Ore product production control system based on Internet of Things
Through the Internet of Things system, real-time monitoring and optimization of the parameters of mineral product production equipment have solved the problems of insufficient coordination between equipment and unreasonable energy consumption control, and achieved improved production stability and economy.
Patent Information
- Application Number
- CN202511133721.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-13
- Publication Date
- 2025-10-17
AI Technical Summary
In the existing production process of mineral products, there is insufficient coordination between production equipment, poor adaptability to raw material fluctuations, and unreasonable energy consumption control, resulting in reduced production continuity and efficiency.
An IoT-based ore product production control system is adopted, which acquires equipment parameters and material flow data in real time through a data sensing module, generates adaptive adjustment schemes, and optimizes parameter matching and energy consumption control by combining raw material characteristics and equipment operating status.
It has achieved organic coordination among production equipment, improved the continuity and stability of production, and achieved reasonable control of energy consumption on the basis of ensuring efficiency, thus achieving a dynamic balance between production efficiency and energy consumption costs.
Smart Images
Figure CN120802884A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of production equipment control, and particularly relates to a mineral product production control system based on Internet of Things. BACKGROUND
[0002] In the production process of mineral products, fluctuations in raw material characteristics (such as brittleness difference, component proportion, etc.) are common, which directly affect the processing effect and operating efficiency of the production equipment. For example, brittle minerals have significant differences in the requirements for the adaptation of the equipment feed quantity and power output intensity in processes such as crushing and grinding; changes in the component proportion can cause fluctuations in processing difficulty, further interfering with the stability of the production process.
[0003] The prior art uses PID parameter self-adaptive control for each production equipment to dynamically adjust the production parameters of the equipment, but the PID control in the prior art focuses on the parameter stability of a single device, and only adjusts through the feedback signal of the device itself, lacking consideration of the coordination relationship between the front and rear series devices. When the raw material characteristics (such as brittleness difference, component proportion) fluctuate, the PID adjustment of a single device can cause the output material state to not match the processing needs of the rear device - for example, a mineral with low brittleness may produce excess fine material after processing, and if the front device only maintains its own parameter stability through PID, it is easy to cause material accumulation in the rear device; while a mineral with high brittleness may not be processed sufficiently, which can cause a shortage of supply to the rear device, thereby affecting the overall production continuity.
[0004] On the other hand, the parameter adjustment logic of the PID control is relatively fixed, mainly relying on pre-set proportional, integral, and differential parameters to respond to disturbances, and it is difficult to flexibly adapt to dynamic changes in raw material characteristics (such as sudden changes in component proportion, intensification of brittleness difference). For example, when the processing difficulty of the raw material is significantly increased due to changes in the composition, the PID adjustment with fixed parameters may not be able to timely optimize the matching relationship between the power output intensity and the feed quantity, resulting in a decrease in the operating efficiency of the equipment or a sharp increase in energy consumption. In addition, the existing PID control strategy often focuses on the static stability of the equipment operating parameters, and does not include the energy consumption state of the rear device in the optimization dimension, making it difficult to achieve reasonable control of overall energy consumption while ensuring production continuity.
[0005] Therefore, there is an urgent need for an intelligent control system that can break through the limitations of traditional PID control, comprehensively consider fluctuations in raw material characteristics, coordination conditions of front and rear devices, and energy consumption factors, to solve the problems of insufficient coordination of device control, poor adaptability to raw material fluctuations, and unreasonable energy consumption control in the prior art. SUMMARY
[0006] The present application aims to provide a mineral product production control system based on Internet of Things, which solves the following technical problems:
[0007] Solve the problems of insufficient regulation coordination between production equipment, poor adaptability to raw material fluctuations, and unreasonable energy consumption control.
[0008] The object of the application can be achieved by the following technical solutions:
[0009] An ore product production control system based on the Internet of Things, comprising:
[0010] A data sensing module for acquiring operation parameters, material flow rate change rates, and unit time energy consumption data of each production equipment in real time through Internet of Things nodes;
[0011] A scheme generation module for calibrating a target device and generating a number of candidate parameter variation schemes when the operation parameters of any production equipment trigger adaptive adjustment;
[0012] A target determination module for acquiring material flow rate change rates of the target device and the subsequent devices in series, and determining a target scheme from the number of candidate parameter variation schemes based on the material flow rate change rates;
[0013] A scheme execution module for adjusting the operation parameters of the target device according to the target scheme.
[0014] As a further scheme of the application, in the scheme generation module, the specific process of triggering adaptive adjustment is:
[0015] When the operation parameters of any production equipment exceed the baseline fluctuation interval in the stable running state, adaptive adjustment is triggered, and the baseline fluctuation interval is determined by statistical parameter running data of the device in the historical qualified production period.
[0016] As a further scheme of the application, in the scheme generation module, the specific process of generating a number of candidate parameter variation schemes is:
[0017] Collecting comprehensive characteristic parameters and real-time running parameters of the target device to be processed, the comprehensive characteristic parameters of the to-be-processed raw material including physical characteristic indicators reflecting the brittleness difference of the raw material and component proportions affecting the processing difficulty, and the real-time running parameters including material conveying rate, power output intensity, and running frequency of the processing unit;
[0018] Comparing the current value of the operation parameter triggering adjustment with the baseline fluctuation interval, calculating the quantitative difference of the deviation interval boundary, analyzing the change rate and cumulative deviation length through continuous parameter sequence, distinguishing between persistent deviation and instantaneous fluctuation, combining the change amplitude of the comprehensive characteristic parameters of the raw material to judge whether the deviation is caused by the difference in raw material characteristics, and combining the matching degree of the real-time running parameters of the device to judge whether the deviation is caused by the mismatch between the current running parameters and the raw material characteristics;
[0019] According to the quantitative difference of the parameter deviation, the change rate, the cumulative duration and the cause, the type of the adjustable control parameter is determined, including the conveying parameter for adjusting the raw material supply amount, the power parameter for changing the processing intensity, and the combined parameter for synchronously adjusting both.
[0020] Based on the equipment design operation range, the adjustment threshold of each control parameter is set, the conveying parameter threshold is limited in the conveying system safety interval, the power parameter threshold is limited in the power system rated range, and the combined parameter threshold meets both limitations.
[0021] For the conveying parameter, a plurality of level difference adjustment amounts are calculated according to the difference proportion of the raw material characteristics and the historical reference; for the power parameter, a plurality of level difference adjustment amounts are calculated according to the processing difficulty change amplitude; for the combined parameter, a plurality of matching combinations of conveying and power adjustment amounts are calculated according to the comprehensive influence of the raw material characteristics; the level difference adjustment amounts and the matching combinations are respectively corresponding to different parameter change values, forming a plurality of candidate parameter change schemes including the conveying parameter, the power parameter and the combined parameter adjustment mode.
[0022] As a further scheme of the application, in the target determination module, the specific process of determining the target scheme is:
[0023] S1, obtaining the real-time material flow rate change rate of the target equipment and the subsequent serial equipment and setting the matching degree basic score, the matching degree basic score corresponds to the ideal state of the material flow of the subsequent serial equipment;
[0024] S2, for a plurality of candidate parameter change schemes, simulating the processing effect of the target equipment on the material after the implementation of each candidate parameter change scheme, obtaining the simulated unit time energy consumption value of the target equipment and the simulated material flow rate change rate of the corresponding subsequent serial equipment;
[0025] S3, calculating the matching degree actual score of each scheme, when there is material accumulation in the subsequent serial equipment, taking the degree of the simulated material flow rate change rate tending to zero as the quantitative basis, the smaller the deviation from zero, the closer the matching degree actual score to the matching degree basic score;
[0026] When there is insufficient material supply in the subsequent serial equipment, taking the absolute value of the difference between the simulated material flow rate change rate and the supply gap as the quantitative basis, the smaller the absolute value, the closer the matching degree actual score to the matching degree basic score;
[0027] S4, setting the energy consumption basic score, obtaining the unit time energy consumption data of the target equipment, taking the simulated unit time energy consumption value and the unit time energy consumption data as the quantitative basis, the smaller the difference, the closer the energy consumption actual score to the energy consumption basic score;
[0028] S5, a preset weight is assigned to the actual matching degree score and the actual energy consumption score, the two are superimposed according to the weight to obtain a comprehensive evaluation score of each candidate scheme, and a candidate parameter variation scheme with the highest comprehensive evaluation score is selected as the target scheme.
[0029] As a further scheme of the present application: in S2, the specific process of simulating the processing effect of the production equipment on the material after implementation of each candidate parameter variation scheme is:
[0030] For a plurality of candidate parameter variation schemes, parameter variation values contained in each candidate parameter variation scheme are extracted, the parameter variation values including an adjustment amount of a material conveying parameter, an adjustment amount of a power parameter, and an adjustment amount of a combination of the two; a running data set of the production equipment under the same parameter type adjustment in historical production is called, the data set containing a corresponding relationship between the parameter variation value and a material processing amount change rate and a unit energy consumption change rate;
[0031] A parameter-effect correlation model is constructed based on the historical running data set, the model input being the parameter variation value and the output being a simulated change value of the material processing amount of the production equipment and a simulated change value of the unit energy consumption; the parameter variation values of each candidate scheme are input into the parameter-effect correlation model respectively to obtain the simulated change value of the material processing amount of the production equipment and the simulated change value of the unit energy consumption under each candidate parameter variation scheme;
[0032] The material transfer coefficient of the target equipment and the subsequent serial equipment is obtained, and the simulated change value of the material processing amount is converted into a simulated material flow rate change rate of the subsequent serial equipment.
[0033] As a further scheme of the present application: in S3, it further includes calculating the continuous change trend of the material flow rate change rate of the subsequent serial equipment, when the change rate is negative and the absolute value shows an increasing trend, it is determined that the subsequent serial equipment has material accumulation; when the change rate is positive and continuously increasing, it is determined that the subsequent serial equipment has insufficient material supply.
[0034] As a further scheme of the present application: the target determination module, if the production equipment cannot exist the subsequent serial equipment, obtains real-time material retention amount and unit time energy consumption data of the production equipment;
[0035] If the retention amount of the production equipment exceeds a preset accumulation threshold, it is determined that the production equipment has material accumulation; when the retention amount is less than or equal to the preset accumulation threshold, it is determined that the production equipment has no material accumulation;
[0036] When the production equipment has material accumulation, the simulated change value of the material processing amount corresponding to each candidate parameter variation scheme is obtained, and the candidate parameter variation scheme with the largest simulated change value of the material processing amount is selected as the target scheme;
[0037] When the production equipment does not exist material accumulation, then the difference value of the simulation unit energy consumption value corresponding to each candidate parameter variation scheme and the unit time energy consumption data is obtained, and the candidate parameter variation scheme with the minimum difference value is selected as the target scheme.
[0038] As a further scheme of the present application: if there are two or more candidate parameter variation schemes corresponding to the simulation change value of the material processing amount equal and all being the maximum value, then the difference value of the simulation unit energy consumption value corresponding to each candidate parameter variation scheme and the unit time energy consumption data is obtained, and the candidate parameter variation scheme with the minimum difference value is selected as the target scheme.
[0039] The beneficial effects of the present application are:
[0040] 1) The present application collects the operating parameters, material flow rate change rate and energy consumption data of each production equipment in real time through the Internet of Things nodes, combines the comprehensive characteristic parameters of the raw materials to be processed and the real-time running state of the equipment when generating the candidate parameter variation scheme, and refers to the material flow state of the subsequent equipment when determining the target scheme, so that the parameter adjustment of the current production equipment can accurately match the processing requirements of the subsequent equipment, effectively avoiding the problems of material accumulation or insufficient supply, and making each device in the production process form an organic cooperation, significantly improving the continuity and stability of the overall production.
[0041] 2) In the process of determining the target scheme, the present application sets a matching degree basic score corresponding to the ideal state of the material flow of the subsequent equipment and an energy consumption basic score corresponding to the historical optimal energy consumption level, calculates the matching degree actual score and the energy consumption actual score of each scheme, and obtains the comprehensive evaluation score by superimposing the preset weight, which takes into account the matching degree of energy consumption and the historical optimal level while ensuring that the material flow state of the subsequent equipment meets the standard, thereby realizing reasonable control of energy consumption on the basis of maintaining production efficiency, achieving dynamic balance between production efficiency and energy consumption cost, and improving the economy of overall production. BRIEF DESCRIPTION OF DRAWINGS
[0042] The present application will be further described below in conjunction with the accompanying drawings.
[0043] Figure 1 The present application is a mineral product production control system based on the Internet of Things. DETAILED DESCRIPTION
[0044] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.
[0045] Please refer toFigure 1 The application is a mine product production control system based on Internet of Things, comprising:
[0046] A data sensing module is configured to acquire operation parameters, material flow rate change rate and energy consumption data of each production equipment in real time through Internet of Things nodes.
[0047] A scheme generation module is configured to, when the operation parameters of any production equipment trigger adaptive adjustment, mark the production equipment as a target equipment and generate a plurality of candidate parameter change schemes.
[0048] A target determination module is configured to acquire the material flow rate change rate of a subsequent serial equipment of the target equipment, and determine a target scheme from the plurality of candidate parameter change schemes based on the material flow rate change rate.
[0049] A scheme execution module is configured to adjust the operation parameters of the target equipment according to the target scheme.
[0050] 1) The application acquires operation parameters, material flow rate change rate and energy consumption data of each production equipment in real time through Internet of Things nodes, combines comprehensive characteristic parameters of to-be-processed raw materials and real-time running state of the equipment when generating candidate parameter change schemes, and references the material flow state of a subsequent serial equipment when determining a target scheme, so that parameter adjustment of the current production equipment can accurately match processing requirements of the subsequent serial equipment, effectively avoids problems of material accumulation or insufficient supply, and makes each equipment in the production process form organic cooperation, thereby significantly improving continuity and stability of overall production.
[0051] 2) In the process of determining a target scheme, the application sets a matching degree basic score corresponding to an ideal state of material flow of a subsequent serial equipment and an energy consumption basic score corresponding to a historical optimal energy consumption level, calculates matching degree actual scores and energy consumption actual scores of each scheme, and adds the scores according to a preset weight to obtain a comprehensive evaluation score, thereby guaranteeing that the material flow state of the subsequent serial equipment meets the standard, taking into account matching degrees of energy consumption and the historical optimal level, so as to realize reasonable control of energy consumption on the basis of maintaining production efficiency, achieve dynamic balance between production efficiency and energy consumption cost, and improve economic efficiency of overall production.
[0052] In another preferred embodiment of the application, in the scheme generation module, the specific process of triggering adaptive adjustment is as follows:
[0053] When the operation parameters of any production equipment exceed a reference fluctuation interval of the equipment in a stable running state, adaptive adjustment is triggered, and the reference fluctuation interval is determined by statistical parameter running data of the equipment in a historical qualified production period.
[0054] When determining the benchmark fluctuation range, first, the historical production cycles of the production equipment in which qualified ore products are produced in the past are screened. The equipment runs stably and the product quality meets the standard in these cycles, which can reflect the normal working state of the equipment. The operating parameters of the equipment are extracted from the running records of these cycles, such as the feeding rate of the crusher, the motor speed of the grinding machine, the vibration frequency of the screening equipment, etc. The actual fluctuation range of these parameters in the qualified cycle is counted, for example, the feeding rate of the crusher is usually 20-30 tons / hour during qualified production, so this range will be determined as the benchmark fluctuation range of the feeding rate of the equipment. This is because the parameters in the qualified cycle can ensure that the production process is stable and the product is qualified, and this benchmark can accurately reflect the normal operation state of the equipment. When the equipment is running, the current operating parameters are continuously monitored, such as the real-time monitoring of the feeding rate of the crusher. If the feeding rate reaches 35 tons / hour at a certain moment, it exceeds the benchmark fluctuation range of 20-30 tons / hour, which will trigger an adaptive adjustment; if the feeding rate is 25 tons / hour, which is within the range, no adjustment will be triggered.
[0055] It can be understood that the benchmark fluctuation range is based on actual qualified production data, which can fit the real running demand of the equipment and avoid the problem of inapplicability caused by using theoretical values. Only when the parameter exceeds the range will the adjustment be triggered, which can reduce unnecessary frequent adjustments, ensure the stability of the production process, and timely discover parameter abnormalities that may affect the stability of the production process or the quality of the product, and correct them before the abnormalities expand. This helps the entire control system to achieve the ultimate goal of coordinated operation of front and rear equipment, reduce material accumulation or insufficient supply, and optimize energy consumption, and ensures the stability of production efficiency and product quality.
[0056] In another preferred embodiment of the present application, the specific process of generating a plurality of candidate parameter variation schemes in the scheme generation module is:
[0057] The comprehensive characteristic parameters of the target equipment to be processed and the real-time operating parameters are collected, the comprehensive characteristic parameters of the to-be-processed raw materials include physical characteristic indexes reflecting the brittleness difference of the raw materials and component proportions affecting the processing difficulty, and the real-time operating parameters include material conveying rate, power output intensity and running frequency of the processing unit;
[0058] The current value of the operating parameter triggering the adjustment is compared with the benchmark fluctuation range, the quantitative difference from the boundary of the range is calculated, the change rate and the cumulative deviation length of the continuous parameter sequence are analyzed to distinguish between persistent deviation and transient fluctuation; combined with the change amplitude of the comprehensive characteristic parameters of the raw materials, it is judged whether the deviation is caused by the difference in the characteristics of the raw materials; combined with the matching degree of the real-time operating parameters of the equipment, it is judged whether the deviation is caused by the mismatch between the current operating parameters and the characteristics of the raw materials;
[0059] According to the quantitative difference, the rate of change, the cumulative duration and the cause of the parameter deviation, the type of adjustable control parameter is determined, including the conveying parameter for adjusting the raw material supply amount, the power parameter for changing the processing intensity, and the combined parameter for synchronously adjusting both;
[0060] Based on the equipment design operation range, the adjustment threshold of each control parameter is set, the conveying parameter threshold is limited in the conveying system safety interval, the power parameter threshold is limited in the power system rated range, and the combined parameter threshold meets both limitations;
[0061] For the conveying parameter, several levels of adjustment amounts are calculated according to the difference proportion of the raw material characteristics and the historical benchmark; for the power parameter, several levels of adjustment amounts are calculated according to the processing difficulty change amplitude; for the combined parameter, several matching combinations of conveying and power adjustment amounts are calculated according to the comprehensive influence of raw material characteristics; each level of adjustment amount and matching combination is correspondingly matched with different parameter variation values to form several candidate parameter variation schemes including the conveying parameter, the power parameter and the combined parameter adjustment mode.
[0062] When generating several candidate parameter variation schemes, first, the comprehensive characteristic parameters and real-time running parameters of the raw materials to be processed by the production equipment are collected. Among them, the physical characteristic index reflecting the brittleness difference of the raw materials can be the compressive strength of the raw materials, which can be determined by detecting the bearing force of the ore under the action of pressure when the ore is crushed. The smaller the compressive strength, the greater the brittleness of the ore, and vice versa. The composition ratio affecting the processing difficulty can be the content of quartz in the ore, which will increase the grinding difficulty. In the real-time running parameters, the material conveying rate can be reflected by the running speed of the conveyor belt, the power output intensity can be reflected by the output power of the crusher motor, and the running frequency of the processing unit can be reflected by the vibration frequency of the screening equipment. This is because the brittleness and composition of the raw materials directly affect the processing difficulty, and the real-time running parameters of the equipment can reflect the current processing state, which provides a basis for subsequent adjustment. Then, the current value of the operation parameter that triggers the adjustment is compared with the reference fluctuation interval, for example, the power output intensity reference fluctuation interval of the crusher is 50-70kW, and the current value is 85kW. Therefore, the quantitative difference from the upper boundary of the interval is 15kW. Through continuous recording of the parameter sequence, such as recording the power output intensity every 5 seconds, if it is in the deviation state for 3 minutes, it is a continuous deviation, and if it is only in the deviation state for a second and then recovers immediately, it is a transient fluctuation. Combined with the change amplitude of the comprehensive characteristic parameters of the raw materials, such as a 30% decrease in the compressive strength of the raw materials compared to the historical average (a significant increase in brittleness), it can be judged that the deviation is caused by the difference in raw material characteristics. Combined with the matching degree of the real-time running parameters of the equipment, such as an increase in the brittleness of the raw materials but no change in the conveying rate, causing the material to accumulate in the equipment, it can be judged that the deviation is caused by the mismatch between the current running parameters and the raw material characteristics, because the raw materials with high brittleness require faster processing speed or lower conveying capacity. Then, according to the quantitative difference, change rate, cumulative duration and cause of the above parameter deviation, the type of adjustable control parameter is determined. If the deviation is mainly caused by too much raw material, the conveying parameter (such as slowing down the conveyor belt speed) can be adjusted. If the deviation is mainly caused by too hard raw material, the power parameter (such as increasing the motor power) can be adjusted. If both factors contribute to the deviation, the combined parameter should be adjusted. This is because different reasons correspond to different solutions, and targeted adjustment is more effective. Then, based on the design running range of the equipment, the adjustment threshold of each control parameter is set, such as the safety interval of the conveying system is 0-6m / s for the conveyor belt speed, so the conveying parameter threshold will not exceed 6m / s. The rated range of the power system is 0-120kW for the motor power, so the power parameter threshold is limited within this range. The combined parameter threshold must meet both the conveyor belt speed not exceeding 6m / s and the motor power not exceeding 120kW, because the equipment has a design limit, and exceeding it will cause a fault, ensuring that the adjustment is within a safe range.Finally, for the conveying parameters, several differential adjustments are calculated according to the difference ratio between the raw material characteristics and the historical benchmark. For example, if the brittleness of the raw material increases by 20% compared with the historical benchmark, the conveying rate can be reduced by 10% and 20% to form different differentials; for the power parameters, the adjustment is based on the change in processing difficulty. For example, if the processing difficulty increases by 30% due to composition changes, the power output intensity can be increased by 20% and 30%; for the combination parameters, the adjustment is based on the comprehensive impact of the raw material characteristics. For example, if the brittleness of the raw material increases by 15% and the processing difficulty increases by 25%, a matching combination of a 10% reduction in conveying rate and a 20% increase in power output, or a 15% reduction in conveying rate and a 25% increase in power output can be formed. These differentials and combinations are set because the degree of change in raw material characteristics determines the rationality of the adjustment range. Finally, these differential adjustments and matching combinations are corresponded to specific parameter change values to form several candidate schemes including conveying, power, and combination parameter adjustment methods.
[0063] By collecting raw material characteristics and equipment operating parameters, we can fully grasp the key factors affecting production and provide an accurate basis for subsequent adjustments; distinguishing the types and causes of parameter deviations can avoid blind adjustments and ensure that the adjustment direction matches the root cause of the problem; setting the adjustment threshold of the control parameters can ensure that the equipment operates within a safe range and prevent failures due to excessive adjustments; calculating the differential adjustment amount according to the proportion of raw material characteristic differences and the amplitude of changes in processing difficulty can make the candidate solutions more in line with actual production needs and cover different adjustment strengths and methods. The generated candidate parameter change solutions are targeted and feasible, which can not only adapt to changes in raw material characteristics, but also match the operating capabilities of the equipment, avoiding the one-sidedness of the solution, and providing rich and reasonable choices for the subsequent determination of the target solution from the candidate solutions, thereby laying the foundation for the entire control system to achieve coordinated operation of front-end and rear-end equipment, reduce material accumulation or insufficient supply problems, and optimize energy consumption levels, helping to achieve the ultimate goal of improving production efficiency and stability.
[0064] In another preferred embodiment of the present invention, in the target determination module, the specific process of determining the target solution is:
[0065] S1, obtaining the real-time material flow rate change rate of the subsequent series-connected device of the target device and setting a matching basic score, wherein the matching basic score corresponds to the ideal state of material flow of the subsequent series-connected device;
[0066] S2, for several candidate parameter change plans, simulate the material processing effect of the target equipment after the implementation of each candidate parameter change plan, and obtain the simulated energy consumption value per unit time of the target equipment and the simulated material flow rate change rate of the corresponding subsequent series equipment;
[0067] S3, calculate the actual score of the matching degree of each scheme, when there is material accumulation in the subsequent serial equipment, take the degree of the simulated material flow rate change rate approaching to zero as the quantitative basis, the smaller the deviation from zero, the closer the actual score of the matching degree to the basic score of the matching degree;
[0068] When there is insufficient material supply in the subsequent serial equipment, take the absolute value of the difference between the simulated material flow rate change rate and the supply gap as the quantitative basis, the smaller the absolute value, the closer the actual score of the matching degree to the basic score of the matching degree;
[0069] S4, set the energy consumption basic score, obtain the unit time energy consumption data of the target equipment, take the difference between the simulated unit time energy consumption value and the unit time energy consumption data as the quantitative basis, the smaller the difference, the closer the actual energy consumption score to the energy consumption basic score;
[0070] S5, assign a preset weight to the actual score of the matching degree and the actual score of the energy consumption, superimpose the two according to the weight to obtain the comprehensive evaluation score of each candidate scheme, and select the candidate parameter variation scheme with the highest comprehensive evaluation score as the target scheme.
[0071] S1, that is, obtaining the real-time material flow rate change rate of the subsequent serial equipment of the production equipment and the unit time energy consumption data, such as the previous stage is a crusher and the subsequent stage is a screening machine, real-time monitoring the change of the screening machine material throughput per hour (such as from 50 tons / hour to 45 tons / hour, the change rate is decreased by 10%), and the power consumption of the screening machine per hour (such as 120 degrees / hour); Set the basic score of the matching degree and the basic score of the energy consumption, wherein the basic score of the matching degree corresponds to the ideal state of the material flow of the screening machine, that is, the material neither accumulates nor is short of, and the flow rate is stable (such as 50 tons per hour without fluctuation), at this time a fixed score is set; The basic score of the energy consumption corresponds to the historical optimal energy consumption level of the screening machine, that is, the lowest power consumption of the screening machine when it stably processes material in the past production, because the real-time state of the subsequent equipment directly reflects the effect of the previous adjustment, and the historical optimal data can be used as a reasonable benchmark for energy consumption control;
[0072] Then execute S2, simulate the processing effect of each scheme on the material after the implementation of a number of candidate parameter variation schemes, such as a scheme is to reduce the conveying rate of the crusher by 10%, by referring to the relationship between the processing capacity of the crusher and the flow rate of the screening machine in the past when the conveying rate was reduced by 10% (in the past, when the conveying rate was reduced by 10%, the screening machine flow rate change rate was reduced by an average of 8%), the simulated material flow rate change rate of the screening machine under this scheme (such as from 10% to 2%) is obtained;
[0073] Then, S3 is performed to calculate the actual score of the matching degree of each scheme. If the screening machine has material accumulation (for example, the material is more and more accumulated at the inlet, the flow rate change rate is -15%, and the absolute value is also increasing), at this time, the closer the simulated material flow rate change rate is to 0 (for example, -2% is closer to 0 than -8%), the more obvious the improvement of the accumulation, and the closer the actual score of the matching degree is to the basic score. If the screening machine has insufficient material supply (for example, the material is less and less at the outlet, the flow rate change rate is 20%, and continues to increase), the supply gap is 20 tons / hour, and the smaller the absolute value of the difference between the simulation value and the gap (for example, the absolute value of the difference between the simulation value of 18 tons / hour and 10 tons / hour is small), the closer the supply is to the demand, and the closer the actual score of the matching degree is to the basic score. This is because a flow rate change rate of 0 means that the material flow is stable, and a small difference means that the supply and demand are more accurately matched.
[0074] Then, S4 is performed to calculate the actual energy consumption score of each scheme. The difference between the simulated unit time energy consumption value and the historical optimal energy consumption level is used as the basis. For example, the historical optimal value is 100 degrees / hour, the simulated value of a certain scheme is 102 degrees / hour, and the simulated value of another scheme is 108 degrees / hour. The actual energy consumption score of the former is closer to the basic score, because a small difference means that the energy consumption control is closer to the historical best level. Finally, S5 is performed to assign a predetermined weight to the actual matching degree score and the actual energy consumption score (for example, matching degree accounts for 60%, and energy consumption accounts for 40%). The two are superimposed according to the weight to obtain a comprehensive evaluation score. For example, scheme A has a matching degree of 80 points and an energy consumption of 90 points, a total score of 80x60%+90x40%=84 points; scheme B has a matching degree of 85 points and an energy consumption of 85 points, a total score of 85x60%+85x40%=85 points. Scheme B is selected as the target scheme because the weight distribution reflects the importance of the two, the comprehensive score balances the stability of the material flow and the control of the energy consumption, and avoids the one-sidedness of a single indicator.
[0075] By obtaining real-time data of the subsequent equipment in series, the parameter adjustment of the previous equipment can fully consider the cooperation of the previous and subsequent equipment, avoiding material accumulation or insufficient supply caused by only focusing on a single device. By referring to the historical correlation rules when simulating the effects of each scheme, the prediction can be more in line with the actual production situation, ensuring the reliability of the scheme evaluation. By calculating the matching degree score according to the situation, the adjustment effect under different working conditions can be evaluated, making the score more accurate. By calculating the weight of the matching degree and the energy consumption, the problem of only pursuing flow stability while ignoring energy consumption, or only controlling energy consumption while affecting production continuity can be avoided. The advantage is that the generated target scheme not only meets the processing needs of the subsequent equipment, but also considers energy consumption control, which is reasonable and comprehensive. The purpose is to select the optimal adjustment scheme from the candidate schemes to help the entire control system achieve the ultimate goal of cooperative operation of the previous and subsequent equipment, reduce material flow abnormalities, and optimize overall energy consumption, improving the stability and economy of production.
[0076] In another preferred embodiment of the present application, in the S2, the specific process of simulating the effect of the production equipment on the material after the implementation of each candidate parameter variation scheme is:
[0077] For several candidate parameter variation schemes, the parameter variation values contained in each candidate parameter variation scheme are extracted, including the adjustment amount of the material conveying parameter, the adjustment amount of the power parameter, and the adjustment amount of the combination of the two; the running data set of the production equipment under the adjustment of the same parameter type in the historical production is retrieved, and the data set contains the corresponding relationship between the parameter variation value and the material processing amount change rate and the unit energy consumption change rate;
[0078] A parameter-effect correlation model is constructed based on the historical running data set, the model input is the parameter variation value, and the output is the simulated change value of the material processing amount and the simulated change value of the unit energy consumption of the production equipment; the parameter variation values of each candidate scheme are input into the parameter-effect correlation model respectively, and the simulated change value of the material processing amount and the simulated change value of the unit energy consumption of the production equipment under each candidate parameter variation scheme are obtained.
[0079] The material transfer coefficient of the target equipment and the subsequent serial equipment is obtained, and the simulated change value of the material processing amount is converted into the simulated material flow rate change rate of the subsequent serial equipment.
[0080] The historical production data records the actual running effect of the equipment under different parameter adjustments, and the correlation model constructed based on these data can truly reflect the rules between parameter variation and processing effect, so that the simulation result is more in line with the actual production situation; the specific parameter variation value is extracted and input into the model, which can predict the effect of each candidate scheme specifically, avoiding blind speculation; the introduction of the material transfer coefficient ensures the accurate correlation between the processing effect of the previous stage equipment and the state of the subsequent stage equipment, so that the simulation can reflect the actual impact on the subsequent stage, the simulation process is based on the actual running rules, and the result has reliability and pertinence, which can provide accurate reference basis for the determination of the subsequent target scheme. Through accurate simulation of the processing effect of each candidate scheme, the influence of different schemes on the subsequent equipment is determined, thereby laying a foundation for screening out a scheme that can match the working condition of the subsequent stage and optimize energy consumption, which helps the entire control system to realize the coordinated operation of the previous stage and the subsequent stage, reduce material flow abnormalities, and improve the final goal of production efficiency and energy economy.
[0081] In another preferred embodiment of the present application, in the S3, a continuous change trend of the material flow rate change rate of the subsequent serial equipment is further calculated, when the change rate is negative and the absolute value shows an increasing trend, it is determined that the subsequent serial equipment exists material accumulation; when the change rate is positive and continuously increasing, it is determined that the subsequent serial equipment exists material supply shortage.
[0082] In another preferred embodiment of the present application, the target determination module obtains the real-time material residence amount and the unit time energy consumption data of the production device if the production device does not have a subsequent serial device.
[0083] If the residence amount of the production device exceeds the preset accumulation threshold, it is determined that the production device has material accumulation; if the residence amount is less than or equal to the preset accumulation threshold, it is determined that the production device has no material accumulation.
[0084] If the production device has material accumulation, the material processing amount simulation change value corresponding to each candidate parameter change scheme is obtained, and the candidate parameter change scheme with the largest material processing amount simulation change value is selected as the target scheme.
[0085] If the production device has no material accumulation, the difference between the simulation unit energy consumption value corresponding to each candidate parameter change scheme and the unit time energy consumption data is obtained, and the candidate parameter change scheme with the smallest difference is selected as the target scheme.
[0086] Because when the production device has no subsequent serial device, its running state only needs to focus on whether the material processing is smooth and the energy consumption is reasonable, without considering the influence on the subsequent device, by monitoring the residence amount to determine whether it is accumulated, the device whether there is a running blockage problem can be directly located, the scheme can be quickly solved according to the processing amount change value to avoid affecting the production continuity, and when there is no accumulation, the scheme can be selected according to the energy consumption difference, which can reduce energy waste while ensuring normal operation. The advantage is to develop accurate adjustment logic for the scene without subsequent devices, which can prevent production interruption caused by material accumulation, and can optimize energy consumption when running stably, taking into account production stability and economy. In the case where the production device does not have a subsequent serial device, the device can still run efficiently and with low energy consumption through reasonable parameter adjustment, which helps the entire control system to achieve optimal control under different device layouts, whether the device has a subsequent serial device or not, the production can be maintained smoothly and economically, thereby improving the adaptability and operation efficiency of the entire ore product production system.
[0087] In another preferred embodiment of the present application, if there are two or more candidate parameter change schemes corresponding to the same maximum material processing amount simulation change value, the difference between the simulation unit energy consumption value corresponding to each candidate parameter change scheme and the unit time energy consumption data is obtained, and the candidate parameter change scheme with the smallest difference is selected as the target scheme.
[0088] When multiple schemes are consistent in the effect of improving the material processing amount, the key indicator of energy consumption needs to be introduced as the basis for further screening to avoid ignoring the energy consumption cost when solving the accumulation problem, leading to energy waste. The benefit is to select the most energy-saving scheme under the premise of ensuring the maximum degree of alleviating material accumulation, which not only guarantees the continuity of production, but also takes into account the economy of operation, avoiding the blindness of scheme selection. The purpose is to select the optimal parameter adjustment scheme through multi-dimensional comparison, which helps the entire control system to still achieve efficient and low-consumption operation under complex working conditions, whether the equipment has a later series or not, the production can be kept in an optimized state, and the accuracy of regulation and control and the economy of the entire ore product production system are improved.
[0089] The above describes one embodiment of the present application in detail, but the content described is only a preferred embodiment of the present application and cannot be considered as limiting the scope of the present application. Any equivalent changes and improvements made within the scope of the present application should still belong to the scope of the present patent.
Claims
1. A mineral product production control system based on the Internet of Things, characterized in that: include: The data perception module is used to obtain the operating parameters of each production equipment, the rate of change of material circulation rate and energy consumption per unit time in real time through the Internet of Things nodes; A scheme generation module is used to mark any production equipment as a target equipment and generate several candidate parameter change schemes when the operating parameters of the production equipment trigger adaptive adjustment; A target determination module is configured to obtain a material flow rate change rate of a device connected in series with a target device, and determine a target solution from a plurality of candidate parameter change solutions based on the material flow rate change rate; The solution execution module is used to adjust the operating parameters of the target device according to the target solution.
2. The mineral product production control system based on the Internet of Things according to claim 1, characterized in that: In the solution generation module, the specific process of triggering adaptive adjustment is as follows: When the operating parameters of any production equipment exceed the reference fluctuation range under stable operation, adaptive adjustment is triggered. The reference fluctuation range is determined by statistically analyzing the parameter operation data of the equipment in the historical qualified production cycle.
3. The mineral product production control system based on the Internet of Things according to claim 1, characterized in that: In the scheme generation module, the specific process of generating several candidate parameter change schemes is as follows: Collecting comprehensive characteristic parameters and real-time operating parameters of the raw materials to be processed by the target equipment. The comprehensive characteristic parameters of the raw materials to be processed include physical property indicators reflecting the brittleness differences of the raw materials and the proportion of components that affect the processing difficulty. The real-time operating parameters include material conveying rate, power output intensity, and operating frequency of the processing unit; Compare the current value of the operating parameter that triggers the adjustment with the benchmark fluctuation range, calculate the quantitative difference from the interval boundary, analyze the change rate and cumulative deviation duration through continuous parameter sequence, and distinguish between continuous deviation and transient fluctuation; combine the change range of the comprehensive characteristic parameters of the raw materials to determine whether the deviation is caused by differences in raw material characteristics; combine the matching degree of the equipment's real-time operating parameters to determine whether the deviation is caused by the mismatch between the current operating parameters and the raw material characteristics; Based on the quantitative difference, rate of change, cumulative duration, and cause of parameter deviation, determine the types of control parameters that can be adjusted, including conveying parameters for adjusting raw material supply, dynamic parameters for changing processing intensity, and simultaneous adjustment of a combination of the two; Based on the equipment's designed operating range, the adjustment thresholds of each control parameter are set separately. The transport parameter threshold is limited to the transport system's safe range, the power parameter threshold is limited to the power system's rated range, and the combined parameter threshold satisfies both limits. For the conveying parameters, several differential adjustments are calculated based on the difference ratio between the raw material characteristics and the historical benchmark; for the power parameters, several differential adjustments are calculated based on the change in processing difficulty; for the combination parameters, several matching combinations of conveying and power adjustments are calculated based on the comprehensive impact of the raw material characteristics; each differential adjustment amount and matching combination are respectively corresponded to different parameter change values, forming several candidate parameter change schemes that include adjustment methods for conveying parameters, power parameters and combination parameters.
4. The mineral product production control system based on the Internet of Things according to claim 1, characterized in that: In the target determination module, the specific process of determining the target solution is as follows: S1, obtaining the real-time material flow rate change rate of the subsequent series-connected device of the target device and setting a matching basic score, wherein the matching basic score corresponds to the ideal state of material flow of the subsequent series-connected device; S2, for several candidate parameter change plans, simulate the material processing effect of the target equipment after the implementation of each candidate parameter change plan, and obtain the simulated energy consumption value per unit time of the target equipment and the simulated material flow rate change rate of the corresponding subsequent series equipment; S3, calculate the actual matching score of each scheme. When there is material accumulation in the downstream series equipment, the degree to which the simulated material flow rate change rate approaches zero is used as the quantitative basis. The smaller the deviation from zero, the closer the actual matching score is to the basic matching score. When there is a shortage of material supply in the downstream series equipment, the absolute value of the difference between the simulated material circulation rate change and the supply gap is used as the quantitative basis. The smaller the absolute value of the difference, the closer the actual matching score is to the basic matching score. S4, setting a basic energy consumption score, obtaining the energy consumption data per unit time of the target device, and using the simulated energy consumption per unit time value and the energy consumption data per unit time as a quantitative basis. The smaller the difference, the closer the actual energy consumption score is to the basic energy consumption score; S5, assign preset weights to the actual matching score and the actual energy consumption score, superimpose the two according to the weights to obtain the comprehensive evaluation score of each candidate solution, and select the candidate parameter change solution with the highest comprehensive evaluation score as the target solution.
5. The mineral product production control system based on the Internet of Things according to claim 4 is characterized in that: In S2, the specific process of simulating the processing effect of the production equipment on the material after the implementation of each candidate parameter change plan is as follows: For several candidate parameter change schemes, extracting parameter change values included in each candidate parameter change scheme, wherein the parameter change values include adjustment amounts of material conveying parameters, adjustment amounts of power parameters, and adjustment amounts of a combination of the two; Retrieving an operating data set of the production equipment under the same parameter type adjustment in historical production, the data set includes the corresponding relationship between the parameter change value and the material throughput change rate and the unit energy consumption change rate; constructing a parameter-effect association model based on the historical operating data set, with the model input being the parameter change value and the output being the simulated change value of the material throughput and the simulated change value of the unit energy consumption of the production equipment; Input the parameter change values of each candidate solution into the parameter-effect association model to obtain the simulated change value of the material handling capacity and the simulated change value of the unit energy consumption of the production equipment under each candidate parameter change solution; The material transfer coefficient between the target device and the subsequent series device is obtained, and the simulated change value of the material handling capacity is converted into the simulated material circulation rate change rate of the subsequent series device.
6. The mineral product production control system based on the Internet of Things according to claim 4 is characterized in that: Said S3 also includes calculating the continuous change trend of the material circulation rate change rate of the subsequent series equipment. When the change rate is negative and the absolute value is increasing, it is determined that there is material accumulation in the subsequent series equipment; when the change rate is positive and continuously increasing, it is determined that there is insufficient material supply in the subsequent series equipment.
7. The mineral product production control system based on the Internet of Things according to claim 4 is characterized in that: The target determination module obtains the real-time material retention and energy consumption per unit time data of the production equipment if the production equipment cannot have a subsequent series device; If the retention volume of the production equipment exceeds the preset accumulation threshold, it is determined that there is material accumulation in the production equipment; When the holdup volume is lower than or equal to the preset accumulation threshold, it is determined that there is no material accumulation in the production equipment; When there is material accumulation in the production equipment, the material throughput simulation change value corresponding to each candidate parameter change scheme is obtained, and the candidate parameter change scheme with the largest material throughput simulation change value is selected as the target scheme; When there is no material accumulation in the production equipment, the difference between the simulated unit energy consumption value and the unit time energy consumption data corresponding to each candidate parameter change scheme is obtained, and the candidate parameter change scheme with the smallest difference is selected as the target scheme.
8. The mineral product production control system based on the Internet of Things according to claim 7 is characterized in that: If there are more than two candidate parameter change plans whose corresponding material processing volume simulated change values are equal and both are maximum values, then the difference between the simulated unit energy consumption value and the unit time energy consumption data corresponding to each candidate parameter change plan is obtained, and the candidate parameter change plan with the smallest difference is selected as the target plan.