Production line adaptive control method based on multi-dimensional and multi-modal environmental data

Abstract

The invention discloses a production line adaptive control method based on multi-dimensional and multi-modal environmental data, and the method comprises the steps: collecting original data on a production line in real time according to collection equipment which is installed on the production line in advance, carrying out the preprocessing of the original data, obtaining effective data, carrying out the key feature extraction of the effective data, and carrying out the calculation of the key features of the effective data; acquiring key feature data of the effective data; a machine learning algorithm and a statistical model are adopted to analyze the key feature data, correlation and modes between the data are identified, analysis results are obtained, and the analysis results comprise a production mode classification result and a production demand prediction result; generating an optimal control strategy of the production line according to the analysis result, generating a control instruction according to the optimal control strategy, and issuing the control instruction to the corresponding to-be-controlled mechanical equipment for operation control; and the real-time operation state of the mechanical equipment to be controlled is monitored in real time, a closed-loop control mechanism for control analysis is established according to a monitoring mechanism, and the optimal control strategy of the production line is updated in real time.

Description

Technical Field

[0001] The present invention relates to the field of industrial automation control technology, and more specifically, to a production line adaptive control method based on multi-dimensional and multi-modal environmental data. Background Art

[0002] In modern manufacturing, the efficiency and safety of production lines are crucial to the competitiveness of enterprises. Traditional production line control is mostly based on static parameters and predetermined rules, which is not flexible enough in the face of changing production needs. Especially on production lines involving continuous operations such as material handling and processing, if changes in material supply cannot be responded to in a timely manner, it is easy to lead to low production efficiency, energy waste and even safety accidents. In addition, relying on human experience and manual adjustments is not only inefficient, but also prone to errors, which increases production uncertainty. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a production line adaptive control method based on multi-dimensional and multi-modal environmental data.

[0004] According to one aspect of the present invention, there is provided a production line adaptive control method based on multi-dimensional and multi-modal environmental data, comprising:

[0005] According to the collection equipment pre-installed on the production line, the raw data on the production line is collected in real time, and the raw data is pre-processed to obtain valid data, and key features of the valid data are extracted to obtain key feature data of the valid data;

[0006] Use machine learning algorithms and statistical models to analyze key feature data, identify correlations and patterns between data, and obtain analysis results, including production model classification results and production demand forecast results;

[0007] Generate the optimal control strategy for the production line based on the analysis results, and generate control instructions based on the optimal control strategy and send them to the corresponding mechanical equipment to be controlled for operation control;

[0008] The real-time operating status of the controlled mechanical equipment is monitored in real time, and a closed-loop control mechanism for control analysis is established based on the monitoring organization to update the optimal control strategy of the production line in real time.

[0009] Optionally, the raw data includes cargo box load, discharge port flow data and the operating status of each mechanical equipment, and

[0010] According to the collection equipment pre-installed on the production line, the raw data on the production line is collected in real time, including:

[0011] According to the pressure sensor and flow meter installed on the working vehicle, the cargo box load and discharge port flow data are obtained in real time;

[0012] Based on the sensors installed on each mechanical device in the production line, the operating status of each mechanical device is obtained in real time, where the operating status includes real-time power and working gear.

[0013] Optionally, preprocess the raw data to obtain valid data, including:

[0014] The collected raw data is cleaned, filtered and corrected to remove noise and outliers in the raw data and obtain valid data.

[0015] Optionally, machine learning algorithms and statistical models are used to analyze key feature data, identify correlations and patterns between data, and obtain analysis results, including:

[0016] Perform cluster analysis on key feature data through machine learning algorithms of cluster analysis to obtain the classification results of production mode of the production line;

[0017] The regression model in the statistical model is used to perform regression analysis on key feature data to predict the production demand forecast results of the production line.

[0018] Optionally, an optimal control strategy for the production line is generated based on the analysis results, including:

[0019] According to the production demand of the production line, the production mode classification results and the production demand prediction results, the current control strategy of the production line is adjusted to determine the optimal control strategy.

[0020] According to another aspect of the present invention, there is provided a production line adaptive control device based on multi-dimensional and multi-modal environmental data, comprising:

[0021] The extraction module is used to collect the raw data on the production line in real time according to the collection equipment pre-installed on the production line, and pre-process the raw data to obtain valid data, extract key features from the valid data, and obtain key feature data of the valid data;

[0022] An identification module is used to analyze key feature data using machine learning algorithms and statistical models, identify correlations and patterns between data, and obtain analysis results, where the analysis results include production mode classification results and production demand forecast results;

[0023] The sending module is used to generate the optimal control strategy of the production line according to the analysis results, and generate control instructions according to the optimal control strategy and send them to the corresponding mechanical equipment to be controlled for operation control;

[0024] The update module is used to monitor the real-time operating status of the controlled mechanical equipment in real time, establish a closed-loop control mechanism for control analysis based on the monitoring mechanism, and update the optimal control strategy of the production line in real time.

[0025] Optionally, the raw data includes cargo box load, discharge port flow data and the operating status of each mechanical equipment, and

[0026] The extraction module collects raw data from the production line in real time based on the collection equipment pre-installed on the production line, including:

[0027] The first acquisition submodule is used to obtain the cargo box load and discharge port flow data in real time based on the pressure sensor and flow meter installed on the working vehicle;

[0028] The second acquisition submodule is used to obtain the operating status of each mechanical device monitored in real time based on the sensors installed on each mechanical device of the production line, wherein the operating status includes real-time power and working gear.

[0029] Optionally, the extraction module preprocesses the raw data to obtain valid data, including:

[0030] The processing submodule is used to clean, filter and correct the collected raw data, remove noise and outliers in the raw data, and obtain valid data.

[0031] Optionally, the identification module includes:

[0032] The clustering submodule is used to perform cluster analysis on key feature data through the machine learning algorithm of cluster analysis to obtain the production mode classification results of the production line;

[0033] The regression submodule is used to perform regression analysis on key feature data through the regression model in the statistical model to predict the production demand forecast results of the production line.

[0034] Optionally, the sending module generates an optimal control strategy for the production line according to the analysis results, including:

[0035] The adjustment submodule is used to adjust the current control strategy of the production line according to the production demand of the production line, the production mode classification results and the production demand prediction results, and determine the optimal control strategy.

[0036] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the method described in any one of the above aspects of the present invention.

[0037] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of the above aspects of the present invention.

[0038] Therefore, compared with the existing grain temperature prediction model that only takes into account the spatial structured characteristics or the temporal characteristics of grain temperature, the present invention takes into account both the spatial structured characteristics and the temporal characteristics of grain temperature, thereby suppressing the influence of a single attribute feature on the grain temperature prediction result. The present invention proposes a three-dimensional encoding method for grain temperature data, and inputs the three-dimensional grain temperature data into 3D U-Net to improve the prediction accuracy of grain temperature. By integrating the LSTM structure, the present invention adds a time dimension to the grain temperature prediction process. Through real-time monitoring and intelligent adjustment, the production line is always ensured to be in the best operating state and maximize production efficiency. Potential dangers caused by equipment overload or material accumulation are reduced, and the safety of the production process is improved. Unnecessary energy loss is avoided, which helps enterprises achieve the goal of green production. The reliance on manual experience is reduced, human errors are reduced, and human resources are saved. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0040] Figure 1 is a flow chart of a production line adaptive control method based on multi-dimensional and multi-modal environmental data provided by an exemplary embodiment of the present invention;

[0041] Figure 2 is a schematic structural diagram of a production line adaptive control device based on multi-dimensional and multi-modal environmental data provided by an exemplary embodiment of the present invention;

[0042] Figure 3 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0043] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0044] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0045] Those skilled in the art can understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0046] It should also be understood that, in the embodiments of the present invention, “plurality” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[0047] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0048] In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0049] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0050] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0051] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0052] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0053] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0054] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0055] Electronic devices such as terminal devices, computer systems, servers, etc. can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0056] Exemplary Methods

[0057] Figure 1 is a flow chart of a production line adaptive control method based on multi-dimensional and multi-modal environmental data provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the production line adaptive control method 100 based on multi-dimensional and multi-modal environmental data includes the following steps:

[0058] Step 101, using a collection device pre-installed on the production line, collect raw data on the production line in real time, pre-process the raw data to obtain valid data, extract key features from the valid data, and obtain key feature data of the valid data;

[0059] Step 102, using machine learning algorithms and statistical models to analyze key feature data, identify correlations and patterns between data, and obtain analysis results, where the analysis results include production mode classification results and production demand forecast results;

[0060] Step 103, generating an optimal control strategy for the production line according to the analysis results, and generating control instructions according to the optimal control strategy and sending them to the corresponding mechanical equipment to be controlled for operation control;

[0061] Step 104 , real-time monitoring of the real-time operating status of the mechanical equipment to be controlled is performed, and a closed-loop control mechanism for control analysis is established based on the monitoring mechanism to update the optimal control strategy of the production line in real time.

[0062] Specifically, the present invention aims to solve the problems existing in the prior art and provide a production line adaptive control method and system based on multi-dimensional and multi-modal environmental data. The method can collect and analyze key data such as the cargo box load and discharge port flow of the operating vehicle in real time, as well as the operating status of each mechanical equipment in the production line (such as power, working mode, etc.), and use a preset control model to intelligently decide the operating status of the production line, including but not limited to adjusting the operating efficiency of the production line, controlling the start and stop timing of mechanical equipment, etc. The present invention can effectively improve the flexibility and response speed of the production line, optimize resource allocation, reduce energy waste, and ensure production safety. The specific technical solution is as follows:

[0063] 1. Data acquisition module: includes pressure sensors and flow meters installed on the operating vehicle, which are used to obtain real-time data on cargo box load and discharge port flow; also includes sensors installed on various mechanical equipment on the production line, which are used to monitor the operating status of the equipment, such as real-time power, working gear, etc.

[0064] 2. Data processing and analysis module:

[0065] oData preprocessing: Clean, filter and correct the collected raw data to remove noise and outliers to ensure the accuracy and reliability of the data.

[0066] o Feature extraction: Extract key features from preprocessed data, such as the changing trend of cargo box load, fluctuation of discharge port flow, power curve of mechanical equipment, etc.

[0067] o Data analysis: Use machine learning algorithms and statistical models to analyze the extracted features and identify correlations and patterns between data. For example, clustering algorithms can be used to classify different types of production patterns, and regression models can be used to predict future production needs.

[0068] oDecision generation: Generate the optimal control strategy based on the analysis results. For example, when a sudden increase in the weight of the cargo box is detected, the system will automatically adjust the speed of the conveyor to match the new production demand; when the energy consumption of a certain production line is detected to be too high, the system will adjust the working mode of the production line to reduce energy consumption.

[0069] 3. Control execution module:

[0070] oInstruction generation: Generate specific control instructions based on the control strategy generated by the data processing and analysis module, such as starting or stopping a certain mechanical equipment, adjusting the speed of the conveyor, etc.

[0071] oInstruction transmission: The generated control instructions are transmitted to the corresponding mechanical equipment through the communication network to ensure the timeliness and accuracy of the instructions.

[0072] oCondition monitoring: Real-time monitoring of the execution status of mechanical equipment to ensure that it operates as expected. If any abnormality is found, immediate measures are taken to correct it, such as resending control instructions or alarming to notify operators.

[0073] oFeedback mechanism: Establish a closed-loop control mechanism to feed back the actual operating status of the mechanical equipment to the data processing and analysis module to form a continuously optimized control loop. For example, if a mechanical equipment fails to execute the control instruction as expected, the system will automatically adjust the control strategy and regenerate a new control instruction.

[0074] 4. Configuration management module: stores basic data such as production line structure, composition information, equipment parameters, etc., and provides necessary context information for data processing and analysis modules.

[0075] In one embodiment of the present invention, it is assumed that there is a transport production line consisting of three bulk cargo conveyors. When the operating vehicle is successfully docked with the production line, the upper computer receives a signal of successful docking and then sends a production line start instruction to the control system. The control system starts the three conveyors in sequence from the docking end to the end according to the predetermined logical sequence based on the production line configuration information stored in the configuration management module. At the same time, the control system continuously receives and processes real-time data from the vehicle and production line equipment, including the real-time load of the cargo box, the real-time flow of the discharge port, the real-time power of the conveyor, etc. Through the built-in algorithm model, the control system can calculate the ratio of the vehicle-side unit efficiency to the production line unit efficiency. If it is found that the vehicle-side efficiency is higher than the current processing capacity of the production line, that is, the ratio is greater than 1, the control system will automatically take measures to improve the operating efficiency of the production line, such as speeding up the conveyor or adding additional conveying paths to ensure the smooth progress of the production process. This process not only improves the overall efficiency of the production line, but also effectively avoids problems such as material spillage caused by the mismatch between the front and rear end efficiencies, reduces the risk of safety accidents, and also reduces the waste of human resources.

[0076] The beneficial effects of the present invention are as follows:

[0077] (1) Compared with the existing grain temperature prediction model that only takes into account the spatial structured characteristics or the temporal characteristics of grain temperature, the present invention takes into account both the spatial structured characteristics and the temporal characteristics of grain temperature, thereby suppressing the influence of a single attribute feature on the grain temperature prediction result.

[0078] (2) A three-dimensional encoding method for grain temperature data is proposed, and the three-dimensional grain temperature data is input into 3D U-Net to improve the prediction accuracy of grain temperature.

[0079] (3) The present invention adds a time dimension to the grain temperature prediction process by integrating the LSTM structure.

[0080] Improve production efficiency: Through real-time monitoring and intelligent adjustment, ensure that the production line is always in the best operating state and maximize production efficiency.

[0081] Enhanced safety: Reduces potential hazards caused by equipment overload or material accumulation, and improves the safety of the production process.

[0082] Energy saving and emission reduction: Avoiding unnecessary energy loss helps companies achieve green production goals.

[0083] Reduce labor costs: Reduce dependence on manual experience, reduce human errors, and save human resources.

[0084] Exemplary Devices

[0085] Figure 2 FIG. 1 is a schematic diagram of a structure of a production line adaptive control device based on multi-dimensional and multi-modal environmental data provided by an exemplary embodiment of the present invention. Figure 2 As shown, the device 200 includes:

[0086] The extraction module 210 is used to collect the raw data on the production line in real time according to the collection equipment pre-installed on the production line, and pre-process the raw data to obtain valid data, extract key features from the valid data, and obtain key feature data of the valid data;

[0087] Identification module 220, used to analyze key feature data using machine learning algorithms and statistical models, identify correlations and patterns between data, and obtain analysis results, wherein the analysis results include production mode classification results and production demand forecast results;

[0088] The sending module 230 is used to generate an optimal control strategy for the production line according to the analysis results, and generate control instructions according to the optimal control strategy and send them to the corresponding mechanical equipment to be controlled for operation control;

[0089] The updating module 240 is used to monitor the real-time operating status of the controlled mechanical equipment in real time, establish a closed-loop control mechanism for control analysis based on the monitoring mechanism, and update the optimal control strategy of the production line in real time.

[0090] Optionally, the raw data includes cargo box load, discharge port flow data and the operating status of each mechanical equipment, and

[0091] The extraction module 210 collects raw data on the production line in real time based on the collection equipment pre-installed on the production line, including:

[0092] The first acquisition submodule is used to obtain the cargo box load and discharge port flow data in real time based on the pressure sensor and flow meter installed on the working vehicle;

[0093] The second acquisition submodule is used to obtain the operating status of each mechanical device monitored in real time based on the sensors installed on each mechanical device of the production line, wherein the operating status includes real-time power and working gear.

[0094] Optionally, the extraction module 210 pre-processes the original data to obtain valid data, including:

[0095] The processing submodule is used to clean, filter and correct the collected raw data, remove noise and outliers in the raw data, and obtain valid data.

[0096] Optionally, the identification module 220 includes:

[0097] The clustering submodule is used to perform cluster analysis on key feature data through the machine learning algorithm of cluster analysis to obtain the production mode classification results of the production line;

[0098] The regression submodule is used to perform regression analysis on key feature data through the regression model in the statistical model to predict the production demand forecast results of the production line.

[0099] Optionally, the sending module 230 generates an optimal control strategy for the production line according to the analysis results, including:

[0100] The adjustment submodule is used to adjust the current control strategy of the production line according to the production demand of the production line, the production mode classification results and the production demand prediction results, and determine the optimal control strategy.

[0101] Exemplary Electronic Devices

[0102] Figure 3 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. Figure 3 As shown, the electronic device 30 includes one or more processors 31 and a memory 32 .

[0103] The processor 31 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0104] The memory 32 may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 31 may run the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may also include: an input device 33 and an output device 34, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0105] In addition, the input device 33 may also include, for example, a keyboard, a mouse, and the like.

[0106] The output device 34 can output various information to the outside. The output device 34 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0107] Of course, to simplify, Figure 3 Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0108] Exemplary computer program products and computer-readable storage media

[0109] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above-mentioned "Exemplary Method" section of this specification.

[0110] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0111] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0112] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0113] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details disclosed above are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0114] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0115] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The word "such as" used here refers to the phrase "such as but not limited to", and can be used interchangeably with it.

[0116] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware or any combination of software, hardware, firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present invention are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

[0117] It should also be noted that in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in the field to make or use the present invention. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but in accordance with the widest range consistent with the principles and novel features disclosed here.

[0118] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A production line adaptive control method based on multi-dimensional and multi-modal environmental data, characterized in that: include: According to the collection equipment pre-installed on the production line, the raw data on the production line is collected in real time, and the raw data is pre-processed to obtain valid data, and key features are extracted from the valid data to obtain key feature data of the valid data; Using machine learning algorithms and statistical models to analyze the key feature data, identify correlations and patterns between the data, and obtain analysis results, wherein the analysis results include production mode classification results and production demand forecast results; Generate an optimal control strategy for the production line according to the analysis results, and generate control instructions based on the optimal control strategy and send them to the corresponding mechanical equipment to be controlled for operation control; The real-time operating status of the mechanical equipment to be controlled is monitored in real time, and a closed-loop control mechanism for control analysis is established based on the monitoring mechanism to update the optimal control strategy of the production line in real time.

2. The method according to claim 1, characterized in that The raw data includes cargo box load, discharge port flow data and the operating status of each mechanical equipment, and According to the collection equipment pre-installed on the production line, the raw data on the production line is collected in real time, including: According to the pressure sensor and flow meter installed on the working vehicle, the cargo box load and the discharge port flow data are obtained in real time; According to the sensors installed on each mechanical device of the production line, the operating status of each mechanical device is obtained in real time, wherein the operating status includes real-time power and working gear.

3. The method according to claim 1, characterized in that Preprocessing the raw data to obtain valid data includes: The collected raw data is cleaned, filtered and corrected to remove noise and abnormal values ​​in the raw data and obtain the valid data.

4. The method according to claim 1, characterized in that: The key feature data are analyzed using machine learning algorithms and statistical models to identify correlations and patterns between the data and obtain analysis results, including: Performing cluster analysis on the key feature data by using a machine learning algorithm of cluster analysis to obtain the production mode classification result of the production line; The key feature data is subjected to regression analysis by means of the regression model in the statistical model to predict the production demand prediction result of the production line.

5. The method according to claim 1 or 4, characterized in that: Generating an optimal control strategy for the production line according to the analysis results, including: The current control strategy of the production line is adjusted according to the production demand of the production line, the production mode classification result and the production demand prediction result to determine the optimal control strategy.

6. A production line adaptive control device based on multi-dimensional and multi-modal environmental data, characterized in that: include: An extraction module is used to collect raw data on the production line in real time according to a collection device pre-installed on the production line, and pre-process the raw data to obtain valid data, extract key features from the valid data, and obtain key feature data of the valid data; An identification module, used to analyze the key feature data using a machine learning algorithm and a statistical model, identify the correlation and pattern between the data, and obtain analysis results, wherein the analysis results include a production mode classification result and a production demand forecast result; A sending module, used to generate an optimal control strategy for the production line according to the analysis results, and generate control instructions according to the optimal control strategy and send them to the corresponding mechanical equipment to be controlled for operation control; The updating module is used to monitor the real-time operating status of the mechanical equipment to be controlled in real time, establish a closed-loop control mechanism for control analysis based on the monitoring mechanism, and update the optimal control strategy of the production line in real time.

7. The device according to claim 6, characterized in that The raw data includes cargo box load, discharge port flow data and the operating status of each mechanical equipment, and The extraction module collects the raw data on the production line in real time based on the collection equipment pre-installed on the production line, including: A first acquisition submodule is used to acquire the cargo box load and the discharge port flow data in real time according to a pressure sensor and a flow meter installed on the working vehicle; The second acquisition submodule is used to acquire the operating status of each mechanical device monitored in real time based on sensors installed on each mechanical device of the production line, wherein the operating status includes real-time power and working gear.

8. The device according to claim 6, characterized in that The extraction module pre-processes the raw data to obtain valid data, including: The processing submodule is used to clean, filter and correct the collected raw data, remove noise and abnormal values ​​in the raw data, and obtain the valid data.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 5.

10. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1 to 5.

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