Industrial production data monitoring system based on Internet of Things

Abstract

The invention relates to the technical field of industrial production data monitoring, in particular to an industrial production data monitoring system based on the Internet of Things, which comprises a production data acquisition module used for acquiring fan data, blast furnace data and gas heat value; the calorific value analysis module is used for analyzing the fuel quality state; the fan monitoring module is used for analyzing the state of the fan; the blast furnace monitoring module is used for analyzing the operation state of the blast furnace; the efficiency analysis module is used for analyzing the energy consumption state of the blast furnace according to the energy consumption data of the blast furnace collected in the monitoring period and the historical energy consumption data of the blast furnace in the historical monitoring period; and the production monitoring module is used for analyzing the production state according to the analysis result of the fuel quality state, the analysis result of the fan state and the analysis results of the blast furnace operation state and the blast furnace energy consumption state in the management period. According to the invention, the monitoring efficiency of industrial production data is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial production data monitoring, and in particular to an industrial production data monitoring system based on the Internet of Things. Background Art

[0002] In the modern steel production process, blast furnace is the key smelting equipment, and it is crucial to ensure its efficient, safe and stable operation. The operation of blast furnace is affected by many factors, including fuel quality, fan efficiency, temperature and pressure state in blast furnace, etc. Therefore, it has become an urgent need for major steel companies to establish a comprehensive monitoring and analysis system to grasp various operating parameters in real time, optimize energy efficiency, reduce energy consumption, and improve production safety.

[0003] Chinese patent publication number CN114237167A discloses an abnormal monitoring system and method for industrial production processes. The monitoring system includes the following modules: a data quality judgment module, which uses a statistical or rule-based method to make a preliminary judgment on the quality of the collected production process data, and then uses a statistical or abnormal monitoring algorithm to realize the quality judgment of the data; an equipment abnormality detection module, which uses rules or machine learning algorithms to judge whether the equipment operation status is abnormal when the data quality is judged to be normal; a control rule suggestion module, which judges the type of equipment abnormality when the equipment abnormality detection module judges that the equipment is abnormal and the data quality judgment module judges that the data quality is normal, triggers control suggestions, and provides reference for operators. This application can accurately judge the abnormal situations that occur in the industrial production process and quickly give processing suggestions; it can be seen that when the scheme monitors the industrial production data, it only analyzes the equipment operation status, and there is a problem of low efficiency in industrial production data monitoring. Summary of the invention

[0004] The object of the present invention is to provide an industrial production data monitoring system based on the Internet of Things to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] An industrial production data monitoring system based on the Internet of Things, comprising:

[0007] Production data acquisition module, used to collect fan data, blast furnace data and gas calorific value;

[0008] The calorific value analysis module is used to analyze the fuel quality status according to the fuel calorific value collected during the monitoring period;

[0009] The fan monitoring module is used to analyze the fan status according to the oxygen supply and air temperature collected during the monitoring period;

[0010] The blast furnace monitoring module is used to analyze the blast furnace operation status according to the analysis results of the blast furnace temperature, blast furnace pressure, carbon dioxide concentration in the blast furnace and fan status collected during the monitoring period;

[0011] An efficiency analysis module is used to analyze the energy consumption status of the blast furnace based on the blast furnace energy consumption data collected during the monitoring period and the historical energy consumption data of the blast furnace during the historical monitoring period;

[0012] The production monitoring module is used to analyze the production status according to the analysis results of the fuel quality status, the fan status, the blast furnace operation status and the blast furnace energy consumption status within the management cycle, and to provide users with abnormal production status alarms based on the analysis results. It is also used to adjust the production status analysis process based on the blast furnace pressure and the collected furnace outlet temperature in each monitoring period within the management cycle.

[0013] Furthermore, the calorific value analysis module constructs a fuel calorific value fluctuation coefficient RB according to the fuel calorific value collected during the monitoring period, and compares the fuel calorific value fluctuation coefficient RB with a preset fluctuation coefficient RB0 to analyze the fuel quality status, which includes qualified and unqualified.

[0014] Further, the fan monitoring module includes an oxygen supply analysis unit, which is used to compare the oxygen supply sy0 collected during the monitoring period with each oxygen supply threshold, and analyze the fan oxygen supply state according to the comparison result, and the fan oxygen supply state includes normal and abnormal;

[0015] The fan monitoring module also includes an air temperature analysis unit, which is used to compare the air temperature t0 collected during the monitoring period with the air temperature threshold t1, and analyze the air temperature state according to the comparison result, and the air temperature state includes normal and abnormal;

[0016] The fan monitoring module also includes a fan analysis unit, which analyzes the fan status according to the analysis results of the fan oxygen supply status and the air temperature status within the monitoring period, and the fan status includes normal and abnormal.

[0017] Furthermore, the blast furnace monitoring module includes a temperature monitoring unit, which is used to compare the blast furnace temperature gt0 collected during the monitoring period with the preset blast furnace temperature gt1, and analyze the blast furnace temperature abnormality based on the comparison result. The blast furnace temperature abnormality includes normal and abnormal. If the blast furnace temperature in the current monitoring period is abnormal, the blast furnace temperature abnormality index is set to GC1; if the blast furnace temperature in the current monitoring period is normal, the blast furnace temperature abnormality index is set to GC2.

[0018] Further, the blast furnace monitoring module also includes a pressure monitoring unit, which is used to compare the blast furnace pressure gy0 collected during the monitoring period with the preset blast furnace pressure gy1, and analyze the abnormality of the blast furnace pressure according to the comparison result. The abnormality of the blast furnace pressure includes normal and abnormal. If the blast furnace pressure in the current monitoring period is normal, the blast furnace pressure abnormality index is set to YC1; if the blast furnace pressure in the current monitoring period is abnormal, the blast furnace pressure abnormality index is set to YC2;

[0019] The blast furnace monitoring module also includes a gas monitoring unit, which is used to compare the carbon dioxide concentration ry0 in the blast furnace collected during the monitoring period with the preset concentration ry1, and analyze the abnormality of the blast furnace carbon dioxide concentration based on the comparison result. The abnormality of the blast furnace carbon dioxide concentration includes normal and abnormal. If the carbon dioxide concentration in the current monitoring period is normal, the blast furnace gas abnormality index is set to LC1; if the carbon dioxide concentration in the current monitoring period is abnormal, the blast furnace gas abnormality index is set to LC2.

[0020] Furthermore, the blast furnace monitoring module also includes a blast furnace analysis unit, which is used to analyze the blast furnace operating status based on the construction results of the blast furnace temperature abnormality index, the blast furnace pressure abnormality index, the blast furnace gas abnormality index and the analysis results of the fan status. The blast furnace operating status includes normal operation and abnormal operation.

[0021] Furthermore, the efficiency analysis module is used to analyze the blast furnace energy consumption status according to the blast furnace energy consumption data n0 collected during the monitoring period and the blast furnace historical energy consumption data during the historical monitoring period, and the blast furnace energy consumption status includes normal energy consumption and high energy consumption.

[0022] Furthermore, the production monitoring module includes an alarm unit, which is used to analyze the production status based on the analysis results of the fuel quality status, the analysis results of the fan status, the blast furnace operation status and the blast furnace energy consumption status within the management cycle. The production status includes normal and abnormal. If the production status in the current management cycle is abnormal, an abnormal production status alarm will be issued to the user.

[0023] Furthermore, the production monitoring module also includes a pressure fluctuation analysis unit, which constructs a blast furnace pressure fluctuation coefficient GB according to the blast furnace pressure of each monitoring period within the management period, and compares the blast furnace pressure fluctuation coefficient GB with the pressure fluctuation threshold GB0, and analyzes the fluctuation state of the blast furnace pressure according to the comparison result. The fluctuation state of the blast furnace pressure includes normal fluctuation and abnormal fluctuation. If the fluctuation of the blast furnace pressure in the current management period is abnormal, the preset state abnormality ratio is adjusted to F'.

[0024] Furthermore, the production monitoring module also includes a furnace temperature monitoring unit, which is used to compare the furnace temperature ct0 collected during the management cycle with the preset furnace temperature ct1, and analyze the abnormality of the furnace temperature based on the comparison result. The abnormality of the furnace temperature includes normal and abnormal. If the furnace temperature in the current management cycle is abnormal, the preset adjustment ratio is adjusted to η1.

[0025] The beneficial effects of the present invention are as follows: Through the collaborative work of various modules, the present invention realizes all-round monitoring and analysis of the blast furnace production status, ensuring the safety, efficiency and environmental protection of the entire production process. Through real-time data monitoring and comprehensive analysis, potential problems can be discovered and dealt with in a timely manner, greatly reducing the production failure rate, improving production efficiency, reducing energy consumption, and ultimately bringing economic and environmental benefits to the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] 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.

[0027] Figure 1 This is a schematic diagram of the structure of the industrial production data monitoring system based on the Internet of Things in this embodiment.

[0028] Figure 2 Schematic diagram of the structure of the fan monitoring module of this embodiment.

[0029] Figure 3 It is a schematic diagram of the structure of the blast furnace monitoring module of this embodiment.

[0030] Figure 4 This is a schematic diagram of the structure of the production monitoring module of this embodiment. DETAILED DESCRIPTION

[0031] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0032] It should be noted that, although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0033] See also Figure 1 As shown, it is a structural diagram of an industrial production data monitoring system based on the Internet of Things in this embodiment, and the system includes:

[0034] The production data acquisition module is used to collect fan data, blast furnace data and gas calorific value, wherein the fan data includes oxygen supply and air temperature, wherein the oxygen supply is the oxygen flow rate delivered by the fan to the blast furnace, and the air temperature is the hot air temperature provided by the fan. The blast furnace data includes blast furnace temperature, blast furnace pressure, carbon dioxide concentration in the blast furnace, blast furnace energy consumption data, blast furnace historical energy consumption data and furnace discharge temperature, wherein the furnace discharge temperature is the furnace discharge temperature of molten iron; it can be understood that in this embodiment, no specific limitation is made on the collection method of fan data, blast furnace data and gas calorific value, and those skilled in the art can freely set it, as long as the collection requirements of fan data, blast furnace data and gas calorific value are met, wherein the oxygen supply can be collected by a flow sensor, the air temperature can be collected by a temperature sensor, the blast furnace temperature can be collected by a temperature sensor, the blast furnace pressure can be collected by a pressure sensor, the carbon dioxide concentration can be collected by a gas sensor, the blast furnace energy consumption data can be collected by a smart meter, the blast furnace historical energy consumption data can be collected interactively, the furnace discharge temperature can be collected by a temperature sensor, and the gas calorific value can be collected by a gas sampling temperature compensator.

[0035] Please continue reading Figure 1 As shown, the system also includes a calorific value analysis module, which is connected to the production data acquisition module. The calorific value analysis module constructs the fluctuation coefficient RB of the fuel calorific value according to the fuel calorific value collected during the monitoring period to avoid production problems caused by low-quality fuel. The setting is:

[0036]

[0037] Wherein, ri is the i-th gas calorific value data collected during the monitoring period, I is the number of gas calorific value data collected during the monitoring period, and rp is the average value of the gas calorific value data collected during the monitoring period;

[0038] The calorific value analysis module compares the fluctuation coefficient RB of the calorific value of the fuel with the preset fluctuation coefficient RB0 to analyze the fuel quality status. If RB≤RB0, the calorific value analysis module determines that the fuel quality status of the current monitoring period is qualified; if RB>RB0, the calorific value analysis module determines that the fuel quality status of the current monitoring period is unqualified. By constructing and analyzing the fuel calorific value fluctuation coefficient RB, the quality status of the fuel is monitored to ensure that the calorific value of the fuel meets production requirements, improve production stability, and thus improve the monitoring efficiency of industrial production data.

[0039] It can be understood that, in this embodiment, there is no specific limitation on the setting of the preset fluctuation coefficient, and those skilled in the art can set it freely, as long as the setting requirements of the preset fluctuation coefficient are met, wherein the optimal value of RB0 is 0.15; in this embodiment, there is no specific limitation on the setting of the monitoring period, and those skilled in the art can set it freely, as long as the setting requirements of the monitoring period are met, wherein the monitoring period can be set to 5 minutes, 6 minutes, etc.

[0040] Please continue reading Figure 1 As shown, the system further includes a fan monitoring module, which is connected to the production data acquisition module. The fan monitoring module is used to analyze the fan status according to the oxygen supply and air temperature collected during the monitoring period.

[0041] See also Figure 2 As shown, the fan monitoring module includes:

[0042] The oxygen supply analysis unit is used to compare the oxygen supply sy0 collected during the monitoring period with each oxygen supply threshold, and analyze the oxygen supply status of the fan according to the comparison result, wherein:

[0043] If sy1≤sy0≤sy2, the oxygen supply analysis unit determines that the oxygen supply state of the fan is normal;

[0044] If sy0>sy2 or sy0<sy1, the oxygen supply analysis unit determines that the oxygen supply state of the fan is abnormal; by setting the upper and lower thresholds sy1 and sy2 for comparing the monitored oxygen supply sy0, it is possible to quickly identify whether the oxygen supply state of the fan is normal or abnormal, and through standardized processes, ensure that the oxygen supply of the fan is stable during the production process;

[0045] Wherein, sy1 is the first oxygen supply threshold, sy2 is the second oxygen supply threshold, and sy1<sy2.

[0046] Please continue reading Figure 2 As shown, the fan monitoring module also includes:

[0047] The air temperature analysis unit is used to compare the air temperature t0 collected during the monitoring period with the air temperature threshold t1, and analyze the air temperature state according to the comparison result, wherein:

[0048] If t0≤t1, the air temperature analysis unit determines that the air temperature state is abnormal;

[0049] If t0>t1, the air temperature analysis unit determines that the air temperature state is normal; by comparing the air temperature with the set threshold t1, the abnormal state is identified to prevent the combustion efficiency from being affected by too low or too high air temperature, thereby ensuring the stable operation of the blast furnace.

[0050] It is understandable that the present embodiment does not specifically limit the setting of the oxygen delivery threshold and the air temperature threshold, and those skilled in the art can freely set them as long as the setting requirements of the oxygen delivery threshold and the air temperature threshold are met. Among them, the optimal value of sy1 is 200Nm 3 / h, the best value of sy2 is 300Nm 3 / h, the optimal value of t1 is 300℃.

[0051] Please continue reading Figure 2 As shown, the fan monitoring module also includes:

[0052] The fan analysis unit is connected to the oxygen supply analysis unit and the air temperature analysis unit. The fan analysis unit analyzes the fan state according to the analysis results of the fan oxygen supply state and the air temperature state within the monitoring period to analyze the overall performance of the fan, wherein:

[0053] If (α1×k1+α2×k2) / k0≤P, the fan analysis unit determines that the fan status in the current monitoring period is normal;

[0054] If (α1×k1+α2×k2) / k0>P, the fan analysis unit determines that the fan state in the current monitoring period is abnormal; by integrating the abnormal states of oxygen supply and air temperature, the overall performance of the fan is analyzed by using a weighted combination of multiple variables, and by presetting the abnormal proportion P, the blast furnace operation monitoring efficiency is improved, thereby improving the monitoring efficiency of industrial production data;

[0055] Among them, α1 is the oxygen supply weight, α2 is the air temperature weight, α1+α2=1, k1 is the duration of the fan oxygen supply state being abnormal during the monitoring period, k2 is the duration of the air temperature state being abnormal during the monitoring period, k0 is the duration of the monitoring period, and P is the preset abnormal proportion.

[0056] It can be understood that, in this embodiment, there is no specific limitation on the setting of each weight and the preset abnormality ratio, and those skilled in the art can set it freely, as long as the setting requirements of each weight and the preset abnormality ratio are met, among which the optimal value of α1 is 0.45, the optimal value of α2 is 0.55, and the optimal value of P is 0.12.

[0057] Please continue reading Figure 1 As shown, the system also includes a blast furnace monitoring module, which is used to analyze the blast furnace operation status according to the analysis results of the blast furnace temperature, blast furnace pressure, carbon dioxide concentration in the blast furnace and fan status collected during the monitoring period, and the blast furnace monitoring module is connected to the fan monitoring module.

[0058] See also Figure 3 As shown, the blast furnace monitoring module includes:

[0059] The temperature monitoring unit is used to compare the blast furnace temperature gt0 collected during the monitoring period with the preset blast furnace temperature gt1, and analyze the blast furnace temperature abnormality according to the comparison result, and construct the blast furnace temperature abnormality index according to the analysis result to quantify the degree of blast furnace temperature abnormality, wherein:

[0060] If gt0≤gt1, the temperature monitoring unit determines that the blast furnace temperature in the current monitoring period is abnormal, and sets the blast furnace temperature abnormality index to GC1, setting: GC1=(gt1-gt0) / gt1;

[0061] If gt0>gt1, the temperature monitoring unit determines that the blast furnace temperature is normal in the current monitoring period, and sets the blast furnace temperature abnormality index to GC2, setting: GC2=0; by comparing the collected blast furnace temperature data gt0 with the preset temperature gt1, the abnormal degree of blast furnace temperature is identified in time to improve the accuracy of blast furnace operation status analysis, thereby improving the monitoring efficiency of industrial production data.

[0062] Please continue reading Figure 3 As shown, the blast furnace monitoring module also includes:

[0063] The pressure monitoring unit is used to compare the blast furnace pressure gy0 collected during the monitoring period with the preset blast furnace pressure gy1, and analyze the abnormality of the blast furnace pressure according to the comparison result, and construct a blast furnace pressure abnormality index according to the analysis result to quantify the abnormal degree of the blast furnace pressure, wherein:

[0064] If gy0≤gy1, the pressure monitoring unit determines that the blast furnace pressure in the current monitoring period is normal, and sets the blast furnace pressure abnormality index to YC1, setting: YC1=0;

[0065] If gy0>gy1, the pressure monitoring unit determines that the blast furnace pressure in the current monitoring period is abnormal, and sets the blast furnace pressure abnormality index to YC2, and sets YC2=ln[4×(gy0-gy1) / gy1+1] / l n5; by comparing the blast furnace pressure gy0 with the preset pressure gy1, it is quickly determined whether the blast furnace is in a normal working state, so as to improve the accuracy of the blast furnace operation status analysis, thereby improving the monitoring efficiency of industrial production data.

[0066] Please continue reading Figure 3 As shown, the blast furnace monitoring module also includes:

[0067] The gas monitoring unit is used to compare the carbon dioxide concentration ry0 in the blast furnace collected during the monitoring period with the preset concentration ry1, and analyze the abnormality of the carbon dioxide concentration in the blast furnace according to the comparison result, and construct the blast furnace gas abnormality index according to the analysis result to quantify the abnormal degree of the blast furnace gas, wherein:

[0068] If ry0≤ry1, the gas monitoring unit determines that the carbon dioxide concentration in the current monitoring period is normal, and sets the blast furnace gas abnormality index to LC1, setting: LC1=0;

[0069] If ry0>ry1, the gas monitoring unit determines that the carbon dioxide concentration in the current monitoring period is abnormal, and sets the blast furnace gas abnormality index to LC2, setting: LC2=(ry0-ry1) / ry1; regularly monitor the carbon dioxide concentration ry0 in the blast furnace, compare it with the set concentration ry1, judge the gas emission situation, ensure the integrity of the combustion process, so as to improve the accuracy of the blast furnace operation status analysis, thereby improving the monitoring efficiency of industrial production data.

[0070] Specifically, the blast furnace temperature collected during the monitoring period, the blast furnace pressure collected during the monitoring period, and the carbon dioxide concentration in the blast furnace collected during the monitoring period described in this embodiment are all average values ​​collected during the monitoring period.

[0071] It can be understood that, in this embodiment, there is no specific limitation on the setting of the preset blast furnace temperature, the preset blast furnace pressure and the preset concentration. Those skilled in the art can set them freely as long as the setting requirements of the preset blast furnace temperature, the preset blast furnace pressure and the preset concentration are met. Among them, the optimal value of gt1 is 1600°C, the optimal value of gy1 is 4Bar, and the optimal value of ry1 is 0.08.

[0072] Please continue reading Figure 3 As shown, the blast furnace monitoring module also includes:

[0073] A blast furnace analysis unit, which is connected to the temperature monitoring unit, the pressure monitoring unit, and the gas monitoring unit, and is used to analyze the blast furnace operation status according to the construction results of the blast furnace temperature abnormality index, the blast furnace pressure abnormality index, and the blast furnace gas abnormality index and the analysis results of the fan status, wherein:

[0074] When the fan is in normal state, if x1×blast furnace temperature abnormality index+x2×blast furnace pressure abnormality index+x3×blast furnace gas abnormality index≤u0, the blast furnace analysis unit determines that the blast furnace operation state in the current monitoring period is normal operation; if x1×blast furnace temperature abnormality index+x2×blast furnace pressure abnormality index+x3×blast furnace gas abnormality index>u0, the blast furnace analysis unit determines that the blast furnace operation state in the current monitoring period is abnormal operation;

[0075] When the fan state is abnormal, if x1×blast furnace temperature abnormality index+x2×blast furnace pressure abnormality index+x3×blast furnace gas abnormality index≤u0×{2-exp[P-α1×k1-α2×k2]}, the blast furnace analysis unit determines that the blast furnace operation state in the current monitoring period is normal operation; if x1×blast furnace temperature abnormality index+x2×blast furnace pressure abnormality index+x3×blast furnace gas abnormality index>u0×{2-exp[P-α1×k1-α2×k2]}, the blast furnace analysis unit determines that the blast furnace operation state in the current monitoring period is abnormal operation; by comprehensively analyzing the temperature, pressure and gas abnormality indexes, the blast furnace operation status is quickly judged, and a comprehensive assessment is obtained in combination with the fan state to improve the accuracy of the blast furnace operation status analysis, thereby improving the monitoring efficiency of industrial production data;

[0076] Among them, x1 is the temperature weight, x2 is the pressure weight, x3 is the gas weight, x1+x2+x3=1, and u0 is the preset anomaly coefficient.

[0077] It can be understood that, in this embodiment, there is no specific limitation on the setting of each weight and the preset abnormality coefficient, and those skilled in the art can set it freely as long as the setting requirements of each weight and the preset abnormality coefficient are met, among which the best value of x1 is 0.4, the best value of x2 is 0.3, the best value of x3 is 0.3, and the best value of u0 is 0.16.

[0078] Please continue reading Figure 1 As shown, the system also includes an efficiency analysis module, which is connected to the production data acquisition module, and the efficiency analysis module is used to analyze the blast furnace energy consumption status according to the blast furnace energy consumption data n0 collected during the monitoring period and the blast furnace historical energy consumption data during the historical monitoring period, wherein:

[0079] If n0≥NJ×(1+γ), the efficiency analysis module determines that the energy consumption state of the blast furnace in the current monitoring period is high energy consumption;

[0080] If n0<NJ×(1+γ), the efficiency analysis module determines that the energy consumption state of the blast furnace in the current monitoring period is normal energy consumption; the energy efficiency state of the blast furnace is judged by comparing the monitored blast furnace energy consumption data n0 with the historical average energy consumption NJ, so that high energy consumption can be identified in time, and the use of resources can be optimized, thereby improving the monitoring efficiency of industrial production data;

[0081] Among them, NJ is the average historical energy consumption data of the blast furnace in each historical monitoring period, γ is the fluctuation ratio threshold, Nh is the historical energy consumption data of the blast furnace in the hth historical monitoring period, and H is the number of historical monitoring periods.

[0082] It can be understood that the present embodiment does not specifically limit the setting of the fluctuation ratio threshold, and those skilled in the art can freely set it as long as the setting requirements of the fluctuation ratio threshold are met, wherein the optimal value of γ is 0.08.

[0083] Please continue reading Figure 1 As shown, the system also includes a production monitoring module, which is used to analyze the production status according to the analysis results of the fuel quality status, the fan status, the blast furnace operation status and the blast furnace energy consumption status within the management period, and to alarm the user of abnormal production status according to the analysis results. The production monitoring module is connected to the calorific value analysis module, the blast furnace monitoring module and the efficiency analysis module.

[0084] It can be understood that the present embodiment does not specifically limit the setting of the management cycle, and those skilled in the art can freely set it as long as the setting requirements of the management cycle are met, wherein the management cycle can be set to the molten iron discharge cycle.

[0085] See also Figure 4 As shown, the production monitoring module includes:

[0086] The alarm unit is used to analyze the production status according to the analysis results of the fuel quality status, the fan status, the blast furnace operation status and the blast furnace energy consumption status within the management period, and to issue an abnormal production status alarm to the user according to the analysis results, wherein:

[0087] If (d1×f1+d2×f2+d3×f3+d4×f4) / f0≤F, the alarm unit determines that the production status of the current management cycle is normal and does not issue an abnormal production status alarm;

[0088] If (d1×f1+d2×f2+d3×f3+d4×f4) / f0>F, the alarm unit determines that the production status of the current management cycle is abnormal, and issues an abnormal production status alarm to the user; by integrating fuel quality, fan status, blast furnace operation status and energy consumption status, a comprehensive analysis of the overall production situation is conducted, potential production problems caused by unqualified parameters are quickly responded to, and an alarm is immediately issued to the operator, thereby reducing potential losses;

[0089] Among them, d1 is the fuel quality weight, d2 is the fan weight, d3 is the blast furnace operation weight, d4 is the blast furnace energy consumption weight, d1+d2+d3+d4=1, f1 is the number of monitoring periods in which the fuel quality status is unqualified within the management period, f2 is the number of monitoring periods in which the fan status is abnormal within the management period, f3 is the number of monitoring periods in which the blast furnace operation status is abnormal within the management period, f4 is the number of monitoring periods in which the blast furnace energy consumption status is high energy consumption within the management period, f0 is the number of monitoring periods within the management period, and F is the preset status abnormality ratio.

[0090] It can be understood that, in this embodiment, there is no specific limitation on the setting of each weight and the abnormal proportion of the preset state, and those skilled in the art can set it freely, as long as the setting requirements of each weight and the abnormal proportion of the preset state are met. Among them, the best value of d1 is 0.12, the best value of d2 is 0.18, the best value of d3 is 0.4, the best value of d4 is 0.3, and the best value of F is 0.13.

[0091] Please continue reading Figure 4 As shown, the production monitoring module also includes:

[0092] A pressure fluctuation analysis unit is connected to the alarm unit, and the pressure fluctuation analysis unit analyzes the fluctuation state of the blast furnace pressure according to the blast furnace pressure of each monitoring period within the management period, and adjusts the analysis process of the production status according to the analysis result, wherein:

[0093] The pressure fluctuation analysis unit constructs the blast furnace pressure fluctuation coefficient GB and sets:

[0094]

[0095] Among them, GLm is the blast furnace pressure of the mth monitoring period within the management period, and GLP is the average value of the blast furnace pressure of each monitoring period within the management period;

[0096] The pressure fluctuation analysis unit compares the blast furnace pressure fluctuation coefficient GB with the pressure fluctuation threshold GB0, and analyzes the fluctuation state of the blast furnace pressure according to the comparison result. If GB≤GB0, the pressure fluctuation analysis unit determines that the fluctuation of the blast furnace pressure in the current management period is normal and does not make any adjustment; if GB>GB0, the pressure fluctuation analysis unit determines that the fluctuation of the blast furnace pressure in the current management period is abnormal, and sets the preset state abnormal proportion to F', setting: F'=F×[1-η×(GB-GB0)], η is the preset adjustment ratio; by constructing the blast furnace pressure fluctuation coefficient GB and comparing it with the preset fluctuation coefficient GB0, the pressure fluctuation exceeding limit condition is effectively identified, and the system parameters are dynamically adjusted to improve the accuracy of issuing alarms to operators;

[0097] It can be understood that, in this embodiment, there is no specific limitation on the setting of the pressure fluctuation threshold and the preset adjustment ratio, and those skilled in the art can set them freely, as long as the setting requirements of the pressure fluctuation threshold and the preset adjustment ratio are met, where the optimal value of GB0 is 0.1, and the optimal value of η is 0.2.

[0098] Please continue reading Figure 4 As shown, the production monitoring module also includes:

[0099] The furnace temperature monitoring unit is connected to the pressure fluctuation analysis unit, and is used to compare the furnace temperature ct0 collected during the management period with the preset furnace temperature ct1, and analyze the abnormality of the furnace temperature according to the comparison result, and make secondary adjustments to the analysis process of the production status according to the analysis result, so as to reduce the influence of the abnormal furnace temperature on the production status analysis, wherein:

[0100] If ct0≤ct1, the furnace temperature monitoring unit determines that the furnace temperature in the current management period is abnormal, and sets the preset adjustment ratio to η1, setting η1=η×{1+exp[3×(ct1-ct0) / ct1-3]};

[0101] If ct0>ct1, the furnace temperature monitoring unit determines that the furnace temperature in the current management cycle is normal and does not make any adjustments. By monitoring the furnace temperature ct0 and comparing it with the preset furnace temperature ct1, it is determined that the temperature of the molten iron discharged from the furnace is maintained within the correct range, thereby reducing the impact of abnormal furnace temperature on production status analysis.

[0102] It is understandable that the present embodiment does not specifically limit the setting of the preset furnace temperature, and those skilled in the art can freely set it as long as the setting requirements of the preset furnace temperature are met, wherein the optimal value of ct1 is 1450°C.

[0103] Specifically, this embodiment is applied to the monitoring of production data in the modern steel production process. Through the Internet of Things technology, it realizes intelligent monitoring and data analysis of various core parameters of the blast furnace, timely identifies abnormal conditions and takes corresponding measures to ensure the operating efficiency and safety of the blast furnace, while optimizing fuel use and reducing production costs.

[0104] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the embodiments here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. An industrial production data monitoring system based on the Internet of Things, characterized in that: include: Production data acquisition module, used to collect fan data, blast furnace data and gas calorific value; The calorific value analysis module is used to analyze the fuel quality status according to the fuel calorific value collected during the monitoring period; The fan monitoring module is used to analyze the fan status according to the oxygen supply and air temperature collected during the monitoring period; The blast furnace monitoring module is used to analyze the blast furnace operation status according to the analysis results of the blast furnace temperature, blast furnace pressure, carbon dioxide concentration in the blast furnace and fan status collected during the monitoring period; An efficiency analysis module is used to analyze the energy consumption status of the blast furnace based on the blast furnace energy consumption data collected during the monitoring period and the historical energy consumption data of the blast furnace during the historical monitoring period; The production monitoring module is used to analyze the production status according to the analysis results of the fuel quality status, the fan status, the blast furnace operation status and the blast furnace energy consumption status within the management cycle, and to provide users with abnormal production status alarms based on the analysis results. It is also used to adjust the production status analysis process based on the blast furnace pressure and the collected furnace outlet temperature in each monitoring period within the management cycle.

2. The industrial production data monitoring system based on the Internet of Things according to claim 1 is characterized in that: The calorific value analysis module constructs a fuel calorific value fluctuation coefficient RB according to the fuel calorific value collected during the monitoring period, and compares the fuel calorific value fluctuation coefficient RB with a preset fluctuation coefficient RB0 to analyze the fuel quality status, which includes qualified and unqualified.

3. The industrial production data monitoring system based on the Internet of Things according to claim 2 is characterized in that: The fan monitoring module includes an oxygen supply analysis unit, which is used to compare the oxygen supply sy0 collected during the monitoring period with each oxygen supply threshold, and analyze the fan oxygen supply status according to the comparison result, and the fan oxygen supply status includes normal and abnormal; The fan monitoring module also includes an air temperature analysis unit, which is used to compare the air temperature t0 collected during the monitoring period with the air temperature threshold t1, and analyze the air temperature state according to the comparison result, and the air temperature state includes normal and abnormal; The fan monitoring module also includes a fan analysis unit, which analyzes the fan status according to the analysis results of the fan oxygen supply status and the air temperature status within the monitoring period, and the fan status includes normal and abnormal.

4. The industrial production data monitoring system based on the Internet of Things according to claim 3 is characterized in that: The blast furnace monitoring module includes a temperature monitoring unit, which is used to compare the blast furnace temperature gt0 collected during the monitoring period with the preset blast furnace temperature gt1, and analyze the blast furnace temperature abnormality based on the comparison result. The blast furnace temperature abnormality includes normal and abnormal. If the blast furnace temperature in the current monitoring period is abnormal, the blast furnace temperature abnormality index is set to GC1; if the blast furnace temperature in the current monitoring period is normal, the blast furnace temperature abnormality index is set to GC2.

5. The industrial production data monitoring system based on the Internet of Things according to claim 4 is characterized in that: The blast furnace monitoring module also includes a pressure monitoring unit, which is used to compare the blast furnace pressure gy0 collected during the monitoring period with the preset blast furnace pressure gy1, and analyze the abnormality of the blast furnace pressure according to the comparison result. The abnormality of the blast furnace pressure includes normal and abnormal. If the blast furnace pressure in the current monitoring period is normal, the blast furnace pressure abnormality index is set to YC1; if the blast furnace pressure in the current monitoring period is abnormal, the blast furnace pressure abnormality index is set to YC2; The blast furnace monitoring module also includes a gas monitoring unit, which is used to compare the carbon dioxide concentration ry0 in the blast furnace collected during the monitoring period with the preset concentration ry1, and analyze the abnormality of the blast furnace carbon dioxide concentration based on the comparison result. The abnormality of the blast furnace carbon dioxide concentration includes normal and abnormal. If the carbon dioxide concentration in the current monitoring period is normal, the blast furnace gas abnormality index is set to LC1; if the carbon dioxide concentration in the current monitoring period is abnormal, the blast furnace gas abnormality index is set to LC2.

6. The industrial production data monitoring system based on the Internet of Things according to claim 5 is characterized in that: The blast furnace monitoring module also includes a blast furnace analysis unit, which is used to analyze the blast furnace operation status according to the construction results of the blast furnace temperature abnormality index, the blast furnace pressure abnormality index, the blast furnace gas abnormality index and the analysis results of the fan status. The blast furnace operation status includes normal operation and abnormal operation.

7. The industrial production data monitoring system based on the Internet of Things according to claim 6 is characterized in that: The efficiency analysis module is used to analyze the blast furnace energy consumption status according to the blast furnace energy consumption data n0 collected during the monitoring period and the blast furnace historical energy consumption data during the historical monitoring period. The blast furnace energy consumption status includes normal energy consumption and high energy consumption.

8. The industrial production data monitoring system based on the Internet of Things according to claim 7 is characterized in that: The production monitoring module includes an alarm unit, which is used to analyze the production status according to the analysis results of the fuel quality status, the fan status, the blast furnace operation status and the blast furnace energy consumption status within the management cycle. The production status includes normal and abnormal. If the production status in the current management cycle is abnormal, an abnormal production status alarm will be issued to the user.

9. The industrial production data monitoring system based on the Internet of Things according to claim 8 is characterized in that: The production monitoring module also includes a pressure fluctuation analysis unit, which constructs a blast furnace pressure fluctuation coefficient GB according to the blast furnace pressure of each monitoring period within the management period, and compares the blast furnace pressure fluctuation coefficient GB with the pressure fluctuation threshold GB0, and analyzes the fluctuation state of the blast furnace pressure according to the comparison result. The fluctuation state of the blast furnace pressure includes normal fluctuation and abnormal fluctuation. If the fluctuation of the blast furnace pressure in the current management period is abnormal, the preset state abnormality ratio is adjusted to F'.

10. The industrial production data monitoring system based on the Internet of Things according to claim 9 is characterized in that: The production monitoring module also includes a furnace temperature monitoring unit, which is used to compare the furnace temperature ct0 collected during the management cycle with the preset furnace temperature ct1, and analyze the abnormality of the furnace temperature based on the comparison result. The abnormality of the furnace temperature includes normal and abnormal. If the furnace temperature in the current management cycle is abnormal, the preset adjustment ratio is adjusted to η1.

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