Blast furnace ironmaking monitoring method based on blast furnace physical examination system and data driving fusion

Abstract

The invention relates to a blast furnace iron-making monitoring method based on a blast furnace physical examination system and data driving fusion, which comprises the following steps: determining a first period and a second period smaller than the first period, and periodically determining a smooth working condition for evaluating the overall advantages and disadvantages of a blast furnace iron-making process according to the first period, and determining a variable set, a score contribution set and a weight vector according to the sequential working conditions of the two adjacent first periods. In the next first period, index data corresponding to at least one operation index in the variable set is periodically collected according to the second period, a preset blast furnace ironmaking monitoring model is calculated according to the weight vector and the index data, a first analysis index and a second analysis index are obtained, and the blast furnace ironmaking monitoring model is obtained according to the first analysis index and the second analysis index. And judging whether alarm is needed in the current second period. According to the invention, macroscopic monitoring is carried out based on the production scene, microscopic monitoring is carried out based on the macroscopic monitoring result and specific data, data scene combination is realized in the monitoring process, and the monitoring effect is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of blast furnace ironmaking, and particularly to a blast furnace ironmaking monitoring method based on the integration of blast furnace physical examination system and data-driven approach. Background Art

[0002] As an important pillar of modern industry, with the improvement of production requirements and the progress of production level, blast furnace ironmaking is gradually developing towards large-scale and high-complexity. The stable and normal operation of the blast furnace ironmaking process is of great significance for improving the production efficiency of the steel industry. And the effective monitoring of the blast furnace operation status is an important guarantee for realizing the stable production of the blast furnace. At present, the monitoring of the blast furnace operation status mainly has two aspects: on the one hand, due to the very complex operation mechanism of the blast furnace, it is impossible to establish an accurate mechanism model, and mainly based on expert experience, a comprehensive analysis of various operation indexes of the blast furnace is carried out to determine the main factors affecting the furnace condition. On the other hand, in order to timely master the production status of the blast furnace ironmaking process, a large number of sensors are installed in the ironmaking system to collect and store a large amount of operation data in real time, and the operation monitoring is realized through the modeling and analysis of the data. However, the related technologies for monitoring blast furnace ironmaking have problems such as relying on manual experience and not combining the physical characteristics of the actual production process for monitoring, resulting in unsatisfactory monitoring effects. Summary of the Invention

[0003] In view of this, the present disclosure proposes a blast furnace ironmaking monitoring method based on the integration of blast furnace physical examination system and data-driven approach, aiming to monitor the blast furnace ironmaking process by combining data and production scenarios and improve the monitoring effect.

[0004] According to the first aspect of the present disclosure, there is provided a method for monitoring the blast furnace ironmaking process, the method comprising:

[0005] Determine a first period and a second period smaller than the first period;

[0006] Periodically determine the smooth operation condition of the blast furnace ironmaking process according to the first period, and the smooth operation condition is used to evaluate the overall quality of the blast furnace ironmaking process;

[0007] Determine a variable set, a score contribution set and a weight vector according to the smooth operation condition corresponding to the current first period and the smooth operation condition corresponding to the previous first period, wherein the variable set includes at least one operation index affecting the smooth operation condition, the score contribution set includes the influence scores corresponding to the operation indexes, the influence scores are used to characterize the influence degree of the corresponding operation indexes on the smooth operation condition, and the weight vector includes the weights corresponding to each operation index;

[0008] Collect the index data corresponding to at least one operation index in the variable set periodically according to the second period within the next first period;

[0009] Based on the weight vector, the index data corresponding to the at least one operating index, and a preset blast furnace ironmaking monitoring model, a first analysis index and a second analysis index are obtained;

[0010] Based on the first analysis index and the second analysis index, it is determined whether an alarm is required for the blast furnace ironmaking process in the current second cycle.

[0011] In a possible implementation manner, the determining the smooth operation condition of the blast furnace ironmaking process periodically according to the first cycle includes:

[0012] Within each of the first cycles, determine the grade scores, index data corresponding to at least one candidate index, and the set range corresponding to each candidate index;

[0013] Calculate the corresponding candidate scores according to the index data corresponding to each candidate index and the set range;

[0014] Calculate the corresponding influence scores according to the grade scores and candidate scores corresponding to each candidate index;

[0015] Determine the sequential condition of the blast furnace ironmaking process according to the sum of the influence scores corresponding to all the candidate indexes.

[0016] In a possible implementation manner, the calculating the corresponding candidate scores according to the index data corresponding to each candidate index and the set range includes:

[0017] Determine the upper quartile, lower quartile, and median according to the set range corresponding to each candidate index;

[0018] Input the upper quartile, lower quartile, median, and index data corresponding to each candidate index into a preset score function to obtain the corresponding candidate scores.

[0019] In a possible implementation manner, the calculating the corresponding influence scores according to the grade scores and candidate scores corresponding to each candidate index includes:

[0020] Calculate the product of the grade scores and candidate scores corresponding to each candidate index to obtain the corresponding influence scores.

[0021] In a possible implementation manner, the determining the variable set, score contribution set, and weight vector according to the smooth operation condition corresponding to the current first cycle and the smooth operation condition corresponding to the previous first cycle includes:

[0022] In response to the smooth operation condition corresponding to the current first cycle being the same as the smooth operation condition corresponding to the previous first cycle, determine that the variable set, score contribution set, and weight vector corresponding to the current first cycle are the same as those of the previous first cycle.

[0023] In a possible implementation, determining the variable set, the score contribution set, and the weight vector according to the forward operating condition corresponding to the current first cycle and the forward operating condition corresponding to the previous first cycle includes:

[0024] In response to the forward operating condition corresponding to the current first cycle being different from the forward operating condition corresponding to the previous first cycle, at least one operating index is screened according to the influence scores corresponding to each of the candidate indexes, a variable set including the at least one operating index is determined, and a score contribution set including the influence scores of each of the operating indexes is determined;

[0025] A relationship matrix characterizing the relationship between every two operating indexes is determined according to the variable set and the score contribution set;

[0026] The relationship matrix is calculated according to the analytic hierarchy process to obtain the weight corresponding to each of the operating indexes, and a weight vector including the weights corresponding to each of the operating indexes is determined.

[0027] In a possible implementation, the method further includes:

[0028] An index data matrix is determined according to multiple index data corresponding to each of the operating indexes in history;

[0029] Principal component analysis is performed on the index data matrix to obtain the same number of principal components as the number of operating indexes in the variable set, and a load vector corresponding to each of the principal components;

[0030] At least one target principal component is screened from the principal components;

[0031] A blast furnace ironmaking monitoring model is established according to the target principal components and the load vectors corresponding to each of the target principal components.

[0032] In a possible implementation, the blast furnace ironmaking monitoring model is X = TP T + E, where T = [t 1 , …, t a ∈ R N×a , P = [p 1 , … p a ∈ R m×a , E ∈ R N×m , X is the index data matrix, t i is the i-th target principal component, p i is the load vector of the i-th target principal component, a is the number of target principal components, E is the residual matrix, and m is the number of principal components.

[0033] In a possible implementation manner, obtaining the first analysis index and the second analysis index according to the weight vector, the index data corresponding to the at least one operation index, and a preset blast furnace ironmaking monitoring model includes:

[0034] Determine a diagonal weight matrix \(W = diag(w)\in R^{m\times m}\), where \(w\) is the weight vector and \(m\) is the number of operation indexes included in the variable set; m×m , \(w\) is the weight vector, and \(m\) is the number of operation indexes included in the variable set;

[0035] Calculate the first analysis index \(T\) according to the diagonal weight matrix and the blast furnace ironmaking monitoring model 2 \(=xWP^{\wedge}P\) T \(Wx\) and the second analysis index \(SPE = xW(I - PP\) T \())Wx\), where \(I\) is the identity matrix, \(x\) is an index data vector including the index data corresponding to each operation index corresponding to the current second period, and \(\wedge\) is a singular value matrix obtained by performing principal component analysis on the index data matrix.

[0036] In a possible implementation manner, determining whether the blast furnace ironmaking process in the current second period needs to give an alarm according to the first analysis index and the second analysis index includes:

[0037] Determine a first index threshold and a second index threshold;

[0038] In response to the first analysis index being greater than the first index threshold and / or the second analysis index being greater than the second index threshold, determine that the blast furnace ironmaking process in the current second period needs to give an alarm.

[0039] According to a second aspect of the present disclosure, there is provided a blast furnace ironmaking process monitoring device, and the device includes:

[0040] A period determination module, configured to determine a first period and a second period smaller than the first period;

[0041] A working condition determination module, configured to periodically determine the smooth running working condition of the blast furnace ironmaking process according to the first period, and the smooth running working condition is used to evaluate the overall quality of the blast furnace ironmaking process;

[0042] A parameter determination module, configured to determine a variable set, a score contribution set, and a weight vector according to the smooth running working condition corresponding to the current first period and the smooth running working condition corresponding to the previous first period. The variable set includes at least one operation index affecting the smooth running working condition, the score contribution set includes the influence scores corresponding to the operation indexes, the influence scores are used to characterize the influence degree of the corresponding operation indexes on the smooth running working condition, and the weight vector includes the weights corresponding to each operation index;

[0043] A data acquisition module, configured to periodically collect index data corresponding to at least one running index in the variable set according to the second period within the next first period;

[0044] An index calculation module, configured to obtain a first analysis index and a second analysis index according to the weight vector, the index data corresponding to the at least one running index, and a preset blast furnace ironmaking monitoring model;

[0045] An alarm response module, configured to determine whether an alarm is required for the blast furnace ironmaking process in the current second period according to the first analysis index and the second analysis index.

[0046] In a possible implementation manner, the working condition determination module is further configured to:

[0047] Within each first period, determine the grade scores, index data corresponding to at least one candidate index, and the set range corresponding to each candidate index;

[0048] Calculate the corresponding candidate scores according to the index data corresponding to each candidate index and the set range;

[0049] Calculate the corresponding influence scores according to the grade scores and candidate scores corresponding to each candidate index;

[0050] Determine the sequential working condition of the blast furnace ironmaking process according to the sum of the influence scores corresponding to all the candidate indexes.

[0051] In a possible implementation manner, the working condition determination module is further configured to:

[0052] Determine the upper quartile, lower quartile, and median according to the set range corresponding to each candidate index;

[0053] Input the upper quartile, lower quartile, median, and index data corresponding to each candidate index into a preset scoring function to obtain the corresponding candidate scores.

[0054] In a possible implementation manner, the working condition determination module is further configured to:

[0055] Calculate the product of the grade scores and candidate scores corresponding to each candidate index to obtain the corresponding influence scores.

[0056] In a possible implementation manner, the parameter determination module is further configured to:

[0057] In response to the smooth working condition corresponding to the current first period being the same as the smooth working condition corresponding to the previous first period, determine that the variable set, score contribution set, and weight vector corresponding to the current first period are the same as those of the previous first period.

[0058] In a possible implementation, the parameter determination module is further configured to:

[0059] In response to the forward driving condition corresponding to the current first cycle being different from the forward driving condition corresponding to the previous first cycle, at least one operating index is screened according to the influence scores corresponding to each of the candidate indexes, a variable set including the at least one operating index is determined, and a score contribution set including the influence scores of each of the operating indexes is determined;

[0060] Determine a relationship matrix representing the relationship between every two operating indexes according to the variable set and the score contribution set;

[0061] Calculate the relationship matrix according to the analytic hierarchy process, obtain the weight corresponding to each of the operating indexes, and determine a weight vector including the weights corresponding to each of the operating indexes.

[0062] In a possible implementation, the apparatus further includes:

[0063] A matrix determination module, configured to determine an index data matrix according to multiple index data corresponding to each of the operating indexes in history;

[0064] A load vector determination module, configured to perform principal component analysis on the index data matrix to obtain the same number of principal components as the number of operating indexes in the variable set, and a load vector corresponding to each of the principal components;

[0065] A principal component screening module, configured to screen at least one target principal component from the principal components;

[0066] A data modeling module, configured to build a blast furnace ironmaking monitoring model according to the target principal components and the load vectors corresponding to each of the target principal components.

[0067] In a possible implementation, the blast furnace ironmaking monitoring model is X = TP T +E, where T = [t 1 , …, t a ∈ R N×a , P = [p 1 , … p a ∈ R m×a , E ∈ R N×m , X is the index data matrix, t i is the i-th target principal component, p i is the load vector of the i-th target principal component, a is the number of target principal components, E is the residual matrix, and m is the number of principal components.

[0068] In a possible implementation, the index calculation module is further configured to:

[0069] Determine the diagonal weight matrix \(W = \text{diag}(w)\in\mathbb{R}\) according to the weight vector m×m , where \(w\) is the weight vector and \(m\) is the number of operation indexes included in the variable set;

[0070] Calculate the first analysis index \(T\) according to the diagonal weight matrix and the blast furnace ironmaking monitoring model 2 \(= xWP\land P\) T \(Wx\) and the second analysis index \(SPE=xW(I - PP\) T )\(Wx\), where \(I\) is the identity matrix, \(x\) is the index data vector including the index data corresponding to each operation index in the current second period, and \(\land\) is the singular value matrix obtained by performing principal component analysis on the index data matrix.

[0071] In a possible implementation manner, the alarm response module is further configured to:

[0072] Determine a first index threshold and a second index threshold;

[0073] In response to the first analysis index being greater than the first index threshold and / or the second analysis index being greater than the second index threshold, determine that the blast furnace ironmaking process in the current second period needs to be alarmed.

[0074] According to a third aspect of the present disclosure, there is provided an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein, the processor is configured to implement the above method when executing the instructions stored in the memory.

[0075] According to a fourth aspect of the present disclosure, there is provided a non-volatile computer-readable storage medium, on which computer program instructions are stored, wherein the computer program instructions implement the above method when executed by a processor.

[0076] According to a fifth aspect of the present disclosure, there is provided a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code, when the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0077] In the embodiments of the present disclosure, a first period and a second period smaller than the first period are determined. The smooth operation conditions for evaluating the overall quality of the blast furnace ironmaking process are periodically determined according to the first period. A variable set, a score contribution set, and a weight vector are determined according to the smooth operation conditions of two adjacent first periods. In the next first period, index data corresponding to at least one operation index in the variable set is periodically collected according to the second period, and a preset blast furnace ironmaking monitoring model is calculated according to the weight vector and the index data to obtain a first analysis index and a second analysis index, so as to determine whether an alarm is required for the current second period according to the first analysis index and the second analysis index. The present disclosure performs macro monitoring based on the production scenario, and performs micro monitoring based on the macro monitoring result and specific data, realizing the combination of data and scenario during the monitoring process, and improving the monitoring effect.

[0078] Other features and aspects of the present disclosure will become clear from the following detailed description of the exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] The accompanying drawings, which are included in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the present disclosure together with the specification, and are used to explain the principles of the present disclosure.

[0080] Figure 1 The flowchart showing a method for monitoring a blast furnace ironmaking process according to an embodiment of the present disclosure;

[0081] Figure 2 The schematic diagram showing a device for monitoring a blast furnace ironmaking process according to an embodiment of the present disclosure;

[0082] Figure 3 The schematic diagram showing an electronic device according to an embodiment of the present disclosure;

[0083] Figure 4 The schematic diagram showing another electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0084] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0085] The special term "exemplary" here means "serving as an example, an embodiment, or illustrative". Any embodiment described as "exemplary" here does not necessarily have to be construed as superior to or better than other embodiments.

[0086] In addition, for a better illustration of the present disclosure, numerous specific details are provided in the following detailed implementation manners. Those skilled in the art should understand that the present disclosure can still be implemented without certain specific details. In some instances, methods, means, elements, and circuits well-known to those skilled in the art are not described in detail so as to highlight the gist of the present disclosure.

[0087] The blast furnace ironmaking process monitoring method according to an embodiment of the present disclosure can be executed by an electronic device such as a terminal device or a server. Among them, the terminal device can be any fixed or mobile terminal such as a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The server can be a single server or a server cluster composed of multiple servers. Any electronic device can implement the blast furnace ironmaking process monitoring method according to an embodiment of the present disclosure by a processor calling computer-readable instructions stored in a memory.

[0088] Figure 1 A flowchart showing a blast furnace ironmaking process monitoring method according to an embodiment of the present disclosure is as follows. Figure 1 As shown, the blast furnace ironmaking process monitoring method according to an embodiment of the present disclosure may include the following steps S10 - S60.

[0089] Step S10, determine a first period and a second period smaller than the first period.

[0090] In a possible implementation manner, the electronic device can preset a first period and a second period for periodically monitoring the blast furnace ironmaking process. Among them, the first period is a relatively long period of time, used for macroscopically monitoring the blast furnace ironmaking process based on the production scenario and expert experience. The second period is a relatively short period of time, used for collecting data generated during the blast furnace ironmaking process within each first period based on the monitoring results of the previous first period, and for microscopically monitoring based on the data. Exemplarily, the first period can be 24 hours, and the second period can be 1 hour, that is, the electronic device can perform a macroscopical monitoring every 24 hours during the blast furnace ironmaking process, and perform a microscopical monitoring every hour between every two macroscopical monitorings.

[0091] Step S20, periodically determine the smooth operation condition of the blast furnace ironmaking process according to the first period.

[0092] In a possible implementation, the electronic device periodically determines the smooth operation condition for evaluating the overall quality of the blast furnace ironmaking process according to the determined first period, so as to macroscopically monitor the overall blast furnace ironmaking process based on the smooth operation condition. Optionally, the smooth operation condition can be judged according to manual experience, the production scenario of blast furnace ironmaking, and the data collected during the current first period of the blast furnace ironmaking process. Exemplarily, the electronic device can determine the grade score, index data, and the set range corresponding to each candidate index for at least one candidate index in each first period. Calculate the corresponding candidate score according to the index data and the set range corresponding to each candidate index. Calculate the corresponding influence score according to the grade score and the candidate score corresponding to each candidate index. Then determine the sequential condition of the blast furnace ironmaking process according to the sum of the influence scores corresponding to all candidate indexes. Among them, the influence score corresponding to each candidate index is used to characterize the magnitude of the influence of the corresponding candidate index on the blast furnace ironmaking process.

[0093] Optionally, at least one candidate index determined by the electronic device is any index that will affect the blast furnace ironmaking process, such as fuel ratio, number of daily wind reductions, top temperature, number of caving pipes, hot metal temperature, and silicon deviation. The grade score corresponding to each candidate index can be determined in advance according to expert experience. The determination process can be to first divide the index grades according to importance, then determine the candidate indexes corresponding to each index grade, and assign values to each corresponding candidate index according to the grade score interval corresponding to each index grade to obtain the grade score of each candidate index. Exemplarily, the index grades can include first grade, second grade, and third grade, and the corresponding grade score intervals are [7, 9], [4, 6], and [1, 3] respectively. For example, according to needs, in accordance with a preset rule, a value within the interval [7, 9] can be assigned to a candidate index with a first-grade index grade as the grade score of the candidate index. The index data can be the actual value of each candidate index collected at the target moment corresponding to the current first period, and the corresponding target moment can be the start moment, end moment of the current first period, or any specified moment within the interval. The set range corresponding to each candidate index can be determined according to the index data of the candidate index collected under normal historical working conditions. For example, the set range can include the preset number of index data of the candidate index collected under normal historical working conditions.

[0094] Further, after the electronic device determines the grade score, index data, and set range corresponding to each candidate index in the current first cycle, it can first calculate the corresponding candidate score according to the index data and set range corresponding to the candidate index. Exemplarily, the upper quartile, lower quartile, and median of the index data included in the set range can be determined according to the set range corresponding to each candidate index, and then the upper quartile, lower quartile, median, and index data corresponding to each candidate index are input into a preset score function to obtain the candidate score corresponding to the candidate index. Among them, the score function can be set according to the actual blast furnace ironmaking scenario. After obtaining the candidate scores of each candidate index, the influence score corresponding to the candidate index is obtained by calculating the product of the grade score and the candidate score corresponding to each candidate index.

[0095] Optionally, the electronic device can preset multiple preset working conditions and the score range corresponding to each preset working condition. After obtaining the influence score corresponding to each candidate index, calculate the sum of the influence scores of each candidate index, and determine the corresponding preset working condition as the smooth working condition corresponding to the current first cycle according to the influence score and the belonging score range. The preset working conditions preset by the electronic device can include working conditions such as "stable and smooth", "basically smooth", "fluctuation warning", and "furnace condition abnormal" for monitoring the blast furnace ironmaking situation from a macro level.

[0096] Step S30: Determine the variable set, score contribution set, and weight vector according to the smooth working condition corresponding to the current first cycle and the smooth working condition corresponding to the previous first cycle.

[0097] In a possible implementation manner, after the electronic device determines the smooth working condition of each first cycle, it determines the variable set, score contribution set, and weight vector according to the smooth working condition of the current first cycle and the smooth working condition of the previous first cycle. Among them, the variable set includes at least one operating index that affects the smooth working condition, and the operating index can be selected from the candidate indexes. The score contribution set includes the influence scores corresponding to each operating index in the variable set, which is used to characterize the influence degree of the corresponding operating index on the smooth working condition. The weight vector includes the weights corresponding to each operating index in the variable set.

[0098] Optionally, when the current first cycle is the first first cycle, or when the forward driving condition corresponding to the current first cycle is different from the forward driving condition corresponding to the previous first cycle, the electronic device may screen at least one operating index according to the influence scores corresponding to each candidate index, determine a variable set including at least one operating index, and a score contribution set including the influence scores of each operating index. Then, a relationship matrix representing the relationship between every two operating indexes is determined according to the variable set and the score contribution set. Next, the relationship matrix is calculated according to the analytic hierarchy process to obtain the weight corresponding to each operating index, and a weight vector including the weights corresponding to each operating index is determined.

[0099] Among them, the electronic device may screen operating indexes from the candidate indexes according to a preset screening rule. The preset screening rule may be to first sort the candidate indexes in descending order according to the corresponding influence scores, and then gradually calculate the sum of the influence scores of the first N candidate indexes starting from the influence score of the first candidate index, and the ratio of the sum of the influence scores of all candidate indexes. When the calculated ratio is greater than the preset threshold, the current N candidate indexes are determined as operating indexes, and a variable set is determined according to the current operating indexes. Further, a score contribution set is determined according to the influence scores corresponding to each screened operating index.

[0100] Further, after determining the variable set and the score contribution set, a relationship matrix representing the relationship between every two operating indexes is determined according to the variable set and the score contribution set. Among them, each value in the relationship matrix represents the ratio of the influence scores corresponding to the operating index represented by its row and the operating index represented by its column, and is used to represent the importance of the operating index in the row relative to the operating index in the column. After determining the relationship matrix, the electronic device may calculate the relationship matrix according to the analytic hierarchy process to obtain the weight corresponding to each operating index, and determine a weight vector including the weights corresponding to each operating index.

[0101] When the forward driving condition corresponding to the current first cycle is the same as the forward driving condition corresponding to the previous first cycle, the electronic device may directly use the variable set, score contribution set, and weight vector corresponding to the previous first cycle without re-determining the variable set, score contribution set, and weight vector. That is, in response to the forward driving condition corresponding to the current first cycle being the same as the forward driving condition corresponding to the previous first cycle, it may be directly determined that the variable set, score contribution set, and weight vector corresponding to the current first cycle are the same as those of the previous first cycle.

[0102] Step S40: In the next first cycle, periodically collect the index data corresponding to at least one operating index in the variable set according to the second cycle.

[0103] In a possible implementation, after determining the variable set, score contribution set, and weight vector corresponding to the current first cycle, the process of blast furnace ironmaking is monitored periodically in the next first cycle according to the above information at the second cycle. Specifically, the electronic device can collect the index data corresponding to at least one operating index in the variable set once in each second cycle, and then monitor the process of blast furnace ironmaking microscopically based on the index data.

[0104] Step S50: Obtain a first analysis index and a second analysis index according to the weight vector, the index data corresponding to the at least one operating index, and a preset blast furnace ironmaking monitoring model.

[0105] In a possible implementation, after the electronic device obtains the index data corresponding to each operating index in the current second cycle, it further obtains a first analysis index and a second analysis index for evaluating the process of blast furnace ironmaking at the microscopic level according to the weight vector, the index data of at least one operating index collected in the current second cycle, and a preset blast furnace ironmaking monitoring model. Among them, the blast furnace ironmaking monitoring model can be determined based on historical operating index data, that is, training data can be determined first according to historical operating index data, and then the blast furnace ironmaking monitoring model can be established according to the training data.

[0106] Optionally, the electronic device can first determine an index data matrix according to multiple index data corresponding to each operating index in history. Then, perform principal component analysis on the index data matrix to obtain the same number of principal components as the number of operating indexes in the variable set, and the load vector corresponding to each principal component. Screen at least one target principal component from the principal components, and then establish a blast furnace ironmaking monitoring model according to the target principal component and the load vector corresponding to each target principal component. Exemplarily, the electronic device can determine the index data matrix \(X\in R\) N×m , where \(m\) is the number of operating indexes, and \(N\) is the number of index data corresponding to each operating index. Then perform singular value decomposition on \(X\): \(X = UAV\) T , where \(U=[u\) 1 , \(u\) 2 ,…, \(u\) m \(\in\) R×m , \(V = [v\) 1 , \(v\) 2 ,…, \(v\) m \(\in R\) m×m , Furthermore, the electronic device determines \(\sigma\) i \(u\) i as the \(i\)-th principal component \(t\) i , and determines \(v\) i as the load vector \(p\) i corresponding to the \(i\)-th principal component. And extract the first \(a\) principal components as the target principal components. The screening condition can be that the first \(a\) principal components satisfy

[0107] Further, after obtaining the target principal components and the corresponding loading vectors, the electronic device models a blast furnace ironmaking monitoring model X = TP according to the target principal components and the loading vectors corresponding to each target principal component T +E, where, T = [t 1 , …, t a ∈ R N×a , P = [p 1 , … p a ∈ R m×a , E ∈ R N×m , X is the index data matrix, t i is the i-th target principal component, p i is the loading vector of the i-th target principal component, a is the number of target principal components, E is the residual matrix, and m is the number of operation indexes

[0108] After determining the blast furnace ironmaking monitoring model, the electronic device determines a diagonal weight matrix W = diag(w) ∈ R m×m , where w is the weight vector and m is the number of operation indexes included in the variable set. Then, according to the diagonal weight matrix, the index data of at least one operation index collected in the current second period, and the blast furnace ironmaking monitoring model, a first analysis index T 2 = xWP∧P T Wx and a second analysis index SPE = xW(I - PP T )Wx are calculated, where I is the identity matrix, x is the index data vector including the index data corresponding to each operation index corresponding to the current second period, and ∧ is the singular value matrix obtained by performing principal component analysis on the index data matrix X. That is, through the modeling process of the index data matrix X based on the blast furnace ironmaking monitoring model X = TP T +E, P can be obtained, and ∧ can be obtained based on X. Then, according to the collected index data vector x, the first analysis index and the second analysis index are obtained

[0109] Step S60: Determine whether an alarm is required for the blast furnace ironmaking process in the current second period according to the first analysis index and the second analysis index

[0110] In a possible implementation, after the electronic device calculates the first analysis index and the second analysis index corresponding to the current second cycle, it can jointly judge the blast furnace ironmaking situation in the current second cycle according to the first analysis index and the second analysis index, and give an alarm in case of danger. Among them, the electronic device can pre-determine the first index threshold and the second index threshold, and then determine that the blast furnace ironmaking process in the current second cycle needs to give an alarm when the first analysis index is greater than the first index threshold and / or the second analysis index is greater than the second index threshold. Optionally, the first index threshold and the second index threshold in the embodiments of the present disclosure can be determined by means of kernel density estimation of a pre-set confidence level.

[0111] Based on the above technical features, the embodiments of the present disclosure conduct macroscopic monitoring of the blast furnace ironmaking process based on expert experience and production scenarios in the first cycle. In each second cycle, microscopic monitoring is carried out based on the macroscopic monitoring results of the previous cycle and the data of the current cycle. This monitoring method realizes the effective integration of expert experience and data-driven models, overcomes the deficiencies of poor real-time performance and inaccurate quantification of the expert experience method, as well as the problems in variable selection, model update, etc. of the data-driven model, and can effectively track the blast furnace ironmaking process with continuously changing working conditions and give effective alarms for possible abnormalities during production.

[0112] Figure 2 Shows a schematic diagram of a blast furnace ironmaking process monitoring device according to an embodiment of the present disclosure. As Figure 2 shown, the blast furnace ironmaking process monitoring device in the embodiments of the present disclosure may include:

[0113] A cycle determination module 20, configured to determine a first cycle and a second cycle smaller than the first cycle;

[0114] A working condition determination module 21, configured to periodically determine the smooth working condition of the blast furnace ironmaking process according to the first cycle, and the smooth working condition is used to evaluate the overall quality of the blast furnace ironmaking process;

[0115] A parameter determination module 22, configured to determine a variable set, a score contribution set, and a weight vector according to the smooth working condition corresponding to the current first cycle and the smooth working condition corresponding to the previous first cycle, where the variable set includes at least one operating index affecting the smooth working condition, the score contribution set includes the influence scores corresponding to the operating indexes, the influence scores are used to characterize the influence degree of the corresponding operating indexes on the smooth working condition, and the weight vector includes the weights corresponding to each operating index;

[0116] A data acquisition module 23, configured to collect the index data corresponding to at least one operating index in the variable set periodically according to the second cycle in the next first cycle;

[0117] An index calculation module 24, configured to obtain a first analysis index and a second analysis index according to the weight vector, the index data corresponding to the at least one operating index, and a preset blast furnace ironmaking monitoring model;

[0118] An alarm response module 25, configured to determine whether an alarm is required for the blast furnace ironmaking process in the current second cycle according to the first analysis index and the second analysis index.

[0119] In a possible implementation manner, the operating condition determination module 21 is further configured to:

[0120] Within each of the first cycles, determine the grade scores, index data, and set ranges corresponding to at least one candidate index;

[0121] Calculate corresponding candidate scores according to the index data and the set ranges corresponding to each of the candidate indexes;

[0122] Calculate corresponding influence scores according to the grade scores and candidate scores corresponding to each of the candidate indexes;

[0123] Determine the sequential operating conditions of the blast furnace ironmaking process according to the sum of the influence scores corresponding to all the candidate indexes.

[0124] In a possible implementation manner, the operating condition determination module 21 is further configured to:

[0125] Determine the upper quartile, lower quartile, and median according to the set ranges corresponding to each of the candidate indexes;

[0126] Input the upper quartile, lower quartile, median, and index data corresponding to each of the candidate indexes into a preset scoring function to obtain corresponding candidate scores.

[0127] In a possible implementation manner, the operating condition determination module 21 is further configured to:

[0128] Calculate the product of the grade scores and candidate scores corresponding to each of the candidate indexes to obtain corresponding influence scores.

[0129] In a possible implementation manner, the parameter determination module 22 is further configured to:

[0130] In response to the smooth operating condition corresponding to the current first cycle being the same as the smooth operating condition corresponding to the previous first cycle, determine that the variable set, score contribution set, and weight vector corresponding to the current first cycle are the same as those of the previous first cycle.

[0131] In a possible implementation manner, the parameter determination module 22 is further configured to:

[0132] In response to the forward driving condition corresponding to the current first cycle being different from the forward driving condition corresponding to the previous first cycle, at least one operating index is screened according to the influence scores corresponding to each of the candidate indexes, a variable set including the at least one operating index is determined, and a score contribution set including the influence scores of each of the operating indexes is determined;

[0133] A relationship matrix characterizing the relationship between every two operating indexes is determined according to the variable set and the score contribution set;

[0134] The relationship matrix is calculated according to the analytic hierarchy process to obtain the weight corresponding to each of the operating indexes, and a weight vector including the weights corresponding to each of the operating indexes is determined.

[0135] In a possible implementation manner, the device further includes:

[0136] A matrix determination module, configured to determine an index data matrix according to multiple index data corresponding to each of the operating indexes in history;

[0137] A load vector determination module, configured to perform principal component analysis on the index data matrix to obtain the same number of principal components as the number of operating indexes in the variable set, and a load vector corresponding to each of the principal components;

[0138] A principal component screening module, configured to screen at least one target principal component from the principal components;

[0139] A data modeling module, configured to model a blast furnace ironmaking monitoring model according to the target principal components and the load vectors corresponding to each of the target principal components.

[0140] In a possible implementation manner, the blast furnace ironmaking monitoring model is X = TP T +E, where T = [t 1 , …, t a ∈ R N×a , P = [p 1 , … p a ∈ R m×a , E ∈ R N×m , X is the index data matrix, t i is the i-th target principal component, p i is the load vector of the i-th target principal component, a is the number of target principal components, E is the residual matrix, and m is the number of principal components.

[0141] In a possible implementation manner, the index calculation module 24 is further configured to:

[0142] Determine a diagonal weight matrix W = diag(w) ∈ R according to the weight vector m×m, where w is the weight vector and m is the number of operation indicators included in the variable set;

[0143] According to the diagonal weight matrix and the blast furnace ironmaking monitoring model, the first analysis index T is calculated 2 = xWP∧P T Wx and the second analysis index SPE = xW(I - PP T )Wx, where I is the identity matrix, x is the index data vector including the index data corresponding to each operation indicator in the current second cycle, and ∧ is the singular value matrix obtained by performing principal component analysis on the index data matrix.

[0144] In a possible implementation manner, the alarm response module 25 is further configured to:

[0145] Determine a first index threshold and a second index threshold;

[0146] In response to the first analysis index being greater than the first index threshold and / or the second analysis index being greater than the second index threshold, determine that the blast furnace ironmaking process in the current second cycle needs to be alarmed.

[0147] In some embodiments, the functions or modules included in the device provided in the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments. For the sake of brevity, it will not be repeated here.

[0148] The embodiments of the present disclosure also propose a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the above methods are implemented. The computer-readable storage medium can be a volatile or non-volatile computer-readable storage medium.

[0149] The embodiments of the present disclosure also propose an electronic device, including: a processor; a memory for storing instructions executable by the processor; wherein, the processor is configured to implement the above methods when executing the instructions stored in the memory.

[0150] The embodiments of the present disclosure also provide a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in the processor of an electronic device, the processor in the electronic device executes the above methods.

[0151] Figure 3 The schematic diagram of an electronic device 800 according to an embodiment of the present disclosure is shown. For example, the electronic device 800 can be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a personal digital assistant, etc.

[0152] Refer to Figure 3, the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input / output interface 812 (I / O interface), a sensor component 814, and a communication component 816.

[0153] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communication, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above methods. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0154] The memory 804 is configured to store various types of data to support the operation of the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disk.

[0155] The power component 806 provides power to the various components of the electronic device 800. The power component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.

[0156] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can sense not only the boundaries of touch or swipe actions, but also detect the duration and pressure associated with the touch or swipe operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each of the front camera and the rear camera can be a fixed optical lens system or have a focal length and optical zoom capabilities.

[0157] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC) that is configured to receive external audio signals when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 further includes a speaker for outputting audio signals.

[0158] The input / output interface 812 provides an interface between the processing component 802 and a peripheral interface module, and the peripheral interface module can be a keyboard, a click wheel, buttons, etc. These buttons can include but are not limited to: a home button, a volume button, a power button, and a lock button.

[0159] The sensor component 814 includes one or more sensors for providing a status assessment of various aspects of the electronic device 800. For example, the sensor component 814 can detect the on / off state of the electronic device 800, the relative positioning of components, such as the display and the keypad of the electronic device 800. The sensor component 814 can also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor component 814 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor component 814 can also include a light sensor, such as a CMOS or a CCD image sensor, for use in imaging applications. In some embodiments, the sensor component 814 can further include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0160] The communication component 816 is configured to facilitate communication between the electronic device 800 and other devices in a wired or wireless manner. The electronic device 800 can access a communication standard-based wireless network, such as WiFi, 2G, or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on Radio Frequency Identification (RFID) technology, Infrared Data Association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0161] In an exemplary embodiment, the electronic device 800 can be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above method.

[0162] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions, and the above computer program instructions can be executed by the processor 820 of the electronic device 800 to complete the above method.

[0163] Figure 4 A schematic diagram showing another electronic device 1900 according to an embodiment of the present disclosure is shown. For example, the electronic device 1900 can be provided as a server or a terminal device. Referring to Figure 4 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by a memory 1932 for storing instructions executable by the processing component 1922, such as application programs. The application programs stored in the memory 1932 can include one or more modules each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0164] The electronic device 1900 can also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output interface 1958 (I / O interface). The electronic device 1900 can operate based on an operating system stored in the memory 1932, such as Windows Server TM , Mac OS X TM , Unix TM, Linux TM , FreeBSD TM or the like.

[0165] In an exemplary embodiment, a non - volatile computer - readable storage medium is also provided, such as a memory 1932 including computer program instructions, and the above - mentioned computer program instructions can be executed by a processing component 1922 of an electronic device 1900 to complete the above - mentioned method.

[0166] The present disclosure may be a system, a method, and / or a computer program product. The computer program product may include a computer - readable storage medium having thereon computer - readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0167] A computer - readable storage medium may be a tangible device that can retain and store instructions for use by an instruction - executing device. A computer - readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non - exhaustive list) of the computer - readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read - only memory (ROM), an erasable programmable read - only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read - only memory (CD - ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punched card or raised structures in grooves having instructions stored thereon, and any suitable combination of the foregoing. The computer - readable storage medium used herein is not construed as being a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0168] The computer - readable program instructions described herein can be downloaded from a computer - readable storage medium to various computing / processing devices, or downloaded to an external computer or an external storage device through a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include a copper transmission cable, an optical fiber transmission, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. A network adapter or network interface in each computing / processing device receives the computer - readable program instructions from the network and forwards the computer - readable program instructions for storage in a computer - readable storage medium in each computing / processing device.

[0169] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine - related instructions, microcode, firmware instructions, state - setting data, or source code or object code written in any combination of one or more programming languages, including object - oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer - readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand - alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer - readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field - programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer - readable program instructions to implement various aspects of the present disclosure.

[0170] Aspects of the present disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer - readable program instructions.

[0171] These computer - readable program instructions can be provided to a processor of a general - purpose computer, a special - purpose computer, or other programmable data processing apparatus to produce a machine such that the instructions, when executed by the processor of the computer or other programmable data processing apparatus, create a means for implementing the functions / acts specified in one or more blocks of the flowchart and / or block diagram. These computer - readable program instructions can also be stored in a computer - readable storage medium, which causes a computer, a programmable data processing apparatus, and / or other devices to operate in a particular manner, so that the computer - readable medium storing the instructions comprises a manufacture, which includes instructions for implementing various aspects of the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0172] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices, causing a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other devices to generate a computer-implemented process, such that the instructions executed on the computer, other programmable data processing apparatus, or other devices implement the functions / acts specified in one or more boxes of the flowchart and / or block diagram.

[0173] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of code, or a portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two consecutive blocks may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the block diagrams and / or flowchart, and combinations of blocks in the block diagrams and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or act, or by a combination of dedicated hardware and computer instructions.

[0174] The embodiments of the present disclosure have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the market, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein.

Claims

1. A method for monitoring the blast furnace ironmaking process, characterized in that, the method includes: determining a first period and a second period smaller than the first period; periodically determining the smooth operation condition of the blast furnace ironmaking process according to the first period, and the smooth operation condition is used to evaluate the overall quality of the blast furnace ironmaking process; determining a variable set, a score contribution set and a weight vector according to the smooth operation condition corresponding to the current first period and the smooth operation condition corresponding to the previous first period, wherein the variable set includes at least one operating index affecting the smooth operation condition, the score contribution set includes the influence scores corresponding to the operating indexes, the influence scores are used to characterize the influence degree of the corresponding operating indexes on the smooth operation condition, and the weight vector includes the weights corresponding to each operating index; collecting the index data corresponding to at least one operating index in the variable set periodically according to the second period within the next first period; obtaining a first analysis index and a second analysis index according to the weight vector, the index data corresponding to the at least one operating index and a preset blast furnace ironmaking monitoring model; judging whether the blast furnace ironmaking process in the current second period needs to give an alarm according to the first analysis index and the second analysis index.

2. The method according to claim 1, characterized in that, the periodically determining the smooth operation condition of the blast furnace ironmaking process according to the first period includes: determining the grade scores, index data and the set ranges corresponding to each candidate index within each first period; calculating the corresponding candidate scores according to the index data corresponding to each candidate index and the set ranges; calculating the corresponding influence scores according to the grade scores and candidate scores corresponding to each candidate index; determining the sequential condition of the blast furnace ironmaking process according to the sum of the influence scores corresponding to all the candidate indexes.

3. The method according to claim 2, characterized in that, the calculating the corresponding candidate scores according to the index data corresponding to each candidate index and the set ranges includes: determining the upper quartile, lower quartile and median according to the set range corresponding to each candidate index; inputting the upper quartile, lower quartile, median and index data corresponding to each candidate index into a preset score function to obtain the corresponding candidate scores.

4. The method according to claim 2 or 3, characterized in that, the calculating the corresponding influence scores according to the grade scores and candidate scores corresponding to each candidate index includes: calculating the product of the grade scores and candidate scores corresponding to each candidate index to obtain the corresponding influence scores.

5. The method according to any one of claims 1-4, characterized in that, the determining the variable set, the score contribution set and the weight vector according to the smooth operation condition corresponding to the current first period and the smooth operation condition corresponding to the previous first period includes: in response to the smooth operation condition corresponding to the current first period being the same as the smooth operation condition corresponding to the previous first period, determining that the variable set, the score contribution set and the weight vector corresponding to the current first period are the same as those of the previous first period.

6. The method according to any one of claims 2-5, wherein, determining the variable set, the score contribution set and the weight vector according to the forward operation condition corresponding to the current first period and the forward operation condition corresponding to the previous first period includes: in response to the forward operation condition corresponding to the current first period being different from the forward operation condition corresponding to the previous first period, screening at least one operation index according to the influence score corresponding to each candidate index, and determining a variable set including the at least one operation index, and a score contribution set including the influence score of each operation index; determining a relationship matrix characterizing the relationship between every two operation indexes according to the variable set and the score contribution set; calculating the relationship matrix according to the analytic hierarchy process to obtain the weight corresponding to each operation index, and determining a weight vector including the weight corresponding to each operation index.

7. The method according to any one of claims 1-6, wherein, the method further includes: determining an index data matrix according to a plurality of index data corresponding to each operation index in history; performing principal component analysis on the index data matrix to obtain the same number of principal components as the number of operation indexes in the variable set, and a load vector corresponding to each principal component; screening at least one target principal component from the principal components; modeling a blast furnace ironmaking monitoring model according to the target principal component and the load vector corresponding to each target principal component.

8. The method according to claim 7, wherein, The blast furnace ironmaking monitoring model is X = TP T + E, where, T = [t 1 , …, t a ∈ R N×a , P = [p 1 , … p a ∈ R m×a , E ∈ R N×m , X is the index data matrix, t i is the i-th target principal component, p i is the load vector of the i-th target principal component, a is the number of target principal components, E is the residual matrix, and m is the number of principal components.

9. The method according to claim 8, wherein, obtaining a first analysis index and a second analysis index according to the weight vector, the index data corresponding to the at least one operation index and a preset blast furnace ironmaking monitoring model includes: Determine the diagonal weight matrix \(W = \text{diag}(w)\in\mathbb{R}\) according to the weight vector m×m , where \(w\) is the weight vector and \(m\) is the number of operation indicators included in the variable set; According to the diagonal weight matrix and the blast furnace ironmaking monitoring model, the first analysis index T is calculated 2 = xWP∧P T Wx and the second analysis index SPE = xW(I - PP T )Wx, where I is the identity matrix, x is the index data vector including the index data corresponding to each operation index in the current second period, and ∧ is the singular value matrix obtained by performing principal component analysis on the index data matrix.

10. The method according to any one of claims 1-9, wherein, judging whether the blast furnace ironmaking process in the current second period needs to give an alarm according to the first analysis index and the second analysis index includes: determining a first index threshold and a second index threshold; in response to the first analysis index being greater than the first index threshold and / or the second analysis index being greater than the second index threshold, determining that the blast furnace ironmaking process in the current second period needs to give an alarm.

11. A blast furnace ironmaking process monitoring device, wherein, the device includes: a period determination module, configured to determine a first period and a second period smaller than the first period; a working condition determination module, configured to periodically determine the forward working condition of the blast furnace ironmaking process according to the first period, and the forward working condition is used to evaluate the overall quality of the blast furnace ironmaking process; A parameter determination module, configured to determine a variable set, a score contribution set, and a weight vector according to the forward operation condition corresponding to the current first cycle and the forward operation condition corresponding to the previous first cycle. The variable set includes at least one operation index affecting the forward operation condition. The score contribution set includes influence scores corresponding to the operation indexes, and the influence scores are used to represent the influence degree of the corresponding operation indexes on the forward operation condition. The weight vector includes weights corresponding to each of the operation indexes; A data acquisition module, configured to periodically collect index data corresponding to at least one operation index in the variable set according to the second cycle within the next first cycle; An index calculation module, configured to obtain a first analysis index and a second analysis index according to the weight vector, the index data corresponding to the at least one operation index, and a preset blast furnace ironmaking monitoring model; An alarm response module, configured to determine whether an alarm is required for the blast furnace ironmaking process in the current second cycle according to the first analysis index and the second analysis index.

12. An electronic device, characterized in that it includes: a processor; a memory for storing processor-executable instructions; wherein, the processor is configured to implement the method according to any one of claims 1 to 10 when executing the instructions stored in the memory.

13. A non-volatile computer-readable storage medium, on which computer program instructions are stored, characterized in that the computer program instructions, when executed by a processor, implement the method according to any one of claims 1 to 10.

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