Real-time material distribution parameter statistical method for blast furnace

Abstract

The invention belongs to the technical field of blast furnace production, and provides a blast furnace real-time material distribution parameter statistical method, which comprises the following steps: when a parameter missing signal is generated, acquiring a missing number ratio and a missing discrete value, and calculating the ratio of the missing number ratio to the missing discrete value to obtain a missing degree value; all the missing degree values in the collection period are added, summed and averaged to obtain a collection adjustment value, the collection adjustment value is compared with a collection adjustment threshold value, and if the collection adjustment value is larger than the collection adjustment threshold value, a collection adjustment signal is generated. The missing number proportion of the cloth parameters in all missing data sets and the dispersion degree of missing value distribution are comprehensively reflected by collecting adjustment values, the larger the value is, the larger the missing number proportion of the parameters in all the missing data sets in the collecting period is, the missing value distribution is concentrated, and the missing value distribution is more accurate. Therefore, the accuracy of blast furnace burden distribution parameter acquisition is reduced, and the blast furnace burden distribution parameter acquisition is incomplete.

Description

Technical Field

[0001] The invention belongs to the technical field of blast furnace data collection, in particular to a real-time blast furnace burden distribution parameter statistics method. Background Art

[0002] Blast furnace charging refers to the process of loading charge (including ore and coke) into the throat of a blast furnace in a certain manner and according to certain parameters through charging equipment. In this process, it is necessary to collect and count the charging parameters of the blast furnace in real time to provide data basis for subsequent adjustment of the blast furnace condition. In actual operation, blast furnace process operators are unable to collect and count charging parameters for each angle, and when using the background system to collect and count parameters, it is also necessary to avoid the problem of incomplete collection of blast furnace charging parameters due to missing parameters.

[0003] In the prior art, the zoned charging method is to divide the interior of the blast furnace into different areas and perform differentiated charging according to the characteristics of the areas. In the actual operation process, the blast furnace process operators are unable to collect and count the charging parameters for each angle, and parameter missing is prone to occur when the background system is used to collect and count the parameters. Therefore, it is necessary to analyze whether the charging parameters collected in real time are abnormal, and evaluate whether the abnormal type is parameter missing. When it is determined that the abnormal type is parameter missing, the degree of missing of the collected charging parameters is analyzed. If the degree of missing of the collected charging parameters is large, a collection adjustment signal is generated to avoid incomplete charging parameter information in the entire collection cycle, thereby solving the problem of incomplete collection of blast furnace charging parameters due to a large degree of missing of charging parameters.

[0004] To this end, the present invention provides a method for calculating real-time burden distribution parameters of a blast furnace. Summary of the invention

[0005] In order to make up for the deficiencies of the prior art, the problem of incomplete collection of blast furnace burden distribution parameters due to a large degree of missingness mentioned in the background technology is solved.

[0006] The technical solution adopted by the present invention to solve its technical problem is:

[0007] A method for calculating real-time burden distribution parameters of a blast furnace, comprising:

[0008] Step 1: During the collection period, real-time collection is performed on the blast furnace burden area to obtain burden parameters, the burden parameters are processed to obtain a burden data group, and the burden parameters in the burden data group are pre-analyzed to obtain an abnormal data group and a non-abnormal data group;

[0009] Step 2: Based on the abnormal data group and the non-abnormal data group, the abnormal data group is processed and analyzed to obtain a type identification value, and compared with the threshold. If the type identification value is greater than or equal to the type identification threshold, a parameter missing signal is generated, and the abnormal data group corresponding to the parameter missing signal is marked as a missing data group;

[0010] Step 3: Based on the parameter missing signal, the missing data group is analyzed and processed to obtain the acquisition adjustment value, and the acquisition adjustment value is compared with the threshold. If the acquisition adjustment value is greater than the acquisition adjustment threshold, an acquisition adjustment signal is generated;

[0011] Step 4: Based on the acquisition adjustment signal, obtain the acquisition adjustment coefficient, and complete the dynamic adjustment of the blast furnace burden parameters by adjusting the acquisition frequency.

[0012] As a further solution of the present invention, the abnormal data group is obtained by:

[0013] In a collection cycle, the collection cycle is divided into a number of collection time periods, and in the collection time period, each collection time period is divided into a number of collection nodes, and the cloth parameters corresponding to each collection node are obtained, and all the cloth parameters in each collection time period are integrated to obtain a cloth data group;

[0014] Calculate the standard deviation of all fabric parameters in the fabric data set to obtain discrete values ​​of the parameters;

[0015] Compare the parameter discrete value with the parameter discrete threshold, and if the parameter discrete value is greater than the parameter discrete threshold, mark the fabric data group as an abnormal data group;

[0016] If the parameter discrete value is less than or equal to the parameter discrete threshold, the fabric data set is marked as a non-abnormal data set.

[0017] As a further solution of the present invention, the type identification value is obtained by:

[0018] Substitute the parameter number difference coefficient and the parameter acquisition difference coefficient into the formula: The type identification value SB is calculated, where GS represents the abnormal number difference coefficient, CJ represents the abnormal collection difference coefficient, and α and β represent the preset weight coefficients.

[0019] As a further solution of the present invention, the abnormal number difference coefficient is obtained in the following manner:

[0020] Get the number of fabric parameters in the abnormal data group and mark it as the number of abnormal parameters;

[0021] Subtract the number of abnormal parameters from the threshold of the number of abnormal parameters, take the absolute value, and get the difference of the number of abnormalities;

[0022] The anomaly number difference value is calculated by ratioing the anomaly number threshold value to obtain the anomaly number difference coefficient.

[0023] As a further solution of the present invention, the abnormal acquisition difference coefficient is obtained in the following manner:

[0024] In the abnormal data group, the number of abnormal collections is obtained, and the number of abnormal collections is subtracted from the threshold of the number of abnormal collections, and the absolute value is taken to obtain the abnormal collection difference;

[0025] The abnormal collection difference value is calculated by ratioing the abnormal collection number threshold value to obtain the abnormal collection difference coefficient.

[0026] As a further solution of the present invention, the acquisition method of the acquisition adjustment value is:

[0027] All missing degree values ​​within the collection period are added and averaged to obtain the collection adjustment value.

[0028] As a further solution of the present invention, the method for obtaining the missing degree value is:

[0029] The missing degree value is obtained by calculating the ratio of the missing number proportion to the missing discrete value.

[0030] As a further solution of the present invention, the method for obtaining the missing number ratio is as follows:

[0031] Obtain the number of missing parameters in each missing data group, add the number of missing parameters in each missing data group, and obtain the number of missing parameters;

[0032] The ratio of the number of missing parameters to the total number of parameters collected during the collection period is calculated to obtain the missing number ratio.

[0033] As a further solution of the present invention, the missing discrete coefficient is obtained by:

[0034] Get the collection nodes corresponding to the missing parameters in each missing data group and mark them as missing nodes;

[0035] Get the interval time between all missing nodes, calculate the standard deviation of the interval time between all missing nodes, and get the missing discrete value;

[0036] The missing discrete value is calculated by ratio calculation with the time length value corresponding to the acquisition period to obtain the missing discrete coefficient.

[0037] As a further solution of the present invention, the dynamic adjustment process of blast furnace burden parameters is as follows:

[0038] Substituting the missingness value into the formula: The acquisition adjustment coefficient K is calculated, where QS represents the missing degree value, QSy It is expressed as the missing degree threshold;

[0039] Based on the collection adjustment factor, substitute the collection adjustment factor and the missing degree value into the formula: F t =F d +F d ×K, calculate the adjusted acquisition frequency F t , where F d It represents the current acquisition frequency, and K represents the acquisition adjustment coefficient.

[0040] The beneficial effects of the present invention are as follows:

[0041] (1) The present invention performs real-time collection of the blast furnace charge distribution area within the collection period, obtains charge distribution parameters, processes the charge distribution parameters to obtain a charge distribution data group, performs pre-analysis processing on the charge distribution parameters in the charge distribution data group to obtain an abnormal data group and a non-abnormal data group, respectively processes and analyzes the charge distribution parameters in the abnormal data group and the non-abnormal data group to obtain a type identification value, and compares it with a threshold value. If the type identification value is greater than or equal to the type identification threshold value, a parameter missing signal is generated, thereby analyzing the parameters in the abnormal data group to identify whether the charge distribution parameters in the abnormal data group are missing;

[0042] (2) When generating a parameter missing signal, the present invention obtains the missing number ratio and the missing discrete value, calculates the ratio of the missing number ratio to the missing discrete value, obtains the missing degree value, adds and averages all the missing degree values ​​in the acquisition period, obtains the acquisition adjustment value, and compares the acquisition adjustment value with the acquisition adjustment threshold. If the acquisition adjustment value is greater than the acquisition adjustment threshold, a collection adjustment signal is generated. Therefore, the acquisition adjustment value comprehensively reflects the missing number ratio of the distribution parameters in all missing data groups and the discrete degree of the missing value distribution. The larger this value is, the larger the missing number ratio of the parameters in all missing data groups in the acquisition period is, and the distribution of the missing values ​​is more concentrated, thereby reducing the accuracy of the blast furnace distribution parameter acquisition, resulting in incomplete blast furnace distribution parameter acquisition;

[0043] (3) When generating a collection adjustment signal, the present invention substitutes the missing degree value into the formula to obtain the collection adjustment coefficient, and substitutes the collection adjustment coefficient and the current collection frequency into the formula to obtain the adjusted collection frequency. Therefore, when real-time collection and statistics of blast furnace burden distribution parameters are performed, if parameter collection is missing, the collection frequency can be adjusted in time to avoid incomplete burden distribution parameter information in the entire collection cycle, thereby solving the problem of incomplete collection of blast furnace burden distribution parameters due to a large missing degree. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The present invention will be further described below in conjunction with the accompanying drawings.

[0045] Figure 1 is a flowchart of the steps of Embodiment 1 of the present invention;

[0046] Figure 2 is a flowchart of the steps of Embodiment 2 of the present invention;

[0047] Figure 3 It is a step flow chart of Example 3 of the present invention. DETAILED DESCRIPTION

[0048] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0049] Example 1

[0050] like Figure 1 As shown, a method for calculating real-time burden distribution parameters of a blast furnace according to an embodiment of the present invention includes:

[0051] Step 1: During the collection period, real-time collection is performed on the blast furnace burden area to obtain burden parameters, the burden parameters are processed to obtain a burden data group, and the burden parameters in the burden data group are pre-analyzed to obtain an abnormal data group and a non-abnormal data group;

[0052] In some embodiments, within a collection cycle, the collection cycle is divided into a number of collection time periods, within the collection time period, each collection time period is divided into a number of collection nodes, the cloth parameters corresponding to each collection node are obtained, and all cloth parameters within each collection time period are integrated to obtain a cloth data group;

[0053] It can be understood that: the collection cycle is divided into a number of collection time periods at equal time intervals;

[0054] It should be noted that: the fabric parameters collected in each collection period are integrated and arranged according to the time sequence of the collection nodes to obtain a fabric data group;

[0055] The pre-analysis and processing process of the cloth parameters in the cloth data group is as follows:

[0056] Calculate the standard deviation of all fabric parameters in the fabric data set to obtain discrete values ​​of the parameters;

[0057] It can be understood that the meaning of the parameter discrete value is: in the fabric data set, by calculating the standard deviation of all fabric parameters, it reflects the degree of discreteness of the fabric parameters in the fabric data set. Specifically, the larger the value is, the more dispersed the fabric parameters in the fabric data set are, the greater the difference between the fabric parameters is, and the greater the possibility of data anomaly. On the contrary, the smaller the value is, the more concentrated the fabric parameters in the fabric data set are, the smaller the difference between the fabric parameters is, and the less likely the possibility of data anomaly is.

[0058] The parameter discrete value is compared with the parameter discrete threshold. The comparison process is as follows:

[0059] If the parameter discrete value is greater than the parameter discrete threshold, it means that the fabric parameters in the fabric data set are more dispersed and the differences between the fabric parameters are greater, and the fabric data set is marked as an abnormal data set;

[0060] If the parameter discrete value is less than or equal to the parameter discrete threshold, it means that the fabric parameters in the fabric data group are more concentrated and the differences between the fabric parameters are smaller, and the fabric data group is marked as a non-abnormal data group;

[0061] Step 2: Based on the abnormal data group, process and analyze the fabric parameters in the abnormal data group to obtain the type identification value, and compare it with the threshold to generate a signal whether the parameter is missing;

[0062] Wherein, the parameter missing signal includes a parameter missing signal and a parameter non-missing signal;

[0063] In some embodiments, the number of fabric parameters in the abnormal data group is obtained and marked as the number of abnormal parameters;

[0064] Subtract the number of abnormal parameters from the threshold of the number of abnormal parameters, take the absolute value, and get the difference of the number of abnormalities;

[0065] Calculate the ratio of the anomaly number difference to the anomaly number threshold to obtain the anomaly number difference coefficient;

[0066] The non-abnormal interval values ​​corresponding to all non-abnormal data groups are added together to obtain the non-abnormal acquisition value;

[0067] In the abnormal data group, the number of abnormal collections is obtained, and the number of abnormal collections is subtracted from the threshold of the number of abnormal collections, and the absolute value is taken to obtain the abnormal collection difference;

[0068] The abnormal collection difference value is calculated by ratio with the abnormal collection number threshold value to obtain the abnormal collection difference coefficient;

[0069] Substitute the parameter number difference coefficient and the parameter acquisition difference coefficient into the formula: The type identification value SB is calculated, where GS represents the abnormal number difference coefficient, CJ represents the abnormal collection difference coefficient, and α and β represent the preset weight coefficients;

[0070] It can be understood that the meaning of the type identification value is: by calculating the abnormal number difference coefficient and the abnormal acquisition difference coefficient, it reflects whether there is a missing cloth parameter in the abnormal data group. Specifically, the larger the value, the greater the difference in the number of parameters in the abnormal data group, the greater the deviation in the number of acquisitions, and the greater the missing degree of cloth parameters in the abnormal data group. On the contrary, the smaller the value, the smaller the difference in the number of parameters in the abnormal data group, the smaller the deviation in the number of acquisitions, and the smaller the missing degree of cloth parameters in the abnormal data group.

[0071] The type identification value is compared with the type identification threshold. The comparison process is as follows:

[0072] If the type identification value is greater than or equal to the type identification threshold, it means that the number of parameters in the abnormal data group is relatively different and the number of acquisition times is relatively different, so a parameter missing signal is generated and the abnormal data group corresponding to the parameter missing signal is marked as a missing data group.

[0073] If the type identification value is less than the type identification threshold, it means that the number of parameters in the abnormal data group is less different and the number of acquisitions is less biased, so a parameter non-missing signal is generated and the abnormal data group corresponding to the parameter missing signal is marked as a non-missing data group.

[0074] The specific implementation scheme of the embodiment of the present invention is: within the collection period, real-time collection is performed on the blast furnace charging area to obtain charging parameters, the charging parameters are processed to obtain a charging data group, the charging parameters in the charging data group are pre-analyzed to obtain an abnormal data group and a non-abnormal data group, and the charging parameters in the abnormal data group and the non-abnormal data group are processed and analyzed respectively to obtain a type identification value, and compared with a threshold value. If the type identification value is greater than or equal to the type identification threshold value, a parameter missing signal is generated, thereby analyzing the parameters in the abnormal data group to identify whether the charging parameters in the abnormal data group are missing.

[0075] Example 2

[0076] like Figure 2 As shown, based on Example 1, a method for calculating real-time burden distribution parameters of a blast furnace according to an embodiment of the present invention includes:

[0077] Step 3: Based on the parameter missing signal, the missing data group is analyzed and processed to obtain the acquisition adjustment value, and compared with the threshold value to generate a signal whether to adjust the acquisition;

[0078] The collecting of whether to adjust the signal includes collecting the adjusted signal and collecting the unadjusted signal;

[0079] In some embodiments, when a parameter missing signal is generated, the missing number ratio and the missing discrete value are obtained to obtain the acquisition adjustment value. The specific process is as follows:

[0080] Obtain the number of missing parameters in each missing data group, add the number of missing parameters in each missing data group, and obtain the number of missing parameters;

[0081] Calculate the ratio of the number of missing parameters to the total number of parameters collected during the collection period to obtain the missing number ratio;

[0082] Get the collection nodes corresponding to the missing parameters in each missing data group and mark them as missing nodes;

[0083] Get the interval time between all missing nodes, calculate the standard deviation of the interval time between all missing nodes, and get the missing discrete value;

[0084] Calculate the ratio of the missing discrete value to the time length value corresponding to the acquisition period to obtain the missing discrete coefficient;

[0085] Calculate the ratio of missing number proportion to missing discrete value to get missing degree value;

[0086] It can be understood that the meaning of the missing degree value is: by calculating the proportion of missing parameters in the missing data group and the proportion of the degree of dispersion of missing values, it comprehensively reflects the degree of missing parameters in the missing data group. Specifically, the larger the value, the larger the proportion of missing parameters in the missing data group, and the distribution of missing values ​​is more concentrated. On the contrary, the smaller the value, the smaller the proportion of missing parameters in the missing data group, and the distribution of missing values ​​is more dispersed.

[0087] All missing degree values ​​within the collection period are added and averaged to obtain the collection adjustment value;

[0088] It should be noted that the meaning of the acquisition adjustment value is: a comprehensive evaluation index after quantifying the missing degree of all missing data groups within the acquisition period. This value comprehensively reflects the proportion of missing numbers of fabric parameters in all missing data groups and the discrete degree of missing value distribution. Specifically, the larger this value is, the larger the proportion of missing numbers of parameters in all missing data groups within the acquisition period, the more concentrated the distribution of missing values, and the less complete the parameter information in all missing data groups within the acquisition period. On the contrary, the smaller this value is, the smaller the proportion of missing numbers of parameters in all missing data groups within the acquisition period, the more dispersed the distribution of missing values, and the more complete the parameter information in all missing data groups within the acquisition period.

[0089] The acquisition adjustment value is compared with the acquisition adjustment threshold. The comparison process is as follows:

[0090] If the acquisition adjustment value is greater than the acquisition adjustment threshold, it means that the number of missing parameters in all missing data groups during the acquisition period accounts for a large proportion, the distribution of missing values ​​is relatively concentrated, and the parameter information in all missing data groups during the acquisition period is relatively incomplete, and an acquisition adjustment signal is generated;

[0091] If the acquisition adjustment value is less than or equal to the acquisition adjustment threshold, it means that the number of missing parameters in all missing data groups during the acquisition period accounts for a small proportion, the distribution of missing values ​​is relatively dispersed, and the parameter information in all missing data groups during the acquisition period is relatively complete, and a signal of no acquisition adjustment is generated;

[0092] The specific implementation scheme of the embodiment of the present invention is: when a parameter missing signal is generated, the missing number ratio and the missing discrete value are obtained, the missing number ratio and the missing discrete value are calculated to obtain a missing degree value, all missing degree values ​​in the collection period are added and averaged to obtain a collection adjustment value, and the collection adjustment value is compared with the collection adjustment threshold. If the collection adjustment value is greater than the collection adjustment threshold, a collection adjustment signal is generated. Therefore, the collection adjustment value comprehensively reflects the missing number ratio of the material distribution parameters in all missing data groups and the discrete degree of the missing value distribution. The larger this value is, the larger the missing number ratio of the parameters in all missing data groups in the collection period is, and the distribution of the missing values ​​is more concentrated, thereby reducing the accuracy of the blast furnace material distribution parameter collection and resulting in incomplete blast furnace material distribution parameter collection.

[0093] Example 3

[0094] like Figure 3 As shown, based on the above-mentioned embodiment 1 and embodiment 2, a method for calculating the real-time distribution parameters of a blast furnace according to an embodiment of the present invention includes:

[0095] Step 4: Based on the acquisition adjustment signal, the acquisition adjustment coefficient is obtained, and the dynamic adjustment of the blast furnace burden parameters is completed by adjusting the acquisition frequency;

[0096] In some embodiments, when the acquisition adjustment signal is generated, the acquisition adjustment coefficient is obtained, and the acquisition process is as follows:

[0097] Substituting the missingness value into the formula: The acquisition adjustment coefficient K is calculated, where QS represents the missing degree value, QS y It is expressed as the missing degree threshold;

[0098] Based on the collection adjustment factor, substitute the collection adjustment factor and the missing degree value into the formula: F t =F d +F d×K, calculate the adjusted acquisition frequency F t , where F d It represents the current acquisition frequency, and K represents the acquisition adjustment coefficient;

[0099] The specific implementation scheme of the embodiment of the present invention is: when generating a collection adjustment signal, the missing degree value is substituted into the formula to obtain the collection adjustment coefficient, and the collection adjustment coefficient and the current collection frequency are substituted into the formula to obtain the adjusted collection frequency, so that when the blast furnace burden distribution parameters are collected and counted in real time, if parameter collection is missing, the collection frequency can be adjusted in time to avoid incomplete burden distribution parameter information in the entire collection cycle, thereby solving the problem of incomplete collection of blast furnace burden distribution parameters due to a large missing degree.

[0100] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for calculating real-time blast furnace burden parameters, characterized in that: include: Step 1: During the collection period, real-time collection is performed on the blast furnace burden area to obtain burden parameters, the burden parameters are processed to obtain a burden data group, and the burden parameters in the burden data group are pre-analyzed to obtain an abnormal data group and a non-abnormal data group; Step 2: Based on the abnormal data group and the non-abnormal data group, the abnormal data group is processed and analyzed to obtain a type identification value, and compared with the threshold. If the type identification value is greater than or equal to the type identification threshold, a parameter missing signal is generated, and the abnormal data group corresponding to the parameter missing signal is marked as a missing data group; Step 3: Based on the parameter missing signal, the missing data group is analyzed and processed to obtain the acquisition adjustment value, and the acquisition adjustment value is compared with the threshold. If the acquisition adjustment value is greater than the acquisition adjustment threshold, an acquisition adjustment signal is generated; Step 4: Based on the acquisition adjustment signal, obtain the acquisition adjustment coefficient, and complete the dynamic adjustment of the blast furnace burden parameters by adjusting the acquisition frequency.

2. A method for calculating real-time blast furnace burden parameters according to claim 1, characterized in that: The abnormal data group is obtained as follows: In a collection cycle, the collection cycle is divided into a number of collection time periods, and in the collection time period, each collection time period is divided into a number of collection nodes, and the cloth parameters corresponding to each collection node are obtained, and all the cloth parameters in each collection time period are integrated to obtain a cloth data group; Calculate the standard deviation of all fabric parameters in the fabric data set to obtain discrete values ​​of the parameters; Compare the parameter discrete value with the parameter discrete threshold, and if the parameter discrete value is greater than the parameter discrete threshold, mark the fabric data group as an abnormal data group; If the parameter discrete value is less than or equal to the parameter discrete threshold, the fabric data set is marked as a non-abnormal data set.

3. A method for calculating real-time blast furnace burden parameters according to claim 1, characterized in that: The type identification value is obtained as follows: Substitute the parameter number difference coefficient and the parameter acquisition difference coefficient into the formula: The type identification value SB is calculated, where GS represents the abnormal number difference coefficient, CJ represents the abnormal collection difference coefficient, and α and β represent the preset weight coefficients.

4. A method for calculating real-time blast furnace burden parameters according to claim 1, characterized in that: The method for obtaining the abnormal number difference coefficient is as follows: Get the number of fabric parameters in the abnormal data group and mark it as the number of abnormal parameters; Subtract the number of abnormal parameters from the threshold of the number of abnormal parameters, take the absolute value, and get the difference of the number of abnormalities; The anomaly number difference value is calculated by ratioing the anomaly number threshold value to obtain the anomaly number difference coefficient.

5. A method for calculating real-time blast furnace burden parameters according to claim 1, characterized in that: The abnormal acquisition difference coefficient is obtained as follows: In the abnormal data group, the number of abnormal collections is obtained, and the number of abnormal collections is subtracted from the threshold of the number of abnormal collections, and the absolute value is taken to obtain the abnormal collection difference; The abnormal collection difference value is calculated by ratioing the abnormal collection number threshold value to obtain the abnormal collection difference coefficient.

6. A method for calculating real-time blast furnace burden parameters according to claim 1, characterized in that: The acquisition adjustment value is obtained as follows: All missing degree values ​​within the collection period are added and averaged to obtain the collection adjustment value.

7. A method for calculating real-time blast furnace burden distribution parameters according to claim 6, characterized in that: The missing degree value is obtained as follows: The missing degree value is obtained by calculating the ratio of the missing number proportion to the missing discrete value.

8. A method for calculating real-time blast furnace burden distribution parameters according to claim 7, characterized in that: The missing number ratio is obtained as follows: Obtain the number of missing parameters in each missing data group, add the number of missing parameters in each missing data group, and obtain the number of missing parameters; The ratio of the number of missing parameters to the total number of parameters collected during the collection period is calculated to obtain the missing number ratio.

9. A method for calculating real-time blast furnace burden distribution parameters according to claim 7, characterized in that: The missing discrete coefficient is obtained as follows: Get the collection nodes corresponding to the missing parameters in each missing data group and mark them as missing nodes; Get the interval time between all missing nodes, calculate the standard deviation of the interval time between all missing nodes, and get the missing discrete value; The missing discrete value is calculated by ratio calculation with the time length value corresponding to the acquisition period to obtain the missing discrete coefficient.

10. A method for calculating real-time blast furnace burden distribution parameters according to claim 1, characterized in that: The dynamic adjustment process of blast furnace burden parameters is as follows: Substituting the missingness value into the formula: The acquisition adjustment coefficient K is calculated, where QS represents the missing degree value, QS y It is expressed as the missing degree threshold; Based on the collection adjustment factor, substitute the collection adjustment factor and the missing degree value into the formula: F t =F d +F d ×K, calculate the adjusted acquisition frequency F t , where F d It represents the current acquisition frequency, and K represents the acquisition adjustment coefficient.

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