An automatic adjustment method and system for a workshop conveyor based on material conveying data

Abstract

The present invention discloses an automatic adjustment method and system for a workshop conveyor based on material conveying data, which relates to the technical field of workshop conveyors. The method includes the following steps: constructing a multi-modal data set based on the collected material conveying data, and processing the multi-modal data set using a multi-modal deep learning network to obtain multi-dimensional conveying features; setting constraint rules according to the operating parameters and historical data of the workshop conveyor, and dividing the operating modes of the workshop conveyor; based on the real-time material flow rate and conveying requirements, selecting the current operating mode of the workshop conveyor and adjusting the operating parameters of the workshop conveyor in the current operating mode. By setting reasonable constraint rules and dividing different operating modes, the present invention can intelligently select and adjust the operating parameters of the workshop conveyor according to the real-time material flow rate and conveying requirements, ensure that the conveyor operates in an optimal state under different working conditions, and improve the conveying efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of workshop conveyors, and in particular to an automatic adjustment method and system for workshop conveyors based on material conveying data. Background Art

[0002] In modern industrial production, material transportation is a key link that connects various production links and realizes the continuity of production processes. Traditional material transportation regulation technologies mostly rely on fixed speed and load settings, which are generally determined based on experience or static plans, and mainly rely on manual experience and simple control devices. These technologies usually adjust the operation of the conveyor based on preset rules and parameters. For example, according to the size of the material flow, the operator may manually adjust the speed or power of the conveyor, and often lack the ability to respond quickly to real-time production needs and the operating status of the conveyor; With the improvement of the level of industrial automation, some workshop conveyors have begun to integrate simple sensors and controllers to achieve basic regulation of conveying speed and load. However, most of these workshop conveyors use open-loop control, lacking comprehensive consideration and precise control of complex variables in the conveying process, such as fluctuations in material flow, optimization of conveying energy consumption, and synchronization with production rhythm.

[0003] There are some significant drawbacks in the existing workshop conveyor adjustment methods. First, these methods often rely on manual experience and intuition, lack of scientific and accurate data support. Due to the complexity and variability of the material conveying process, it is difficult to achieve accurate and real-time adjustment based on manual experience alone; secondly, the existing adjustment methods can often only consider limited parameters, such as material flow or conveying speed, while ignoring other important factors, such as conveying load, energy consumption and conveying rhythm. This one-sided consideration leads to unsatisfactory adjustment effects and the inability to achieve optimal conveying efficiency; in addition, the existing adjustment methods lack adaptive capabilities and cannot be adjusted according to real-time data and changing production needs, resulting in low conveying efficiency, high energy consumption and costs.

[0004] Due to the above-mentioned drawbacks of the existing technology, the efficiency and effect in the field of material transportation are often limited. The traditional workshop conveyor adjustment method relies on fixed control strategies and parameter settings, lacks flexibility and adaptability. This method cannot cope with the complex and changeable situations in the material transportation process, nor can it make full use of a large amount of material transportation data to achieve real-time and accurate adjustment, making it difficult for the workshop conveyor to operate in the optimal state, resulting in energy waste and low efficiency; therefore, a new workshop conveyor adjustment method is urgently needed. By combining multimodal deep learning technology with real-time data analysis, the intelligent adjustment of workshop conveyor operating parameters can be realized to improve the efficiency and quality of material transportation.

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the Invention

[0006] In view of the problems in the related art, the present invention provides an automatic adjustment method and system for a workshop conveyor based on material conveying data, which has the advantages of being able to intelligently select and adjust the operating parameters of the workshop conveyor according to the real-time material flow rate and conveying requirements, ensuring that the workshop conveyor can operate in an optimal state under different working conditions, and further solving the problem in the prior art that it depends on fixed control strategies and parameter settings and cannot cope with the complex and changeable situations in the material conveying process.

[0007] Therefore, the specific technical solutions adopted by the present invention are as follows:

[0008] According to one aspect of the present invention, there is provided an automatic adjustment method for a workshop conveyor based on material conveying data, and the automatic adjustment method for the workshop conveyor based on material conveying data includes the following steps:

[0009] S1. Based on the collected material conveying data, construct a multi-modal data set, and use a multi-modal deep learning network to process the multi-modal data set to obtain multi-dimensional conveying features;

[0010] S2. Set constraint rules according to the operating parameters and historical data of the workshop conveyor, and divide the operating modes of the workshop conveyor;

[0011] S3. Based on the real-time material flow rate and conveying requirements, select the current operating mode of the workshop conveyor and adjust the operating parameters of the workshop conveyor in the current operating mode;

[0012] Setting constraint rules according to the operating parameters and historical data of the workshop conveyor and dividing the operating modes of the workshop conveyor include the following steps:

[0013] S21. Set the constraint rules of the automatic adjustment model of the workshop conveyor, including safe operation constraints, efficient production demand constraints, cost control constraints and conveying beat constraints;

[0014] S22. Divide the operating modes of the workshop conveyor, including peak flow mode, low valley flow mode and idle mode;

[0015] S23. Based on the division results, design corresponding adjustment strategies for different operating modes.

[0016] Furthermore, the material conveying data includes material conveying sequence data and material conveying non-sequence data;

[0017] Among them, the material conveying sequence data includes: conveying speed and transportation time;

[0018] The material conveying non-sequence data includes: conveying load, material flow rate and transportation energy consumption.

[0019] Further, based on the collected material conveying data, a multi-modal data set is constructed, and the multi-modal data set is processed by a multi-modal deep learning network to obtain multi-dimensional conveying features, including the following steps:

[0020] S11. Integrate the material conveying sequence data and the non-sequence data of material conveying to construct a multi-modal data set;

[0021] S12. Based on the multi-modal deep learning network, use a one-dimensional convolutional layer to extract the sequence data features of the multi-modal data set, use a fully connected layer to extract the non-sequence data features of the multi-modal data set, and use a long short-term memory layer to fuse the sequence data features and the non-sequence data features;

[0022] S13. Obtain the output vector of the last hidden layer and perform normalization processing to obtain multi-dimensional conveying features.

[0023] Further, set the constraint rules of the automatic adjustment model of the workshop conveyor, including safety operation constraints, efficiency production demand constraints, cost control constraints, and conveying rhythm constraints, including the following steps:

[0024] S211. According to the design specifications and historical operation data of the workshop conveyor, calculate and set the maximum safety load rate to construct safety operation constraints;

[0025] S212. Based on the efficiency curve of the workshop conveyor and production requirements, set the minimum efficiency load rate to construct efficiency production demand constraints;

[0026] S213. Combine the energy consumption mode and transportation cycle of the workshop conveyor to set the cost control threshold to construct cost control constraints;

[0027] S214. According to the transportation plan and work schedule of the workshop conveyor, set the conveying rhythm to construct conveying rhythm constraints.

[0028] Further, divide the operation modes of the workshop conveyor, including peak flow mode, low valley flow mode, and idle mode, including the following steps:

[0029] S221. Based on the statistical analysis of historical material flow data, define the flow thresholds of the peak flow mode, low valley flow mode, and idle mode;

[0030] S222. Set the corresponding parameter ranges for each operation mode, including the speed, energy consumption power, and load rate of the workshop conveyor;

[0031] S223. Define the switching logic between different operation modes, including trigger conditions and switching processes.

[0032] Further, based on the partitioning result, designing corresponding adjustment strategies for different operating modes includes the following steps:

[0033] S231. Classify and define the types of policy rules according to the requirements of different operating modes;

[0034] S232. Based on each type of policy rule, specify the associated operating parameters, including the operating parameters during the startup, operation, and stop phases of the workshop conveyor;

[0035] S233. Set the logical conditions for parameter adjustment according to the policy rule type and the associated operating parameters.

[0036] Further, based on the real-time material flow and conveying requirements, selecting the current operating mode of the workshop conveyor and adjusting the operating parameters of the workshop conveyor in the current operating mode includes the following steps:

[0037] S31. Select the current operating mode of the workshop conveyor according to the real-time material flow and conveying requirements;

[0038] S32. Based on the constraint rules, with the goal of maximizing the transportation efficiency, construct an automatic adjustment model for the workshop conveyor in the current operating mode;

[0039] S33. Based on the automatic adjustment model of the workshop conveyor, input multi-dimensional conveying characteristics and output the adjusted operating parameters of the workshop conveyor.

[0040] Further, based on the constraint rules, with the goal of maximizing the transportation efficiency, constructing an automatic adjustment model for the workshop conveyor in the current operating mode includes the following steps:

[0041] S321. Extract the constraint rules of the automatic adjustment model of the workshop conveyor and construct an expression for the constraint conditions;

[0042] S322. According to the adjustment strategy of the current operating mode, set the objective function to maximize the transportation efficiency and define the expression of the objective function;

[0043] S323. Based on the constraint conditions and the objective function, establish an automatic adjustment model for the workshop conveyor in the current operating mode.

[0044] Further, the expression of the constraint conditions is:

[0045] ;

[0046] ;

[0047] ;

[0048] ;

[0049] In the formula, L represents the load rate of the workshop conveyor;

[0050] L max represents the maximum safe load rate;

[0051] L min represents the minimum efficiency load rate;

[0052] p ( t ) represents the energy consumption of the workshop conveyor, C cost represents the cost control threshold;

[0053] v represents the conveying speed of the workshop conveyor, v pro represents the conveying speed of the workshop conveyor under the conveying beat;

[0054] The expression of the objective function is:

[0055] ;

[0056] In the formula, f ( x ) represents the objective function, which is used to maximize the transportation efficiency while considering the control cost;

[0057] α and β represent the weight coefficients, which are used to balance the importance of transportation efficiency and cost control;

[0058] Q material is the material flow rate, which represents the amount of material conveyed per unit time;

[0059] Eenergy is the energy consumption, which represents the energy consumed during the conveying process;

[0060] Ccurrent is the current cost, which represents the cost consumption under the current operation mode.

[0061] According to another aspect of the present invention, there is also provided an automatic adjustment system for a workshop conveyor based on material conveying data. The automatic adjustment system for a workshop conveyor based on material conveying data includes:

[0062] A multi-dimensional conveying feature module, which is used to construct a multi-modal data set based on the collected material conveying data, and process the multi-modal data set by using a multi-modal deep learning network to obtain multi-dimensional conveying features;

[0063] An operation mode construction module, configured to set constraint rules according to the operation parameters and historical data of the workshop conveyor, and divide the operation modes of the workshop conveyor;

[0064] An operation parameter adjustment module, configured to select the current operation mode of the workshop conveyor based on the real-time material flow rate and conveying requirements, and adjust the operation parameters of the workshop conveyor in the current operation mode;

[0065] Wherein, the multi-dimensional conveying feature module is connected to the operation parameter adjustment module through the operation mode construction module.

[0066] The beneficial effects of the present invention are as follows:

[0067] (1) By setting reasonable constraint rules and dividing different operation modes, the present invention can intelligently select and adjust the operation parameters of the workshop conveyor according to the real-time material flow rate and conveying requirements. This flexible adjustment strategy can ensure that the conveyor operates in an optimal state under different working conditions, improving the conveying efficiency, reducing energy consumption and costs; at the same time, by comprehensively collecting and multi-modal processing the material conveying data, more accurate and comprehensive multi-dimensional conveying features can be obtained, providing a solid foundation for the automatic adjustment of the workshop conveyor. Not only sequence data such as conveying speed and time are considered, but also non-sequence data such as conveying load, material flow rate and energy consumption are comprehensively considered, making the adjustment of the conveyor more in line with the actual operation situation; in addition, the present invention adopts a deep learning network and linear programming technology, which can automatically learn and optimize the adjustment strategy from a large amount of data. This method greatly reduces the need for manual intervention, reduces the possibility of human errors, and improves the reliability and stability of the workshop conveyor.

[0068] (2) By real-time monitoring and analyzing the material conveying data, the present invention can accurately adjust parameters such as the speed, power and load rate of the workshop conveyor to adapt to different production requirements. This accurate adjustment can ensure that the workshop conveyor operates efficiently in the high-flow mode and saves energy in the low-flow mode, thereby improving the production efficiency, reducing energy consumption and operating costs; secondly, by setting reasonable cost control constraints and the minimum efficiency load rate, the present invention can ensure that the workshop conveyor minimizes energy consumption and operating costs while meeting production requirements, achieving refined cost control, which helps to improve market competitiveness.

[0069] (3) By setting safety operation constraints and conveying rhythm constraints, the present invention can ensure that the workshop conveyor operates within a safe range and can adapt to the rhythm requirements of the production plan. This precise adjustment and control can effectively reduce the failure rate of the conveying system and improve the reliability and stability of the system. In addition, by real-time monitoring and analyzing the material conveying data, the present invention can timely detect and handle abnormal situations in the conveying system, thereby reducing the occurrence of failures. This real-time monitoring and analysis not only helps to timely detect potential problems, but also helps to quickly respond to and handle these problems, reducing the impact of failures on production. Description of the Drawings

[0070] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0071] Figure 1 is a flowchart of an automatic adjustment method for a workshop conveyor based on material conveying data according to an embodiment of the present invention;

[0072] Figure 2 is a principle block diagram of an automatic adjustment system for a workshop conveyor based on material conveying data according to an embodiment of the present invention. Detailed Embodiments

[0073] To further illustrate the embodiments, the present invention provides drawings. These drawings are part of the disclosure of the present invention. They are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, those of ordinary skill in the art should be able to understand other possible implementation manners and the advantages of the present invention. The components in the drawings are not drawn to scale, and similar component symbols are usually used to represent similar components.

[0074] According to an embodiment of the present invention, an automatic adjustment method and system for a workshop conveyor based on material conveying data are provided.

[0075] Now, the present invention will be further described in conjunction with the drawings and specific embodiments. As Figure 1 shown, according to an embodiment of the present invention, an automatic adjustment method for a workshop conveyor based on material conveying data is provided. The automatic adjustment method for a workshop conveyor based on material conveying data includes the following steps:

[0076] S1. Based on the collected material conveying data, construct a multi-modal data set, and use a multi-modal deep learning network to process the multi-modal data set to obtain multi-dimensional conveying features;

[0077] S2. Set constraint rules according to the operating parameters and historical data of the workshop conveyor, and divide the operating modes of the workshop conveyor;

[0078] S3. Based on the real-time material flow and conveying requirements, select the current operating mode of the workshop conveyor and adjust the operating parameters of the workshop conveyor in the current operating mode;

[0079] Setting constraint rules according to the operating parameters and historical data of the workshop conveyor and dividing the operating modes of the workshop conveyor include the following steps:

[0080] S21. Set the constraint rules of the automatic adjustment model of the workshop conveyor, including safety operation constraints, efficient production demand constraints, cost control constraints and conveying rhythm constraints;

[0081] S22. Divide the operating modes of the workshop conveyor, including peak flow mode, low flow mode and idle mode;

[0082] S23. Based on the division results, design corresponding adjustment strategies for different operating modes.

[0083] In one embodiment, the material conveying data includes material conveying sequence data and material conveying non-sequence data;

[0084] Among them, the material conveying sequence data includes: conveying speed and transportation time;

[0085] The material conveying non-sequence data includes: conveying load, material flow and transportation energy consumption.

[0086] In one embodiment, based on the collected material conveying data, constructing a multi-modal data set and using a multi-modal deep learning network to process the multi-modal data set to obtain multi-dimensional conveying features includes the following steps:

[0087] S11. Integrate the material conveying sequence data and the material conveying non-sequence data to construct a multi-modal data set;

[0088] Specifically, using a NoSQL database, import the real-time collected sequence data (conveying speed, transportation time) and non-sequence data (conveying load, material flow, transportation energy consumption) into a unified data table, and then arrange the data in chronological order through the timestamp field to ensure that the time points of each record are consistent. Then, use the moving average method to fill in the missing data to establish a multi-modal data set.

[0089] S12. Based on the multi-modal deep learning network, use a one-dimensional convolutional layer to extract the sequence data features of the multi-modal data set, use a fully connected layer to extract the non-sequence data features of the multi-modal data set, and use a long short-term memory layer to fuse the sequence data features and the non-sequence data features;

[0090] Specifically, a deep learning network with a convolutional layer and a recurrent layer is adopted. In this embodiment, a multi-modal deep learning network with a CNN-LSTM architecture is used.

[0091] Specifically, CNN is used to extract the sequence data features of the sequence data. Here, the number and size of the filters of CNN can be adaptively adjusted according to the data. A 1D CNN model is designed, which contains learnable filters that can automatically adjust according to the input sequence data to extract the most useful features. Then, for the non-sequence data, the data is input into the Dense layer, and the fully connected layer is used to extract the non-sequence data features. Then, the feature vectors of the two branches are concatenated in the time dimension and then input into the LSTM layer to simultaneously consider the dynamic changes of the time series data and the static features of the non-sequence data.

[0092] S13. Obtain the output vector of the last hidden layer and perform normalization processing to obtain multi-dimensional conveying features.

[0093] Specifically, obtain the output from the last hidden layer of the multi-modal deep learning network as the multi-dimensional conveying feature vector, and then perform batch normalization processing on this feature vector and output it in the standard form of 0 mean and unit variance as the input of the subsequent automatic adjustment model of the workshop conveyor.

[0094] In one embodiment, setting the constraint rules of the automatic adjustment model of the workshop conveyor, including safety operation constraints, efficiency production demand constraints, cost control constraints, and conveying rhythm constraints, includes the following steps:

[0095] S211. Calculate and set the maximum safety load rate according to the design specifications and historical operation data of the workshop conveyor, and construct safety operation constraints;

[0096] Specifically, query the design specifications of the workshop conveyor and collect the historical operation data of the workshop conveyor in the past period (one year in this embodiment). Find the maximum stable operation load rate reached by the conveyor without failures and safety problems. Considering the long-term operation reliability and safety, a safety factor is usually set based on the maximum load-bearing capacity to reduce the operation risk, and then calculate and set the maximum safety load rate L max , and the expression is:

[0097] ;

[0098] In the formula, L max represents the maximum safety load rate of the workshop conveyor;

[0099] Lh represents the maximum load rate allowed by the design specifications of the workshop conveyor;

[0100] L d represents the maximum stable operating load rate shown by historical data;

[0101] a q represents the safety factor.

[0102] In this embodiment, the maximum load rate allowed by the design specifications of the workshop conveyor is queried to be 98%. However, historical operation data shows that the workshop conveyor can operate stably without failure at a maximum stable operating load rate of 80%. For safety reasons, it is decided to use a safety factor of 90%. Finally, the maximum safety load rate L max = 80% × 0.9 = 72% is calculated and used as the safety operation constraint to ensure that the workshop conveyor operates within a safe range.

[0103] S212. Based on the efficiency curve of the workshop conveyor and production requirements, set the minimum efficiency load rate and construct an efficiency production requirement constraint;

[0104] Specifically, by referring to the technical specifications provided by the manufacturer, collect the efficiency data of the workshop conveyor at different load rates, and then determine the daily or shift production targets, including the total amount of materials required and the available working hours. Plot the collected efficiency data as a graph with the load rate on the horizontal axis and the efficiency on the vertical axis. Then convert the production requirements into load rate requirements. For example, if the production requirement is to convey 100 tons of materials per day and there are 8 working hours per day, calculate the amount of materials that need to be conveyed per hour, and based on the bandwidth of the conveyor and the density of the materials, calculate the required average load rate.

[0105] In the above embodiment, the efficiency curve of the workshop conveyor shows that when the load rate is 60%, the efficiency of the conveyor reaches a reasonable working point, which can not only meet the production requirements but also avoid excessive energy consumption or excessive wear of the equipment. At the same time, through the comparative analysis with the production plan, it is confirmed that at a load rate of 60%, the conveyor can complete the daily production target within the specified working hours. Therefore, based on these data and analysis, the minimum efficiency load rate is set to 60%, that is L min = 0.6, thereby obtaining the efficiency production requirement constraint.

[0106] S213. Combine the energy consumption pattern and transportation cycle of the workshop conveyor, set the cost control threshold, and construct a cost control constraint;

[0107] Specifically, set a cost control threshold Ccost , the threshold is the maximum allowable value of the energy consumption of the workshop conveyor per unit time, which is equal to the maximum kilowatt-hours of the energy consumption of the workshop conveyor per hour. Then, a cost control constraint is constructed to ensure that the real-time energy consumption of the conveyor does not exceed the set energy consumption threshold at any given time. The expression of the cost control constraint is:

[0108] ;

[0109] ;

[0110] In the formula, p ( t ) represents the energy consumption of the workshop conveyor;

[0111] P represents the power of the workshop conveyor, with the unit of kW;

[0112] v represents the operating speed of the workshop conveyor, with the unit of m / min;

[0113] L represents the load rate of the workshop conveyor.

[0114] S214. According to the transportation plan and work schedule of the workshop conveyor, set the conveying rhythm and construct the conveying rhythm constraint.

[0115] Specifically, the transportation plan and work schedule of the workshop conveyor usually have a predetermined rhythm time, that is, the time required to produce a unit of product. The conveying speed v must match the rhythm time T pro of the production plan to ensure the continuity and efficiency of the production process. The expression of the conveying rhythm constraint is:

[0116] ;

[0117] In the formula, v pro represents the conveying speed of the workshop conveyor under the conveying rhythm, with the unit of m 2 / min;

[0118] Q represents the amount of material that the workshop conveyor needs to convey, with the unit of m 3 ;

[0119] T pro represents the rhythm time of the production plan, with the unit of min;

[0120] W represents the material conveying bandwidth of the workshop conveyor, with the unit of m.

[0121] In one embodiment, dividing the operating modes of the workshop conveyor, including the peak flow mode, the low valley flow mode, and the idle mode, includes the following steps:

[0122] S221. Based on the statistical analysis of historical material flow data, define the flow thresholds for the peak flow mode, the low valley flow mode, and the idle mode;

[0123] Specifically, according to the statistical analysis of historical material flow data, define the flow threshold for the peak flow mode as F peak , define the flow threshold for the low valley flow mode as F low , when the material flow is close to zero, define it as the idle mode, and the flow threshold for the idle mode is F idle .

[0124] S222. Set the corresponding parameter ranges for each operating mode, including the speed, energy consumption power, and load rate of the workshop conveyor;

[0125] Specifically, set the speed V , energy consumption power P , and load rate L of the workshop conveyor for each operating mode. In the above embodiment, for the peak flow mode, V peak = 2 m / min, P peak = 5 kW, L peak = 0.8; for the low valley flow mode, V low = 1 m / min, P low = 3 kW, L low = 0.6; for the idle mode, V idle = 0 m / min, P idle = 0.5 kW, L idle = 0.

[0126] S223. Define the switching logic between different operating modes, including the trigger conditions and the switching process.

[0127] Specifically, define the switching logic between different operating modes, including the trigger conditions and the switching process. In the above embodiment, when the real-time material flow exceeds F peak and lasts for 5 minutes, then switch from the current mode to the peak flow mode. When the real-time material flow is lower thanF low If it lasts for 10 minutes, then switch from the current mode to the low-flow mode. When no material flow is detected by the workshop conveyor for 20 consecutive minutes, switch to the idle mode.

[0128] In one embodiment, based on the partitioning result, designing corresponding adjustment strategies for different operating modes includes the following steps:

[0129] S231. Classify and define the types of policy rules according to the requirements of different operating modes;

[0130] Specifically, classify and define the types of policy rules according to the requirements of different operating modes. Among them, the type of policy rule for the high-flow mode is: prioritize ensuring the conveying speed and load rate to cope with high-flow demands; the type of policy rule for the low-flow mode is: optimize energy consumption, reduce speed and load rate, and reduce energy consumption; the type of policy rule for the idle mode is: maintain the lowest operating state and be ready to quickly respond to the upcoming material flow.

[0131] S232. Specify the associated operating parameters based on each type of policy rule, including the operating parameters during the startup, operation, and stop phases of the workshop conveyor;

[0132] Specifically, based on each type of policy rule, specify the associated operating parameters. Among them, for the high-flow mode, V = V peak , P = P peak , L = L peak ; for the low-flow mode, V = V low , P = P low , L = L low ; for the idle mode, V = V idle , P = P idle , L = L idle .

[0133] S233. Set the logical conditions for parameter adjustment according to the policy rule type and the associated operating parameters.

[0134] Specifically, according to the policy rule type and associated operating parameters, set the logical conditions for parameter adjustment. For example, if the current is in the peak flow mode and the real-time load rate is lower than L peak , increase the power of the conveyor to increase the load rate; if the current is in the low flow mode and the energy consumption exceeds C cost , reduce the speed of the conveyor to reduce energy consumption.

[0135] In one embodiment, based on the real-time material flow and conveying requirements, select the current operating mode of the workshop conveyor, and adjust the operating parameters of the workshop conveyor in the current operating mode, including the following steps:

[0136] S31. According to the real-time material flow and conveying requirements, select the current operating mode of the workshop conveyor;

[0137] Specifically, if the real-time material flow is greater than or equal to the peak flow threshold F peak , select the peak flow mode; if the real-time material flow is less than the peak flow threshold and greater than or equal to the low flow threshold F low , select the low flow mode; if the real-time material flow is less than the low flow threshold F low , select the idle mode.

[0138] S32. Based on the constraint rules, with the goal of maximizing the transportation efficiency, construct an automatic adjustment model for the workshop conveyor in the current operating mode;

[0139] S33. Based on the automatic adjustment model of the workshop conveyor, input multi-dimensional conveying characteristics, and output the adjusted operating parameters of the workshop conveyor.

[0140] Specifically, input the multi-dimensional conveying characteristics into the automatic adjustment model of the workshop conveyor, and adjust the speed and load rate of the workshop conveyor in real time according to the output adjusted operating parameters to match the production requirements and meet the constraint conditions.

[0141] In one embodiment, based on the constraint rules, with the goal of maximizing the transportation efficiency, constructing an automatic adjustment model for the workshop conveyor in the current operating mode includes the following steps:

[0142] S321. Extract the constraint rules of the automatic adjustment model of the workshop conveyor and construct an expression for the constraint conditions;

[0143] S322. According to the adjustment strategy of the current operating mode, set the objective function to maximize the transportation efficiency and define the expression of the objective function;

[0144] S323. Based on the constraint conditions and the objective function, establish an automatic adjustment model for the workshop conveyor in the current operation mode.

[0145] In one embodiment, the expression of the constraint conditions is:

[0146] ;

[0147] ;

[0148] ;

[0149] ;

[0150] Wherein, L represents the load rate of the workshop conveyor;

[0151] L max represents the maximum safe load rate;

[0152] L min represents the minimum efficiency load rate;

[0153] p ( t ) represents the energy consumption of the workshop conveyor, C cost represents the cost control threshold;

[0154] v represents the conveying speed of the workshop conveyor, v pro represents the conveying speed of the workshop conveyor under the conveying beat;

[0155] The expression of the objective function is:

[0156] ;

[0157] Wherein, f ( x ) represents the objective function, which is used to maximize the transportation efficiency while considering the control cost;

[0158] α and β represent the weight coefficients, which are used to balance the importance of transportation efficiency and cost control;

[0159] Q material is the material flow rate, which represents the amount of material conveyed per unit time;

[0160] Eenergy is the energy consumption, which represents the energy consumed during the conveying process;

[0161] Ccurrent The current cost represents the cost consumption in the current operating mode, and in this embodiment, it is calculated by multiplying the real-time energy consumption by the unit energy consumption cost.

[0162] As Figure 2 shown, according to another embodiment of the present invention, there is also provided a workshop conveyor automatic adjustment system based on material conveying data. The workshop conveyor automatic adjustment system based on material conveying data includes:

[0163] A multi-dimensional conveying feature module 1, configured to construct a multi-modal data set based on the collected material conveying data, and process the multi-modal data set using a multi-modal deep learning network to obtain multi-dimensional conveying features;

[0164] An operating mode construction module 2, configured to set constraint rules according to the operating parameters and historical data of the workshop conveyor, and divide the operating modes of the workshop conveyor;

[0165] An operating parameter adjustment module 3, configured to select the current operating mode of the workshop conveyor based on the real-time material flow rate and conveying demand, and adjust the operating parameters of the workshop conveyor in the current operating mode;

[0166] Among them, the multi-dimensional conveying feature module 1 is connected to the operating parameter adjustment module 3 through the operating mode construction module 2.

[0167] In summary, by means of the above technical solutions of the present invention, by setting reasonable constraint rules and dividing different operating modes, it is possible to intelligently select and adjust the operating parameters of the workshop conveyor according to the real-time material flow rate and conveying requirements. This flexible adjustment strategy can ensure that the conveyor operates in an optimal state under different working conditions, improving the conveying efficiency, reducing energy consumption and costs, and by comprehensively collecting and multi-modal processing of material conveying data, more accurate and comprehensive multi-dimensional conveying characteristics can be obtained, thus providing a solid foundation for the automatic adjustment of the workshop conveyor. It not only considers sequential data such as conveying speed and time, but also comprehensively considers non-sequential data such as conveying load, material flow rate and energy consumption, making the adjustment of the conveyor more in line with the actual operating conditions. By using deep learning networks and linear programming techniques, the present invention can automatically learn and optimize the adjustment strategy from a large amount of data. This method greatly reduces the need for manual intervention, reduces the possibility of human errors, and improves the reliability and stability of the workshop conveyor. At the same time, by real-time monitoring and analyzing material conveying data, the present invention can accurately adjust parameters such as the speed, power and load rate of the workshop conveyor to adapt to different production requirements. This precise adjustment can ensure that the workshop conveyor operates efficiently in the high-flow mode and saves energy in the low-flow mode, thus improving production efficiency, reducing energy consumption and operating costs. Secondly, by setting reasonable cost control constraints and the minimum efficiency load rate, the present invention can ensure that the workshop conveyor minimizes energy consumption and operating costs while meeting production requirements, achieving refined cost control and helping to improve market competitiveness. In addition, by setting safety operation constraints and conveying beat constraints, the present invention can ensure that the workshop conveyor operates within a safe range and can adapt to the beat requirements of the production plan. This precise adjustment and control can effectively reduce the failure rate of the conveying system and improve the reliability and stability of the system. By real-time monitoring and analyzing material conveying data, the present invention can timely detect and handle abnormal situations in the conveying system, thereby reducing the occurrence of faults. This real-time monitoring and analysis not only helps to timely discover potential problems, but also helps to quickly respond to and handle these problems, reducing the impact of faults on production.

[0168] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for automatic adjustment of a workshop conveyor based on material transportation data, characterized in that: include: S1. Based on the collected material transportation data, a multimodal data set is constructed, and the multimodal deep learning network is used to process the multimodal data set to obtain multidimensional transportation features; S2. Set constraint rules according to the operation parameters and historical data of the workshop conveyor, and divide the operation mode of the workshop conveyor; S3. Based on the real-time material flow and conveying demand, select the current operation mode of the workshop conveyor, and adjust the operation parameters of the workshop conveyor under the current operation mode; The S2 includes: S21. Setting constraint rules for the automatic adjustment model of the workshop conveyor, including safety operation constraints, efficiency production demand constraints, cost control constraints and conveying rhythm constraints; S22. Divide the operation modes of the workshop conveyor into peak flow mode, valley flow mode and idle mode; specifically include: Based on the statistical analysis of historical material flow data, define the flow thresholds for peak flow mode, valley flow mode and idle mode; set the corresponding parameter range for each operation mode, including the speed, energy consumption and load rate of the workshop conveyor; define the switching logic between different operation modes, including trigger conditions and switching processes; S23. Based on the division results, design corresponding adjustment strategies for different operation modes; specifically including: According to the requirements of different operating modes, the strategy rule types are defined by category; based on each strategy rule type, the associated operating parameters are specified, including the operating parameters of the start-up, operation and stop stages of the workshop conveyor; according to the strategy rule type and the associated operating parameters, the logical conditions for parameter adjustment are set.

2. The method for automatic adjustment of a workshop conveyor based on material transportation data according to claim 1, characterized in that: The material transportation data includes material transportation sequence data and material transportation non-sequence data; Wherein, the material transport sequence data includes: transport speed and transport time; The material transportation non-sequential data includes: transportation load, material flow and transportation energy consumption.

3. The method for automatic adjustment of a workshop conveyor based on material transportation data according to claim 2 is characterized in that: The multimodal data set is constructed based on the collected material transportation data, and the multimodal deep learning network is used to process the multimodal data set to obtain multidimensional transportation features including: S11. Integrate material transportation sequence data and material transportation non-sequence data to construct a multimodal data set; S12. Based on the multimodal deep learning network, the sequence data features of the multimodal data set are extracted using a single-dimensional convolutional layer, the non-sequence data features of the multimodal data set are extracted using a fully connected layer, and the sequence data features and non-sequence data features are fused using a long short-term memory layer. S13. Obtain the output vector of the last hidden layer and normalize it to obtain multi-dimensional transmission features.

4. The method for automatic adjustment of a workshop conveyor based on material transportation data according to claim 3 is characterized in that: The constraint rules for setting the automatic adjustment model of the workshop conveyor include safety operation constraints, efficiency production demand constraints, cost control constraints and conveying rhythm constraints, including: S211. Calculate and set the maximum safe load rate and build safe operation constraints based on the design specifications and historical operation data of the workshop conveyor; S212. Based on the efficiency curve and production demand of the workshop conveyor, set the minimum efficiency load rate and construct the efficiency production demand constraint; S213. Combining the energy consumption pattern and transportation cycle of the workshop conveyor, setting the cost control threshold and constructing the cost control constraint; S214. According to the transportation plan and work schedule of the workshop conveyor, the transportation rhythm is set and the transportation rhythm constraints are constructed.

5. The method for automatic adjustment of a workshop conveyor based on material transportation data according to claim 1, characterized in that: The selecting the current operation mode of the workshop conveyor based on the real-time material flow and the conveying demand, and adjusting the operation parameters of the workshop conveyor under the current operation mode include: S31. Select the current operation mode of the workshop conveyor according to the real-time material flow and transportation demand; S32. Based on constraint rules and aiming at maximizing transportation efficiency, an automatic adjustment model of workshop conveyors in the current operation mode is constructed; S33. Based on the automatic adjustment model of the workshop conveyor, multi-dimensional conveying characteristics are input, and the operating parameters of the adjusted workshop conveyor are output.

6. The method for automatic adjustment of a workshop conveyor based on material transportation data according to claim 5, characterized in that: The automatic adjustment model of the workshop conveyor in the current operation mode is constructed based on constraint rules and with the goal of maximizing transportation efficiency, including: S321, extracting the constraint rules of the automatic adjustment model of the workshop conveyor and constructing the expression of the constraint conditions; S322. According to the adjustment strategy of the current operation mode, the objective function is set to maximize the transportation efficiency, and an expression of the objective function is defined; S323. Based on the constraints and objective function, an automatic adjustment model of the workshop conveyor in the current operation mode is established.

7. The method for automatic adjustment of a workshop conveyor based on material transportation data according to claim 6, characterized in that: The constraint condition is expressed as: ； ； ； ； In the formula, L Indicates the load rate of the workshop conveyor; L max Indicates the maximum safe load rate; L min Indicates the minimum efficiency load rate; p ( t ) represents the energy consumption of the workshop conveyor, C cost Indicates the cost control threshold; v Indicates the conveying speed of the workshop conveyor, v pro Indicates the conveying speed of the workshop conveyor under the conveying beat; The expression of the objective function is: ； In the formula, f ( x ) represents the objective function, which is used to maximize the transportation efficiency while taking into account the control cost; α and β Represents the weight coefficient, which is used to balance the importance of transportation efficiency and cost control; Q material is the material flow rate, which indicates the amount of material transported per unit time; Eenergy is energy consumption, which indicates the energy consumed during the transportation process; Ccurrent The current cost indicates the cost consumption in the current operation mode.

8. A workshop conveyor automatic adjustment system based on material transportation data, used to implement the workshop conveyor automatic adjustment method based on material transportation data according to any one of claims 1 to 7, characterized in that: The workshop conveyor automatic adjustment system based on material transportation data includes: The multi-dimensional transport feature module is used to construct a multi-modal data set based on the collected material transport data, and use the multi-modal deep learning network to process the multi-modal data set to obtain multi-dimensional transport features; The operation mode building module is used to set constraint rules according to the operation parameters and historical data of the workshop conveyor and divide the operation mode of the workshop conveyor; An operation parameter adjustment module is used to select the current operation mode of the workshop conveyor based on the real-time material flow and conveying demand, and adjust the operation parameters of the workshop conveyor under the current operation mode; Among them, the multi-dimensional transport feature module is connected to the operating parameter adjustment module through the operating mode construction module.

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