A method and system for optimizing equipment scheduling based on automated operation

Abstract

The present invention discloses an equipment scheduling optimization method and system based on automated operation, and relates to the technical field of equipment scheduling optimization, wherein the method comprises the following steps: collecting operation data and environmental condition data of the equipment; constructing a scheduling evaluation model based on a machine learning algorithm for the operation data and the environmental condition data, and generating a scheduling evaluation coefficient; acquiring the constraint conditions of the equipment, combining the scheduling evaluation coefficient, constructing a scheduling optimization model based on a machine learning algorithm, generating a scheduling optimization coefficient, and optimizing the equipment scheduling according to the scheduling optimization coefficient; the present invention can realize a more comprehensive, efficient and sustainable equipment scheduling optimization strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment scheduling optimization, and more specifically, to an equipment scheduling optimization method and system based on automated operation. Background Art

[0002] Equipment scheduling optimization refers to the process of improving production efficiency, reducing costs and resource consumption by effectively arranging and managing the use of equipment. Modern equipment scheduling optimization covers many aspects, mainly reflecting

[0003] The application of intelligent algorithms and optimization technologies uses mathematical modeling, optimization algorithms and artificial intelligence technologies to optimize equipment scheduling. These technologies can automatically adjust equipment usage plans based on real-time data and predictive models to maximize production capacity and improve equipment utilization; simulate equipment operation through simulation technology and evaluate the effects of different scheduling strategies to optimize equipment scheduling plans before actual application; connect equipment to sensors and Internet of Things technology to monitor equipment status, production progress and resource consumption in real time, and provide data support for optimization algorithms; and adjust equipment scheduling strategies in a timely manner through real-time data analysis to respond to changes and emergencies in production.

[0004] Deficiencies of existing technologies:

[0005] Traditional scheduling optimization usually focuses on equipment utilization, which measures the proportion of equipment actually working in a certain period of time. Improving equipment utilization can reduce idle time, thereby improving production efficiency and resource utilization efficiency; temperature impact refers to the heat generated by equipment during operation. Excessive temperature may cause equipment performance degradation or even damage. Therefore, considering temperature impact is an important factor in ensuring long-term stable operation of equipment; however, traditional scheduling optimization often ignores economic cost factors. In practical applications, economic cost is usually one of the important considerations for enterprise operations. Therefore, comprehensive consideration of these four factors can achieve a more comprehensive, efficient and sustainable equipment scheduling optimization strategy.

[0006] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0007] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an equipment scheduling optimization method and system based on automated operation, which solves the problems raised in the above-mentioned background technology through an equipment scheduling optimization method and system based on automated operation.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for optimizing equipment scheduling based on automated operation, comprising: collecting equipment operation data and environmental condition data; constructing a scheduling evaluation model based on a machine learning algorithm for the operation data and environmental condition data to generate a scheduling evaluation coefficient; acquiring equipment constraint conditions, combining the scheduling evaluation coefficient, constructing a scheduling optimization model based on a machine learning algorithm to generate a scheduling optimization coefficient, and optimizing equipment scheduling according to the scheduling optimization coefficient.

[0010] In a preferred embodiment, the operating data includes an equipment deviation coefficient and a dynamic load balancing coefficient, and the environmental condition data includes a temperature influence coefficient.

[0011] In a preferred embodiment, the specific method for obtaining the equipment deviation coefficient is as follows: obtain the actual operating time of the equipment within a preset time period, and calculate the processing time deviation value based on the actual operating time and the scheduled working time of the equipment within the preset time period, combined with a preset processing time deviation weight coefficient; compare the actual operating time with the preset time period to obtain the equipment utilization rate, and combine the expected equipment utilization rate and the preset equipment utilization efficiency deviation weight coefficient to obtain the equipment utilization efficiency deviation value; obtain the actual resource demand of the equipment, and combine the predetermined resource demand of the equipment and the preset resource deviation weight coefficient to obtain the resource deviation value; combine the equipment utilization efficiency deviation value, and calculate the equipment deviation coefficient by weighted summation of the processing time deviation value and the resource deviation value.

[0012] In a preferred embodiment, the specific method for obtaining the dynamic load balancing coefficient is as follows:

[0013] Get the actual running time of the equipment and the total running time of all equipment, and take the ratio of the actual running time of the equipment to the total running time of all equipment as the equipment load value; take the difference between the equipment load value and the preset rated load as the load deviation; get the rated load time within the preset time period, subtract the actual running time of the equipment from the rated load time to get the overload time; calculate the load cycle adjustment coefficient based on the overload time combined with the preset load cycle adjustment factor; get the sum of the load deviations of all equipment based on the load deviation of each equipment, and calculate the dynamic load balancing coefficient in combination with the load cycle adjustment coefficient.

[0014] In a preferred embodiment, the specific method for obtaining the temperature influence coefficient is as follows: obtaining the actual operating time of the equipment within a preset time period, comparing the actual operating time with the preset time period, and obtaining the equipment utilization rate; collecting a data set of the temperature and the corresponding equipment utilization rate within the preset time period, establishing a standard influence model of temperature on equipment utilization rate based on a multivariate regression model, and using the least squares method to perform regression analysis on the standard influence model of temperature on equipment utilization rate to obtain the optimal regression parameter;

[0015] Substitute the optimal regression parameters into the standard influence model of temperature on equipment utilization to obtain the influence model of temperature on equipment utilization; substitute the temperature into the influence model of temperature on equipment utilization to obtain the regressed equipment utilization, and differentiate the regressed equipment utilization with respect to temperature to obtain the temperature influence coefficient.

[0016] In a preferred embodiment, the specific steps of optimizing equipment scheduling according to the scheduling optimization coefficient are as follows: comparing and analyzing the scheduling optimization coefficient with a preset scheduling threshold; when the scheduling optimization coefficient is less than the preset scheduling threshold, the maintenance personnel adjust the scheduling optimization coefficient by reducing the response time, lowering the temperature, maximizing the equipment utilization rate and reducing the cost to achieve optimized scheduling of the equipment; when the scheduling optimization coefficient is greater than the preset scheduling threshold, there is no need to optimize the equipment and maintain it, and continuous detection is performed.

[0017] A system using the equipment scheduling optimization method based on automated operation as described in any one of the above embodiments, comprising an acquisition module, an evaluation module, and an optimization module, wherein the modules are connected: the acquisition module collects the operation data and environmental condition data of the equipment; the evaluation module constructs a scheduling evaluation model based on the operation data and the environmental condition data based on a machine learning algorithm, and generates a scheduling evaluation coefficient; the optimization module obtains the constraint conditions of the equipment, combines the scheduling evaluation coefficient, constructs a scheduling optimization model based on a machine learning algorithm, generates a scheduling optimization coefficient, and optimizes the equipment scheduling according to the scheduling optimization coefficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a structural schematic diagram of an equipment scheduling optimization method based on automated operation of the present invention.

[0019] Figure 2 This is a schematic diagram of the structure of an equipment scheduling optimization system based on automated operation according to the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Embodiment 1, Figure 1 The present invention provides an equipment scheduling optimization method based on automated operation.

[0022] S10, collecting equipment operation data and environmental condition data;

[0023] The operation data includes equipment deviation coefficient and dynamic load balancing coefficient, and the environmental condition data includes temperature influence coefficient;

[0024] The specific method for obtaining the device deviation coefficient is as follows:

[0025] Obtain the actual running time of the device within the preset time period, and calculate the processing time deviation value according to the actual running time of the device within the preset time period and the scheduled working time, combined with the processing time deviation weight coefficient;

[0026] The actual operation time is compared with the preset time period to obtain the equipment utilization rate, and the equipment utilization efficiency deviation value is obtained by combining the expected equipment utilization rate and the equipment utilization efficiency deviation weight coefficient;

[0027] The actual resource demand of the device is obtained, and the resource deviation value is obtained by combining the predetermined resource demand of the device and the resource deviation weight coefficient;

[0028] The equipment deviation coefficient is obtained by combining the equipment utilization efficiency deviation value, the processing time deviation value and the resource deviation value;

[0029] The specific calculation formula of the equipment deviation coefficient is as follows:

[0030]

[0031] In the formula, is the equipment deviation coefficient, is the actual running time of the device, It is the scheduled working time.

[0032] is a preset time period, is the expected equipment utilization, is the actual resource demand, is the predetermined resource demand, is the processing time deviation weight coefficient, is the equipment utilization efficiency deviation weight coefficient, is the resource bias weight coefficient, where , , ;

[0033] The equipment deviation coefficient plays an important role in equipment scheduling optimization. It is mainly used to measure the degree of deviation between the actual operating state of the equipment and the ideal or set state. The role of this deviation coefficient in equipment scheduling optimization is mainly reflected in the following aspects:

[0034] Improve production efficiency: By monitoring the equipment deviation coefficient, the deviation between the equipment operating status and the ideal state can be discovered in time, so that corresponding adjustment measures can be taken to ensure that the equipment operates in the optimal state, which helps to reduce downtime and failure rate in the production process and improve production efficiency and output; the equipment deviation coefficient can help the scheduling system more accurately evaluate the operating status and performance of each equipment, so as to reasonably allocate production tasks and resources according to actual needs, avoiding resource waste and bottlenecks;

[0035] Improve product quality: The equipment deviation coefficient reflects the stability of the equipment during the process. By calibrating and reducing the equipment deviation coefficient, the stability and consistency of the process can be ensured, thereby reducing the fluctuation of product quality and improving the overall quality of the product. For production processes that require precise control of process parameters, the equipment deviation coefficient can help the scheduling system adjust equipment parameters in real time to ensure that each parameter in the production process remains within the optimal range, thereby improving product accuracy and consistency.

[0036] Reduce operating costs: By regularly monitoring and analyzing the equipment deviation coefficient, potential problems and failures of the equipment can be discovered in time, so that preventive maintenance measures can be taken to reduce downtime and repair costs caused by equipment failures; the equipment deviation coefficient can also reflect the energy consumption of the equipment. By optimizing the equipment operating status and parameters, the energy consumption of the equipment can be reduced, and energy waste in the production process can be reduced, thereby reducing operating costs.

[0037] The specific method for obtaining the dynamic load balancing coefficient is as follows:

[0038] The actual running time of the device and the total running time of all devices are obtained, the ratio of the actual running time of the device to the total running time of all devices is used as the device load value, and the difference between the device load value and the preset rated load is used as the load deviation;

[0039] Obtain the rated load time within the preset time period, subtract the actual operating time of the equipment from the rated load time to obtain the overload time, and combine the load cycle adjustment factor to obtain the load cycle adjustment coefficient;

[0040] The sum of the load deviations of all devices is obtained according to the load deviation of each device, and the dynamic load balancing coefficient is calculated in combination with the load cycle adjustment coefficient;

[0041] The specific calculation formula of the dynamic load balancing coefficient is as follows:

[0042]

[0043] In the formula, is the dynamic load balancing coefficient, is the actual running time of the device, is the total running time of all devices, is the rated load, is the load cycle adjustment factor, is the rated load time within a preset time period, , is the number of devices;

[0044] The dynamic load balancing factor plays a core role in device scheduling optimization. It dynamically adjusts resource allocation and task execution order to cope with real-time changes in system load, thereby optimizing the overall performance and efficiency of the system. The following are the specific roles of the dynamic load balancing factor in device scheduling optimization:

[0045] Improve system responsiveness: The dynamic load balancing coefficient can monitor the system load in real time and dynamically adjust resource allocation according to the real-time changes in the load. When the system load increases, by increasing the load balancing coefficient, more resources can be directed to the high-load area, thereby ensuring that the system can quickly respond to and process new task requests; by dynamically adjusting the load balancing coefficient, the system can give priority to tasks that require a high response time, such as real-time data processing, interactive applications, etc., thereby improving the execution efficiency and response speed of these tasks;

[0046] Optimize resource utilization: Dynamic load balancing coefficient can ensure the reasonable allocation of resources in the system and avoid resources being idle for a long time. When the load of a device or node is low, by reducing its load balancing coefficient, some resources can be allocated to other high-load devices or nodes, thereby improving the overall resource utilization;

[0047] Improve system stability and reliability: The dynamic load balancing coefficient can monitor the system load in real time and take corresponding measures when the load approaches or exceeds the threshold, such as adding resources, adjusting task allocation, etc., to avoid system crash or performance degradation due to overload. In distributed systems, the dynamic load balancing coefficient can also be combined with the fault detection and recovery mechanism. When a device or node fails, the load balancing coefficient can be quickly adjusted to reallocate the tasks originally assigned to the device or node to other healthy devices or nodes, thereby improving the system's fault tolerance and reliability.

[0048] In summary, the dynamic load balancing coefficient plays a vital role in equipment scheduling optimization. By real-time monitoring of the system load and dynamically adjusting resource allocation and task execution order, the dynamic load balancing coefficient can significantly improve the system's responsiveness, optimize resource utilization, improve system stability and reliability, and support flexible scheduling strategies to adapt to different application scenarios and needs.

[0049] The specific method for obtaining the temperature influence coefficient is as follows:

[0050] Obtaining the actual running time of the equipment within a preset time period, comparing the actual running time with the preset time period, and obtaining the equipment utilization rate;

[0051] Collect data sets of temperature and corresponding equipment utilization within a preset time period, use a multivariate regression model to establish a model of the impact of temperature on equipment utilization, and use the least squares method to regress the model of the impact of temperature on equipment utilization to obtain the optimal regression parameters;

[0052] The temperature influence coefficient is obtained by derivation of the temperature influence model on equipment utilization.

[0053] The specific calculation formula of the temperature influence coefficient is as follows:

[0054]

[0055] The formula of the model of the influence of temperature on equipment utilization is as follows:

[0056]

[0057] In the formula, is the temperature influence coefficient, is the temperature within a preset time period, is the optimal regression parameter in the multivariate regression model. is the equipment utilization rate;

[0058] The influence of the collection temperature influence coefficient on scheduling optimization is mainly reflected in the following aspects:

[0059] Performance prediction and tuning:

[0060] Temperature has a direct impact on device performance. For example, high temperature may cause processor speed to decrease or memory read and write latency to increase. By collecting data on temperature impact coefficients, we can more accurately predict device performance changes under different temperature conditions.

[0061] This data can be used to adjust scheduling strategies. For example, when the device is in a high temperature state, light loads or tasks that are not sensitive to response time can be prioritized to reduce sensitivity to performance impacts.

[0062] Energy saving and performance balance:

[0063] The collection of temperature impact coefficients can help achieve a balance between energy saving and performance. At lower temperatures, more equipment performance can be used to improve system response speed; at high temperatures, more emphasis is placed on stability and energy saving.

[0064] According to the changes in the temperature influence coefficient, the scheduling system can dynamically adjust resource allocation to optimize the energy efficiency and performance of the equipment under different temperature conditions;

[0065] Load prediction and dynamic scheduling:

[0066] The temperature impact coefficient data helps predict the load of the equipment under future temperature changes. Such prediction capabilities can help the scheduling system better reserve and manage resources to cope with upcoming high-load or low-load periods;

[0067] Real-time temperature impact coefficient monitoring can also prompt the scheduling system to make dynamic scheduling decisions to adapt to the real-time temperature changes of the equipment, thereby maintaining efficient operation of the system;

[0068] Equipment Health and Reliability:

[0069] Analysis of the temperature impact coefficient helps to evaluate the health status and reliability of equipment under different temperature conditions. This data can be used to develop maintenance plans or implement failure prevention measures to reduce equipment damage or performance degradation caused by high temperature.

[0070] S20, constructing a scheduling evaluation model based on the operation data and environmental condition data based on a machine learning algorithm to generate a scheduling evaluation coefficient;

[0071] The specific calculation method of the scheduling evaluation coefficient is as follows:

[0072] According to the calculated equipment deviation coefficient and dynamic load balancing coefficient, equipment utilization rate and temperature influence coefficient, a scheduling evaluation model is built based on machine learning to generate a scheduling evaluation coefficient;

[0073] The specific calculation formula of the scheduling evaluation coefficient is as follows:

[0074]

[0075] In the formula, is the scheduling evaluation coefficient, is the equipment deviation coefficient, is the dynamic load balancing coefficient, is the temperature influence coefficient, is the equipment utilization rate;

[0076] The impact of the scheduling evaluation coefficient on scheduling optimization can be summarized as follows:

[0077] Decision Support and Optimization:

[0078] The scheduling evaluation coefficient provides data-based guidance to help decision makers more accurately understand the impact of the current environment and resource status on scheduling. By analyzing the scheduling evaluation coefficient, resource allocation and task scheduling can be optimized to maximize production efficiency, reduce waiting time, and thus improve overall operational efficiency.

[0079] Real-time adjustment and adaptability:

[0080] The scheduling evaluation coefficient can support real-time scheduling decisions. When environmental conditions or resource status change, the system can quickly adjust the scheduling plan to meet new challenges or optimization opportunities. Based on the evaluation coefficient, the system can automatically adjust the workflow or production plan to adapt to different production needs or market changes to maintain efficient operation.

[0081] Resource optimization and cost control:

[0082] Optimal resource allocation: The scheduling evaluation coefficient helps determine the best resource allocation plan to ensure that each resource is used most effectively and avoid waste or overuse of resources;

[0083] Cost-benefit analysis: By evaluating the coefficients, the cost-benefit comparison of different scheduling schemes can be evaluated. This helps to formulate more economical operation strategies and achieve cost control and savings in the long run;

[0084] Predictive maintenance and failure prevention:

[0085] Equipment status prediction: Some scheduling evaluation coefficients may involve the health status or maintenance requirements of the equipment. By analyzing these coefficients, the failure probability or maintenance cycle of the equipment can be predicted, and maintenance can be performed in advance to avoid production interruptions or reduced production efficiency.

[0086] Reduce downtime: Effective scheduling evaluation factors can help reduce equipment downtime and enhance production continuity and stability;

[0087] Improve customer satisfaction:

[0088] Timely delivery and service quality: By optimizing scheduling, timely delivery of products or services can be guaranteed, improving customer satisfaction and trust. The evaluation coefficient can ensure that tasks are completed on time and reduce customer complaints or delays.

[0089] In summary, the scheduling evaluation coefficient can significantly improve the effect of scheduling optimization and the accuracy of implementation by providing data support and analysis, helping enterprises maintain competitive advantages and achieve long-term economic benefits in a complex operating environment.

[0090] S30, obtaining the constraint conditions of the equipment, combining the scheduling evaluation coefficient, building a scheduling optimization model based on the machine learning algorithm, generating the scheduling optimization coefficient, and optimizing the equipment scheduling according to the scheduling optimization coefficient;

[0091] The constraints include equipment return costs, which include equipment output, equipment price, equipment operating costs, and equipment update costs;

[0092] The specific calculation formula for equipment return cost is as follows:

[0093]

[0094] In the formula, is the equipment payback cost, is the output of the equipment, The price of the equipment itself. is the equipment operating cost, is the equipment replacement cost;

[0095] Combine the scheduling evaluation coefficient with the constraint conditions, build a scheduling optimization model based on machine learning, and generate the scheduling optimization coefficient;

[0096] The specific calculation formula of the scheduling optimization coefficient is as follows:

[0097]

[0098] In the formula, is the scheduling optimization coefficient, is the scheduling evaluation coefficient, is the output of the equipment, The price of the equipment itself. is the equipment operating cost, It is the cost of equipment renewal.

[0099] The specific steps of optimizing equipment scheduling according to the scheduling optimization coefficient are as follows:

[0100] Compare and analyze the scheduling optimization coefficient with the preset scheduling threshold;

[0101] When the scheduling optimization coefficient is less than the preset scheduling threshold, the maintenance personnel adjust the scheduling optimization coefficient to achieve optimal scheduling of the equipment by reducing response time, lowering temperature, maximizing equipment utilization, and reducing costs;

[0102] When the scheduling optimization coefficient is greater than the preset scheduling threshold, there is no need to optimize and maintain the equipment, and continuous detection is required.

[0103] Understanding the benefits of constraints for equipment optimization requires considering the following aspects, especially in the context of equipment payback costs:

[0104] Equipment output:

[0105] Improved production efficiency: Equipment output directly affects production efficiency and output. By optimizing the use and configuration of equipment, the throughput and quality stability of the production line can be improved, thereby increasing output, which means that more products or services can be obtained with the same resource input;

[0106] Reduce production costs: Increasing the output of the equipment will usually reduce the production cost per unit of product, such as reducing the scrap rate, increasing the processing speed or enhancing the flexibility of production, which can directly affect the control of production costs;

[0107] Price of the device itself:

[0108] Cost-benefit analysis: The purchase cost of the equipment is one of the important constraints. By reasonably evaluating the performance, technical indicators and price of the equipment, the most economical and suitable equipment can be selected, which not only reduces the risk of initial investment, but also ensures that the equipment can provide stable economic benefits during its service life;

[0109] Equipment operating costs:

[0110] Resource optimization: Equipment operating costs include energy consumption, maintenance costs, and labor costs. By optimizing the operation mode of the equipment, regular maintenance, and modern management, operating costs can be reduced and the overall economic benefits of the equipment can be increased;

[0111] Preventive maintenance: Effective equipment management and maintenance can reduce unexpected downtime and repair costs, keep equipment in optimal operating condition, and thus ensure the continuity and stability of production;

[0112] Equipment update cost:

[0113] Technology upgrade and adaptability: With the development of technology and changes in the market, timely updating of equipment can improve production efficiency, reduce energy consumption or improve product quality. By analyzing and managing the cost of equipment update, it can ensure that the equipment remains competitive and profitable in the long run;

[0114] Comprehensively analyzing the benefits of these constraints on equipment optimization, the following advantages can be obtained:

[0115] Maximize economic benefits: The economic benefits of equipment can be maximized by optimizing the output of equipment, controlling acquisition and operating costs, and updating when necessary;

[0116] Improved resource utilization: Accurately manage various costs, including operating costs and renewal costs, to help improve resource utilization and return on investment;

[0117] Enhanced competitive advantage: Optimizing equipment can help companies reduce production costs, improve product quality and delivery capabilities, thereby gaining an advantage in market competition;

[0118] Continuous improvement and market adaptation: Regular evaluation and adjustment of constraints will help companies achieve continuous improvement and adapt to market demands, maintain competitiveness and sustainable development;

[0119] Therefore, comprehensive consideration of various economic benefit constraints of equipment can effectively guide equipment optimization strategies and maximize long-term economic benefits and enterprise value;

[0120] Optimize equipment scheduling according to the scheduling optimization coefficient:

[0121] Optimize scheduling algorithm selection:

[0122] According to the characteristics of the equipment and the optimization coefficient, choosing the appropriate scheduling algorithm is a key step. Different algorithms are suitable for different optimization goals and data characteristics:

[0123] Greedy algorithm: Applicable to simple scheduling problems. It builds an overall solution by selecting the current optimal solution each time. For example, it can prioritize scheduling based on equipment utilization to reduce equipment idle time.

[0124] Genetic algorithm: Applicable to complex optimization problems, it searches for the optimal solution by simulating the biological evolution process. This algorithm can balance and optimize between multiple optimization coefficients, while considering the balance between equipment energy consumption and production efficiency;

[0125] Simulated annealing algorithm: used to find the global optimal solution in a large range of searches. It can handle problems with large search spaces and uncertainties. For example, in multi-objective optimization, it can be used to weigh different optimization metrics.

[0126] The selection of an appropriate algorithm also depends on the availability of computing resources and real-time requirements. In practical applications, multiple algorithms may be combined or customized algorithm development may be performed to better meet specific device optimization requirements;

[0127] Real-time scheduling adjustment:

[0128] Equipment optimization strategies need to take into account changes in real-time data. By monitoring equipment operation and environmental conditions in real time, the scheduling plan can be adjusted in a timely manner to optimize the equipment's operating results. For example:

[0129] Real-time data collection: Use sensors and monitoring systems to collect data while the equipment is running.

[0130] Real-time analysis and decision-making: Based on real-time data analysis, determine the effectiveness of the current scheduling plan and adjust it according to the real-time situation;

[0131] Adaptive adjustment strategy: Set automated rules or response strategies to react quickly when equipment performance degrades or the environment changes. For example, if the temperature of the equipment exceeds a safe threshold, the system can automatically reduce the production rate or shut down.

[0132] Implementation and Monitoring:

[0133] Implementing an optimization strategy is the process of putting the developed plan into practice. The key steps include:

[0134] Deploy the scheduling system: implement the selected scheduling algorithm and adjustment strategy into the equipment management system;

[0135] Continuous monitoring: Continuously monitor the operation status and optimization effect of equipment through equipment monitoring system and data analysis tools;

[0136] Feedback and adjustment: Based on real-time data feedback, evaluate the effectiveness of the optimization strategy and adjust the algorithm, parameters or scheduling scheme as needed. This continuous feedback loop ensures that the equipment is continuously in the best operating state and continuously improves the optimization effect.

[0137] The optimization effect of the scheduling optimization coefficient on equipment scheduling can be understood as an indicator or method to measure and evaluate the quality of the scheduling plan. In the context of equipment scheduling, especially in the field of industrial production or services, the efficient use of equipment is crucial to improving production efficiency and service quality. The following are several important roles of the scheduling optimization coefficient in equipment scheduling:

[0138] Evaluate the quality of scheduling plans: The scheduling optimization coefficient can be used to evaluate the pros and cons of different equipment scheduling plans. It can measure the efficiency, cost, resource utilization and other aspects of a scheduling plan, thereby helping decision makers choose the best scheduling plan;

[0139] Guiding the design of scheduling algorithms: When designing scheduling algorithms, the scheduling optimization coefficient can be used as an objective function or evaluation function. By maximizing or minimizing the scheduling optimization coefficient, the algorithm can be guided to find the optimal equipment scheduling solution to achieve the desired effect.

[0140] Optimize production resource utilization: The optimization of equipment scheduling usually involves the rational allocation and utilization of resources. The scheduling optimization coefficient can help determine how to arrange the working order and time of the equipment to maximize the utilization of the equipment and reduce idle time, thereby improving production efficiency and reducing costs;

[0141] Respond to real-time demand changes: In dynamic environments, such as task changes or emergencies, equipment scheduling needs to be adjusted in real time. The scheduling optimization coefficient can help quickly evaluate the adaptability of existing scheduling plans and make adjustments based on real-time data to meet changing needs;

[0142] Support decision-making: The application of scheduling optimization coefficients can also provide decision-making support for management. By quantitatively evaluating the pros and cons of different scheduling schemes, management can make decisions based on data, optimize resource allocation and production plans, and achieve the company's strategic goals;

[0143] In summary, the scheduling optimization coefficient plays multiple roles in equipment scheduling, including evaluation, guidance, optimization and decision support. It is an important tool and method to improve production efficiency and resource utilization efficiency.

[0144] Embodiment 2, Figure 2 The invention provides an equipment scheduling optimization system based on automatic operation.

[0145] Acquisition module: collects the operation data and environmental condition data of the equipment; the operation data includes response time and equipment utilization rate, and the environmental condition data includes temperature influence coefficient;

[0146] Evaluation module: Uses the operation data and environmental condition data to build a scheduling evaluation model based on machine learning algorithms and generate scheduling evaluation coefficients;

[0147] Optimization module: obtains the constraints of the equipment, which include the equipment payback cost, where the equipment payback cost includes the equipment output, the price of the equipment itself, the equipment operating cost, and the equipment update cost; combines the scheduling evaluation coefficient, builds a scheduling optimization model based on the machine learning algorithm, generates the scheduling optimization coefficient, and optimizes the equipment scheduling according to the scheduling optimization coefficient.

[0148] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0149] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0150] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0151] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0152] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0153] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing equipment scheduling based on automated operation, characterized in that: The following steps are involved: Collect equipment operation data and environmental condition data; The operation data and environmental condition data are used to construct a scheduling evaluation model based on a machine learning algorithm to generate a scheduling evaluation coefficient. The specific calculation formula of the scheduling evaluation coefficient is as follows: In the formula, is the scheduling evaluation coefficient, is the equipment deviation coefficient, is the dynamic load balancing coefficient, is the temperature influence coefficient, is the equipment utilization rate; Obtain the constraints of the equipment, combine the scheduling evaluation coefficient, build a scheduling optimization model based on the machine learning algorithm, and generate the scheduling optimization coefficient. The specific calculation formula of the scheduling optimization coefficient is as follows: In the formula, is the scheduling optimization coefficient, is the scheduling evaluation coefficient, is the output of the equipment, The price of the equipment itself. is the equipment operating cost, is the equipment replacement cost; Optimize equipment scheduling according to the scheduling optimization coefficient.

2. The equipment scheduling optimization method based on automated operation according to claim 1 is characterized in that: The operation data includes an equipment deviation coefficient and a dynamic load balancing coefficient, and the environmental condition data includes a temperature influence coefficient.

3. The equipment scheduling optimization method based on automated operation according to claim 2 is characterized in that: The specific method for obtaining the device deviation coefficient is as follows: Obtaining the actual running time of the device within a preset time period, and calculating the processing time deviation value according to the actual running time of the device within the preset time period and the scheduled working time, combined with a preset processing time deviation weight coefficient; The actual operation time is compared with the preset time period to obtain the equipment utilization rate, and the equipment utilization efficiency deviation value is obtained by combining the estimated equipment utilization rate and the preset equipment utilization efficiency deviation weight coefficient; The actual resource demand of the device is obtained, and the resource deviation value is obtained by combining the predetermined resource demand of the device and the preset resource deviation weight coefficient; The equipment deviation coefficient is calculated by combining the equipment utilization efficiency deviation value, the processing time deviation value and the resource deviation value.

4. The equipment scheduling optimization method based on automated operation according to claim 2 is characterized in that: The specific method for obtaining the dynamic load balancing coefficient is as follows: Obtain the actual running time of the device and the total running time of all devices, and use the ratio of the actual running time of the device to the total running time of all devices as the device load value; The difference between the equipment load value and the preset rated load is taken as the load deviation; Obtain the rated load time within a preset time period, and subtract the actual operating time of the equipment from the rated load time to obtain the overload time; The load cycle adjustment coefficient is calculated based on the overload time and the preset load cycle adjustment factor; The sum of the load deviations of all devices is obtained according to the load deviation of each device, and the dynamic load balancing coefficient is calculated in combination with the load cycle adjustment coefficient.

5. The equipment scheduling optimization method based on automated operation according to claim 2 is characterized in that: The specific method for obtaining the temperature influence coefficient is as follows: Obtaining the actual running time of the equipment within a preset time period, comparing the actual running time with the preset time period, and obtaining the equipment utilization rate; Collect data sets of temperature and corresponding equipment utilization within a preset time period, establish a standard impact model of temperature on equipment utilization based on a multivariate regression model, and use the least squares method to perform regression analysis on the standard impact model of temperature on equipment utilization to obtain the optimal regression parameters; Substituting the optimal regression parameters into the standard impact model of temperature on equipment utilization, the impact model of temperature on equipment utilization is obtained; Substitute the temperature into the model of the influence of temperature on equipment utilization to obtain the regressed equipment utilization, and then take the derivative of the regressed equipment utilization with respect to temperature to obtain the temperature influence coefficient.

6. The equipment scheduling optimization method based on automated operation according to claim 1 is characterized in that: The specific steps of optimizing equipment scheduling according to the scheduling optimization coefficient are as follows: Compare and analyze the scheduling optimization coefficient with the preset scheduling threshold; When the scheduling optimization coefficient is less than the preset scheduling threshold, the maintenance personnel adjust the scheduling optimization coefficient to achieve optimal scheduling of the equipment by reducing response time, lowering temperature, maximizing equipment utilization, and reducing costs; When the scheduling optimization coefficient is greater than the preset scheduling threshold, there is no need to optimize and maintain the equipment, and continuous detection is required.

7. The equipment scheduling optimization method based on automated operation according to claim 2 is characterized in that: The specific calculation formula of the equipment deviation coefficient is as follows: In the formula, is the equipment deviation coefficient, is the actual running time of the device, It is the scheduled working time. is a preset time period, is the expected equipment utilization, is the actual resource demand, is the predetermined resource demand, is the processing time deviation weight coefficient, is the equipment utilization efficiency deviation weight coefficient, is the resource bias weight coefficient, where , , .

8. The equipment scheduling optimization method based on automated operation according to claim 2 is characterized in that: The specific calculation formula of the dynamic load balancing coefficient is as follows: In the formula, is the dynamic load balancing coefficient, is the actual running time of the device, is the total running time of all devices, is the rated load, is the load cycle adjustment factor, is the rated load time within a preset time period, , is the number of devices.

9. A system using the equipment scheduling optimization method based on automated operation as described in any one of claims 1 to 8, characterized in that: It includes acquisition module, evaluation module and optimization module, and there are connections between the modules: Collection module: used to collect equipment operation data and environmental condition data; Evaluation module: used to construct a scheduling evaluation model based on the operation data and environmental condition data based on the machine learning algorithm and generate the scheduling evaluation coefficient; Optimization module: obtains the constraints of the equipment, combines the scheduling evaluation coefficient, builds a scheduling optimization model based on the machine learning algorithm, generates the scheduling optimization coefficient, and optimizes the equipment scheduling according to the scheduling optimization coefficient.

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