Intelligent scheduling management method and system for cement production raw materials

Abstract

This invention provides an intelligent scheduling and management method and system for cement production raw materials, belonging to the field of scheduling and management technology. Specifically, it includes: determining available blending schemes for a batch of raw materials based on the degree of blending compatibility; determining a priority blending control batch by combining the overlap of blending data among available blending schemes and the blending compatibility type; determining a blending observation scheme for the priority blending control batch by considering the blending data of the priority blending control batch, the overlap of available blending schemes in other batches, and the blending compatibility type of other batches; determining the observation and processing results during the blending process based on the blending observation scheme; and determining the blending scheduling scheme for raw materials in other batches based on the observation and processing results and the raw material consumption data in available blending schemes of other batches. This improves the efficiency of blending scheme identification and processing.

Description

Technical Field

[0001] This invention belongs to the field of scheduling and management technology, and in particular relates to an intelligent scheduling and management method and system for cement production raw materials. Background Technology

[0002] In the cement production process, in order to achieve intelligent scheduling and processing of cement production raw materials, the invention patent application CN202410584343.0, "Cement Production Scheduling Method, System, Medium, Equipment and Products," aims to minimize production scheduling costs and is based on constraints such as power balance, output, power load, and storage capacity. However, the following technical problems exist: During cement production, batch differences in raw materials may lead to discrepancies between the content of some components and the standard requirements. Therefore, determining the blending control strategy between different batches to reduce the difficulty of blending control and improve the quality of clinker after blending has become an urgent technical problem to be solved.

[0003] Therefore, there is an urgent need for an intelligent scheduling and management method and system for cement production raw materials. Summary of the Invention

[0004] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides an intelligent scheduling and management method for cement production raw materials, which includes: S1 uses the composition analysis results of different batches of raw materials in the stockpile to determine the matching with historical production data, and uses the matching results, combined with the degree of blending compatibility with other batches, to determine the blending compatibility type of different batches of raw materials. S2 determines the available blending schemes for the raw materials in the batch based on the degree of blending compatibility, and determines the priority blending control batch in the batch by combining the overlap of blending data between the available blending schemes and the blending compatibility type. S3 uses the blending data of the priority blending control batch, the overlap of available blending schemes with other batches, and the blending compatibility type of other batches to determine the blending observation scheme of the priority blending control batch. S4 determines the observation and processing results during the blending process based on the blending observation scheme, and determines the blending scheduling scheme for raw materials in other batches based on the observation and processing results and the consumption data of raw materials in other batches of available blending schemes.

[0005] The beneficial effects of this invention are as follows: Based on the available blending schemes for the raw materials in the batch, the overlap of blending data between available blending schemes, and the blending compatibility type, priority blending control batches are determined. This enables the assessment of the reliability of blending treatment for different batches based on the number of available blending schemes, the overlap of blending batches between available blending schemes, and the blending compatibility type. Blending control is then prioritized for blending batches with higher reliability, thus improving the efficiency of identifying and processing available blending schemes.

[0006] Based on the observation and processing results and the raw material consumption data in other batches of available blending schemes, a blending scheduling scheme for raw materials in other batches is determined. This avoids the technical problem that occurs when the blending processing results in the priority blending control batch do not meet the requirements, resulting in excessive consumption of raw materials in the blending batch and thus preventing other batches from being effectively blended. By performing other blending adjustments in a timely manner, the reliability of the blending process is further improved.

[0007] Furthermore, the composition analysis results of the raw materials are determined based on the test results of the original components.

[0008] Furthermore, the matching with historical production data is determined based on the deviation between the composition analysis results of the raw materials and the control range of the components required for production.

[0009] Furthermore, the method for determining the blending compatibility type of the batch of raw materials is as follows: Based on the composition analysis results of the raw materials, the deviation from the control range of the composition required for production is determined. Based on the deviation, the components that are not within the control range are identified and identified as the deviation components. Based on the composition data of the deviation components in the batch of raw materials, and in combination with the composition data of the deviation components in raw materials excluding the batch, the blending and compatibility type of the batch of raw materials is determined.

[0010] Furthermore, the method for determining the blending and scheduling scheme of raw materials in the other batches is as follows: Based on the observation and processing results, the time period in which the monitored component of the priority blending control batch is inconsistent with the reference component during the blending control process is determined and this period is taken as the component deviation period. Based on the observation and processing results, the monitoring data of the clinker in different available blending schemes for the priority blending control batch are determined, and the time period of clinker composition deviation in different available blending schemes is determined based on the monitoring data; By utilizing the component deviation time period of the priority blending control batch, as well as the clinker component deviation time period in different available blending schemes, and combining the raw material consumption data in the available blending schemes of other batches, the blending scheduling scheme for raw materials in other batches is determined.

[0011] Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described intelligent scheduling and management method for cement production raw materials when running the computer program.

[0012] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0013] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0014] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings; Figure 1 This is a flowchart of an intelligent scheduling and management method for raw materials in cement production. Figure 2 This is a flowchart illustrating the method for determining the blending compatibility type of raw materials in a batch. Figure 3 This is a flowchart illustrating the method for determining the preferred blending control batch in a batch. Detailed Implementation

[0015] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0016] Example 1 like Figure 1 As shown, this application provides an intelligent scheduling and management method for cement production raw materials, specifically including: S1 uses the composition analysis results of different batches of raw materials in the stockpile to determine the matching with historical production data, and uses the matching results, combined with the degree of blending compatibility with other batches, to determine the blending compatibility type of different batches of raw materials. S2 determines the available blending schemes for the raw materials in the batch based on the degree of blending compatibility, and determines the priority blending control batch in the batch by combining the overlap of blending data between the available blending schemes and the blending compatibility type. S3 uses the blending data of the priority blending control batch, the overlap of available blending schemes with other batches, and the blending compatibility type of other batches to determine the blending observation scheme of the priority blending control batch. S4 determines the observation and processing results during the blending process based on the blending observation scheme, and determines the blending scheduling scheme for raw materials in other batches based on the observation and processing results and the consumption data of raw materials in other batches of available blending schemes.

[0017] Furthermore, the composition analysis results of the raw materials are determined based on the test results of the original components.

[0018] Furthermore, the matching with historical production data is determined based on the deviation between the composition analysis results of the raw materials and the control range of the components required for production.

[0019] Specifically, such as Figure 2 As shown, the method for determining the blending compatibility type of the raw materials in the batch is as follows: Business Objective: For a batch of limestone raw materials with substandard chemical composition, determine the potential and difficulty of achieving cement production standards through scientific blending with other batches of raw materials. Based on this, classify the batches to guide subsequent processing. Mixing different batches of substandard limestone with complementary components in appropriate proportions to bring their overall chemical composition within the acceptable range is key to maximizing resource utilization. The "blending compatibility type" indicates the priority of a particular batch of raw materials in this optimization process.

[0020] In one possible embodiment, the raw materials are: limestone (the main raw material for cement production), CaO (calcium oxide): the main component, the higher the content, the better, SiO2 (silicon dioxide): needs to be controlled within an appropriate range, and MgO (magnesium oxide): a harmful component that must be strictly limited.

[0021] Production control range (enterprise standard): CaO: ≥ 48%, SiO2: ≤ 10%, MgO: ≤ 3%.

[0022] Batch to be evaluated: There are 3 non-compliant limestone batches, and their composition analysis results are as follows: Table 1 Component Analysis Results

[0023] S11 uses the composition analysis results of the raw materials to determine the deviation from the control range of the composition required for production, and based on the deviation, determines the components that are not within the control range and identifies them as deviation components. In the above steps, the deviation components are identified. Action: Compare the composition of each batch with the control range, list the items that exceed the standard, and deviate from the standard: refers to the chemical components in the raw materials of this batch that exceed the control range required for production.

[0024] Results (as shown in the table above): Batch A deviation component: [CaO] -> Quantity = 1, Batch B deviation component: [SiO2] -> Quantity = 1, Batch C deviation component: [CaO, SiO2, MgO] -> Quantity = 3.

[0025] S12 determines the blending compatibility type of the raw materials in the batch based on the composition data of the deviation components in the batch of raw materials and in combination with the composition data of the deviation components in raw materials excluding the batch.

[0026] In the above steps, a usable blending scheme is generated. Based on the preset blending control conditions (the total number of deviation components in all batches is ≤3, all components are qualified after blending, and the number of batches in which all components are deviation components after blending is no more than 1), all possible blending combinations are searched.

[0027] Available blending schemes: These refer to blending plans that meet all of the following conditions: Controllability: The total number of types of deviating components in all batches involved in blending is ≤ 3 (difficult to adjust more than three components simultaneously); Compliance: After blending in a specific ratio, all chemical components fall within the controllable range; Stability: After blending, the number of batches containing "deviating components" is ≤ 1 (ensuring stable results and not overly relying on a single problematic batch). Case Analysis and Results: Scheme 1 (A+B): Total number of deviation components: [CaO] + [SiO2] = 2 (Satisfies condition 1) After blending: CaO content can be increased, SiO2 content can be decreased, and both can be made to meet standards by adjusting the ratio. MgO is also within acceptable limits. (Condition 2 is met). After blending, both CaO and SiO2 originate from only one deviation batch. (Condition 3 is met) Conclusion: This is a usable blending method. It works for both batch A and batch B.

[0028] Scheme 2 (A+C): Total number of deviation components: [CaO] + [CaO, SiO2, MgO], there is overlap, conclusion: not a usable scheme.

[0029] Option 3 (B+C): Total number of deviation components: [SiO2] + [CaO, SiO2, MgO] = [CaO, SiO2, MgO], there is overlap, conclusion: not a usable option.

[0030] Scheme 4 (A+B+C): Total number of deviation components: [CaO] + [SiO2] + [CaO, SiO2, MgO] = [CaO,SiO2, MgO], there is overlap, conclusion: not a usable scheme.

[0031] Furthermore, based on the composition data of the deviation components in the batch of raw materials, and in combination with the composition data of the deviation components in raw materials excluding the batch, the blending compatibility type of the batch of raw materials is determined, specifically including: Based on preset blending control conditions, determine the available blending schemes for the batch of raw materials based on the composition data of the deviation components; Based on the available blending scheme data, determine the quantity of available blending schemes for the batch; Using the number of available blending schemes and the composition data of the deviation components, the blending compatibility type of the batch of raw materials is determined.

[0032] It should be noted that the available blending scheme ensures that the total number of deviation components in all batches is within a set range, that the different components after blending are all controlled within a control range, and that the number of batches in which all components after blending belong to deviation components is no more than 1, thereby ensuring the stability and reliability of the raw material composition after blending.

[0033] Specifically, using the number of available blending schemes and the composition data of the deviation components, the blending compatibility type of the batch of raw materials is determined, including: S121 determines whether the number of deviation components in the batch of raw materials is less than a preset deviation component number threshold. If yes, proceed to the next step; otherwise, determine that the blending adaptation type of the batch of raw materials is a three-type adaptation type. In the above steps, the initial screening is based on the number of deviation components. The judgment is as follows: if the number of deviation components in a batch is <2, then for batch A: 1 < 2 -> Yes, proceed to S122; for batch B: 1 < 2 -> Yes, proceed to S122; for batch C: 3 < 2 -> No, directly determine that the blending adaptation type of batch C is the third type of adaptation.

[0034] S122 obtains the number of available blending schemes for the batch of raw materials, sorts them from largest to smallest based on the number of available blending schemes, determines the sorting result of the batch of raw materials, and determines the blending adaptation type of the batch of raw materials based on the sorting result.

[0035] It is understood that the determination of whether the number of available blending schemes for the batch of raw materials is greater than the preset threshold for the number of available blending schemes is made. If so, the blending adaptation type of the batch of raw materials is a type II adaptation type. If not, proceed to the next step. Determine whether the number of items ranked higher than the sorting result of the raw materials in the batch meets the requirement (i.e., less than one-third of the total number of batches). If yes, the blending adaptation type of the raw materials in the batch is a type II adaptation type; otherwise, the blending adaptation type of the raw materials in the batch is a type III adaptation type.

[0036] It should be noted that the degree of compatibility of the second type of compatibility mixing treatment is greater than that of the third type of compatibility.

[0037] The final judgment is as follows: Is the number of available blending schemes > 2? Since the number of schemes for batches A and B is 1, not greater than 2, proceed to the next step: Determine the ranking: The number of batches ranked higher than this batch is < 1 / 3 of the total number of batches. With a total of 3 batches, 1 / 3 ≈ 1, meaning the number of batches ranked higher than batches A and B is 0.

[0038] Batch A: The blending adaptation type is type II; Batch B: The blending adaptation type is type II; Batch C: The blending adaptation type is type III.

[0039] Specifically, such as Figure 3 As shown, the method for determining the preferred blending control batch in the batch is as follows: Business objective: Among the multiple batches already identified as "Category II compatibility type" (suitable for blending), identify the most critical and essential batch—the priority blending control batch. This batch acts as the "hub" for resolving the entire issue of substandard raw materials. Prioritizing its handling can most efficiently "activate" or "resolve" multiple blending solutions, thereby revitalizing the entire inventory.

[0040] Scenario setting: Continue using batches A, B, and C, and introduce a new batch D to enrich the scenario. Batch A: Low CaO (Type II adaptation), Batch B: High MgO (Type II adaptation), Batch C: Low CaO, High SiO2, High MgO (Type III adaptation), Batch D: High MgO (Type II adaptation).

[0041] Available blending schemes for batch A: Scheme 1: A + B (solves the problem of low CaO in A and high SiO2 in B), Scheme 2: A + D (solves the problem of low CaO in A, but the problem of high MgO in D still exists, requiring a small amount to be added). Available blending schemes for batch B: Scheme 1: B + A (same as above).

[0042] Available blending schemes for batch D: Scheme 1: D + A (same as above).

[0043] S21 determines the quantity of available blending schemes for the batch of raw materials based on the available blending schemes for the batch of raw materials; In the above steps (number of schemes): Batch A: 2, Batch B: 1, Batch D: 1.

[0044] S22 Based on the overlap of blending data among the available blending schemes, determine the overlapping blending batches among different available blending schemes; Definition: Overlapping Blending Batches: For two different available blending schemes, identify the common raw material batches they contain, and determine the correlation between the different solutions. The stronger the correlation, the more likely a particular batch is a "common denominator" of the multiple schemes.

[0045] Example Analysis: Comparing Scheme 1 (A+B) and Scheme 2 (A+D) for batch A: Overlapping blended batches = [batch A].

[0046] S23 determines whether the batch is a priority blending control batch by using the number of available blending schemes, blending adaptation type, and overlapping blending batches between different available blending schemes.

[0047] It is understood that determining whether a batch is a priority blending control batch involves using the number of available blending schemes, blending compatibility types, and overlapping blending batches among different available blending schemes. Specifically, this includes: Case 1: If the batch has a blending compatibility type of three, then the batch is determined not to be a priority blending control batch. Scenario 1: Excluding the three types of compatibility, judgment: batch C belongs to the three types of compatibility, decision: batch C does not belong to the priority blending control batch.

[0048] Case 2: If the blending adaptation type of the batch is a type II adaptation type, obtain the number of available blending schemes for the batch, and determine whether the number of available blending schemes for the batch is greater than the preset blending scheme number threshold. If yes, proceed to the next step; otherwise, determine that the batch does not belong to the priority blending control batch. Scenario 2: Filter candidates from the two types of adaptation and determine: if the number of available blending schemes is > 1, batch A: 2 > 1 -> Yes, proceed to the next step.

[0049] Logic: Batches B and D only have one path (they can only be mixed with A). It is not a "hub" but an "endpoint" and does not belong to the batches under effective mixing control.

[0050] Based on the overlapping blending batches between different available blending schemes, determine the number of blending batches that overlap between the available blending scheme and other available blending schemes, and the ratio of this number to the number of blending batches of the available blending scheme, determine the overlap factor between the available blending scheme and other available blending schemes, and based on the overlap factor, determine the priority blending control batches among the batches.

[0051] It is understood that, based on the overlap factor, determining the preferred blending control batches within the batches specifically includes: The batch with the largest overlap factor among different available blending schemes is designated as the priority blending control batch.

[0052] For batch A: it has two options: option 1 (A+B) and option 2 (A+D). Calculate the overlap factor of option 1: the batch that overlaps with option 2 is [A], with a quantity of 1. The total number of blended batches of option 1 itself is 2 (A and B). The overlap factor of option 1 = 1 / 2 = 0.5. Therefore, batch A is determined to be the preferred blending control batch.

[0053] Specifically, the method for determining the blending observation scheme for the priority blending control batch is as follows: A blending execution strategy is developed for the "priority blending control batch," deciding whether to rapidly rotate through all its blending schemes or focus on a single scheme for extended blending. If a priority batch is closely associated with many other important batches, prolonged use for a single blending scheme will block the execution of other blending schemes. Therefore, a rapid rotation strategy (pre-defined observation scheme) is required; otherwise, a persistent strategy (second pre-defined observation scheme) can be used.

[0054] Available blending schemes for batch A: Scheme A1: A + B, Scheme A2: A + D, Scheme A3: A + E (new) Available blending schemes for batch B: B + A (i.e., A1); Available blending schemes for batch D: D + A (i.e., A2); Available blending schemes for batch E: E + A (i.e., A3).

[0055] S31 takes the blending batches involved in the other batches of available blending schemes as matching blending batches, and determines the number of available blending schemes in the other batches involved in the matching blending batches. In the steps above (identifying matching blending batches): for priority batch A, the other batches involved in all available blending schemes are B, D, and E. These are the matching blending batches.

[0056] S32 determines the number of available blending schemes involved in the priority blending control batch based on the blending data of the priority blending control batch; S33 determines the blending observation scheme for the priority blending control batch based on the number of available blending schemes involved in the matching blending batch in the other batches and the number of available blending schemes involved in the matching blending batch in the priority blending control batch.

[0057] It is understood that, based on the number of available blending schemes involved in the matching blending batch in the other batches, and the number of available blending schemes involved in the matching blending batch in the priority blending control batch, the blending observation scheme for the priority blending control batch is determined, specifically including: The number of available blending schemes involved in the matching blending batch in the other batches, and the number of available blending schemes involved in the matching blending batch in the priority blending control batch, are used to determine the blending influence factor between the priority blending control batch and the other batches. In the above steps, the blending influence factor measures the blending correlation strength between preferred batch A and another batch X, and is calculated as: (number of available blending schemes for batch X) × (number of times batch X appears in the available blending schemes for batch A). Logic: The more options a batch X has, and the more it depends on batch A, the higher the factor. The longer A is occupied, the greater its impact on X. This quantifies the intensity of the "chain reaction" caused by the blending operation of priority batch A on the entire inventory system.

[0058] Batch B: The number of available blending schemes for batch B = 1 (only B+A), the number of times batch B appears in the schemes of batch A = 1 (appearing in A1), and the blending influence factor (B) = 1 × 1 = 1.0.

[0059] Batch D: The number of available blending schemes for batch D = 1 (only D+A), the number of times batch D appears in the schemes of batch A = 1 (appears in A2), and the blending influence factor (D) = 1 × 1 = 1.0.

[0060] Batch E: The number of available blending schemes for batch E = 1 (only E+A), the number of times batch E appears in the schemes of batch A = 1 (appears in A3), and the blending influence factor (E) = 1 × 1 = 1.0.

[0061] Based on the blending influence factors between the batch and other batches, as well as the blending compatibility types of other batches, the blending observation scheme for the priority blending control batch is determined.

[0062] It should be noted that the blending impact factor is determined based on the number of available blending schemes involved in different matching blending batches in the other batches, and the number of available blending schemes involved in the matching blending batches in the priority blending control batches. The more available blending schemes a matching blending batch involves in the other batches, and the more available blending schemes it involves in the priority blending control batches, the more the blending time will be affected. This will affect the available blending schemes in the other batches, thus the blending impact factor is determined to be larger.

[0063] It should be noted that, based on the blending influence factors with other batches and the blending compatibility types of other batches, the blending observation scheme for the priority blending control batch is determined, specifically including: S331 Based on the mixing influence factor, determine whether there are other batches with a mixing influence factor greater than the preset influence factor threshold. If so, determine the mixing observation scheme of the priority mixing control batch as the preset observation scheme. If not, proceed to the next step. In the above steps, we checked whether there were any high-impact individuals and determined whether there were other batches with a contamination impact factor > 0.6. The results showed that the impact factors of batches B, D, and E were all 1.0, all greater than 0.6, and the condition was met! We directly determined that the contamination observation scheme for batch A was the preset observation scheme (rapid rotation strategy).

[0064] S332 determines the average value of the mixing influence factor between the batch and other batches based on the mixing influence factor between the batch and other batches, and determines whether the average value of the mixing influence factor between the batch and other batches is greater than the influence factor threshold. If so, proceed to the next step; otherwise, determine the mixing observation scheme of the priority mixing control batch as the second preset observation scheme. Assuming that the impact factors of batches B, D, and E are all 0.5, proceed to the next step, S332: assess the average impact level, calculate: average impact factor = (0.5 + 0.5 + 0.5 + 0) / 4 = 0.375, determine: 0.375 is not greater than 0.5, proceed to S333.

[0065] S333 determines whether there are batches with a type II compatibility among the other batches. If so, proceed to the next step. If not, determine that the compatibility observation scheme of the priority compatibility control batch is the second preset observation scheme. S334 determines whether the number of batches with the second type of blending adaptation in the other batches is greater than the preset batch number threshold. If yes, the blending observation scheme of the priority blending control batch is determined to be the preset observation scheme. If no, proceed to the next step. S333: Are there any two types of adaptation in other batches? Yes (B, D, E are all), the number of batches with two types of adaptation (3) > the preset threshold (2)? Yes, decision: determine the mixing observation scheme of batch A as the preset observation scheme (fast rotation strategy).

[0066] Although the individual correlation is not strong, batch A is associated with a large number of important batches (3 Category II batches). Considering the overall risk, rapid rotation is still necessary.

[0067] S335 determines the blending weight coefficient based on the blending compatibility type of other batches, and determines the blending influence coefficient of the priority blending control batch based on the blending weight coefficient of other batches and the blending influence factor between it and other batches, and determines the blending observation scheme of the priority blending control batch based on the blending influence coefficient.

[0068] Furthermore, the blending observation scheme for the priority blending control batch is determined based on the blending influence coefficient, specifically including: Determine whether the blending influence coefficient is greater than a preset influence coefficient threshold. If yes, then determine the blending observation scheme of the priority blending control batch as the preset observation scheme. If no, then determine the blending observation scheme of the priority blending control batch as the second preset observation scheme.

[0069] Assuming batch A is only associated with one second-class batch (e.g., only batch B), the final comprehensive score involves calculating the contamination impact coefficient. The formula (example) is: Contamination impact coefficient = Σ(Contamination impact factors of other batches × their weight coefficients). Weighting coefficients: Category II batch = 1.0, Category III batch = 0.2. Calculation: Mixing influence coefficient = (0.5 × 1.0) = 0.5. Judgment: 0.5 is not greater than 1, so the mixing observation scheme for batch A is determined to be the second preset observation scheme (persistent strategy).

[0070] It should be noted that if the blending observation scheme of the priority blending control batch is a preset observation scheme, then during the blending process, if the blending control duration in a certain available blending scheme exceeds the preset control duration threshold, the process switches to the next available blending scheme for blending control processing, until blending control is completed in all available blending schemes. If the blending observation scheme of the priority blending control batch is a second preset observation scheme, then if the blending control duration in a certain available blending scheme exceeds the second preset control duration threshold, the process switches to the next available blending scheme for blending control processing, until blending control is completed in all available blending schemes.

[0071] Table 2. Schematic diagram of the observation scheme

[0072] It should be noted that the preset control duration threshold is less than the second preset control duration threshold.

[0073] Specifically, the method for determining the blending and scheduling scheme for raw materials in the other batches is as follows: When quality fluctuations occur during the blending process of the core raw material (preferred blending control batch), intelligent decision-making determines how to adjust the scheduling and blending plans of other batches of raw materials to minimize the impact on the quality of the final product (clinker) and maintain the stable operation of the production line.

[0074] This is a dynamic emergency response mechanism. Based on real-time monitoring data, it decides whether to continue optimizing the current plan, activate alternative plans, or even restructure the entire blending scheduling plan.

[0075] Based on the observation and processing results, S41 determines the time period in which the monitored component and the reference component of the priority blending control batch are inconsistent with each other during the blending control process, and takes this period as the component deviation period. In the above steps (identifying core raw material fluctuations): the system detects that the composition of batch A is unstable during the blending process and records the periods of these composition deviations. The composition deviation period refers to the period during which the composition of the priority blending control batch (here, A) fluctuates significantly during the blending process.

[0076] S42 determines the monitoring data of clinker in different available blending schemes for the priority blending control batch based on the observation and processing results, and determines the clinker composition deviation period in different available blending schemes based on the monitoring data; In the above steps (identifying product quality fluctuations): the system monitors the period when the clinker produced by schemes A1 and A2 has unqualified quality. The period of clinker composition deviation refers to the period when the composition (such as silicon content, aluminum content, etc.) of the clinker produced by the blending scheme exceeds the qualified range.

[0077] S43 uses the component deviation time period of the priority blending control batch, as well as the clinker component deviation time period in different available blending schemes, and combines the raw material consumption data in the available blending schemes of other batches to determine the blending scheduling scheme for raw materials in other batches.

[0078] It should be noted that the clinker composition deviation period refers to the period during which the clinker composition is outside the acceptable range.

[0079] Furthermore, by utilizing the component deviation time period of the prioritized blending control batch, and the clinker component deviation time period in different available blending schemes, and combining the raw material consumption data of other batches in available blending schemes, a blending scheduling scheme for raw materials in other batches is determined, specifically including: S431 determines whether there is a usable blending scheme that meets the requirements for the number of clinker component deviation periods. If yes, proceed to the next step. If no, determine that the blending scheduling scheme of the raw materials in the other batches is to switch to the blending scheduling scheme with the largest overlap factor, excluding the priority blending control batch. In the above steps, assess the prevalence of product quality problems and whether there are available blending schemes that meet the requirements (i.e., "small quantity") for the period of clinker composition deviation. Example: The clinker quality of scheme A3 is stable and meets the requirements. The condition is met (A3 exists), and the case branch is entered.

[0080] Case 1: When the total duration of the component deviation period of the priority blending batch does not meet the requirements, the blending scheduling scheme of the raw materials in the other batches is determined to be switched to the blending scheduling scheme with the largest overlap factor, excluding the priority blending control batch. Case 2: When the total duration of the component deviation period of the preferred blending batch meets the requirements, determine whether the consumption data of raw materials in the available blending schemes of other batches all meet the requirements (i.e., the consumption rate is less than 1%). If so, the available blending scheme that meets the requirements for the total duration of the clinker component deviation period is taken as the preferred blending scheme, and the observation processing is carried out in the preferred blending scheme according to the second preset observation scheme. If not, proceed to the next step. In the above steps, it is determined whether the consumption rate of other batches of raw materials is less than 1% (i.e., sufficient inventory). Assuming that batches B, D, and E have sufficient inventory and very low consumption rates, the decision is made to select the shortest available blending scheme that meets the requirement of the total duration of the clinker composition deviation period (e.g., less than 1 hour). Scheme A3 is selected as the preferred blending scheme, and observation is performed according to the second preset observation scheme (persistent strategy) within the preferred blending scheme. That is, the production line is switched and kept running on scheme A3 (A+E+B), and it is allowed to continue blending for a relatively long time (e.g., 24 hours), because it is currently the only scheme that can stably produce qualified clinker. It is determined whether there is any abnormality. If there is no abnormality, the above scheme is used for blending control.

[0081] Based on the similarity between the batches blended in the preferred blending scheme and the available blending schemes in other batches, the matching batches of the preferred blending scheme are determined. It is then determined whether the number of matching batches of the preferred blending control scheme is greater than a preset matching batch threshold. If yes, proceed to the next step; otherwise, determine that the blending scheduling scheme of the raw materials in the other batches is to switch to the blending scheduling scheme with the largest overlap factor, excluding the preferred blending control batches. Assumption: In case S431, it is found that the inventory of batch E, which is compatible with plan A3, is tight (consumption rate > 1%). If the mixing is abnormal, it may affect the mixing of other batches. Decision: The condition is not met, proceed to the next step.

[0082] Based on the preferred blending control scheme with the largest number of matching batches, and combined with the consumption data of raw materials in the available blending schemes in different matching batches, the blending scheduling scheme of raw materials in the matching batch is determined.

[0083] Furthermore, based on the consumption data of raw materials in the available blending schemes in the matching batch, the proportion of available blending schemes in the matching batch whose consumption data does not meet the requirements is determined, and this proportion is used as the blending influence coefficient of the matching batch. Assessing Inventory Pressure in Matched Batches: Action: Calculate the blending impact factor. Definition: Blending Impact Factor: The number of available blending options within a matched batch where consumption data does not meet requirements (tight inventory) / the total number of matched batches. This factor reflects the overall inventory risk of activating alternative options.

[0084] Case calculation: In matching batch E, the blending influence coefficient is 1. In matching batch B, only in ABE is there a usable blending scheme where the consumption data does not meet the requirements (short inventory). Assuming there are 3 usable blending schemes, the blending influence coefficient in matching batch B is determined to be 0.33.

[0085] In the preferred blending control scheme with the most matching times, it is determined whether the blending influence coefficient of all matching batches is greater than the preset influence coefficient threshold. If so, the blending batches in the preferred blending control scheme, excluding the priority blending batches, are kept unchanged according to a preset time period. The matching batches are then processed using available blending schemes that overlap with the blending batches in the preferred blending control scheme, excluding the priority blending batches, according to a preset time period. This determines whether the reliability of the matching batch blending control process meets the requirements. If not, the matching batch blending control process is only required if the total duration of the component deviation period within the most recent unit time period in the preferred blending control scheme does not meet the requirements.

[0086] Judgment: Are the blending impact coefficients of all matching batches greater than the preset impact coefficient threshold (assuming 0.5)? No (0.33 < 0.5). Since the overall inventory risk is controllable, the system adopts a strategy of activating alternative solutions on demand.

[0087] Main scheme: Preferred blending scheme A3 (A+B+E) is used first. Only when the total duration of component deviation in scheme A3 within a recent period (per unit time) is greater than 0.05, matching batches B and E are dynamically initiated, that is, A is adjusted. For example, (F+B+E) is a scheme where all components are the same except for A. This is to determine whether B or E is reliable when blending.

[0088] Logic: With core ingredient A stable, prioritize maintaining the only stable product quality line (A3). Only when there are component deviations in this line should adjustments to B and E be made, thereby improving the efficiency of identifying blending schemes.

[0089] Composition deviation period: refers to the period during which the chemical composition of the raw materials in the priority blending control batch (such as batch A limestone in our case) undergoes significant abnormal fluctuations, as monitored in real time by the system during the raw material blending production process.

[0090] Monitoring object: Input raw material, specifically the "priority blending control batch" that serves as the core of blending.

[0091] Judgment criteria: The system performs continuous or high-frequency sampling analysis on the core raw materials entering the production line. When the real-time detection value of its key components (such as CaO content) exceeds its inherent and expected normal fluctuation range (a control upper and lower limit set based on historical data), the period is marked as the "component deviation period".

[0092] For example, the CaO content of batch A limestone typically fluctuates within the range of 45% ± 0.5%. However, at a certain time, the online analyzer showed that its CaO content continuously dropped to 43.5% and remained there for 10 minutes. This 10-minute period represents a component deviation.

[0093] The clinker composition deviation period refers to the time period during which the chemical composition of the produced clinker (final product) does not meet the product quality standards, as monitored in real time by the system during the raw material blending production process.

[0094] Monitoring object: Output product, that is, cement clinker from the rotary kiln. The system performs continuous or high-frequency sampling and analysis on the clinker produced by the production line.

[0095] When the real-time detection values ​​of key clinker ratios or components (such as saturation ratio (KH), silicon content (SM), and aluminum content (IM)) exceed the range of qualified clinker standards set by the company, this period is marked as the "clinker composition deviation period".

[0096] For example, the company standard requires the clinker saturation ratio (KH) to be controlled between 0.88 and 0.92. However, during a certain period, the KH value of the produced clinker remained above 0.94 for half an hour. This half-hour period represents a deviation in clinker composition, and its core characteristic is that it reflects a problem with the quality of the final product, resulting from production fluctuations.

[0097] Example 2 Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described intelligent scheduling and management method for cement production raw materials when running the computer program.

[0098] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0099] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0100] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for intelligent scheduling and management of raw materials in cement production, characterized in that, Specifically, it includes: Based on the composition analysis results of different batches of raw materials in the stockpile, the matching situation with historical production data is determined. Based on the matching situation and the degree of blending compatibility with other batches, the blending compatibility type of different batches of raw materials is determined. Based on the degree of blending compatibility, the available blending schemes for the raw materials in the batch are determined, and the priority blending control batch in the batch is determined by combining the overlap of blending data between the available blending schemes and the blending compatibility type. Based on the blending data of the priority blending control batch, the overlap of available blending schemes with other batches, and the blending compatibility type of other batches, the blending observation scheme of the priority blending control batch is determined. Based on the aforementioned blending observation scheme, the observation and processing results during the blending process are determined. Based on the observation and processing results and the consumption data of raw materials in other batches of available blending schemes, the blending scheduling scheme for raw materials in other batches is determined.

2. The intelligent scheduling and management method for cement production raw materials as described in claim 1, characterized in that, The composition analysis results of the raw materials are determined based on the test results of the original components.

3. The intelligent scheduling and management method for cement production raw materials as described in claim 1, characterized in that, The matching with historical production data is determined based on the deviation between the composition analysis results of the raw materials and the control range of the components required for production.

4. The intelligent scheduling and management method for cement production raw materials as described in claim 1, characterized in that, The method for determining the blending compatibility type of the raw materials in the batch is as follows: Based on the composition analysis results of the raw materials, the deviation from the control range of the composition required for production is determined. Based on the deviation, the components that are not within the control range are identified and identified as the deviation components. Based on the composition data of the deviation components in the batch of raw materials, and in combination with the composition data of the deviation components in raw materials excluding the batch, the blending and compatibility type of the batch of raw materials is determined.

5. The intelligent scheduling and management method for cement production raw materials as described in claim 4, characterized in that, Based on the composition data of the deviation components in the batch of raw materials, and in combination with the composition data of the deviation components in raw materials excluding the batch, the blending compatibility type of the batch of raw materials is determined, specifically including: Based on preset blending control conditions, determine the available blending schemes for the batch of raw materials based on the composition data of the deviation components; Based on the available blending scheme data, determine the quantity of available blending schemes for the batch; Using the number of available blending schemes and the composition data of the deviation components, the blending compatibility type of the batch of raw materials is determined.

6. The intelligent scheduling and management method for cement production raw materials as described in claim 5, characterized in that, The available blending scheme is one in which the total number of deviation components in all batches is within a set range, the different components after blending can be controlled within a control range, and the number of batches in which all components after blending belong to deviation components is no more than 1.

7. The intelligent scheduling and management method for cement production raw materials as described in claim 1, characterized in that, The method for determining the blending observation scheme for the priority blending control batch is as follows: Take the blending batches involved in the other batches of available blending schemes as matching blending batches, and determine the number of available blending schemes in the other batches involved in the matching blending batches. Based on the blending data of the priority blending control batch, determine the number of available blending schemes involved in the priority blending control batch in the matching blending batch; Based on the number of available blending schemes involved in the matching blending batch in the other batches, and the number of available blending schemes involved in the matching blending batch in the priority blending control batch, the blending observation scheme for the priority blending control batch is determined.

8. The intelligent scheduling and management method for cement production raw materials as described in claim 1, characterized in that, The method for determining the blending and scheduling scheme for raw materials in the other batches is as follows: Based on the observation and processing results, the time period in which the monitored component of the priority blending control batch is inconsistent with the reference component during the blending control process is determined and this period is taken as the component deviation period. Based on the observation and processing results, the monitoring data of the clinker in different available blending schemes for the priority blending control batch are determined, and the time period of clinker composition deviation in different available blending schemes is determined based on the monitoring data; By utilizing the component deviation time period of the priority blending control batch, as well as the clinker component deviation time period in different available blending schemes, and combining the raw material consumption data in the available blending schemes of other batches, the blending scheduling scheme for raw materials in other batches is determined. It should be noted that the clinker composition deviation period refers to the period during which the clinker composition is outside the acceptable range.

9. The intelligent scheduling and management method for cement production raw materials as described in claim 8, characterized in that, By utilizing the component deviation time period of the prioritized blending control batch, and the clinker component deviation time periods in different available blending schemes, and combining the raw material consumption data of other batches in available blending schemes, a blending scheduling scheme for raw materials in other batches is determined, specifically including: When there is no available blending scheme that meets the requirements for the quantity of clinker composition deviation period, the blending scheduling scheme for the raw materials in the other batches is determined to be switched to the blending scheduling scheme with the largest overlap factor, excluding the priority blending control batch.

10. A computer system, comprising: A memory and processor connected by communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a method for intelligent scheduling and management of raw materials for cement production as described in any one of claims 1-9.

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