of production equipment during the production stage on the abnormal sulfur content analysis during the production stage was not analyzed, which reduced the accuracy of the abnormal sulfur content analysis during the production stage. Based on inaccurate sulfur content analysis results, the company may make wrong production decisions and quality control measures. At the same time, the aging of production equipment may also increase safety hazards and risks. Summary of the invention

[0007] In view of this, in order to solve the problems raised in the above background technology, a method and device for detecting sulfur content in industrial gas based on big data are proposed.

[0008] The purpose of the present invention can be achieved through the following technical solutions: The first aspect of the present invention provides a method for detecting the sulfur content of industrial gases based on big data, including: S1. Confirmation of actual sulfur content: by using various sensors to collect the sulfur content of industrial gases generated in each production stage in each monitoring time period in the target factory on the current monitoring day, and at the same time collect the temperature, pressure and humidity of the industrial gases corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day, confirm the actual sulfur content corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day.

[0009] S2. Analysis of abnormal sulfur content: Confirm the actual sulfur content of each production stage in the target factory during each monitoring period on each historical monitoring day in the same way as the actual sulfur content of each production stage in the target factory during each monitoring period on the current monitoring day, and analyze the abnormal sulfur content index of each production stage in the target factory during each monitoring period.

[0010] S3. Confirmation of data collection frequency: based on the sulfur content abnormality index corresponding to each production stage in the target factory in each monitoring time period, confirm the data collection frequency corresponding to each production stage in the target factory in each monitoring time period.

[0011] S4. Feedback on problems in the production stage: Extract the aging information corresponding to each production equipment in each production stage in the target factory, and evaluate whether there is a problem with the sulfur content generated in each production stage in the target factory. If there is a problem, feedback will be provided.

[0012] The second aspect of the present invention provides an industrial gas sulfur content detection device based on big data, including: an actual sulfur content confirmation module, which is used to collect the industrial gas sulfur content corresponding to each production stage in the target factory on the current monitoring day and each monitoring time period by using various sensors, and at the same time collect the temperature, pressure and humidity of the industrial gas corresponding to each production stage in the target factory on the current monitoring day and each monitoring time period, to confirm the actual sulfur content corresponding to each production stage in the target factory on the current monitoring day and each monitoring time period.

[0013] The sulfur content anomaly analysis module is used to confirm the actual sulfur content corresponding to each production stage in the target factory in each monitoring time period on each historical monitoring day in the same way as the actual sulfur content corresponding to each production stage in the target factory in each monitoring time period on the current monitoring day, and analyze the sulfur content anomaly index corresponding to each production stage in the target factory in each monitoring time period.

[0014] The data collection frequency confirmation module is used to confirm the data collection frequency corresponding to each production stage in the target factory in each monitoring time period based on the sulfur content abnormality index corresponding to each production stage in the target factory in each monitoring time period.

[0015] The database is used to store the sulfur content of industrial gases affected by the influencing factors of unit sulfur content determination, and to store the sulfur content abnormality index range corresponding to each data collection frequency.

[0016] The production stage problem feedback module is used to extract the aging information corresponding to each production equipment in each production stage in the target factory, and evaluate whether there is a problem with the sulfur content generated in each production stage in the target factory. If there is a problem, feedback is provided.

[0017] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention collects the sulfur content of industrial gas by utilizing multiple types of sensors, thereby covering all possible ranges of sulfur content in industrial gas as comprehensively as possible, avoiding limiting data accuracy and improving the reliability of collecting sulfur content in industrial gas.

[0018] (2) The present invention determines the data collection frequency corresponding to the monitoring time period by combining the abnormal sulfur content corresponding to the monitoring time period. In the time period when the sulfur content is stable or changes slightly, the data collection frequency is avoided to be too high, which leads to a large amount of manpower, material and time resources being wasted. On the contrary, in the time period when the sulfur content changes greatly, the data collection frequency is avoided to be too low, which leads to the inability to timely discover and deal with potential environmental problems.

[0019] (3) The present invention analyzes the impact of the abnormal sulfur content analysis corresponding to the production stage in combination with the aging information of the production equipment in the production stage, thereby improving the accuracy of the abnormal sulfur content analysis corresponding to the production stage, avoiding the enterprise from making wrong production decisions and quality control measures based on inaccurate sulfur content analysis results, and reducing the safety hazards and risks caused by the aging of production equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0021] Figure 1 The figure is a schematic flow chart of the method steps of the present invention.

[0022] Figure 2 It is a schematic diagram of the system structure connection of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention.

[0024] See also Figure 1 As shown, the first aspect of the present invention provides a method for detecting sulfur content in industrial gases based on big data, including: S1, confirmation of actual sulfur content: by using various sensors to collect the sulfur content of industrial gases generated in each production stage in each monitoring time period in the target factory on the current monitoring day, and at the same time collecting the temperature, pressure and humidity of the industrial gases corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day, confirming the actual sulfur content corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day.

[0025] In a specific embodiment of the present invention, the various types of sensors include but are not limited to hydrogen sulfide gas sensors, electrochemical sensors and gas chromatographs with sulfur chemiluminescence detectors, wherein the hydrogen sulfide gas sensors are specifically used to detect the concentration of hydrogen sulfide gas and are made based on ionic conductivity, the electrochemical sensors can be used to analyze gas composition, and the gas chromatograph with sulfur chemiluminescence detector detects sulfur content based on the chemiluminescence generated by the reaction of sulfur monoxide generated by the combustion of the analyte with ozone.

[0026] It should be noted that the temperature, pressure and humidity of the industrial gas corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day are collected by infrared thermometers, pressure measuring instruments and humidity sensors respectively.

[0027] In a specific embodiment of the present invention, the specific process of confirming the actual sulfur content corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day is as follows: A1, based on the temperature, pressure and humidity of the industrial gas corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day, setting the sulfur content determination influence factor λ corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day ij , where i represents the number of the production stage, i=1,2,...,n, and j represents the number of the monitoring time period, j=1,2,...,m.

[0028] In a specific embodiment of the present invention, the specific process of setting the sulfur content determination influencing factor corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day is as follows: B1, the temperature, pressure and humidity of the industrial gas corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day are recorded as W ij , P ij and β ij .

[0029] B2. Set the sulfur content determination influencing factor λ corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day. ij , Wherein, W′, P′ and β′ represent the temperature, pressure and humidity of the industrial gas of the set reference, respectively; ΔW, ΔP and Δβ represent the temperature deviation, pressure deviation and humidity deviation of the industrial gas of the set reference, respectively; and e represents a natural constant.

[0030] A2. The sulfur content of industrial gas generated in each production stage of the target factory during each monitoring period on the current monitoring day collected by various sensors is recorded as ε qij , where q represents the number of each type of sensor, q=1,2,...,p.

[0031] A3. Extract the sulfur content of industrial gas corresponding to the influence factor of unit sulfur content determination from the database and record it as ε 0 .

[0032] A4. Confirm the actual sulfur content ε corresponding to each production stage in each monitoring period in the target factory on the current monitoring day. ij , Where p represents the number of sensor types.

[0033] The embodiments of the present invention collect the sulfur content of industrial gas by using multiple types of sensors, thereby covering all possible ranges of sulfur content in industrial gas as comprehensively as possible, avoiding limiting the accuracy of data, and improving the reliability of collecting the sulfur content of industrial gas.

[0034] S2. Analysis of abnormal sulfur content: Confirm the actual sulfur content of each production stage in the target factory during each monitoring period on each historical monitoring day in the same way as the actual sulfur content of each production stage in the target factory during each monitoring period on the current monitoring day, and analyze the abnormal sulfur content index of each production stage in the target factory during each monitoring period.

[0035] In a specific embodiment of the present invention, the specific process of analyzing the sulfur content abnormality index corresponding to each production stage in the target factory in each monitoring time period is as follows: C1, based on the actual sulfur content corresponding to each production stage in the target factory in each monitoring time period on each historical monitoring day, respectively calculate the sulfur content compliance degree δ corresponding to each production stage in the target factory in each monitoring time period ij and sulfur content variation

[0036] It should be noted that the specific process of calculating the compliance of the sulfur content corresponding to each production stage in the target factory in each monitoring time period is as follows: the actual sulfur content corresponding to each production stage in the target factory in each monitoring time period on each historical monitoring day is compared with the maximum sulfur content permitted; if the actual sulfur content of a certain monitoring day corresponding to a certain production stage in the target factory in a certain monitoring time period is greater than or equal to the maximum sulfur content permitted, the historical monitoring day corresponding to the production stage in the target factory in the monitoring time period is regarded as an abnormal monitoring day, and the number of abnormal monitoring days corresponding to each production stage in the target factory in each monitoring time period is counted and recorded as T. ij .

[0037] Calculate the sulfur content compliance δ corresponding to each production stage in the target factory during each monitoring period ij , Wherein, T′ represents the number of abnormal monitoring days for the set reference.

[0038] In a specific embodiment of the present invention, the specific process of calculating the sulfur content variation degree corresponding to each production stage in each monitoring time period in the target factory is as follows: D1. Taking the monitoring day as the horizontal coordinate and the actual sulfur content as the vertical coordinate, a sulfur content deviation curve corresponding to each production stage in each monitoring time period is constructed, and the slope value is located from the curve as the sulfur content growth rate corresponding to each production stage in each monitoring time period, which is marked as K ij .

[0039] D2. Calculate the sulfur content variation of each production stage in the target factory during each monitoring period Wherein, K′ represents the sulfur content growth rate of the set reference.

[0040] C2. Calculate the sulfur content abnormality index χ corresponding to each production stage in each monitoring time period in the target factory. ij , Among them, a 1 and a 2 They represent the weights of the set sulfur content compliance and sulfur content change corresponding to the sulfur content abnormality assessment, respectively. 1 +a 2 =1.

[0041] In a specific embodiment of the present invention, a 1 The setting value of is 0.5, a 2 The setting value is 0.5. When calculating the sulfur content abnormality index corresponding to each production stage in the target factory during each monitoring period, it is recommended to comprehensively consider the two factors of sulfur content compliance and sulfur content change. The weight can be set according to the actual situation to reflect the importance of the two in the assessment.

[0042] S3. Confirmation of data collection frequency: based on the sulfur content abnormality index corresponding to each production stage in the target factory in each monitoring time period, confirm the data collection frequency corresponding to each production stage in the target factory in each monitoring time period.

[0043] In a specific embodiment of the present invention, the specific method of confirming the data collection frequency corresponding to each production stage in the target factory in each monitoring time period is: comparing the sulfur content abnormality index corresponding to each production stage in the target factory in each monitoring time period with the sulfur content abnormality index range corresponding to each data collection frequency stored in the database; if the sulfur content abnormality index corresponding to a certain production stage in the target factory in a certain monitoring time period is within the sulfur content abnormality index range corresponding to a certain data collection frequency, then the data collection frequency is used as the data collection frequency corresponding to the production stage in the target factory in the monitoring time period.

[0044] The embodiment of the present invention confirms the data collection frequency corresponding to the monitoring time period in combination with the abnormal sulfur content corresponding to the monitoring time period, and avoids the data collection frequency being too high during the time period when the sulfur content is stable or changes little, which leads to a large amount of manpower, material and time resources being wasted. On the contrary, in the time period when the sulfur content changes greatly, the data collection frequency is avoided to be too low, which leads to the failure to timely discover and deal with potential environmental problems.

[0045] S4. Feedback on problems in the production stage: Extract the aging information corresponding to each production equipment in each production stage in the target factory, and evaluate whether there is a problem with the sulfur content generated in each production stage in the target factory. If there is a problem, feedback will be provided.

[0046] In a specific embodiment of the present invention, the aging information includes working days, maintenance times and warranty times.

[0047] It should be noted that the working days, maintenance times and warranty times corresponding to each production equipment in each production stage in the target factory are all extracted from the production equipment management manual.

[0048] In a specific embodiment of the present invention, the specific process of evaluating whether there is a problem with the sulfur content generated in each production stage in the target factory is as follows: E1, extracting the working days, maintenance times and warranty times from the aging information corresponding to each production equipment in each production stage in the target factory, and calculating the aging degree θ of the equipment corresponding to each production stage in the target factory. i .

[0049] In a specific embodiment of the present invention, the specific process of calculating the aging degree of equipment corresponding to each production stage in the target factory is as follows: F1, the working days, maintenance times and warranty times corresponding to each production equipment in each production stage in the target factory are recorded as μ ir , τ ir and σ ir , where r represents the number of the production equipment, r = 1, 2, ..., g.

[0050] F2. Calculate the aging degree of each production equipment in each production stage in the target factory Among them, μ′, τ′ and σ′ represent the reference working days, maintenance times and warranty times respectively.

[0051] F3. Compare the aging degree of each production equipment in each production stage of the target factory with the aging degree of the set reference. If the aging degree of a production equipment in a production stage of the target factory is greater than the aging degree of the set reference, the production equipment is recorded as an aged equipment. The number of aged equipment in each production stage of the target factory is counted and recorded as φ i .

[0052] F4. Calculate the equipment aging degree θ corresponding to each production stage in the target factory i , Among them, g represents the number of production equipment, and η represents the proportion of aging equipment set as a reference.

[0053] E2. Calculate the sulfur content abnormality index χ corresponding to each production stage in the target factory i , Wherein, m represents the number of monitoring time periods.

[0054] E3. Compare the sulfur content abnormality index corresponding to each production stage in the target factory with the set reference sulfur content abnormality index. If the sulfur content abnormality index corresponding to a production stage in the target factory is greater than or equal to the set reference sulfur content abnormality index, it indicates that there is a problem with the sulfur content generated in the production stage of the target factory. Otherwise, it indicates that there is no problem with the sulfur content generated in the production stage of the target factory.

[0055] The embodiment of the present invention analyzes the influence of the abnormal sulfur content analysis corresponding to the production stage in combination with the aging information of the production equipment in the production stage, thereby improving the accuracy of the abnormal sulfur content analysis corresponding to the production stage, avoiding the enterprise from making wrong production decisions and quality control measures based on inaccurate sulfur content analysis results, and reducing the safety hazards and risks caused by the aging of production equipment.

[0056] Reference Figure 2 As shown, the second aspect of the present invention provides an industrial gas sulfur content detection device based on big data, including: an actual sulfur content confirmation module, a sulfur content abnormality analysis module, a data acquisition frequency confirmation module, a database and a production stage problem feedback module.

[0057] The actual sulfur content confirmation module is connected to the sulfur content abnormality analysis module, the sulfur content abnormality analysis module is connected to the data acquisition frequency confirmation module, the sulfur content abnormality analysis module is connected to the production stage problem feedback module, and both the actual sulfur content confirmation module and the data acquisition frequency confirmation module are connected to the database.

[0058] The actual sulfur content confirmation module is used to collect the sulfur content of industrial gas corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day by using various sensors, and at the same time collect the temperature, pressure and humidity of the industrial gas corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day, to confirm the actual sulfur content corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day.

[0059] The sulfur content anomaly analysis module is used to confirm the actual sulfur content corresponding to each production stage in the target factory in each monitoring time period on each historical monitoring day in the same way as the actual sulfur content corresponding to each production stage in the target factory in each monitoring time period on the current monitoring day, and analyze the sulfur content anomaly index corresponding to each production stage in the target factory in each monitoring time period.

[0060] The data collection frequency confirmation module is used to confirm the data collection frequency corresponding to each production stage in the target factory in each monitoring time period based on the sulfur content abnormality index corresponding to each production stage in the target factory in each monitoring time period.

[0061] The database is used to store the sulfur content of industrial gases affected by the unit sulfur content determination influencing factor and the sulfur content abnormality index range corresponding to each data collection frequency. The data sources in the database of this embodiment are shown in Table 1 below.

[0062] Table 1 Data sources in the database

[0063]

[0064] The production stage problem feedback module is used to extract the aging information corresponding to each production equipment in each production stage in the target factory, evaluate whether there is a problem with the sulfur content generated in each production stage in the target factory, and provide feedback if there is a problem.

[0065] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A method for detecting sulfur content in industrial gas based on big data, characterized in that: include: S1. Confirmation of actual sulfur content: by using various sensors to collect the sulfur content of industrial gas generated in each production stage in each monitoring time period of the target factory on the current monitoring day, and at the same time collecting the temperature, pressure and humidity of industrial gas in each production stage in each monitoring time period of the target factory on the current monitoring day, confirm the actual sulfur content in each production stage in each monitoring time period of the target factory on the current monitoring day; S2. Analysis of abnormal sulfur content: confirm the actual sulfur content of the target factory at each production stage in each monitoring period on each historical monitoring day in the same way as the actual sulfur content of the target factory at each production stage in each monitoring period on the current monitoring day, and analyze the abnormal sulfur content index of the target factory at each production stage in each monitoring period; S3. Confirmation of data collection frequency: based on the sulfur content abnormality index corresponding to each production stage in each monitoring time period in the target factory, confirm the data collection frequency corresponding to each production stage in each monitoring time period in the target factory; S4. Feedback on problems in the production stage: Extract the aging information corresponding to each production equipment in each production stage in the target factory, and evaluate whether there is a problem with the sulfur content generated in each production stage in the target factory. If there is a problem, feedback will be provided.

2. The method for detecting sulfur content in industrial gas based on big data according to claim 1, characterized in that: The specific process of confirming the actual sulfur content corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day is: A1. Based on the temperature, pressure and humidity of the industrial gas corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day, set the sulfur content determination influence factor λ corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day ij , where i represents the number of the production stage, i=1,2,...,n, and j represents the number of the monitoring time period, j=1,2,...,m; A2. The sulfur content of industrial gas generated in each production stage of the target factory during each monitoring period on the current monitoring day collected by various sensors is recorded as ε qij , where q represents the number of each type of sensor, q = 1, 2, ..., p; A3. Extract the sulfur content of industrial gas corresponding to the unit sulfur content determination influencing factor from the database and record it as ε0; A4. Confirm the actual sulfur content ε corresponding to each production stage in each monitoring period in the target factory on the current monitoring day. ij , Where p represents the number of sensor types.

3. The method for detecting sulfur content in industrial gas based on big data according to claim 2, characterized in that: The specific process of setting the sulfur content determination influencing factors corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day is: B1. The temperature, pressure and humidity of the industrial gas corresponding to each production stage in each monitoring period in the target factory on the current monitoring day are recorded as W ij , P ij and β ij ; B2. Set the sulfur content determination influencing factor λ corresponding to each production stage in each monitoring time period in the target factory on the current monitoring day. ij , Wherein, W′, P′ and β′ represent the temperature, pressure and humidity of the industrial gas of the set reference, respectively; ΔW, ΔP and Δβ represent the temperature deviation, pressure deviation and humidity deviation of the industrial gas of the set reference, respectively; and e represents a natural constant.

4. The method for detecting sulfur content in industrial gas based on big data according to claim 3 is characterized in that: The specific process of analyzing the sulfur content abnormality index corresponding to each production stage in each monitoring time period in the target factory is as follows: C1. Based on the actual sulfur content of each production stage in the target factory during each monitoring period on each historical monitoring day, calculate the sulfur content compliance δ of each production stage in the target factory during each monitoring period. ij and sulfur content variation C2. Calculate the sulfur content abnormality index χ corresponding to each production stage in each monitoring time period in the target factory. ij , Among them, a1 and a2 represent the weights of the sulfur content abnormality assessment corresponding to the set sulfur content compliance degree and sulfur content change degree, respectively, and a1+a2=1.

5. The method for detecting sulfur content in industrial gas based on big data according to claim 4, characterized in that: The specific process of calculating the sulfur content variation corresponding to each production stage in the target factory during each monitoring time period is as follows: D1. With the monitoring day as the horizontal axis and the actual sulfur content as the vertical axis, a sulfur content deviation curve corresponding to each production stage in each monitoring time period is constructed, and the slope value is located from the curve as the sulfur content growth rate corresponding to each production stage in each monitoring time period, which is marked as K ij ; D2. Calculate the sulfur content variation of each production stage in the target factory during each monitoring period Wherein, K′ represents the sulfur content growth rate of the set reference.

6. The method for detecting sulfur content in industrial gas based on big data according to claim 5, characterized in that: The specific method for confirming the data collection frequency corresponding to each production stage in the target factory in each monitoring time period is: comparing the sulfur content abnormality index corresponding to each production stage in the target factory in each monitoring time period with the sulfur content abnormality index range corresponding to each data collection frequency stored in the database; if the sulfur content abnormality index corresponding to a certain production stage in the target factory in a certain monitoring time period is within the sulfur content abnormality index range corresponding to a certain data collection frequency, then the data collection frequency is used as the data collection frequency corresponding to the production stage in the target factory in the monitoring time period.

7. The method for detecting sulfur content in industrial gas based on big data according to claim 4, characterized in that: The aging information includes working days, maintenance times and warranty times.

8. The method for detecting sulfur content in industrial gas based on big data according to claim 7, characterized in that: The specific process of evaluating whether there is a problem with the sulfur content generated at each production stage in the target plant is as follows: E1. Extract the working days, maintenance times and warranty times from the aging information corresponding to each production equipment in each production stage in the target factory, and calculate the aging degree θ of the equipment corresponding to each production stage in the target factory. i ; E2. Calculate the sulfur content abnormality index χ corresponding to each production stage in the target factory i , Where m represents the number of monitoring time periods; E3. Compare the sulfur content abnormality index corresponding to each production stage in the target factory with the set reference sulfur content abnormality index. If the sulfur content abnormality index corresponding to a production stage in the target factory is greater than or equal to the set reference sulfur content abnormality index, it indicates that there is a problem with the sulfur content generated in the production stage of the target factory. Otherwise, it indicates that there is no problem with the sulfur content generated in the production stage of the target factory.

9. The method for detecting sulfur content in industrial gas based on big data according to claim 8, characterized in that: The specific process of calculating the equipment aging degree corresponding to each production stage in the target factory is as follows: F1. The number of working days, maintenance times and warranty times corresponding to each production equipment in each production stage in the target factory are recorded as μ ir , τ ir and σ ir , where r represents the number of the production equipment, r = 1, 2, ..., g; F2. Calculate the aging degree of each production equipment in each production stage in the target factory Among them, μ′, τ′ and σ′ represent the reference working days, maintenance times and warranty times respectively; F3. Compare the aging degree of each production equipment in each production stage of the target factory with the aging degree of the set reference. If the aging degree of a production equipment in a production stage of the target factory is greater than the aging degree of the set reference, the production equipment is recorded as an aged equipment. The number of aged equipment in each production stage of the target factory is counted and recorded as φ i ; F4. Calculate the equipment aging degree θ corresponding to each production stage in the target factory i , Among them, g represents the number of production equipment, and η represents the proportion of aging equipment set as a reference.

10. An industrial gas sulfur content detection device based on big data, characterized in that: include: The actual sulfur content confirmation module is used to collect the sulfur content of industrial gas generated in each production stage of the target factory on the current monitoring day in each monitoring time period by using various sensors, and at the same time collect the temperature, pressure and humidity of the industrial gas corresponding to each production stage of the target factory on the current monitoring day in each monitoring time period, to confirm the actual sulfur content corresponding to each production stage of the target factory on the current monitoring day in each monitoring time period; The sulfur content abnormality analysis module is used to confirm the actual sulfur content corresponding to each production stage in each monitoring time period of the target factory on each historical monitoring day in the same way as the confirmation method of the actual sulfur content corresponding to each production stage in each monitoring time period of the target factory on the current monitoring day, and analyze the sulfur content abnormality index corresponding to each production stage in each monitoring time period of the target factory; A data collection frequency confirmation module is used to confirm the data collection frequency corresponding to each production stage in each monitoring time period in the target factory based on the sulfur content abnormality index corresponding to each production stage in each monitoring time period in the target factory; A database for storing the sulfur content of industrial gases affected by the influencing factors of unit sulfur content determination, and storing the sulfur content abnormality index range corresponding to each data collection frequency; The production stage problem feedback module is used to extract the aging information corresponding to each production equipment in each production stage in the target factory, and evaluate whether there is a problem with the sulfur content generated in each production stage in the target factory. If there is a problem, feedback is provided.

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