Intelligent dosing method and system based on powder metallurgy production

Abstract

The application relates to an intelligent batching method and system based on powder metallurgy production, and relates to the field of intelligent production technology.The method comprises the following steps: obtaining a required feeding type; determining a basic feeding opening corresponding to the required feeding type according to a preset basic matching relationship, controlling a discharging port to work at the basic feeding opening for a preset fixed time length, and obtaining a fixed discharging weight in real time within the fixed time length; calculating the fixed discharging weight and the fixed time length to determine a flow fluctuation coefficient; constructing a history interval with a current time point as a rear end point and a preset history time length as a width on a preset time axis, determining a similar point in the history interval according to the flow fluctuation coefficient and the required feeding type; determining a material speed threshold scheme at the similar point, and controlling raw materials of the current required feeding type to perform a batching operation according to the material speed threshold scheme.The application has the function of improving the batching operation effect of the raw materials.

Description

Technical Field

[0001] This application relates to the field of intelligent manufacturing technology, and in particular to an intelligent batching method and system based on powder metallurgy production. Background Technology

[0002] Powder metallurgy is an important metal forming process, and the batching process directly determines the compositional uniformity, dimensional accuracy, and mechanical properties of the final product. In powder metallurgy batching systems, it is typically necessary to weigh and mix various metal or non-metal powders of different densities, particle sizes, and flowability according to a preset formula with high precision. Because powdered materials are easily affected by factors such as flowability, humidity, static electricity, and changes in bulk density, the precision and stability of material feeding control have always been a technical challenge in the industry.

[0003] Currently, in automated powder batching equipment, a multi-stage feeding control strategy of "fast-medium-slow" is commonly adopted to balance feeding efficiency and weighing accuracy. This involves using high-speed feeding to quickly convey most of the material in the initial stage, switching to medium speed in the middle stage to reduce inertial overshoot, and then switching to slow speed in the later stage to achieve precise approximation of the target value. In this strategy, the timing of switching between different speed stages is usually determined based on a fixed threshold of "remaining feed amount." For example, when the remaining feed amount is greater than 100 grams, fast feeding is maintained; when the remaining feed amount drops to between 100 grams and 10 grams, medium-speed feeding is switched; and when the remaining feed amount is less than 10 grams, slow feeding is switched until the end.

[0004] While the aforementioned technologies can achieve accurate material feeding to a certain extent, the flowability of different powder raw materials varies under different environments. If a fixed threshold speed adjustment method is used, some free-flowing raw materials may experience over-weighting, resulting in poor material batching performance and room for improvement. Summary of the Invention

[0005] To improve the efficiency of raw material batching, this application provides an intelligent batching method and system based on powder metallurgy production.

[0006] Firstly, this application provides an intelligent batching method based on powder metallurgy production, employing the following technical solution: A smart batching method based on powder metallurgy production includes: Obtain the required feed type; The basic feeding opening is determined according to the preset basic matching relationship, and the feeding port is controlled to operate at the basic feeding opening for a preset fixed duration, and the fixed feeding weight is obtained in real time within the fixed duration. The flow rate fluctuation coefficient is determined by calculation based on a fixed feed weight and a fixed duration. Construct a historical interval on a preset timeline with the current time point as the endpoint and a width of a preset historical duration, and determine similarity points within the historical interval based on the flow fluctuation coefficient and the required feeding type; Determine the material rate threshold scheme based on similarity points, and control the feeding operation of raw materials of the current demand type according to the material rate threshold scheme.

[0007] Optionally, the steps for calculating and determining the flow fluctuation coefficient based on a fixed feed weight and a fixed duration include: A fixed interval is defined based on a fixed duration, and a unit interval is defined within the fixed interval based on a preset unit duration. The unit cutting weight is determined by calculating based on the fixed cutting weights at the front and rear points within the unit interval; The unit flow coefficient is determined by calculation and analysis based on the unit material weight and unit time. Randomly select a unit flow coefficient to define the main flow coefficient, and construct a main similar range based on the main flow coefficient and preset similar coefficients. Count the unit flow coefficients within the main similar range to determine the number within the range. The effective interval is defined as the unit interval corresponding to the unit flow coefficient in the main similar range corresponding to the largest range. The effective intervals are combined to construct the virtual normal interval, and the flow fluctuation coefficient is determined by calculation and analysis within the virtual normal interval.

[0008] Optionally, after the quantity within the range is determined, the intelligent batching method based on powder metallurgy production also includes: Determine whether there exist at least two primary adjacent ranges with the same and largest number of internal ranges; If there are no two primary adjacent ranges with the same and largest number of internal components, then the valid interval is defined based on the primary adjacent range corresponding to the largest number of internal components. If there are at least two main similar ranges with the same and largest number of internal components, the main similar range corresponding to the largest number of internal components is defined as the candidate similar range, and the virtual fluctuation coefficient is determined based on the candidate similar range. The unit flow coefficient within the similar range of the candidates is defined as the internal flow coefficient, and the unit flow coefficient outside the similar range of the candidates is defined as the external flow coefficient. The internal separation coefficient is determined based on the virtual fluctuation coefficient and the internal flow coefficient, and the external separation coefficient is determined based on the virtual fluctuation coefficient and the external flow coefficient. The reasonable separation coefficient is determined by calculating the internal separation coefficient, the preset internal weight coefficient, the external separation coefficient, and the preset external weight coefficient. The virtual fluctuation coefficient corresponding to the largest reasonable separation coefficient is determined as the flow fluctuation coefficient.

[0009] Optionally, the steps for determining similarities based on flow fluctuation coefficients and demand feed types within a historical timeframe include: The historical flow coefficient is determined based on the type of material required within the historical timeframe. The single-point deviation coefficient is determined by calculating the difference between the current flow fluctuation coefficient and the historical flow coefficient. Determine whether the minimum single-point deviation coefficient is less than the preset equivalent deviation coefficient; If the smallest single-point deviation coefficient is less than the equivalent deviation coefficient, then the production point of the historical flow coefficient corresponding to the smallest single-point deviation coefficient is defined as the similar point. If the minimum single-point deviation coefficient is not less than the equivalent deviation coefficient, then the reference allowable range is constructed using the current flow fluctuation coefficient and the preset reference interval coefficient. Within the reference allowable range, the left-side volatility coefficient and the right-side volatility coefficient are determined based on historical flow coefficients. The threshold range of each material rate is determined based on the left and right fluctuation coefficients, and the left and right deviation coefficients are calculated based on the left fluctuation coefficient, the current flow fluctuation coefficient, and the right fluctuation coefficient. The specific material speed threshold is determined based on the left and right deviation coefficients within the material speed threshold range, and the material speed threshold scheme is determined by combining all the specific material speed thresholds.

[0010] Optionally, after the left-side and right-side fluctuation coefficients are determined, the intelligent batching method based on powder metallurgy production also includes: Determine whether at least one of the left-side volatility coefficient and the right-side volatility coefficient is empty; If neither the left-side fluctuation coefficient nor the right-side fluctuation coefficient is empty, then the material rate threshold scheme is determined based on the left-side fluctuation coefficient and the right-side fluctuation coefficient. If at least one of the left-side volatility coefficients and the right-side volatility coefficients is empty, then the similarity of properties is determined by calculating and analyzing the unit flow coefficient of the unit interval that determines the historical flow coefficient and the current unit flow coefficient within the historical interval. The type corresponding to the property similarity is defined as the reasonable reference type when the property similarity is greater than the preset reasonable similarity, and the material rate threshold scheme is determined according to the reasonable reference type.

[0011] Optionally, after the material rate threshold scheme is determined, the intelligent batching method based on powder metallurgy production also includes: The theoretical feeding time is determined based on the material rate threshold scheme for each type of required feeding. The maximum theoretical feeding time is defined as the upper limit feeding time, and the reasonable feeding time is determined based on the upper limit feeding time and the preset floating time. The material rate threshold scheme for the demand feeding type with a theoretical feeding time less than the reasonable feeding time is defined as the permissible adjustment scheme, and the initial material rate threshold is determined according to the permissible adjustment scheme. Based on the initial material rate threshold and preset variation parameters, a threshold selection range is constructed, and a value is randomly selected from each threshold selection range to construct a simulated threshold scheme. The simulated feeding time is determined based on the simulated threshold scheme, and the simulated threshold scheme whose simulated feeding time is consistent with the reasonable feeding time is defined as the high-quality threshold scheme. An alternative threshold scheme is determined from the high-quality threshold scheme to be updated as the new material rate threshold scheme.

[0012] Optionally, the step of determining alternative threshold schemes within the high-quality threshold scheme includes: The width of fast feeding operation, medium feeding operation, and slow feeding operation are determined based on the optimal threshold scheme. The evaluation value of the scheme is determined by calculating based on the width of the fast feeding operation, the width of the medium feeding operation, the width of the slow feeding operation, and the preset material speed evaluation coefficient. The optimal threshold scheme corresponding to the scheme with the highest evaluation value is determined as the alternative threshold scheme.

[0013] Secondly, this application provides an intelligent batching system based on powder metallurgy production, which adopts the following technical solution: A smart batching system based on powder metallurgy production includes: The acquisition module is used to acquire the required feed type; The processing module, connected to the acquisition module, is used for information storage and processing; The processing module determines the basic feeding opening corresponding to the required feeding type based on the preset basic matching relationship, controls the feeding port to operate at the basic feeding opening and maintains it for a preset fixed duration, and enables the acquisition module to acquire the fixed feeding weight in real time within the fixed duration. The processing module calculates and determines the flow fluctuation coefficient based on a fixed feed weight and a fixed duration. The processing module constructs a historical interval on a preset time axis with the current time point as the end point and a width of a preset historical duration, and determines similar points within the historical interval based on the flow fluctuation coefficient and the required feeding type. The processing module determines the material rate threshold scheme at similar points and controls the feeding operation of raw materials of the current demand type according to the material rate threshold scheme.

[0014] In summary, this application includes at least one of the following beneficial technical effects: During the powder metallurgy production process, the flowability of each raw material powder can be analyzed, and a reasonable speed switching threshold can be set according to the flowability to reduce overshooting of some raw materials with good flowability and improve the efficiency of raw material batching. During the batching process, the required feeding time for each raw material can be determined, thereby adjusting the material speed threshold as much as possible while meeting efficiency requirements, so as to further improve the batching accuracy. Attached Figure Description

[0015] Figure 1 This is a flowchart of an intelligent batching method based on powder metallurgy production.

[0016] Figure 2 This is a module flowchart of an intelligent batching method based on powder metallurgy production. Detailed Implementation

[0017] To make the purpose, technical solution, and advantages of this application clearer, the following is combined with Figures 1-2 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0018] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0019] This application discloses an intelligent batching method based on powder metallurgy production, referring to... Figure 1 The process flow of the intelligent batching method based on powder metallurgy production includes the following steps: Step S100: Obtain the required material feeding type.

[0020] The required feed type refers to the type of raw materials that are currently needed for batching. In a single powder metallurgy production process, multiple types of raw materials often appear simultaneously.

[0021] Step S101: Determine the basic feeding opening corresponding to the required feeding type based on the preset basic matching relationship, control the discharge port to operate at the basic feeding opening and maintain the preset fixed time, and obtain the fixed feeding weight in real time within the fixed time.

[0022] The basic feeding opening is the operating opening of the discharge port during material preparation. Different feeding types require different raw material weights, so the initial feeding speed will also be different. The fixed duration is a set value duration set by the operator. By controlling the fixed feeding duration, the flow data of the raw material in the current environment can be obtained, which is convenient for subsequent analysis. The fixed discharge weight is the weight of the raw material discharged within the fixed duration, which can be obtained in real time through the corresponding sensor.

[0023] Step S102: Calculate and determine the flow fluctuation coefficient based on the fixed material weight and fixed time.

[0024] The flow fluctuation coefficient is a parameter that reflects the fluidity of raw materials. The smaller the value, the better the fluidity. The specific calculation formula is as follows: ,in For flow fluctuation coefficient, This refers to the fixed weight of the material collected at the end of a fixed time period. For a fixed duration, This reflects the material feeding speed, or the average flow rate of the material. ; For the first The instantaneous flow rate detected at a given moment can be obtained through... Calculation determined, Right now The change in the fixed material weight at a given time point. It represents the total number of moments within a fixed duration.

[0025] Step S103: Construct a historical interval on the preset time axis with the current time point as the end point and the width as the preset historical duration, and determine similar points in the historical interval based on the flow fluctuation coefficient and the required feeding type.

[0026] The time axis is a coordinate axis formed by combining various time points. This coordinate axis points from the time points that have been passed to the time points that have not yet been reached, with the direction of the time points that have been passed being forward. The historical duration is the duration for which data on historical batching can be collected, as set by the staff. By constructing historical intervals, it is possible to acquire and analyze data within the historical duration. Similarity points are processing points where the flow is highly similar to the current flow. These processing points are the operation points for a single batching.

[0027] Step S104: Determine the material rate threshold scheme at similar points, and control the feeding operation of raw materials of the current demand type according to the material rate threshold scheme.

[0028] The material rate threshold scheme is the batching threshold scheme executed when there is a similarity point. For example, when there is 150g left, the speed is switched from fast to medium, and when there is 50g left, the speed is switched from medium to slow. At this time, the batching operation is controlled according to the material rate threshold scheme, so as to ensure that the threshold set for the fluid material is reasonable, which facilitates the accurate feeding of materials, avoids overrushing and other situations, and improves the overall batching operation effect.

[0029] The steps for determining the flow fluctuation coefficient based on a fixed feed weight and a fixed duration include: Step S200: Define a fixed interval based on a fixed duration, and define a unit interval within the fixed interval based on a preset unit duration.

[0030] In step S102 above, it is determined that the flow fluctuation coefficient may be affected by local flow velocity anomalies, so further analysis is required; the fixed interval is the time interval on the time axis with the current time point as the end point and the width as a fixed duration, which is also the operation time interval for material unloading; the unit duration is the fixed duration set by the staff, which is less than the fixed duration and is set as an integer multiple of the fixed duration, and the unit interval is the time interval within the fixed interval with a width of the unit duration.

[0031] Step S201: Calculate the unit cutting weight based on the fixed cutting weights of the front and rear points within the unit interval.

[0032] Unit feed weight refers to the feed weight of material within a unit interval.

[0033] Step S202: Calculate and analyze based on the unit feed weight and unit duration to determine the unit flow coefficient.

[0034] The unit flow coefficient is the flow fluctuation coefficient of the material within a unit range, calculated using the formula in step S102 above.

[0035] Step S203: Randomly select a unit flow coefficient as the main flow coefficient, construct a main similar range based on the main flow coefficient and preset similar coefficients, and count the unit flow coefficients within the main similar range to determine the number within the range.

[0036] The flow coefficients within each unit interval can be analyzed by randomly defining the primary flow coefficient; the similarity coefficient is the maximum difference in flow coefficients allowed when the flowability of materials is considered to be highly similar, as set by the staff. By adding and subtracting the similarity coefficient from the primary flow coefficient, the required numerical range of other parameters that are close to the primary flow coefficient can be determined, i.e., the primary similarity range; the number within the range is the number of unit flow coefficients that are within the primary similarity range.

[0037] Step S204: Define the unit interval corresponding to the unit flow coefficient in the main similar range corresponding to the largest range as the effective interval, and combine the effective intervals to construct the virtual normal interval, and perform calculation and analysis within the virtual normal interval to determine the flow fluctuation coefficient.

[0038] The largest number within the range indicates that the current unit flow coefficient is relatively concentrated, which means that the unit flow coefficient within this range can reflect the flowability of the powder under normal conditions. Therefore, an effective range is defined to identify and distinguish different unit ranges. At this time, the effective ranges are combined to form a virtual normal range. The flowability coefficient under normal conditions can be determined through the scheme in step S102. The calculated value at this time is the flow fluctuation coefficient that can objectively reflect the actual flowability of the material after eliminating interference.

[0039] Once the quantity within the range is determined, the intelligent batching method based on powder metallurgy production also includes: Step S300: Determine whether there exist at least two primary adjacent ranges with the same and largest number of internal ranges.

[0040] The purpose of this judgment is to determine whether there are multiple main similar ranges that meet the requirements, so as to facilitate subsequent analysis.

[0041] Step S3001: If there are no two main similar ranges with the same and largest number of internal components, then define the valid interval based on the main similar range corresponding to the largest number of internal components.

[0042] When there are no at least two main similar ranges with the same and largest number of elements within them, it means that there is only one main similar range that meets the requirements. In this case, the effective interval can be determined based on this main similar range.

[0043] Step S3002: If there are at least two main similar ranges with the same and largest number of internal components, the main similar range corresponding to the largest number of internal components is defined as the candidate similar range, and the virtual fluctuation coefficient is determined based on the candidate similar range.

[0044] When there are at least two main similar ranges with the same number of internal components and the largest number of internal components, it indicates that there are multiple main similar ranges that meet the requirements. At this time, they are defined as candidate similar ranges to distinguish different main similar ranges. The virtual fluctuation coefficient is the flow fluctuation coefficient that can be obtained after performing the analysis of step S204 based on the candidate similar ranges.

[0045] Step S301: Define the unit flow coefficient within the similar range of the candidates as the internal flow coefficient, and define the unit flow coefficient outside the similar range of the candidates as the external flow coefficient.

[0046] Define internal and external flow coefficients to identify and distinguish different unit flow coefficients, facilitating subsequent analysis.

[0047] Step S302: Determine the internal phase separation coefficient based on the virtual fluctuation coefficient and the internal flow coefficient, and determine the external phase separation coefficient based on the virtual fluctuation coefficient and the external flow coefficient.

[0048] The internal separation coefficient is the difference between the virtual volatility coefficient and the internal flow coefficient, and the external separation coefficient is the difference between the virtual volatility coefficient and the external flow coefficient. Both of these differences are absolute values.

[0049] Step S303: Calculate and determine the reasonable separation coefficient based on the internal separation coefficient, the preset internal weight coefficient, the external separation coefficient, and the preset external weight coefficient, and determine the virtual fluctuation coefficient of the candidate similar range corresponding to the largest reasonable separation coefficient as the flow fluctuation coefficient.

[0050] The appropriate separation coefficient can be obtained by dividing the internal weighting coefficient by each internal separation coefficient and adding the external weighting coefficient divided by each external separation coefficient. The larger the appropriate separation coefficient, the more suitable the selected candidate range is. The internal weighting coefficient is greater than the external weighting coefficient. The specific parameters are set by the staff according to the actual required accuracy. At this time, the largest appropriate separation coefficient indicates that the selected candidate range is more suitable. Therefore, the virtual fluctuation coefficient determined within the candidate range is the required flow fluctuation coefficient.

[0051] The steps for determining similarities based on flow fluctuation coefficients and demand feed types within a historical timeframe include: Step S400: Determine the historical flow coefficient based on the required feed type within the historical interval.

[0052] The historical flow coefficient is the flow fluctuation coefficient determined when batching raw materials of the required feed type within a historical period.

[0053] Step S401: Calculate the difference between the current flow fluctuation coefficient and the historical flow coefficient to determine the single-point deviation coefficient.

[0054] The single-point deviation coefficient is the difference between the current flow fluctuation coefficient and the historical flow coefficient.

[0055] Step S402: Determine whether the smallest single-point deviation coefficient is less than the preset equivalent deviation coefficient.

[0056] The equivalent deviation coefficient is the maximum single-point deviation coefficient allowed when the liquidity performance is extremely similar in two situations, as set by the staff. The purpose of the judgment is to determine whether the most similar processing situation in the historical period is of reference significance.

[0057] Step S4021: If the smallest single-point deviation coefficient is less than the equivalent deviation coefficient, then the production point of the historical flow coefficient corresponding to the smallest single-point deviation coefficient is defined as the similar point.

[0058] When the smallest single-point deviation coefficient is less than the equivalent deviation coefficient, it indicates that the most similar situation is of reference significance. Therefore, the corresponding production point can be defined as a similar point for historical data reference.

[0059] Step S4022: If the minimum single-point deviation coefficient is not less than the equivalent deviation coefficient, then construct a reference allowable range based on the current flow fluctuation coefficient and the preset reference interval coefficient.

[0060] When the minimum single-point deviation coefficient is not less than the equivalent deviation coefficient, it indicates that there is no directly referable historical data and further analysis is needed. The reference interval coefficient is the difference between the flow fluctuation coefficients that the staff set for the data to be considered to have certain reference significance. The reference allowable range is constructed by adding and subtracting the reference interval coefficient from the current flow fluctuation coefficient.

[0061] Step S403: Determine the left-side volatility coefficient and the right-side volatility coefficient based on the historical flow coefficient within the reference allowable range.

[0062] The left-side volatility coefficient is the coefficient value that is to the left of the historical liquidity coefficient and closest to it within the reference permissible range, while the right-side volatility coefficient is the coefficient value that is to the right of the historical liquidity coefficient and closest to it within the reference permissible range.

[0063] Step S404: Determine the threshold range of each material rate based on the left-side fluctuation coefficient and the right-side fluctuation coefficient, and calculate the left and right deviation coefficients based on the left-side fluctuation coefficient, the current flow rate fluctuation coefficient, and the right-side fluctuation coefficient.

[0064] The material speed threshold range refers to the range of threshold values ​​that can be taken at each material speed. Taking the threshold for quickly switching to medium speed as an example, the corresponding range is constructed based on the threshold values ​​for this situation under the left fluctuation coefficient and the right fluctuation coefficient as the two endpoints. The left and right deviation coefficients are the deviation coefficients that reflect the current flow fluctuation coefficients from the interval constructed by the left and right fluctuation coefficients. They can be determined by dividing the distance from the left fluctuation coefficient by the distance from the right fluctuation coefficient.

[0065] Step S405: Determine the specific material speed threshold based on the left and right deviation coefficients within the material speed threshold range, and combine all the specific material speed thresholds to determine the material speed threshold scheme.

[0066] By using the left and right deviation coefficients, we can determine the position of the current flow fluctuation coefficient within the interval constructed by the left and right fluctuation coefficients. Therefore, we can determine the position of the threshold within the material speed threshold range by analogy. At this point, we can combine the threshold for quickly switching to medium speed and the threshold for switching from medium speed to slow speed to obtain the required material speed threshold scheme.

[0067] After determining the left-side and right-side fluctuation coefficients, the intelligent batching method based on powder metallurgy production also includes: Step S500: Determine whether at least one of the left-side volatility coefficient and the right-side volatility coefficient is empty.

[0068] The purpose of the judgment is to determine whether there is any data available for reference.

[0069] Step S5001: If neither the left-side fluctuation coefficient nor the right-side fluctuation coefficient is empty, then determine the material rate threshold scheme based on the left-side fluctuation coefficient and the right-side fluctuation coefficient.

[0070] When neither the left-side fluctuation coefficient nor the right-side fluctuation coefficient is empty, it means that the data analysis can be performed through steps S403-S405 above, and then normal execution can be performed.

[0071] Step S5002: If at least one of the left-side fluctuation coefficient and the right-side fluctuation coefficient is empty, then calculate and analyze the property similarity in the historical interval based on the unit flow coefficient of the unit interval that determined the historical flow coefficient and the current unit flow coefficient.

[0072] When at least one of the fluctuation coefficients on the left and right is empty, it indicates that there is no historical data available for reference under the current demand feeding type, and further analysis is required. The property similarity is a parameter value that reflects the similarity of the properties of two types of materials. It can be determined by the unit flow coefficients of the two materials. The specific determination method is as follows: match the unit intervals of the two materials one by one, that is, match the first unit interval with the first unit interval, match the last unit interval with the last unit interval, and so on. At this time, calculate the difference between the two unit flow coefficients of the corresponding unit intervals, and calculate the property similarity by taking the reciprocal of the sum of the absolute values ​​of all differences.

[0073] Step S501: Define the type corresponding to the property similarity greater than the preset reasonable similarity as a reasonable reference type, and determine the material rate threshold scheme based on the reasonable reference type.

[0074] Reasonable similarity is the minimum property similarity that staff set when the properties of materials are considered extremely similar. Reasonable reference types are defined to determine similar material types. At this time, the material rate threshold scheme determined under the corresponding conditions is the material rate threshold scheme required for this type of material. When there are multiple reasonable reference types that meet the requirements, the largest property similarity is used as the reference for analysis.

[0075] After the material rate threshold scheme is determined, the intelligent batching method based on powder metallurgy production also includes: Step S600: Determine the theoretical feeding time based on the material rate threshold scheme for each required feeding type.

[0076] The theoretical feeding time is the total time required for material feeding when the feeding speed is adjusted according to the material rate threshold scheme.

[0077] Step S601: Define the maximum theoretical feeding time as the upper limit feeding time, and determine the reasonable feeding time based on the upper limit feeding time and the preset floating time.

[0078] Define the upper limit feeding time to identify and distinguish the longest feeding time. The floating time is the time preset by the staff for the material feeding to make fine adjustments to the weight. The reasonable feeding time can be determined by subtracting the floating time from the upper limit feeding time. That is, the feeding operation of other materials can be completed within the reasonable feeding time.

[0079] Step S602: Define the material rate threshold scheme for the demand feeding type whose theoretical feeding time is less than the reasonable feeding time as the permissible adjustment scheme, and determine the initial material rate threshold according to the permissible adjustment scheme.

[0080] When the theoretical feeding time is less than the reasonable feeding time, it indicates that the corresponding required feeding type can extend the feeding time. For example, it can enter the medium speed or slow speed earlier. This earlier entry method can facilitate precise material control, thereby further reducing the occurrence of material inaccuracies. At this time, a permissible adjustment scheme is defined to identify and distinguish different material speed threshold schemes for easy subsequent analysis. The initial material speed threshold is the threshold of each speed change under the permissible adjustment scheme. For example, fast-medium 150g, medium-slow 50g, then the initial material speed thresholds are 150g and 50g, respectively.

[0081] Step S603: Construct a threshold selection range based on the initial material rate threshold and preset variable parameters, and randomly select a value within each threshold selection range to combine them to construct a simulated threshold scheme.

[0082] The variable parameter is the maximum value of the allowable threshold set by the staff. The upper endpoint can be determined by adding the variable parameter to the initial material speed threshold. The lower endpoint can be constructed by using the initial material speed threshold as the lower endpoint. At this time, the values ​​within the threshold selection range meet the threshold selection requirements. Therefore, by randomly selecting values ​​from the threshold selection range to construct a simulated threshold scheme, various situations can be simulated and analyzed.

[0083] Step S604: Determine the simulated feeding time based on the simulated threshold scheme, and define the simulated threshold scheme whose simulated feeding time is consistent with the reasonable feeding time as the excellent threshold scheme, and determine the alternative threshold scheme in the excellent threshold scheme to update it as the new material rate threshold scheme.

[0084] The simulated feeding time is the total time required for the current material to be fed according to the simulated threshold scheme. When the simulated feeding time is consistent with the reasonable feeding time, it means that the current simulated threshold scheme meets the efficiency requirements and is most likely to improve the batching accuracy. Therefore, it is defined as a high-quality threshold scheme for identification and differentiation. At this time, one of them is selected as an alternative threshold scheme to update the material rate threshold scheme. The selection of the alternative threshold scheme can be random or determined through steps S700-S702.

[0085] The steps for determining alternative threshold schemes within a high-quality threshold scheme include: Step S700: Determine the fast feeding operation width, medium feeding operation width, and slow feeding operation width based on the quality threshold scheme.

[0086] The width of a fast feeding operation is the width of the fast feeding period. For example, if 1000g-150g is fast feeding, the corresponding width is 850g. Similarly, the width of a medium-speed feeding operation is the width of the medium-speed feeding period, and the width of a slow feeding operation is the width of the slow feeding period.

[0087] Step S701: Calculate and determine the scheme evaluation value based on the fast feeding operation width, medium feeding operation width, slow feeding operation width, and the preset material speed evaluation coefficient.

[0088] The material speed evaluation coefficient includes values ​​for each speed, with the highest value for slow speed and the lowest value for fast speed. By multiplying each width by the corresponding material speed evaluation coefficient and then adding them all together, the scheme evaluation value can be obtained. This scheme evaluation value objectively evaluates the suitability of the scheme, and the larger the value, the more suitable the scheme.

[0089] Step S702: Determine the high-quality threshold scheme corresponding to the scheme with the largest scheme evaluation value as the alternative threshold scheme.

[0090] At this point, determining the optimal threshold scheme corresponding to the scheme with the highest evaluation value as the alternative threshold scheme can meet the actual production needs.

[0091] Reference Figure 2 Based on the same inventive concept, embodiments of the present invention provide an intelligent batching system for powder metallurgy production, comprising: The acquisition module is used to acquire the required feed type; The processing module, connected to the acquisition module, is used for information storage and processing; The processing module determines the basic feeding opening corresponding to the required feeding type based on the preset basic matching relationship, controls the feeding port to operate at the basic feeding opening and maintains it for a preset fixed duration, and enables the acquisition module to acquire the fixed feeding weight in real time within the fixed duration. The processing module calculates and determines the flow fluctuation coefficient based on a fixed feed weight and a fixed duration. The processing module constructs a historical interval on a preset time axis with the current time point as the end point and a width of a preset historical duration, and determines similar points within the historical interval based on the flow fluctuation coefficient and the required feeding type. The processing module determines the material rate threshold scheme based on similarity points, and controls the feeding operation of raw materials of the current demand type according to the material rate threshold scheme; The flow fluctuation coefficient determination module is used to determine the flow fluctuation coefficient. The main similar range filtering module is used to filter multiple main similar ranges that meet the requirements; The similarity point determination module is used to determine similar points; The property similarity analysis module is used to analyze the property similarities between different types of raw materials; The material rate threshold scheme update module updates the material rate threshold scheme based on efficiency and accuracy. An alternative threshold scheme determination module is used to determine a unique alternative threshold scheme from multiple high-quality threshold schemes.

[0092] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

Claims

1. A smart batching method based on powder metallurgy production, characterized in that, include: Obtain the required feed type; The basic feeding opening is determined according to the preset basic matching relationship, and the feeding port is controlled to operate at the basic feeding opening for a preset fixed duration, and the fixed feeding weight is obtained in real time within the fixed duration. The flow rate fluctuation coefficient is determined by calculation based on a fixed feed weight and a fixed duration. Construct a historical interval on a preset timeline with the current time point as the endpoint and a width of a preset historical duration, and determine similarity points within the historical interval based on the flow fluctuation coefficient and the required feeding type; Determine the material rate threshold scheme based on similarity points, and control the material batching operation of the current demand feeding type of raw materials according to the material rate threshold scheme; The steps for determining the flow fluctuation coefficient based on a fixed feed weight and a fixed duration include: A fixed interval is defined based on a fixed duration, and a unit interval is defined within the fixed interval based on a preset unit duration. The unit cutting weight is determined by calculating based on the fixed cutting weights at the front and rear points within the unit interval; The unit flow coefficient is determined by calculation and analysis based on the unit material weight and unit time. Randomly select a unit flow coefficient to define the main flow coefficient, and construct a main similar range based on the main flow coefficient and preset similar coefficients. Count the unit flow coefficients within the main similar range to determine the number within the range. The effective interval is defined as the unit interval corresponding to the unit flow coefficient in the main similar range corresponding to the largest range. The effective intervals are combined to construct the virtual normal interval, and the flow fluctuation coefficient is determined by calculation and analysis within the virtual normal interval.

2. The intelligent batching method based on powder metallurgy production according to claim 1, characterized in that, Once the quantity within the range is determined, the intelligent batching method based on powder metallurgy production also includes: Determine whether there exist at least two primary adjacent ranges with the same and largest number of internal ranges; If there are no two primary adjacent ranges with the same and largest number of internal components, then the valid interval is defined based on the primary adjacent range corresponding to the largest number of internal components. If there are at least two main similar ranges with the same and largest number of internal components, the main similar range corresponding to the largest number of internal components is defined as the candidate similar range, and the virtual fluctuation coefficient is determined based on the candidate similar range. The unit flow coefficient within the similar range of the candidates is defined as the internal flow coefficient, and the unit flow coefficient outside the similar range of the candidates is defined as the external flow coefficient. The internal separation coefficient is determined based on the virtual fluctuation coefficient and the internal flow coefficient, and the external separation coefficient is determined based on the virtual fluctuation coefficient and the external flow coefficient. The reasonable separation coefficient is determined by calculating the internal separation coefficient, the preset internal weight coefficient, the external separation coefficient, and the preset external weight coefficient. The virtual fluctuation coefficient corresponding to the largest reasonable separation coefficient is determined as the flow fluctuation coefficient.

3. The intelligent batching method based on powder metallurgy production according to claim 1, characterized in that, The steps for determining similarities based on flow fluctuation coefficients and demand feed types within a historical timeframe include: The historical flow coefficient is determined based on the type of material required within the historical timeframe. The single-point deviation coefficient is determined by calculating the difference between the current flow fluctuation coefficient and the historical flow coefficient. Determine whether the minimum single-point deviation coefficient is less than the preset equivalent deviation coefficient; If the smallest single-point deviation coefficient is less than the equivalent deviation coefficient, then the production point of the historical flow coefficient corresponding to the smallest single-point deviation coefficient is defined as the similar point. If the minimum single-point deviation coefficient is not less than the equivalent deviation coefficient, then the reference allowable range is constructed using the current flow fluctuation coefficient and the preset reference interval coefficient. Within the reference allowable range, the left-side volatility coefficient and the right-side volatility coefficient are determined based on historical flow coefficients. The threshold range of each material rate is determined based on the left and right fluctuation coefficients, and the left and right deviation coefficients are calculated based on the left fluctuation coefficient, the current flow fluctuation coefficient, and the right fluctuation coefficient. The specific material speed threshold is determined based on the left and right deviation coefficients within the material speed threshold range, and the material speed threshold scheme is determined by combining all the specific material speed thresholds.

4. The intelligent batching method based on powder metallurgy production according to claim 3, characterized in that, After determining the left-side and right-side fluctuation coefficients, the intelligent batching method based on powder metallurgy production also includes: Determine whether at least one of the left-side volatility coefficient and the right-side volatility coefficient is empty; If neither the left-side fluctuation coefficient nor the right-side fluctuation coefficient is empty, then the material rate threshold scheme is determined based on the left-side fluctuation coefficient and the right-side fluctuation coefficient. If at least one of the left-side volatility coefficients and the right-side volatility coefficients is empty, then the similarity of properties is determined by calculating and analyzing the unit flow coefficient of the unit interval that determines the historical flow coefficient and the current unit flow coefficient within the historical interval. The type corresponding to the property similarity is defined as the reasonable reference type when the property similarity is greater than the preset reasonable similarity, and the material rate threshold scheme is determined according to the reasonable reference type.

5. The intelligent batching method based on powder metallurgy production according to claim 1, characterized in that, After the material rate threshold scheme is determined, the intelligent batching method based on powder metallurgy production also includes: The theoretical feeding time is determined based on the material rate threshold scheme for each type of required feeding. The maximum theoretical feeding time is defined as the upper limit feeding time, and the reasonable feeding time is determined based on the upper limit feeding time and the preset floating time. The material rate threshold scheme for the demand feeding type with a theoretical feeding time less than the reasonable feeding time is defined as the permissible adjustment scheme, and the initial material rate threshold is determined according to the permissible adjustment scheme. Based on the initial material rate threshold and preset variation parameters, a threshold selection range is constructed, and a value is randomly selected from each threshold selection range to construct a simulated threshold scheme. The simulated feeding time is determined based on the simulated threshold scheme, and the simulated threshold scheme whose simulated feeding time is consistent with the reasonable feeding time is defined as the high-quality threshold scheme. An alternative threshold scheme is determined from the high-quality threshold scheme to be updated as the new material rate threshold scheme.

6. The intelligent batching method based on powder metallurgy production according to claim 5, characterized in that, The steps for determining alternative threshold schemes within a high-quality threshold scheme include: The width of fast feeding operation, medium feeding operation, and slow feeding operation are determined based on the optimal threshold scheme. The evaluation value of the scheme is determined by calculating based on the width of the fast feeding operation, the width of the medium feeding operation, the width of the slow feeding operation, and the preset material speed evaluation coefficient. The optimal threshold scheme corresponding to the scheme with the highest evaluation value is determined as the alternative threshold scheme.

7. An intelligent batching system based on powder metallurgy production, used to implement the intelligent batching method based on powder metallurgy production as described in any one of claims 1-6, characterized in that, include: The acquisition module is used to acquire the required feed type; The processing module, connected to the acquisition module, is used for information storage and processing; The processing module determines the basic feeding opening corresponding to the required feeding type based on the preset basic matching relationship, controls the feeding port to operate at the basic feeding opening and maintains it for a preset fixed duration, and obtains the fixed feeding weight in real time within the fixed duration. The processing module calculates and determines the flow fluctuation coefficient based on a fixed feed weight and a fixed duration. The processing module constructs a historical interval on a preset time axis with the current time point as the end point and a width of a preset historical duration, and determines similar points within the historical interval based on the flow fluctuation coefficient and the required feeding type. The processing module determines the material rate threshold scheme at similar points and controls the feeding operation of raw materials of the current demand type according to the material rate threshold scheme.

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