An adaptive material data tracking method suitable for variable speed and complex logistics

Abstract

This invention relates to the field of material tracking technology, specifically to an adaptive material data tracking method suitable for variable speed and complex logistics. By acquiring real-time operating parameter data of the target process equipment and dynamically calculating the data processing window parameters for the current sampling period based on these parameters, the time base for data processing can adaptively adjust to real-time changes in equipment operating speed and internal material inventory, achieving dynamic matching between the data aggregation model and the physical equipment operating conditions. Furthermore, the data processing window parameters, along with input material characteristic data representing the state of materials entering the equipment, are input into a pre-established adaptive aggregation algorithm to generate and output output material characteristic data representing the state of materials exiting the equipment. This accurately reflects the actual state changes of materials under variable speed and complex logistics conditions, achieving synchronization between information flow and physical flow in the dynamic process.

Description

Technical Field

[0001] This invention relates to the field of material tracking technology, and more specifically to an adaptive material data tracking method suitable for variable speed and complex logistics. Background Technology

[0002] In process industries such as steel, metallurgy, and chemicals, precise tracking of key material parameters (such as composition, moisture, and particle size) from inlet to outlet is crucial for achieving process optimization, quality traceability, and refined control. Current technologies commonly employ data processing methods based on fixed time windows or fixed model parameters for material tracking. For example, a fixed residence time is set for a particular piece of equipment and used as the window length for the moving average, or the mixing and conveying process of materials is simplified into an ideal model with constant parameters.

[0003] However, this type of fixed-parameter tracking method has inherent flaws. In actual production, the operating speed of many process equipment, such as mixers, granulators, silo feeders, and annular coolers in sintering plants, can be adjusted according to production needs. Changes in equipment speed directly alter the actual residence time of materials within the equipment. If fixed parameters are continued for tracking, a mismatch between "information flow" and "physical flow" will inevitably occur, resulting in tracking data that is either lagging or leading, failing to accurately reflect the state of the exiting material.

[0004] Furthermore, in processes such as transferring sintered ore from the sintering machine to the annular cooler, the physical form of the material undergoes changes and reorganizations, ranging from "line-to-surface" or "surface-to-line." Tracking models with fixed parameters struggle to effectively maintain the data chain at these complex logistics interfaces, leading to information flow interruptions and hindering continuous tracking and optimized control throughout the entire process. Summary of the Invention

[0005] The main objective of this invention is to provide an adaptive material data tracking method suitable for variable speed and complex logistics, in order to solve the defects of existing technologies that use fixed parameter models for material tracking and cannot adapt to the dynamic mismatch between "information flow" and "physical flow" caused by real-time speed adjustment of process equipment. At the same time, it solves the technical problem of difficulty in effectively continuing the data chain at complex logistics interfaces where the material form changes.

[0006] To achieve the above objectives, the present invention provides an adaptive material data tracking method suitable for variable speed and complex logistics, comprising the following steps: S1. Real-time acquisition of operating parameter data of the target process equipment in the current sampling period; wherein, the operating parameter data includes speed data for characterizing the operating speed of the target process equipment, and / or inventory data for characterizing the material inventory inside the target process equipment; S2. Based on the operating parameter data, dynamically calculate the data processing window parameters of the target process equipment corresponding to the current sampling period; S3. Obtain the input material characteristic data of the target process equipment in the current sampling period; wherein, the input material characteristic data is used to characterize the state of the material entering the target process equipment; S4. Obtain the pre-established adaptive aggregation algorithm, input the data processing window parameters and the input material feature data into the adaptive aggregation algorithm, generate and output the output material feature data for the current sampling period; wherein, the output material feature data is used to characterize the material state at the outlet of the target process equipment; S5. The output material characteristic data is stored and transmitted to the control system or monitoring system of the downstream process for process control or quality traceability of the downstream process. Then, the next sampling cycle is entered, and steps S1 to S4 are repeated.

[0007] Furthermore, step S1 specifically includes the following steps: Within each sampling period, the operating speed of the target process equipment in the current sampling period is collected in real time by a speed detection unit installed on the target process equipment, and is used as the speed data; the speed detection unit includes at least one of the following: a frequency converter and a speed encoder; And / or, within each sampling period, the internal material inventory of the target process equipment in the current sampling period is collected in real time by an inventory detection unit set on the target process equipment, as the inventory data; the inventory detection unit includes at least one of the following: a weighing sensor, a radar level gauge, and a laser level gauge.

[0008] Furthermore, step S2 specifically includes the following steps: S21. Identify the equipment type of the target process equipment; wherein, the equipment type includes physical length type equipment and volumetric type equipment; S22. When the target process equipment is identified as a physical length type equipment, the dynamic window duration of the current sampling period is calculated based on the inherent physical length of the target process equipment and the speed data, and the dynamic window duration is used as the data processing window parameter. S23. Alternatively, when the target process equipment is identified as a volumetric equipment, the dynamic window duration of the current sampling period is calculated based on the existing data and the speed data, and the dynamic window duration is used as the data processing window parameter.

[0009] More preferably, step S23 specifically includes the following steps: S231. Determine the current material volume of the target process equipment in the current sampling period based on the existing data; the existing data includes at least one of the following: material weight data and material level data; When the stock data is material weight data, obtain the bulk density of the material in the target process equipment, and calculate the current material volume based on the bulk density; When the inventory data is material level data, obtain the effective cross-sectional area of ​​the target process equipment, and calculate the current material volume based on the effective cross-sectional area; S232. Obtain the discharge capacity coefficient and discharge speed of the target process equipment, and determine the current discharge rate of the target process equipment in the current sampling period based on the discharge speed and the discharge capacity coefficient. S233. Determine the dynamic window duration of the current sampling period based on the current material volume and the current discharge rate.

[0010] More preferably, step S4 specifically includes the following steps: S41. According to the preset algorithm selection logic, determine the target sub-algorithm to be executed in the current sampling week from the pre-established adaptive aggregation algorithm; wherein, the adaptive aggregation algorithm includes multiple sub-algorithms, including: dynamic simple moving average algorithm, dynamic exponential weighted moving average algorithm, dynamic dwell time distribution convolution model algorithm, and dynamic serial full mixed flow model algorithm. S42. Using the data processing window parameters and the input material characteristic data as input parameters for the target sub-algorithm, execute the target sub-algorithm to generate the output material characteristic data for the current sampling period.

[0011] More preferably, when the target sub-algorithm is a dynamic simple moving average algorithm, step S42 specifically includes the following steps: Obtain a preset sampling period, determine the equivalent number of sampling points for the current sampling period based on the data processing window parameters and the preset sampling period, and round the equivalent number of sampling points. The arithmetic mean of the input material characteristic data for the current sampling period and the previous sampling period is calculated according to the formula. Calculate the output material characteristic data ; In the formula, i ranges from 0 to N(t)-1; Indicates the first Input material characteristic data for each sampling period; i is the i-th sampling point; The preset sampling period is N(t); the equivalent number of sampling points is N(t); and t represents the current time.

[0012] More preferably, when the target sub-algorithm is a dynamic exponentially weighted moving average algorithm, step S42 specifically includes the following steps: Obtain the preset sampling period, and determine the equivalent number of sampling points for the current sampling period based on the data processing window parameters and the preset sampling period; Based on the equivalent number of sampling points, according to the formula Calculate the smoothing coefficient for the current sampling period. Where N(t) is the equivalent number of sampling points; t represents the current time. According to the formula Calculate the output material characteristic data ; In the formula, This is the input material characteristic data for the current sampling period. This is the output material characteristic data from the previous sampling period. This is the preset sampling period.

[0013] More preferably, when the target sub-algorithm is a dynamic dwell time distribution convolution model algorithm, step S42 specifically includes the following steps: Obtain the preset baseline dwell time distribution curve and the corresponding baseline equipment operating speed; Based on the current sampling period's device operating speed and the reference device's operating speed, according to the formula... The baseline dwell time distribution curve is scaled to generate the dynamic dwell time distribution curve for the current sampling period. In the formula, As the baseline residence time distribution curve, The dwell time is represented by v0, the reference device speed is v0, and the device speed is v(t) in the current sampling period. The input material characteristic data of the current sampling period and previous sampling periods are compared with the dynamic residence time distribution curve. Perform convolution operations according to the formula Calculate the output material characteristic data ; Where i ranges from 0 to M, M is the preset convolution calculation length; and i is the i-th sampling point. The preset sampling period is t; t represents the current time.

[0014] More preferably, when the target sub-algorithm is a dynamic serial fully mixed-flow model algorithm, step S42 specifically includes the following steps: The target process equipment is equivalent to N series-connected fully mixed flow reactors, and the preset number N of fully mixed flow reactors is obtained; The residence time of each fully mixed-flow reactor in the current sampling period is determined based on the data processing window parameters and the number of fully mixed-flow reactors. According to the recursive formula Iteratively calculate the outlet material characteristic data of N fully mixed-flow reactors. ; In the formula, j represents the j-th fully mixed-flow reactor, and j ranges from 1 to N. Input material characteristic data for the current sampling period And the outlet material characteristic data of the Nth fully mixed reactor. As the output material characteristic data , The preset sampling period; t represents the current time. This represents the dwell time for the current sampling period. More preferably, the preset algorithm selection logic specifically includes the following steps: Obtain the availability identifier of the dwell time distribution data corresponding to the target process equipment, and the control accuracy requirement level corresponding to the target process equipment; wherein, the dwell time distribution data availability identifier is used to indicate whether the reference dwell time distribution curve of the target process equipment is available; the control accuracy requirement level is set to include a first level and a second level. Based on the availability identifier of the dwell time distribution data and the control precision requirement level, and according to the preset mapping rules, the target sub-algorithm to be executed in the current sampling week is determined from the pre-established adaptive aggregation algorithm: When the availability identifier of the dwell time distribution data indicates that the baseline dwell time distribution curve is present, and the control accuracy requirement level is Level 1, the target sub-algorithm is determined to be the dynamic dwell time distribution convolution model algorithm. When the availability indicator of the dwell time distribution data indicates that the baseline dwell time distribution curve is not available, and the control accuracy requirement level is Level 1, the target sub-algorithm is determined to be the dynamic series fully mixed-flow model algorithm. When the control accuracy requirement level is Level 2, the target sub-algorithm is determined to be either the dynamic simple moving average algorithm or the dynamic exponential weighted moving average algorithm. The specific selection can be preset according to the actual control requirements.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention acquires real-time operating parameter data of the target process equipment and dynamically calculates the data processing window parameters for the current sampling period based on these parameters. This allows the data processing time base to adaptively adjust to real-time changes in equipment operating speed and internal material inventory, achieving dynamic matching between the data aggregation model and the physical equipment operating conditions. Furthermore, the data processing window parameters, along with input material characteristic data representing the state of materials entering the equipment, are input into a pre-established adaptive aggregation algorithm to generate and output output material characteristic data representing the state of materials exiting the equipment. This accurately reflects the actual state changes of materials under variable speed and complex logistics conditions, achieving synchronization between information flow and physical flow in the dynamic process. Finally, the output material characteristic data is stored and transmitted to the control or monitoring system of downstream processes, establishing a data link throughout the entire process. This provides a real-time and accurate data foundation for process control and quality traceability in downstream processes, enabling continuous and accurate tracking of material data across the entire process and significantly improving the level of automation control and refined management in the process industry. This method is based on speed and inventory detection signals commonly found in industrial settings, making it highly versatile and easy to implement in engineering. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating an embodiment of the adaptive material data tracking method applicable to variable speed and complex logistics according to the present invention. Figure 2 This is a schematic diagram illustrating the relationship between the dynamic window duration and device speed in one embodiment of the present invention. The horizontal axis represents the device operating speed v(t), in meters per second (m / s); the vertical axis represents the dynamic window duration T(t), in seconds (s). The curve shows an inverse relationship where T(t) decreases as v(t) increases, reflecting the core adaptive mechanism of the present invention.

[0018] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0022] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0023] Please see Figures 1 to 2 This embodiment provides an adaptive material data tracking method suitable for variable speed and complex logistics. This method achieves continuous and accurate tracking of material characteristic data throughout the entire process by sensing the operating status of process equipment in real time, dynamically adjusting data processing parameters, and executing an adaptive aggregation algorithm.

[0024] First, to implement this method, corresponding detection units need to be configured on the target process equipment. Specifically, for equipment with a defined physical length, such as annular coolers and conveyors, speed encoders are installed on their drive motors or connected to the equipment's frequency converter to acquire the equipment's operating speed in real time. For volumetric equipment such as mixing silos and buffer silos in sintering plants, load cells are installed at the bottom to acquire the material weight, or radar level gauges or laser level gauges are installed on the top of the silo to acquire the material level. Simultaneously, speed encoders are installed on their discharge mechanisms (such as roller feeders) to acquire the discharge speed. These detection units are common configurations in process industry sites, and their signals are easy to acquire, providing a reliable hardware foundation for the implementation of this method.

[0025] This method is executed cyclically with a fixed sampling period (e.g., 10 seconds). The processing flow within each sampling period is as follows: S1. Real-time acquisition of operating parameter data of the target process equipment in the current sampling period; wherein, the operating parameter data includes speed data for characterizing the operating speed of the target process equipment, and / or inventory data for characterizing the material inventory inside the target process equipment; Specifically, within each sampling period (e.g., from second t to second t+Δt), the operating parameter data of the target process equipment is collected in real time through the aforementioned speed detection unit and inventory detection unit. The operating parameter data includes speed data characterizing the operating speed of the target process equipment, and / or inventory data characterizing the material inventory inside the target process equipment (e.g., material weight or material level).

[0026] This step establishes a real-time sensing channel between the physical and information worlds by directly connecting basic detection signals from the industrial site to the data tracking system. This transforms the data processing system from an isolated mathematical operation module into one capable of sensing changes in equipment operating conditions, providing a real-time data-driven foundation for subsequent adaptive adjustments. This overcomes the technical problem of existing technologies where the data processing model is disconnected from the physical process due to the inability to sense equipment status.

[0027] S2. Based on the operating parameter data, dynamically calculate the data processing window parameters of the target process equipment corresponding to the current sampling period; Specifically, based on the operating parameter data obtained in step S1 and combined with the inherent physical properties of the target process equipment, the data processing window parameter T(t) corresponding to the current sampling period is dynamically calculated. This parameter is used to characterize the predicted time required for the material to move from the equipment inlet to the outlet under the current operating state.

[0028] The specific calculation method depends on the device type: For equipment with a physical length (such as an annular chiller), the system obtains its inherent physical length L (e.g., 220 meters) and combines it with the current speed data v(t) of the equipment, calculating the dynamic window duration according to the formula T(t) = L / v(t). Taking an annular chiller as an example, when v(t) = 3.1 m / min, T(t) = 220 / 3.1 ≈ 71.0 minutes; when v(t) = 3.7 m / min, T(t) = 220 / 3.7 ≈ 59.5 minutes. It can be seen that T(t) decreases as v(t) increases and increases as v(t) decreases, achieving real-time dynamic matching with the equipment speed.

[0029] For volumetric equipment (such as the mixing silo in a sintering plant), the system first calculates the current material volume V(t) based on the inventory data: if the obtained data is weight M(t), it is calculated using the formula V(t) = M(t) / ρ based on the material's bulk density ρ; if the obtained data is material level H(t), it is calculated using the formula V(t) = A × H(t) based on the silo's cross-sectional area A. Simultaneously, it calculates the volume based on the discharge rate data v. s The feeder discharge capacity coefficient K is calculated using the formula Q(t) = K × v. s Calculate the current discharge rate Q(t). Finally, calculate the dynamic window duration using the formula T(t) = V(t) / Q(t). When the material level in the silo is high (V(t) is large) or the discharge rate is slow (Q(t) is small), T(t) is automatically extended; conversely, it is automatically shortened.

[0030] This step maps the speed and inventory parameters of the physical equipment to the time window parameters of data processing in real time, enabling the time base for data processing to be dynamically adjusted in accordance with changes in the physical process. This mechanism directly solves the mismatch between "information flow" and "physical flow" caused by the use of fixed time windows in existing technologies, ensuring that data processing is always synchronized with the actual residence time of materials in the equipment.

[0031] S3. Obtain the input material characteristic data of the target process equipment in the current sampling period; wherein, the input material characteristic data is used to characterize the state of the material entering the target process equipment; Specifically, through a data communication interface, historical output material characteristic data from upstream processes within the corresponding historical sampling period is received in real time and used as input material characteristic data for the target process equipment in the current sampling period. This data is used to characterize the state of materials output from upstream processes and entering this equipment, including but not limited to: composition data for characterizing the inherent properties of materials (such as FeO content and alkalinity of sintered ore), temperature data for characterizing the physical state of materials (such as thermal imaging characteristic data of sintered ore), and quality traceability identification data for uniquely identifying the source of material batches (such as batch number and production timestamp).

[0032] Taking material tracking from the sintering process to the ring cooling process as an example, the ring cooling system requests and receives the thermal imaging feature data of the sintered ore output by the sintering system 5 minutes ago (corresponding to the physical transmission delay of the material from the sintering machine outlet to the ring cooling machine inlet) in each sampling cycle (e.g., 10 seconds), as the input material feature data for the current cycle.

[0033] This step establishes data communication links between processes and aligns the data according to physical transmission delays, enabling continuous transmission of material characteristic data at process interfaces. This solves the problem of data chain interruption in existing technologies when material forms change or when materials are transferred across processes, providing a data foundation for continuous tracking throughout the entire process.

[0034] S4. Obtain the pre-established adaptive aggregation algorithm, input the data processing window parameters and the input material feature data into the adaptive aggregation algorithm, generate and output the output material feature data for the current sampling period; wherein, the output material feature data is used to characterize the material state at the outlet of the target process equipment; S5. The output material characteristic data is stored and transmitted to the control system or monitoring system of the downstream process for process control or quality traceability of the downstream process. Then, the next sampling cycle is entered, and steps S1 to S4 are repeated.

[0035] This embodiment acquires real-time operating parameter data of the target process equipment and dynamically calculates the data processing window parameters for the current sampling period based on these parameters. This allows the data processing time base to adaptively adjust to real-time changes in equipment operating speed and internal material inventory, achieving dynamic matching between the data aggregation model and the physical equipment operating conditions. Furthermore, the data processing window parameters and input material characteristic data representing the state of materials entering the equipment are input into a pre-established adaptive aggregation algorithm to generate and output output material characteristic data representing the state of materials exiting the equipment. This accurately reflects the actual state changes of materials under variable speed and complex logistics conditions, achieving synchronization between information flow and physical flow in the dynamic process. Finally, the output material characteristic data is stored and transmitted to the control or monitoring system of downstream processes, establishing a data link throughout the entire process. This provides a real-time and accurate data foundation for process control and quality traceability in downstream processes, achieving continuous and accurate tracking of material data across the entire process and significantly improving the level of automation control and refined management in the process industry. This method is based on speed and inventory detection signals commonly found in industrial settings, making it highly versatile and easy to implement in engineering.

[0036] As a further embodiment, step S1 specifically includes the following steps: Within each sampling period, the operating speed of the target process equipment in the current sampling period is collected in real time by a speed detection unit installed on the target process equipment, and is used as the speed data; the speed detection unit includes at least one of the following: a frequency converter and a speed encoder; And / or, within each sampling period, the internal material inventory of the target process equipment in the current sampling period is collected in real time by an inventory detection unit set on the target process equipment, as the inventory data; the inventory detection unit includes at least one of the following: a weighing sensor, a radar level gauge, and a laser level gauge.

[0037] In one embodiment, step S2 specifically includes the following steps: S21. Identify the equipment type of the target process equipment; wherein, the equipment type includes physical length type equipment and volumetric type equipment; S22. When the target process equipment is identified as a physical length type equipment, the dynamic window duration of the current sampling period is calculated based on the inherent physical length of the target process equipment and the speed data, and the dynamic window duration is used as the data processing window parameter. S23. Alternatively, when the target process equipment is identified as a volumetric equipment, the dynamic window duration of the current sampling period is calculated based on the existing data and the speed data, and the dynamic window duration is used as the data processing window parameter.

[0038] As a further preferred embodiment, step S23 specifically includes the following steps: S231. Determine the current material volume of the target process equipment in the current sampling period based on the existing data; the existing data includes at least one of the following: material weight data and material level data; When the stock data is material weight data, obtain the bulk density of the material in the target process equipment, and calculate the current material volume based on the bulk density; When the inventory data is material level data, obtain the effective cross-sectional area of ​​the target process equipment, and calculate the current material volume based on the effective cross-sectional area; S232. Obtain the discharge capacity coefficient and discharge speed of the target process equipment, and determine the current discharge rate of the target process equipment in the current sampling period based on the discharge speed and the discharge capacity coefficient. S233. Determine the dynamic window duration of the current sampling period based on the current material volume and the current discharge rate.

[0039] As a further preferred embodiment, step S4 specifically includes the following steps: S41. According to the preset algorithm selection logic, determine the target sub-algorithm to be executed in the current sampling week from the pre-established adaptive aggregation algorithm; wherein, the adaptive aggregation algorithm includes multiple sub-algorithms, including: dynamic simple moving average algorithm, dynamic exponential weighted moving average algorithm, dynamic dwell time distribution convolution model algorithm, and dynamic serial full mixed flow model algorithm. S42. Using the data processing window parameters and the input material characteristic data as input parameters for the target sub-algorithm, execute the target sub-algorithm to generate the output material characteristic data for the current sampling period.

[0040] As a further preferred embodiment, when the target sub-algorithm is a dynamic simple moving average algorithm, step S42 specifically includes the following steps: Obtain a preset sampling period, determine the equivalent number of sampling points for the current sampling period based on the data processing window parameters and the preset sampling period, and round the equivalent number of sampling points. The arithmetic mean of the input material characteristic data for the current sampling period and the previous sampling period is calculated according to the formula. Calculate the output material characteristic data ; In the formula, i ranges from 0 to N(t)-1; Indicates the first Input material characteristic data for each sampling period; i is the i-th sampling point; The preset sampling period is N(t); the equivalent number of sampling points is N(t); and t represents the current time.

[0041] As a further preferred embodiment, when the target sub-algorithm is a dynamic exponentially weighted moving average algorithm, step S42 specifically includes the following steps: Obtain the preset sampling period, and determine the equivalent number of sampling points for the current sampling period based on the data processing window parameters and the preset sampling period; Based on the equivalent number of sampling points, according to the formula Calculate the smoothing coefficient for the current sampling period. Where N(t) is the equivalent number of sampling points; t represents the current time. According to the formula Calculate the output material characteristic data ; In the formula, This is the input material characteristic data for the current sampling period. This is the output material characteristic data from the previous sampling period. This is the preset sampling period.

[0042] As a further preferred embodiment, when the target sub-algorithm is a dynamic dwell time distribution convolution model algorithm, step S42 specifically includes the following steps: Obtain the preset baseline dwell time distribution curve and the corresponding baseline equipment operating speed; Based on the current sampling period's device operating speed and the reference device's operating speed, according to the formula... The baseline dwell time distribution curve is scaled to generate the dynamic dwell time distribution curve for the current sampling period. In the formula, As the baseline residence time distribution curve, The dwell time is represented by v0, the reference device speed is v0, and the device speed is v(t) in the current sampling period. The input material characteristic data of the current sampling period and previous sampling periods are compared with the dynamic residence time distribution curve. Perform convolution operations according to the formula Calculate the output material characteristic data ; Where i ranges from 0 to M, M is the preset convolution calculation length; and i is the i-th sampling point. The preset sampling period is t; t represents the current time.

[0043] As a further preferred embodiment, when the target sub-algorithm is a dynamic serial fully mixed-flow model algorithm, step S42 specifically includes the following steps: The target process equipment is equivalent to N series-connected fully mixed flow reactors, and the preset number N of fully mixed flow reactors is obtained; The residence time of each fully mixed-flow reactor in the current sampling period is determined based on the data processing window parameters and the number of fully mixed-flow reactors. According to the recursive formula Iteratively calculate the outlet material characteristic data of N fully mixed-flow reactors. ; In the formula, j represents the j-th fully mixed-flow reactor, and j ranges from 1 to N. Input material characteristic data for the current sampling period And the outlet material characteristic data of the Nth fully mixed reactor. As the output material characteristic data , The preset sampling period; t represents the current time. This represents the dwell time for the current sampling period. In one embodiment, the preset algorithm selection logic specifically includes the following steps: Obtain the availability identifier of the dwell time distribution data corresponding to the target process equipment, and the control accuracy requirement level corresponding to the target process equipment; wherein, the dwell time distribution data availability identifier is used to indicate whether the reference dwell time distribution curve of the target process equipment is available; the control accuracy requirement level is set to include a first level and a second level. Based on the availability identifier of the dwell time distribution data and the control precision requirement level, and according to the preset mapping rules, the target sub-algorithm to be executed in the current sampling week is determined from the pre-established adaptive aggregation algorithm: When the availability identifier of the dwell time distribution data indicates that the baseline dwell time distribution curve is present, and the control accuracy requirement level is Level 1, the target sub-algorithm is determined to be the dynamic dwell time distribution convolution model algorithm. When the availability indicator of the dwell time distribution data indicates that the baseline dwell time distribution curve is not available, and the control accuracy requirement level is Level 1, the target sub-algorithm is determined to be the dynamic series fully mixed-flow model algorithm. When the control accuracy requirement level is Level 2, the target sub-algorithm is determined to be either the dynamic simple moving average algorithm or the dynamic exponential weighted moving average algorithm. The specific selection can be preset according to the actual control requirements.

[0044] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the adaptive material data tracking method applicable to variable speed and complex logistics as described above.

[0045] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. An adaptive material data tracking method suitable for variable speed and complex logistics, characterized in that, Includes the following steps: S1. Real-time acquisition of operating parameter data of the target process equipment in the current sampling period; wherein, the operating parameter data includes speed data for characterizing the operating speed of the target process equipment, and / or inventory data for characterizing the material inventory inside the target process equipment; S2. Based on the operating parameter data, dynamically calculate the data processing window parameters of the target process equipment corresponding to the current sampling period; S3. Obtain the input material characteristic data of the target process equipment in the current sampling period; wherein, the input material characteristic data is used to characterize the state of the material entering the target process equipment; S4. Obtain the pre-established adaptive aggregation algorithm, input the data processing window parameters and the input material feature data into the adaptive aggregation algorithm, generate and output the output material feature data for the current sampling period; wherein, the output material feature data is used to characterize the material state at the outlet of the target process equipment; S5. The output material characteristic data is stored and transmitted to the control system or monitoring system of the downstream process for process control or quality traceability of the downstream process. Then, the next sampling cycle is entered, and steps S1 to S4 are repeated.

2. The adaptive material data tracking method for variable speed and complex logistics as described in claim 1, characterized in that, Step S1 specifically includes the following steps: Within each sampling period, the operating speed of the target process equipment in the current sampling period is collected in real time by a speed detection unit installed on the target process equipment, and is used as the speed data; the speed detection unit includes at least one of the following: a frequency converter and a speed encoder; And / or, within each sampling period, the internal material inventory of the target process equipment in the current sampling period is collected in real time by an inventory detection unit set on the target process equipment, as the inventory data; the inventory detection unit includes at least one of the following: a weighing sensor, a radar level gauge, and a laser level gauge.

3. The adaptive material data tracking method for variable speed and complex logistics as described in claim 1, characterized in that, Step S2 specifically includes the following steps: S21. Identify the equipment type of the target process equipment; wherein, the equipment type includes physical length type equipment and volumetric type equipment; S22. When the target process equipment is identified as a physical length type equipment, the dynamic window duration of the current sampling period is calculated based on the inherent physical length of the target process equipment and the speed data, and the dynamic window duration is used as the data processing window parameter. S23. Alternatively, when the target process equipment is identified as a volumetric equipment, the dynamic window duration of the current sampling period is calculated based on the existing data and the speed data, and the dynamic window duration is used as the data processing window parameter.

4. The adaptive material data tracking method for variable speed and complex logistics as described in claim 3, characterized in that, Step S23 specifically includes the following steps: S231. Determine the current material volume of the target process equipment in the current sampling period based on the existing data; the existing data includes at least one of the following: material weight data and material level data; When the stock data is material weight data, obtain the bulk density of the material in the target process equipment, and calculate the current material volume based on the bulk density; When the inventory data is material level data, obtain the effective cross-sectional area of ​​the target process equipment, and calculate the current material volume based on the effective cross-sectional area; S232. Obtain the discharge capacity coefficient and discharge speed of the target process equipment, and determine the current discharge rate of the target process equipment in the current sampling period based on the discharge speed and the discharge capacity coefficient. S233. Determine the dynamic window duration of the current sampling period based on the current material volume and the current discharge rate.

5. The adaptive material data tracking method for variable speed and complex logistics as described in claim 1, characterized in that, Step S4 specifically includes the following steps: S41. According to the preset algorithm selection logic, determine the target sub-algorithm to be executed in the current sampling week from the pre-established adaptive aggregation algorithm; wherein, the adaptive aggregation algorithm includes multiple sub-algorithms, including: dynamic simple moving average algorithm, dynamic exponential weighted moving average algorithm, dynamic dwell time distribution convolution model algorithm, and dynamic serial full mixed flow model algorithm. S42. Using the data processing window parameters and the input material characteristic data as input parameters for the target sub-algorithm, execute the target sub-algorithm to generate the output material characteristic data for the current sampling period.

6. The adaptive material data tracking method for variable speed and complex logistics as described in claim 5, characterized in that, When the target sub-algorithm is a dynamic simple moving average algorithm, step S42 specifically includes the following steps: Obtain a preset sampling period, determine the equivalent number of sampling points for the current sampling period based on the data processing window parameters and the preset sampling period, and round the equivalent number of sampling points. The arithmetic mean of the input material characteristic data for the current sampling period and the previous sampling period is calculated according to the formula. Calculate the output material characteristic data ; In the formula, i ranges from 0 to N(t)-1; Indicates the first Input material characteristic data for each sampling period; i is the i-th sampling point; The preset sampling period is N(t); the equivalent number of sampling points is N(t); and t represents the current time.

7. The adaptive material data tracking method for variable speed and complex logistics as described in claim 5, characterized in that, When the target sub-algorithm is a dynamic exponentially weighted moving average algorithm, step S42 specifically includes the following steps: Obtain the preset sampling period, and determine the equivalent number of sampling points for the current sampling period based on the data processing window parameters and the preset sampling period; Based on the equivalent number of sampling points, according to the formula Calculate the smoothing coefficient for the current sampling period. Where N(t) is the equivalent number of sampling points; t represents the current time. According to the formula Calculate the output material characteristic data ; In the formula, This is the input material characteristic data for the current sampling period. This is the output material characteristic data from the previous sampling period. This is the preset sampling period.

8. The adaptive material data tracking method for variable speed and complex logistics as described in claim 5, characterized in that, When the target sub-algorithm is a dynamic dwell time distribution convolution model algorithm, step S42 specifically includes the following steps: Obtain the preset baseline dwell time distribution curve and the corresponding baseline equipment operating speed; Based on the current sampling period's device operating speed and the reference device's operating speed, according to the formula... The baseline dwell time distribution curve is scaled to generate the dynamic dwell time distribution curve for the current sampling period. In the formula, As the baseline residence time distribution curve, The dwell time is represented by v0, the reference device speed is v0, and the device speed is v(t) in the current sampling period. The input material characteristic data of the current sampling period and previous sampling periods are compared with the dynamic residence time distribution curve. Perform convolution operations according to the formula Calculate the output material characteristic data ; Where i ranges from 0 to M, M is the preset convolution calculation length; and i is the i-th sampling point. The preset sampling period is t; t represents the current time.

9. The adaptive material data tracking method for variable speed and complex logistics as described in claim 5, characterized in that, When the target sub-algorithm is a dynamic serial fully mixed-flow model algorithm, step S42 specifically includes the following steps: The target process equipment is equivalent to N series-connected fully mixed flow reactors, and the preset number N of fully mixed flow reactors is obtained; The residence time of each fully mixed-flow reactor in the current sampling period is determined based on the data processing window parameters and the number of fully mixed-flow reactors. According to the recursive formula Iteratively calculate the outlet material characteristic data of N fully mixed-flow reactors. ; In the formula, j represents the j-th fully mixed-flow reactor, and j ranges from 1 to N. Input material characteristic data for the current sampling period And the outlet material characteristic data of the Nth fully mixed reactor. As the output material characteristic data , The preset sampling period; t represents the current time. This represents the dwell time for the current sampling period.

10. The adaptive material data tracking method for variable speed and complex logistics according to claim 5, characterized in that, The preset algorithm selection logic specifically includes the following steps: Obtain the availability identifier of the dwell time distribution data corresponding to the target process equipment, and the control accuracy requirement level corresponding to the target process equipment; wherein, the dwell time distribution data availability identifier is used to indicate whether the reference dwell time distribution curve of the target process equipment is available; the control accuracy requirement level is set to include a first level and a second level. Based on the availability identifier of the dwell time distribution data and the control accuracy requirement level, the target sub-algorithm to be executed in the current sampling week is determined from the pre-established adaptive aggregation algorithm according to the preset mapping rules. When the availability identifier of the dwell time distribution data indicates that the baseline dwell time distribution curve is present, and the control accuracy requirement level is Level 1, the target sub-algorithm is determined to be the dynamic dwell time distribution convolution model algorithm. When the availability indicator of the dwell time distribution data indicates that the baseline dwell time distribution curve is not available, and the control accuracy requirement level is Level 1, the target sub-algorithm is determined to be the dynamic series fully mixed-flow model algorithm. When the control accuracy requirement level is Level 2, the target sub-algorithm is determined to be either the dynamic simple moving average algorithm or the dynamic exponential weighted moving average algorithm.

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