Equipment operation control optimization method and system based on artificial intelligence

Abstract

The invention discloses an equipment operation control optimization method and system based on artificial intelligence, and the method comprises the steps: determining an operation requirement of operation equipment, and carrying out the analysis to determine a specific operation requirement standard; acquiring operation data of the operation equipment, and comprehensively analyzing the operation data and the operation requirement standard to determine operation difference characteristics; determining a corresponding operation control strategy from a control strategy library based on the operation difference characteristics, and performing evaluation based on artificial intelligence; optimizing the operation control strategy based on the evaluation result, controlling the operation of the operation equipment according to the optimized control strategy, and storing the control strategy in a control strategy library; and obtaining an operation result of the operation equipment, and determining further operation requirements of the operation equipment based on the operation result. According to the invention, intelligent management and control optimization of equipment operation can be realized, the operation efficiency and performance of the equipment are improved, the failure rate is reduced, and the maintenance cost is reduced, so that higher production efficiency and economic benefits are brought to enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment operation optimization, and in particular to an equipment operation control optimization method and system based on artificial intelligence. Background Art

[0002] With the continuous development and popularization of artificial intelligence technology, more and more industries are beginning to apply it to equipment operation optimization. AI-based equipment operation optimization methods use technologies such as big data analysis, machine learning, and intelligent algorithms to achieve real-time monitoring, analysis, and optimization of equipment operating status, thereby improving equipment efficiency, performance, and reliability.

[0003] Traditional equipment operation optimization methods usually require manual intervention and experience accumulation, and have the disadvantages of low efficiency and large limitations. However, artificial intelligence-based methods can quickly and accurately identify problems and optimization space in equipment operation through automated data collection, analysis and decision-making processes, and provide intelligent solutions. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides an artificial intelligence-based equipment operation control optimization method and system, comprising: Determine the operating requirements of the equipment, analyze the operating requirements, and determine the specific operating requirement standards; Obtain the operating data of the operating equipment, conduct a comprehensive analysis of the operating data and operating requirements and standards, and determine the operating difference characteristics of the operating equipment; Determine the corresponding operation control strategy from the control strategy library based on the operation difference characteristics, and evaluate the operation control strategy based on artificial intelligence; Optimize the operation control strategy based on the evaluation results, control the operation of the operating equipment according to the optimized control strategy, and save the optimized control strategy to the control strategy library; Obtain the operating results of the operating equipment and determine further operating requirements of the operating equipment based on the operating results.

[0005] Furthermore, the operation requirements of the operating equipment are determined, and the operation requirements are analyzed to determine specific operation requirement standards, including: determining an operating requirement for the operating device, and determining a first operating parameter included in the operating requirement for the operating device; Based on the first operating parameter, a parameter requirement range corresponding to each first operating parameter is determined from the operating requirement, and the operating requirement standard is obtained by combining the first operating parameter and the parameter requirement range corresponding to the first operating parameter.

[0006] Furthermore, the acquisition of operating data of the operating equipment and the comprehensive analysis of the operating data and the operating requirements and standards to determine the operating difference characteristics of the operating equipment include: Acquiring operating data of the operating device and determining a second operating parameter included in the operating data of the operating device; Decomposing the operation requirement standard into a first operation parameter and a parameter requirement range corresponding to the first operation parameter, and dividing the operation data into a plurality of data groups according to the second operation parameter; Matching the first operating parameter and the second operating parameter with each other according to the parameter type, and according to the matching result, corresponding the parameter requirement range and the data group; Filtering the data in the data group that is not within the parameter requirement range to obtain difference data, and determining the minimum difference between the difference data and the parameter requirement range; The second operating parameter including the difference data and the minimum difference between the difference data and the parameter requirement range are determined as the operating difference characteristic of the operating device.

[0007] Furthermore, the determining of the corresponding operation control strategy from the control strategy library based on the operation difference characteristics includes: determining a second operating parameter containing difference data from the operating difference characteristic, and determining a parameter type of the second operating parameter containing the difference data; Determine a strategy corresponding to the parameter type from a control strategy library as a candidate operation control strategy; Determine the minimum difference between the difference data and the parameter requirement range from the operation difference characteristics, and calculate the average value of the minimum difference between all difference data and the parameter requirement range to obtain the comprehensive average value; A preset average value corresponding to each candidate operation control strategy is determined, and a candidate operation control strategy whose comprehensive average value is less than the corresponding preset average value is determined as the operation control strategy corresponding to the operation difference feature.

[0008] Furthermore, the evaluation of the operation control strategy includes: Obtain a pre-set artificial intelligence strategy simulation model, simulate the operation control strategy according to the artificial intelligence strategy simulation model, and evaluate the operation control strategy based on the simulation results.

[0009] Furthermore, the operation control strategy is optimized based on the evaluation result, the operation of the operating equipment is controlled according to the optimized control strategy, and the optimized control strategy is saved in the control strategy library, including: A correspondence between an artificial intelligence optimization method and an evaluation value interval is pre-set, wherein the correspondence between the artificial intelligence optimization method and the evaluation value interval is associated with a corresponding artificial intelligence optimization method for each evaluation value interval; Obtaining an evaluation value of the operation control strategy after simulation by the artificial intelligence strategy simulation model, and based on a mapping relationship between the evaluation value interval to which the evaluation value belongs and the artificial intelligence optimization method-evaluation value interval correspondence relationship, selecting the optimization method corresponding to the evaluation value interval as the artificial intelligence optimization method corresponding to the operation control strategy; The operation of the running equipment is controlled according to the optimized control strategy, and the optimized control strategy is saved in the control strategy library.

[0010] Furthermore, obtaining the operating result of the operating device and determining further operating requirements of the operating device based on the operating result includes: Obtaining an operation result of the operation device, and determining from the operation result each second operation parameter still containing difference data after optimization, and determining a second amount of difference data in the data group corresponding to the second operation parameter; Determining a first quantity of difference data in the data group corresponding to the second operating parameter before optimization, and determining an adjustment coefficient based on the first quantity and the second quantity; The calculation formula of the adjustment coefficient is: K=a*(S1-S2), Wherein, K is the adjustment coefficient, a is the preset conversion coefficient, S1 is the first quantity, and S2 is the second quantity; The corresponding parameter requirement range before optimization is adjusted according to the corresponding adjustment coefficient to obtain further operation requirements of the operating equipment.

[0011] The present invention also provides an artificial intelligence-based equipment operation control optimization system, comprising: The determination module is used to determine the operating requirements of the operating equipment, analyze the operating requirements, and determine the specific operating requirement standards; The analysis module is used to obtain the operating data of the operating equipment, conduct a comprehensive analysis of the operating data and the operating requirements and standards, and determine the operating difference characteristics of the operating equipment; An evaluation module is used to determine the corresponding operation control strategy from the control strategy library based on the operation difference characteristics, and evaluate the operation control strategy based on artificial intelligence; An optimization module is used to optimize the operation control strategy based on the evaluation results, control the operation of the operating equipment according to the optimized control strategy, and save the optimized control strategy to the control strategy library; The acquisition module is used to obtain the operation results of the operation equipment and determine further operation requirements of the operation equipment based on the operation results.

[0012] Compared with the prior art, the device operation control optimization method and system based on artificial intelligence in the embodiment of the present invention have the following beneficial effects: The present invention collects real-time operating data and compares and analyzes it with the equipment operating requirements, determines the operational difference characteristics, selects the corresponding operation control strategy, and optimizes the operation control strategy through artificial intelligence and applies it to the equipment. This can achieve precise control of equipment operation, improve equipment performance and efficiency, optimize the control strategy, realize a data-driven decision-making process, and improve the accuracy and reliability of decision-making; By continuously optimizing control strategies and adjusting operating requirements based on actual operating results, the present invention can achieve continuous improvement and optimization, allowing equipment to maintain an optimal state in a constantly changing environment. It can also achieve intelligent management and optimization of equipment operation, improve the efficiency and performance of operating equipment, reduce failure rates, and lower maintenance costs, thereby bringing higher production efficiency and economic benefits to enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 1 is a schematic diagram of the process structure of the device operation control optimization method based on artificial intelligence in an embodiment of the present invention; Figure 2 Schematic diagram of the composition of the equipment operation control optimization system based on artificial intelligence in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0015] In the description of this application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the platform or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on this application.

[0016] The terms "second" and "second" are used for descriptive purposes only and should not be understood as indicating or implying a relative degree of importance or implicitly indicating the number of the indicated technical features. Therefore, a feature specified with "second" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0017] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood broadly. For example, they can refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Persons of ordinary skill in the art will understand the specific meanings of the above terms in this application based on specific circumstances.

[0018] like Figure 1 As shown, in an embodiment of the present application, an artificial intelligence-based equipment operation control optimization method is provided, including: S100: determining the operation requirements of the operating equipment, and parsing the operation requirements to determine specific operation requirement standards; S200: obtaining the operation data of the operating equipment, and comprehensively analyzing the operation data and the operation requirement standards to determine the operation difference characteristics of the operating equipment; S300: determining the corresponding operation control strategy from the control strategy library based on the operation difference characteristics, and evaluating the operation control strategy based on artificial intelligence; S400: optimizing the operation control strategy based on the evaluation result, controlling the operation of the operating equipment according to the optimized control strategy, and saving the optimized control strategy to the control strategy library; S500: obtaining the operation result of the operating equipment, and determining further operation requirements of the operating equipment based on the operation result.

[0019] Furthermore, the present invention collects real-time operating data and compares and analyzes it with the equipment operating requirements to determine the operating difference characteristics, so as to select the corresponding operating control strategy, and optimizes the operating control strategy through artificial intelligence and applies it to the equipment, thereby achieving precise control of equipment operation, improving equipment performance and efficiency, optimizing the control strategy, realizing a data-driven decision-making process, and improving the accuracy and reliability of decision-making; the present invention continuously optimizes the control strategy and adjusts the operating requirements according to the actual operating results, thereby achieving continuous improvement and optimization, so that the equipment can maintain the best state in a constantly changing environment, and can realize intelligent management and optimization of equipment operation, improve the efficiency and performance of operating equipment, reduce failure rates, and reduce maintenance costs, thereby bringing higher production efficiency and economic benefits to the enterprise.

[0020] In an embodiment of the present application, an artificial intelligence-based equipment operation control optimization method is provided, which determines the operation requirements of the operating equipment, analyzes the operation requirements, and determines specific operation requirement standards, including: determining the operation requirements of the operating equipment, and determining the first operating parameters contained in the operation requirements of the operating equipment; determining the parameter requirement range corresponding to each first operating parameter from the operation requirements based on the first operating parameter, and obtaining the operation requirement standard by combining the first operating parameter and the parameter requirement range corresponding to the first operating parameter.

[0021] Specifically, the operating requirements of operating equipment refer to the various technical indicators and parameter requirements that the equipment needs to meet during operation; the first operating parameters are parameters that are important and prioritized in the operating requirements, such as power, temperature, pressure, speed, etc. These parameters are the basis for ensuring the normal operation of the equipment and achieving the expected performance. The parameter requirement range corresponding to the first operating parameters has a significant impact on the equipment performance and operating status. For example, for an engine, the speed is an important first operating parameter, and for each first operating parameter, it is necessary to determine its corresponding parameter requirement range, that is, the minimum and maximum value range allowed for the parameter. For example, for the first operating parameter of speed, there is a reasonable speed range; the first operating parameters and their corresponding parameter requirement ranges are combined to form a complete operating requirement standard. These standards will become the basis for subsequent optimization and monitoring to ensure that the equipment operates stably within the specified range. This step can achieve accurate monitoring and control of the equipment's operating status by determining the operating requirements and parameter requirement ranges, ensuring that the equipment operates in a safe and stable state, and improving production efficiency and quality; setting reasonable operating requirement standards can help predict possible problems with the equipment, perform maintenance and repairs in a timely manner, reduce equipment failure rates, and extend equipment life; through operating requirement standards, a data-driven decision-making process is implemented, and equipment operating parameters are adjusted and optimized based on actual data, improving the accuracy and effectiveness of decision-making.

[0022] In an embodiment of the present application, an artificial intelligence-based equipment operation control optimization method is provided, which obtains operation data of the operating equipment, and conducts a comprehensive analysis of the operation data and operation requirement standards to determine the operation difference characteristics of the operating equipment, including: obtaining the operation data of the operating equipment, and determining a second operation parameter contained in the operation data of the operating equipment; decomposing the operation requirement standard into a first operation parameter and a parameter requirement range corresponding to the first operation parameter, and dividing the operation data into several data groups according to the second operation parameter; matching the first operation parameter with the second operation parameter according to the parameter type, and corresponding the corresponding parameter requirement range with the data group according to the matching result; filtering the data in the data group that is not within the parameter requirement range to obtain difference data, and determining the minimum difference between the difference data and the parameter requirement range; determining the second operation parameter containing the difference data and the minimum difference between the difference data and the parameter requirement range as the operation difference characteristics of the operating equipment.

[0023] Specifically, the operating data is obtained through sensors, monitoring equipment, etc., and these data reflect the status and performance of the equipment during operation; the second operating parameters are parameters contained in the operating data that need to be focused on, and are closely related to the equipment performance, operating status, etc.; the operating requirement standards are decomposed into the first operating parameters and the parameter requirement range corresponding to the first operating parameters to ensure that the requirements of each parameter are clear and unambiguous; according to the second operating parameters, the operating data are divided into several data groups for subsequent comparative analysis and processing; the first operating parameters and the second operating parameters are matched with each other according to the parameter type, and the parameter requirement range is matched with the data group according to the matching results to establish an association relationship; the data that does not meet the parameter requirement range is filtered out in the data group to obtain difference data, that is, data that does not meet the operating requirement standards; finally, the minimum difference with the parameter requirement range is determined, and the second operating parameter containing the difference data and the minimum difference between the difference data and the parameter requirement range are determined as the operating difference characteristics of the operating equipment. This step can achieve real-time monitoring of the equipment's operating status through real-time acquisition and analysis of operating data, promptly identify problems, and take measures to make adjustments; through analysis and comparison of differential data, problems and differences in equipment operation can be accurately diagnosed, providing a basis for subsequent optimization; based on the operational difference characteristics, equipment parameters and control strategies can be adjusted in a targeted manner to achieve data-driven equipment operation optimization and improve equipment performance and efficiency; by identifying differential data and problems, preventive maintenance measures can be implemented to reduce equipment failure rates, extend equipment life, and improve equipment reliability and stability.

[0024] In an embodiment of the present application, an artificial intelligence-based equipment operation control optimization method is provided, which determines the corresponding operation control strategy from the control strategy library based on the operation difference characteristics, including: determining a second operation parameter containing difference data from the operation difference characteristics, and determining the parameter type of the second operation parameter containing difference data; determining the strategy corresponding to the parameter type from the control strategy library as a candidate operation control strategy; determining the minimum difference between the difference data and the parameter requirement range from the operation difference characteristics, and calculating the average value of the minimum difference between all the difference data and the parameter requirement range to obtain a comprehensive average value; determining the preset average value corresponding to each candidate operation control strategy, and determining the candidate operation control strategy whose comprehensive average value is less than the corresponding preset average value as the operation control strategy corresponding to the operation difference characteristics.

[0025] Specifically, the operation difference characteristics are used to determine which parameters contain difference data, and then the types of these parameters are determined; the strategies corresponding to the determined parameter types are found from the control strategy library and used as candidate operation control strategies. These strategies are usually operation control schemes for different parameter types; the minimum difference between the difference data and the parameter requirement range is determined through the operation difference characteristics, and the average value of the minimum difference between all difference data and the parameter requirement range is calculated to obtain a comprehensive average value; the preset average value corresponding to each candidate operation control strategy is determined, that is, the expected difference range set for different strategies; the comprehensive average value is compared with the preset average value of each candidate operation control strategy, and the candidate operation control strategy with a comprehensive average value less than the corresponding preset average value is selected as the final operation control strategy corresponding to the operation difference characteristics. This step realizes an intelligent decision-making process by identifying problem parameters from the operation difference characteristics and automatically selecting the appropriate operation control strategy, thereby improving the efficiency and accuracy of operation management; based on the comparison of difference data and parameter requirement range, it realizes real-time monitoring and optimization adjustment of the equipment operation status to ensure that the equipment operates in the best state; it automatically determines the best operation control strategy, reduces the need for manual intervention and decision-making, improves the level of automated operation and maintenance of the equipment, and reduces operation and maintenance costs; through continuous comparison and adjustment of operation control strategies, it realizes continuous improvement and optimization of equipment operation performance, improves the stability and reliability of the equipment, and extends the service life of the equipment.

[0026] In an embodiment of the present application, an artificial intelligence-based equipment operation control optimization method is provided, and the evaluation of the operation control strategy includes: obtaining a pre-set artificial intelligence strategy simulation model, simulating the operation control strategy according to the artificial intelligence strategy simulation model, and evaluating the operation control strategy according to the simulation results.

[0027] Specifically, the artificial intelligence strategy simulation model is a pre-set model used to simulate and evaluate different operational control strategies. This model may be built based on technologies such as machine learning and deep learning, and can simulate various situations and changes during equipment operation. The artificial intelligence strategy simulation model is used to simulate the selected operational control strategy to simulate the performance and effect of the strategy in actual operation. Based on the simulation results, the operational control strategy is evaluated and valued to determine the pros and cons and applicability of the strategy. This step, through simulation, can more accurately evaluate the effects and impacts of different operational control strategies, providing an objective basis for decision-making. Through simulation evaluation before actual application, the risks brought about by trying new strategies can be reduced and possible losses can be avoided. Simulation can help quickly test and verify multiple possibilities, saving time and resource costs and improving work efficiency.

[0028] In an embodiment of the present application, an artificial intelligence-based equipment operation control optimization method is provided, which optimizes the operation control strategy based on the evaluation result, controls the operation of the operating equipment according to the optimized control strategy, and saves the optimized control strategy in the control strategy library, including: pre-setting the artificial intelligence optimization method-evaluation value interval correspondence, wherein the artificial intelligence optimization method-evaluation value interval correspondence is associated with a corresponding artificial intelligence optimization method for each evaluation value interval; obtaining the evaluation value of the operation control strategy after simulation by the artificial intelligence strategy simulation model, and based on the mapping relationship between the evaluation value interval to which the evaluation value belongs within the artificial intelligence optimization method-evaluation value interval correspondence, selecting the optimization method corresponding to the evaluation value interval as the artificial intelligence optimization method corresponding to the operation control strategy; controlling the operation of the operating equipment according to the optimized control strategy, and saving the optimized control strategy in the control strategy library.

[0029] Specifically, a set of artificial intelligence optimization methods and corresponding evaluation value intervals are pre-set, and each evaluation value interval is associated with a specific artificial intelligence optimization method; after simulation of the artificial intelligence strategy simulation model, the evaluation value of the operation control strategy is obtained, and these evaluation values can reflect the performance of the strategy in the simulation environment; according to the evaluation value interval to which the evaluation value belongs, the optimization method corresponding to the interval is found in the artificial intelligence optimization method-evaluation value interval correspondence, and this optimization method is selected as the artificial intelligence optimization method corresponding to the operation control strategy; the control strategy is optimized using the selected artificial intelligence optimization method, and then the optimized control strategy is applied to control the operation of the running equipment to improve the performance, efficiency and stability of the equipment; the optimized control strategy is saved in the control strategy library for future use and reference. This step automatically selects the most suitable optimization method based on the mapping relationship between the evaluation value intervals, realizes the automated optimization of the operation control strategy, and improves the efficiency and performance of the system; different optimization methods are selected for different evaluation value intervals, realizing personalized optimization strategies and ensuring targeted improvements to operation control; using artificial intelligence optimization methods, the system can intelligently select the optimization method that best suits the current situation, improving the intelligence level of decision-making; by continuously optimizing the control strategy and saving it to the control strategy library, the system can achieve continuous improvement and optimization, ensuring that the equipment operation control strategy is always in the best state; saving the optimized control strategy to the library accumulates rich optimization experience and knowledge, providing valuable reference and reference for future decision-making.

[0030] In an embodiment of the present application, an artificial intelligence-based device operation control optimization method is provided, wherein the method of obtaining the operation results of the operating device and determining further operation requirements of the operating device based on the operation results includes: obtaining the operation results of the operating device, and determining from the operation results each second operating parameter that still contains difference data after optimization, and determining a second amount of difference data in a data group corresponding to the second operating parameter; determining a first amount of difference data in a data group corresponding to the second operating parameter before optimization, and determining an adjustment coefficient based on the first amount and the second amount; the calculation formula of the adjustment coefficient is: K=a*(S1-S2), Among them, K is the adjustment coefficient, a is the preset conversion coefficient, S1 is the first quantity, and S2 is the second quantity; according to the corresponding adjustment coefficient, the corresponding parameter requirement range before optimization is adjusted to obtain further operation requirements of the operating equipment.

[0031] Specifically, the second operating parameter that still contains difference data after each optimization is determined from the operating results, and the number of difference data in the data group corresponding to the parameter is determined; first, the number of difference data in the data group corresponding to the second operating parameter before optimization is determined, which is defined as a first number, and then an adjustment coefficient is calculated based on the first number and the second number; based on the calculated adjustment coefficient, the corresponding parameter requirement range before optimization is adjusted. By adjusting the parameter range, the system can better adapt to the actual operating conditions and improve the stability and performance of the system. This step calculates the adjustment coefficient based on the actual operating data, which can achieve real-time adjustment of system parameters, so that the system can respond to changes and abnormal situations in a timely manner; by analyzing the difference data and calculating the adjustment coefficient, the system parameter requirement range can be finely adjusted to improve the adaptability and performance of the system; the parameter requirement range is dynamically adjusted according to the changes in the difference data, which enhances the adaptability of the system and enables it to better adapt to different working conditions and environments; by calculating the adjustment coefficient and adjusting the parameter range, the performance difference of the system before and after optimization can be evaluated to help further improve and optimize the system; by adjusting the parameter requirement range, the stability and reliability of the system can be improved, the possibility of problems occurring during the operation of the system can be reduced, and the operating efficiency and safety of the equipment can be improved.

[0032] like Figure 2As shown, in an embodiment of the present application, an artificial intelligence-based equipment operation control optimization system is provided, including: a determination module, used to determine the operation requirements of the operating equipment, and to parse the operation requirements to determine specific operation requirement standards; an analysis module, used to obtain the operation data of the operating equipment, and to conduct a comprehensive analysis of the operation data and the operation requirement standards to determine the operation difference characteristics of the operating equipment; an evaluation module, used to determine the corresponding operation control strategy from the control strategy library based on the operation difference characteristics, and to evaluate the operation control strategy based on artificial intelligence; an optimization module, used to optimize the operation control strategy based on the evaluation result, and to control the operation of the operating equipment according to the optimized control strategy, and to save the optimized control strategy to the control strategy library; an acquisition module, used to obtain the operation result of the operating equipment, and to determine further operation requirements of the operating equipment based on the operation result.

[0033] In summary, the embodiments of the present invention provide an artificial intelligence-based equipment operation control optimization method and system, which includes: determining the operation requirements of the operating equipment, and analyzing and determining the specific operation requirement standards; obtaining the operation data of the operating equipment, and comprehensively analyzing the operation data and the operation requirement standards to determine the operation difference characteristics; determining the corresponding operation control strategy from the control strategy library based on the operation difference characteristics, and evaluating it based on artificial intelligence; optimizing the operation control strategy based on the evaluation results, and controlling the operation of the operating equipment according to the optimized control strategy, and saving it to the control strategy library; obtaining the operation results of the operating equipment, and determining further operation requirements of the operating equipment based on the operation results. The present invention can realize intelligent management and control optimization of equipment operation, improve the operating efficiency and performance of the equipment, reduce the failure rate, and reduce maintenance costs, thereby bringing higher production efficiency and economic benefits to the enterprise.

[0034] Finally, it should be noted that it is apparent that various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the present invention. Thus, the present invention is intended to include such modifications and variations as long as they fall within the scope of the present invention and its equivalents.

[0035] The above description is only an example of an embodiment of the present invention, but it does not limit the scope of the present invention. Any structural changes made according to the present invention, as long as they do not lose the essence of the present invention, should be considered to fall within the scope of protection of the present invention and be subject to restrictions. Technical personnel in the relevant technical field can clearly understand that for the convenience and simplicity of description, the specific working process and related instructions of the platform described above can refer to the corresponding process in the aforementioned platform embodiment, and will not be repeated here.

[0036] The term "comprise," "comprising," or any other similar term is intended to cover a non-exclusive inclusion such that a process, platform, article, or apparatus / platform that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, platform, article, or apparatus / platform.

[0037] Thus far, the technical solutions of the present invention have been described in conjunction with the further embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to closely related technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0038] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention.

Claims

1. An artificial intelligence-based equipment operation control optimization method, characterized in that: include: Determine the operating requirements of the equipment, analyze the operating requirements, and determine the specific operating requirement standards; Obtain the operating data of the operating equipment, conduct a comprehensive analysis of the operating data and operating requirements and standards, and determine the operating difference characteristics of the operating equipment; Determine the corresponding operation control strategy from the control strategy library based on the operation difference characteristics, and evaluate the operation control strategy based on artificial intelligence; Optimize the operation control strategy based on the evaluation results, control the operation of the operating equipment according to the optimized control strategy, and save the optimized control strategy to the control strategy library; Obtain the operating results of the operating equipment and determine further operating requirements of the operating equipment based on the operating results.

2. The method for optimizing equipment operation control based on artificial intelligence according to claim 1, characterized in that: Determining the operating requirements of the operating equipment, analyzing the operating requirements, and determining specific operating requirement standards include: determining an operating requirement for the operating device, and determining a first operating parameter included in the operating requirement for the operating device; Based on the first operating parameter, a parameter requirement range corresponding to each first operating parameter is determined from the operating requirement, and the operating requirement standard is obtained by combining the first operating parameter and the parameter requirement range corresponding to the first operating parameter.

3. The device operation control optimization method based on artificial intelligence according to claim 2 is characterized in that: The obtaining of the operating data of the operating equipment and performing a comprehensive analysis of the operating data and the operating requirements and standards to determine the operating difference characteristics of the operating equipment include: Acquiring operating data of the operating device and determining a second operating parameter included in the operating data of the operating device; Decomposing the operation requirement standard into a first operation parameter and a parameter requirement range corresponding to the first operation parameter, and dividing the operation data into a plurality of data groups according to the second operation parameter; Matching the first operating parameter and the second operating parameter with each other according to the parameter type, and according to the matching result, corresponding the parameter requirement range and the data group; Filtering the data in the data group that is not within the parameter requirement range to obtain difference data, and determining the minimum difference between the difference data and the parameter requirement range; The second operating parameter including the difference data and the minimum difference between the difference data and the parameter requirement range are determined as the operating difference characteristic of the operating device.

4. The method for optimizing equipment operation control based on artificial intelligence according to claim 3, characterized in that: The determining of the corresponding operation control strategy from the control strategy library based on the operation difference characteristics includes: determining a second operating parameter containing difference data from the operating difference characteristic, and determining a parameter type of the second operating parameter containing the difference data; Determine a strategy corresponding to the parameter type from a control strategy library as a candidate operation control strategy; Determine the minimum difference between the difference data and the parameter requirement range from the operation difference characteristics, and calculate the average value of the minimum difference between all difference data and the parameter requirement range to obtain the comprehensive average value; A preset average value corresponding to each candidate operation control strategy is determined, and a candidate operation control strategy whose comprehensive average value is less than the corresponding preset average value is determined as the operation control strategy corresponding to the operation difference feature.

5. The method for optimizing equipment operation control based on artificial intelligence according to claim 4, characterized in that: The evaluation of the operation control strategy includes: Obtain a pre-set artificial intelligence strategy simulation model, simulate the operation control strategy according to the artificial intelligence strategy simulation model, and evaluate the operation control strategy based on the simulation results.

6. The method for optimizing equipment operation control based on artificial intelligence according to claim 5, characterized in that: The step of optimizing the operation control strategy based on the evaluation result, controlling the operation of the operation equipment according to the optimized control strategy, and saving the optimized control strategy to the control strategy library includes: A correspondence between an artificial intelligence optimization method and an evaluation value interval is pre-set, wherein the correspondence between the artificial intelligence optimization method and the evaluation value interval is associated with a corresponding artificial intelligence optimization method for each evaluation value interval; Obtaining an evaluation value of the operation control strategy after simulation by the artificial intelligence strategy simulation model, and based on a mapping relationship between the evaluation value interval to which the evaluation value belongs and the artificial intelligence optimization method-evaluation value interval correspondence relationship, selecting the optimization method corresponding to the evaluation value interval as the artificial intelligence optimization method corresponding to the operation control strategy; The operation of the running equipment is controlled according to the optimized control strategy, and the optimized control strategy is saved in the control strategy library.

7. The method for optimizing equipment operation control based on artificial intelligence according to claim 6, characterized in that: The obtaining of the operation result of the operation device and determining further operation requirements of the operation device based on the operation result includes: Obtaining an operation result of the operation device, and determining from the operation result each second operation parameter still containing difference data after optimization, and determining a second amount of difference data in the data group corresponding to the second operation parameter; Determining a first quantity of difference data in the data group corresponding to the second operating parameter before optimization, and determining an adjustment coefficient based on the first quantity and the second quantity; The calculation formula of the adjustment coefficient is: K=a*(S1-S2), Wherein, K is the adjustment coefficient, a is the preset conversion coefficient, S1 is the first quantity, and S2 is the second quantity; The corresponding parameter requirement range before optimization is adjusted according to the corresponding adjustment coefficient to obtain further operation requirements of the operating equipment.

8. An artificial intelligence-based equipment operation control optimization system, characterized in that: include: The determination module is used to determine the operating requirements of the operating equipment, analyze the operating requirements, and determine the specific operating requirement standards; The analysis module is used to obtain the operating data of the operating equipment, conduct a comprehensive analysis of the operating data and the operating requirements and standards, and determine the operating difference characteristics of the operating equipment; An evaluation module is used to determine the corresponding operation control strategy from the control strategy library based on the operation difference characteristics, and evaluate the operation control strategy based on artificial intelligence; An optimization module is used to optimize the operation control strategy based on the evaluation results, control the operation of the operating equipment according to the optimized control strategy, and save the optimized control strategy to the control strategy library; The acquisition module is used to obtain the operation results of the operation equipment and determine further operation requirements of the operation equipment based on the operation results.

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