Production detection method and system for automated cluster operation equipment

Abstract

The present invention belongs to the technical field of production operation detection, and specifically discloses a production detection method and system for automated cluster operation equipment, the method comprising: constructing a production operation plan and a production evaluation model that take into account the operation time and operation accuracy of the operation equipment; obtaining performance parameters of each operation equipment, and generating multiple production operation plans based on the performance parameters and currently set production expectations, and inputting the production operation plans into the production evaluation model to obtain corresponding evaluation indicators; detecting the operation time and operation accuracy of the operation equipment in real time, and using them together as a detection set; optimizing all production operation plans based on the evaluation indicators, and outputting the production operation plan with the highest evaluation indicator; using the highest evaluation indicator and detection set as the detection result and optimizing the operation time or operation accuracy of the operation equipment; having the following advantages: automatically generating a production plan that adapts to production expectations, accurately controlling the operation time and optimizing equipment performance.

Description

Technical Field

[0001] The present invention relates to the technical field of production operation detection, and in particular to a production detection method and system for automated cluster operation equipment. Background Art

[0002] In modern manufacturing, to improve production efficiency and ensure product quality, more and more companies are turning to automated clustered equipment. These devices, leveraging sophisticated mechanical systems and intelligent control, collaboratively complete complex production tasks such as processing, assembly, packaging, and testing. With technological advancements, single automated equipment is no longer sufficient to meet rapidly changing market demands, making clustered operations a significant trend. Multiple devices in a cluster are interconnected via a network, enabling information sharing, optimized task allocation, and coordinated actions, resulting in higher production efficiency and lower operating costs.

[0003] However, the ever-changing nature of production demands requires automated cluster equipment to be highly adaptable and flexible. Existing automation systems often struggle to adapt quickly to increased production speeds or adjust operations to improve yields while maintaining a constant production speed. These adjustments involve complex parameter optimization and inter-device coordination, resulting in existing automation systems often lacking the flexibility to respond to these changes in real time.

[0004] Therefore, a production detection method and system for automated cluster operation equipment are proposed to solve the above-mentioned problems. Summary of the Invention

[0005] The present invention aims to provide a production detection method and system for automated cluster operation equipment to solve or improve at least one of the above-mentioned technical problems.

[0006] In view of this, a first aspect of the present invention is to provide a production detection method for automated cluster operation equipment.

[0007] A second aspect of the present invention is to provide a system.

[0008] The first aspect of the present invention provides a production detection method for automated cluster operation equipment, comprising the following steps: constructing a production operation plan that takes into account the operation time and operation accuracy of the operation equipment, and a production evaluation model that evaluates the production operation plan; obtaining the performance parameters of each of the operation equipment, and generating multiple production operation plans based on the performance parameters and the currently set production expectations, and inputting each of the production operation plans into the production evaluation model to obtain corresponding evaluation indicators; real-time detection of the operation time of multiple operation equipment on the same production line, as well as the operation accuracy at the current operation time, and collectively serving as a detection set; optimizing all the production operation plans according to the evaluation indicators, and outputting the production operation plan with the highest evaluation indicator before each optimization; taking the highest evaluation indicator and the detection set as the detection result and optimizing the operation time or operation accuracy of the operation equipment.

[0009] In any of the above technical solutions, the production evaluation model is constructed through the following steps: configuring time evaluation parameters and precision evaluation parameters on the independent variables of the production evaluation model respectively, and configuring comprehensive index parameters on the dependent variables of the production evaluation model; configuring a first weight factor adjusted according to the production expectation on the time evaluation parameter, and configuring a second weight factor adjusted according to the production expectation on the precision evaluation parameter; judging whether there is an influence relationship between the production evaluation model and the time evaluation parameter on the current production expectation, and configuring the influence mode of the precision evaluation parameter and the time evaluation parameter on the comprehensive index parameter according to the result of the judgment.

[0010] In any of the above technical solutions, the first weighting factor is used to characterize the efficiency expectation set in the production expectation, and the second weighting factor is used to characterize the economic expectation set in the production expectation.

[0011] In any of the above technical solutions, the production evaluation model is the following formula: Wherein, E is the comprehensive index parameter; S is the time evaluation parameter; P is the accuracy evaluation parameter; Used to characterize the influence mode. When there is no influence relationship, Characterized as a plus sign, when there is an impact relationship Represented as a multiplication sign; the α1 and α2 together constitute the first weight factor; the β1 and β2 together constitute the second weight factor.

[0012] In any of the above technical solutions, when there is no influence relationship, the α2 is assigned a value of 1 and the first weight factor is adjusted through the α1, and the β2 is assigned a value of 1 and the second weight factor is adjusted through the β1; when there is an influence relationship, the α1 is assigned a value of 1 and the first weight factor is adjusted through the α2, and the β1 is assigned a value of 1 and the second weight factor is adjusted through the β2.

[0013] In any of the above technical solutions, the step of generating multiple production operation plans based on the performance parameters and the currently set production expectations specifically includes: judging the constraint type set in the current production expectations; configuring the calculation method of the time evaluation parameters and the accuracy evaluation parameters according to the constraint type; defining the operation time range of the operation equipment in the performance parameters through the production expectations; and generating multiple production operation plans within the operation time range.

[0014] In any of the above technical solutions, the time evaluation parameter is calculated by the following formula: S -1 = or The accuracy evaluation parameter is calculated by the following formula: or Among them, the S -1 and the S -2 The calculation formula for the time evaluation parameter configured according to the constraint type; the P -1 and the P -2 The calculation formula for the accuracy evaluation parameter configured according to the constraint type; the T a is the operation time; a is the operation accuracy; A={A1,A2,…,A a} is the number of the operating equipment, and a is a positive integer.

[0015] In any of the above technical solutions, the step of optimizing all the production operation plans according to the evaluation index specifically includes: sorting all the production operation plans generated at the current moment according to the evaluation index, and filtering the production operation plans according to the constraint type; using the operation time of each operation equipment in the production operation plan as an update factor, and exchanging the update factor between the production operation plans to generate a new production operation plan; calibrating the operation accuracy of all the production operation plans at the current moment through the detection set of the previous moment.

[0016] The second aspect of the present invention provides a system for implementing the production inspection method described in any of the above-mentioned technical solutions, including: a solution generation module, used to construct multiple production operation solutions based on the performance parameters of the operating equipment and the production expectations; an evaluation and optimization module, which evaluates all the production operation solutions through the production evaluation model and obtains the corresponding evaluation indicators; and selects and optimizes the production operation solutions based on the evaluation indicators; a real-time monitoring module, used to monitor all operating equipment on the production line and obtain the operation time and the operation accuracy.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] By adapting current production expectations, multiple production operation plans are automatically generated. This process leverages algorithms to adjust in real time to changing production demands, ensuring the optimal production plan is always available. This not only increases production flexibility but also enables rapid response to market changes, optimizes resource allocation, and improves production efficiency.

[0019] By precisely controlling the timing of operations, production equipment can be fine-tuned, achieving comprehensive control over actual operations on the production line. This is achieved by periodically adjusting and optimizing equipment operating parameters, ensuring that each production link achieves the desired efficiency and quality standards, thereby gradually improving overall production performance over the course of the production cycle.

[0020] Considering the importance of real-time inspection data, an integrated data monitoring system collects data from the production process and applies this data to production model estimation and prediction. After each production run, the model is calibrated based on actual inspection data to ensure the accuracy and reliability of the model predictions, thereby making production decisions more scientific and precise.

[0021] Additional aspects and advantages of embodiments according to the present invention will become apparent in the following description or may be learned through practice of embodiments according to the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments with reference to the following drawings, in which:

[0023] Figure 1 is a flow chart of the method steps of the present invention;

[0024] Figure 2 This is a schematic diagram of an operating equipment cluster on a production line of the present invention;

[0025] Figure 3 It is a system logic block diagram of the present invention;

[0026] Figure 4 The figure is a schematic structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0027] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0028] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0029] See also Figure 1-Figure 4 , the following describes a production detection method and system for automated cluster operation equipment according to some embodiments of the present invention.

[0030] The embodiment of the first aspect of the present invention provides a production detection method for automated cluster operation equipment. In some embodiments of the present invention, such as Figure 1 and Figure 2 As shown, the method includes:

[0031] S101, constructing a production operation plan that takes into account the operation time and operation accuracy of the operation equipment, and a production evaluation model for evaluating the production operation plan.

[0032] The first task in this step is to build a detailed production operation plan that takes into account the operating time and accuracy of each machine in the cluster. Operating time refers to the length of time each machine takes to complete its assigned task, while accuracy focuses on the machine's ability to accurately complete the task and meet quality standards. The goal of this plan is to ensure efficient operation of the entire production line while also guaranteeing that product quality meets established standards.

[0033] As can be seen above, to evaluate the effectiveness of the constructed production operation plan, this step also constructs a production evaluation model. Based on mathematical and statistical principles, this model comprehensively evaluates the time and accuracy parameters set in the operation plan. This model can predict the outcomes of different production configurations, such as the expected production speed, the expected product quality level, and resource utilization efficiency. Evaluation models typically include algorithms and calculation programs that can process large amounts of data and provide optimization recommendations, helping decision makers make adjustments to improve production efficiency and product quality.

[0034] In addition, the evaluation model also supports iterative improvement, and can continuously adjust and optimize the original production operation plan based on the feedback data from actual production. The dynamic adjustment mechanism ensures that the production system can flexibly respond to various challenges and changes that may arise during the production process.

[0035] Specifically, the operating accuracy refers to the processing and production yield rate of the operating equipment.

[0036] As can be seen above, the core function of operational precision, as expressed through yield, is to provide a quantitative standard for evaluating the production performance and quality control capabilities of each piece of equipment on the production line. A higher yield means less scrap and defective products produced by the equipment, resulting in higher production efficiency and product quality. This helps management monitor and control production quality, ensuring that products meet industry and market quality requirements, reducing additional costs due to scrap and rework, and indicating equipment that may require repair or adjustment to improve operational precision.

[0037] Sensors and quality inspection equipment are installed at each key node on the production line to capture the quality status of each product in real time. These devices may include visual inspection systems, dimensional measuring instruments, or other automatic inspection equipment; all collected data will be transmitted to the central processing system. Here, the data is analyzed to determine the number of qualified and unqualified products in each batch of products, and the yield rate is calculated by dividing the number of qualified products by the total number of products; based on the yield rate results, the production system can be adjusted automatically or manually by the operator. For example, if a drop in yield rate is detected, the system may prompt to check the calibration status of a specific device or check the quality of the raw materials used. In addition, this data can be used for long-term production trend analysis to help identify potential problems in the production process and formulate improvement measures; continuous monitoring of yield rate and combined with other production parameters such as equipment speed and operating conditions can implement comprehensive process optimization. This includes adjusting machine parameters, improving workflows, or updating production strategies to improve the efficiency and quality of the entire production line.

[0038] Specifically, the production evaluation model is constructed through the following steps:

[0039] The time evaluation parameters and the accuracy evaluation parameters are respectively configured on the independent variables of the production evaluation model, and the comprehensive indicator parameters are configured on the dependent variables of the production evaluation model.

[0040] A first weighting factor adjusted according to production expectations is configured on the time evaluation parameter, and a second weighting factor adjusted according to production expectations is configured on the accuracy evaluation parameter.

[0041] Determine whether the production evaluation model and time evaluation parameters have an impact on the current production expectations, and configure the impact of accuracy evaluation parameters and time evaluation parameters on comprehensive index parameters based on the judgment results.

[0042] In the specific description above, the time evaluation parameter typically refers to the time required for production equipment to complete a task. Shorter operation times often indicate efficient production speeds. The accuracy evaluation parameter relates to the accuracy of product quality during the production process, particularly the yield rate, which is the proportion of qualified products in the total production volume. The first weighting factor is an adjustment factor that adjusts the influence of the time evaluation parameter based on specific production expectations (such as speed priority or cost priority). The second weighting factor is an adjustment factor that adjusts the influence of the accuracy evaluation parameter based on specific production needs. The comprehensive indicator parameter is the output of the model, representing the overall evaluation of the production process, and is typically a weighted combination of the time and accuracy parameters.

[0043] In summary, weighting factors are set and adjusted based on specific production expectations. For example, if the current production goal is to complete orders as quickly as possible, the weight of time evaluation will increase; if the focus is on ensuring product quality, the weight of accuracy evaluation will be higher. During the production process, the model monitors the actual performance of time and accuracy parameters in real time and compares them with preset expectations. If a change in the relationship between time and expectations is detected (for example, a decrease in production speed due to aging or improper maintenance of equipment), the model reassesses the impact of this change on the comprehensive indicator and adjusts the weight of the parameters. The comprehensive indicator is calculated using a weighted average, with weights reflecting the priority of different production objectives. This indicator is used to assess production efficiency and help managers make decisions to adjust or optimize production processes. In this way, the production evaluation model not only provides a quantitative tool for measuring production performance, but also can flexibly adapt to changes in production objectives and optimize the production process in real time, thereby ensuring that production activities can more effectively meet market and enterprise needs. The application of this model helps companies improve production efficiency, reduce costs, and enhance product quality.

[0044] In any of the above embodiments, the first weighting factor is used to characterize the efficiency expectation set in the production expectation, and the second weighting factor is used to characterize the economic expectation set in the production expectation.

[0045] In this embodiment, the first and second weighting factors in the production evaluation model are set to ensure that the model can be flexibly adjusted to meet the different needs and goals of the production process. These two weighting factors each reflect specific expectations of the production process: the first weighting factor focuses on efficiency expectations, while the second weighting factor focuses on economic expectations.

[0046] The first weighting factor (efficiency expectation) is used to adjust the influence of the time evaluation parameter in the production evaluation model. It represents the emphasis on efficiency in the production process, specifically the priority given to production speed and task completion rate. In a production environment where efficiency is prioritized, this weighting factor is higher, indicating that quickly completing production tasks is the primary goal, which is particularly important during high-demand seasons or when processing urgent orders.

[0047] The second weighting factor (economic expectation) adjusts the importance of the accuracy evaluation parameters in the production evaluation model. It reflects the focus on production costs and resource efficiency, including material costs, energy consumption, and minimizing scrap rates. In cost-sensitive or resource-constrained production environments, this weighting is higher, emphasizing economy and cost-effectiveness, with the goal of maximizing output per unit of input.

[0048] During the planning and operation phases of the production process, these two weighting factors are dynamically adjusted based on current market demand, cost conditions, and production capacity. For example, if raw material prices rise, the second weighting factor may need to be increased to place greater emphasis on cost efficiency. Weighting factor adjustments are typically made using decision support tools within the production management system or through manual input, based on real-time data and long-term production strategies. The production evaluation model uses the adjusted weighting factors, combined with actual collected operation time and accuracy data, to calculate a composite index parameter. This composite index helps management understand current production efficiency and economic performance and serves as a basis for improving the production process. The results of the composite index are fed back to production line managers for operational adjustments, such as changing the production cadence, adjusting quality control standards, or reallocating resources.

[0049] In any of the above embodiments, the production evaluation model is the following formula:

[0050]

[0051] Among them, E is the comprehensive index parameter; S is the time evaluation parameter; P is the accuracy evaluation parameter; Used to characterize the influence mode when there is no influence relationship Characterized as a plus sign when there is an influence relationship Represented as a multiplication sign; α1 and α2 together constitute the first weight factor; β1 and β2 together constitute the second weight factor.

[0052] In this embodiment, the time evaluation weights (α1 and α2) are used. α1 adjusts the baseline influence of the parameter S, while α2 is an exponent that adjusts the strengthening or weakening effect on the parameter. This means that the influence of S is not just linear, but can be adjusted to a more complex nonlinear relationship through α2. The accuracy evaluation weights (β1 and β2) are used: similarly, β1 adjusts the baseline influence of the parameter P, and β2, as an exponent, provides the nonlinear ability to adjust the influence of accuracy. When time and accuracy are independent of each other in the production process, the addition operation is used to indicate that the contributions of time and accuracy to the overall performance are separate, that is, When there is a significant interaction effect, a multiplicative operation is used to indicate that time and accuracy are causal or mutually reinforcing to some extent, i.e. This situation can arise in production tasks that require extremely high precision and are time-sensitive. This flexible model design allows the evaluation model to be adjusted based on actual production conditions and specific needs, accurately reflecting the actual production process and optimization requirements. This dynamic adjustment capability makes production management more efficient, helping decision-makers improve production efficiency while maintaining quality.

[0053] The formula provides a highly flexible and comprehensive approach to evaluating production processes, suitable for diverse production environments. Through adjustable weights α1 and α2, as well as β1 and β2, companies can customize evaluation criteria based on specific needs, effectively balancing production speed and product quality. Furthermore, by selecting addition or multiplication operators, the formula can adaptively express the interplay between time and accuracy, more realistically reflecting actual production conditions. This evaluation approach not only supports immediate adjustments to production decisions but also provides a data-driven decision-making support tool for production planning through long-term data analysis. This helps achieve continuous improvement in production processes, improve efficiency, reduce costs, and enhance product market competitiveness.

[0054] In any of the above embodiments, when there is no influence relationship, α2 is assigned a value of 1 and the first weight factor is adjusted by α1, and β2 is assigned a value of 1 and the second weight factor is adjusted by β1.

[0055] When an influence relationship exists, α1 is assigned a value of 1 and the first weight factor is adjusted by α2, and β1 is assigned a value of 1 and the second weight factor is adjusted by β2.

[0056] In this embodiment, when there is no influence relationship: In this case, the time evaluation parameter S and the precision evaluation parameter P are regarded as independent factors, that is, their contributions to the comprehensive production index E are calculated separately without cross-influence. β2 and α2 are assigned a value of 1, which means that the influence of S and P is direct, without any nonlinear adjustment or amplification; by adjusting α1 and β1, the contribution of time efficiency and precision to the comprehensive evaluation index E can be directly controlled. Because α2 = 1 and β2 = 1, the model is simplified to E = α1S + β1P, which is a linear model in which the contributions of time and precision are directly added, allowing managers to directly observe and evaluate the contribution of each factor to the overall goal, facilitating rapid adjustments without considering the interactions between factors.

[0057] When there is an influence relationship: In this configuration, time evaluation S and precision evaluation P are regarded as interdependent factors, and the impact of their interaction on the production comprehensive index E is mutually reinforcing or offsetting.

[0058] α1 and β1 are assigned 1, which means that the basic weights are equal, and the nonlinear effects of S and P are adjusted by α2 and β2. Because α1=1 and β1=1, the model is transformed into This is a nonlinear multiplicative model that emphasizes the interaction between time and precision. This setting allows the model to provide deeper analysis by considering how the two enhance or inhibit each other. For example, if increasing production speed S sacrifices precision P, the settings of α2 and β2 will determine how this trade-off affects the overall performance E.

[0059] Through such a configuration, the production evaluation model can not only more accurately reflect the actual relationship between time and precision under different production conditions, but also flexibly adjust the evaluation criteria according to specific production needs, thereby optimizing production efficiency while ensuring product quality. It provides strong adaptability and decision-making support for production management, enabling it to effectively cope with complex and changing production environments.

[0060] Furthermore, the production evaluation model also stores the operation accuracy of each operation device at different operation times.

[0061] As can be seen above, the model not only records the operating time of each device, but also the corresponding operating accuracy. This allows the evaluation model to comprehensively analyze the performance of the equipment, including the two key indicators of speed and quality. It can clearly understand the potential impact on product quality while increasing production speed, or at which operating speed the equipment can maintain or achieve optimal operating accuracy. By analyzing data on different operating times and corresponding operating accuracy, the evaluation model helps decision makers find the optimal production balance. For example, when producing a high-precision product, the model can recommend the most suitable operating speed to ensure that quality standards are met while maximizing efficiency.

[0062] S102, obtaining the performance parameters of each operating equipment, and generating multiple production operation plans based on the performance parameters and the currently set production expectations, and inputting each production operation plan into the production evaluation model to obtain corresponding evaluation indicators.

[0063] The primary function of this step is to collect and analyze the performance parameters of each operating device in the automated cluster. These parameters may include, but are not limited to, the device's operating speed, accuracy, failure rate, maintenance history, and energy consumption. Based on this data, combined with current production goals and expectations (such as output targets and quality requirements), the system automatically generates multiple potential production operation scenarios. Each scenario attempts to configure the device parameters and production processes differently to achieve or optimize production goals.

[0064] As can be seen above, the system first acquires real-time equipment performance data through an integrated sensor network or device interfaces. This data is fed into a central processing system, where advanced algorithms analyze these parameters and, based on production objectives, calculate multiple possible production scenarios. These scenarios are based on pre-defined algorithmic models, such as optimization, heuristics, or machine learning, designed to explore the production effects of different parameter combinations.

[0065] Once these production scenarios are generated, the next step is to input them into the production evaluation model already constructed in S101. This model evaluates each scenario and generates corresponding evaluation metrics. These metrics may include projected production efficiency, cost-benefit analysis, quality control indicators, resource consumption estimates, and more. The model evaluation process utilizes a variety of data analysis techniques, including statistical analysis, performance prediction, and potential failure analysis.

[0066] Using these evaluation metrics, production managers can intuitively identify the advantages and potential risks of various options, enabling them to make informed decisions. After selecting the optimal production plan, it is implemented on the production line. This process not only enhances production flexibility and adaptability but also, through continuous data feedback and model iteration, continuously improves the overall efficiency and stability of the production system.

[0067] Specifically, the steps of generating multiple production operation plans based on performance parameters and currently set production expectations include:

[0068] Determine the type of constraints set in the current production expectations.

[0069] Configure the calculation method of time evaluation parameters and accuracy evaluation parameters according to the constraint type.

[0070] The operating time range of the operating equipment is defined in the performance parameters based on production expectations.

[0071] Generate multiple production operation plans within the operation time range.

[0072] For the above specific description, the constraint type set in the current production expectation is determined based on the production goals and requirements, and the main constraint types that need to be considered in the production process are determined, such as time optimization, cost minimization, quality maximization, etc. This step helps to determine the parameters and restrictions that should be given priority in the subsequent solution generation; the calculation method of configuring the time evaluation parameters and accuracy evaluation parameters according to the constraint type is to select the appropriate calculation method and formula to calculate the time and accuracy parameters based on the determined constraint type. For example, if time efficiency is prioritized, the calculation of time parameters may focus more on speed, ensuring that the calculation of each production parameter can accurately reflect the current production needs and constraints, thereby improving the practicality and effectiveness of the production plan; defining the operating time range of the operating equipment in the performance parameters through production expectations is to set a specific operating time range within the acceptable performance parameter range based on equipment performance and production goals. This step helps determine the fastest and slowest possible operating times for each device, providing actual operating boundaries for plan generation; generating multiple production operation plans within the operating time range is to generate a variety of different production operation plans based on the set operating time range and evaluation parameters using algorithms and models, and provide multiple plans for selection, so that managers can select the optimal or most suitable plan based on actual production conditions and effect predictions.

[0073] In summary, by collecting and analyzing data on equipment performance and production needs, the system can automatically identify and adapt to different production constraints and goals; use optimization algorithms (such as linear programming, genetic algorithms, etc.) to deal with complex constraints and multi-objective problems, and generate a series of production plans that are both time-efficient and meet quality standards; by simulating the execution results of different plans, predict the performance of each plan in actual production, thereby making the decision-making process more scientific and accurate.

[0074] Specifically, the performance parameters are the adjustable range of the operating speed of the operating equipment during production and processing. Minimum operating speed: refers to the lowest speed at which the equipment can operate safely and effectively. This is usually set to ensure product quality or meet specific process requirements. The maximum operating speed refers to the highest speed that the equipment can reach while ensuring safety and meeting production standards. Within the lower operating speed range, the equipment can usually maintain a higher yield rate, because the slower speed allows the equipment to process or assemble products more accurately, reducing errors or defects caused by rapid operation. As the operating speed increases, the equipment may not be able to maintain the same operating accuracy, so the error rate increases, resulting in a decrease in yield rate. This phenomenon is especially common in precision manufacturing or production lines that require delicate operations. Through historical data analysis and experimental testing, the maximum operating speed of different equipment while maintaining high-quality yields can be determined, and this maximum operating speed is the dividing threshold between operating accuracy and operating time that can have an impact and does not have an impact.

[0075] In any of the above embodiments, the time evaluation parameter is calculated using the following formula:

[0076] or

[0077] The accuracy evaluation parameters are calculated using the following formula:

[0078] or

[0079] Among them, S -1 and S -2 The calculation formula for the time evaluation parameter configured according to the constraint type; P -1 and P -2 T is the calculation formula for the accuracy evaluation parameter configured according to the constraint type; a is the working time; Y a is the operation accuracy; A={A1,A2,…,A a} is the number of the operating equipment, and a is a positive integer.

[0080] In this embodiment, the time evaluation parameter S is used to measure the time efficiency of each operation in the production process. -1 and S -2 , can be used to evaluate time performance based on specific production constraints (such as shortest completion time or optimal time utilization); select the appropriate formula S according to the constraint type -1 and S -2 For example, if the constraint is to minimize the total job time, S -1 ; If you want to optimize the time allocation of a single operation, you can use S -2 The formula will be based on the actual operation time Ta The time evaluation parameter is calculated by a predetermined mathematical relationship (such as linear, exponential or other functional relationship) based on the set production target. The precision evaluation parameter P is used to evaluate the quality control and yield rate in the production process. This parameter reflects the accuracy and quality level of the production activity; select P -1 or P -2 Formula, depending on the quality target or specific quality constraints of production; using the operation accuracy Y a And related production data, through the preset calculation formula (which may involve the calculation of error rate or statistical analysis of pass rate), to obtain the accuracy evaluation parameters.

[0081] By comprehensively considering the evaluation parameters of time and accuracy, the production process can be comprehensively evaluated and optimized. For example, under the premise of meeting certain quality standards, the production time can be shortened as much as possible; equipment number A = A1, A2, ..., A a Each device has a unique number, which helps to track and distinguish the performance of different devices during data analysis and performance evaluation. In this way, companies can improve production efficiency while ensuring product quality and better meet market demand and production goals.

[0082] Specifically, the formula S -1 The objective function is to minimize the maximum time, and the goal is to find a solution that makes the longest single operating device among all operating devices as short as possible; Formula S -2 To minimize the objective function of average time, the goal is to find a solution that minimizes the average operating time of all operating equipment.

[0083] Formula P -1 The objective function for maximizing the minimum yield rate is to find a solution that makes the yield rate of the single operating equipment with the lowest yield rate among all the operating equipment as large as possible; Formula P -2 The objective function of maximizing the average yield rate is to find a solution that maximizes the average yield rate of all operating equipment.

[0084] And the formula is selected based on the current operating equipment and production expectations.

[0085] S103, detecting the operation time of multiple operation devices on the same production line and the operation accuracy under the current operation time in real time, and using them together as a detection set.

[0086] This step aims to continuously monitor and evaluate the actual operating conditions of each piece of equipment on the production line, focusing specifically on its operating time and accuracy. Operating time refers to the actual time it takes for a piece of equipment to complete a specific task, while accuracy measures its ability to meet predetermined quality standards. These two metrics are crucial for assessing overall production efficiency and product quality. By monitoring these parameters in real time and analyzing them as a comprehensive dataset, we can more precisely understand the dynamics of the production process and their impact on production results.

[0087] As mentioned above, the system collects data on operation time and accuracy by installing sensors on each piece of equipment. These sensors provide highly accurate real-time data, such as the exact time when a machine starts and ends a task, as well as the results of quality control inspections during the production process.

[0088] The collected data is sent to a central data processing system, where software applications organize the data into detection sets and then analyze it in real time using data analysis and processing algorithms. This analysis may include comparing actual data with expected data from the production operation plan to identify any significant deviations. In addition, advanced analytical techniques such as statistical analysis, trend prediction, and anomaly detection are used to identify potential performance degradation or production efficiency issues.

[0089] Through real-time monitoring and data analysis, the production management system can promptly adjust production parameters or intervene to correct deviations or optimize the production process. For example, if the operating accuracy of a certain device is detected to be below standard, the system can immediately notify the maintenance team to investigate and repair it. Similarly, if the operation time is too long, the equipment configuration or production process may need to be re-evaluated. Step S103 not only improves the transparency and monitoring capabilities of the production line, but also strengthens the control of the production process, ensuring that production activities can continue to operate in an optimal state, thereby achieving the goal of improving production efficiency and product quality.

[0090] S104, optimize all production operation plans according to the evaluation index, and output the production operation plan with the highest evaluation index before each optimization; use the highest evaluation index and the detection set as the detection result and optimize the operation time or operation accuracy of the operation equipment.

[0091] This step is a key component of the automated cluster equipment production inspection method. Its primary purpose is to optimize production operation plans based on the evaluation metrics generated in the previous steps. By analyzing the metrics generated by the evaluation model, the system can identify and recommend the best-performing production plan. Furthermore, the system uses real-time inspection data (inspection sets) to further optimize equipment operation time and accuracy, ensuring not only efficient but also high-quality production processes.

[0092] As can be seen above, the system first ranks all possible production operation plans according to their evaluation indicators. These evaluation indicators include but are not limited to production efficiency, product quality, cost-effectiveness, etc. The plan with the highest evaluation indicator is considered the optimal plan under the current conditions. Before the start of each optimization cycle, the system automatically outputs the plan with the highest current evaluation indicator to provide decision-making basis for production managers.

[0093] After outputting the optimal solution, the system continues to optimize equipment performance using the real-time monitoring data on operating time and accuracy (i.e., the detection set) from step S103. During the optimization process, the algorithm analyzes this real-time data to identify any factors that may reduce production efficiency or substandard product quality. Based on these analysis results, the system can adjust equipment operating parameters, such as adjusting operating speed, improving operating procedures, or reconfiguring equipment layout.

[0094] Optimization is not a one-time activity but an ongoing process. The system will re-evaluate production plans periodically or as needed to ensure that the production process continues to meet or exceed expected performance standards. Through this dynamic feedback mechanism, the production system can continuously adapt to new production needs and environmental changes, thereby achieving long-term optimization and improvement.

[0095] Specifically, the steps to optimize all production operation plans based on evaluation indicators include:

[0096] Sort all the production operation plans generated at the current moment according to the evaluation indicators, and filter the production operation plans according to the constraint type.

[0097] The operating time of each operating equipment in the production operation plan is used as an update factor, and the factors are updated interactively between production operation plans to generate a new production operation plan.

[0098] The operation accuracy of all production operation plans at the current moment is calibrated through the detection set of the previous moment.

[0099] In the specific description above, sorting and filtering production operation plans involves ranking all generated production operation plans based on preset evaluation metrics (such as time efficiency, production cost, and product quality). These evaluation metrics are typically derived from analyzing previous production data and reflect the overall performance of each plan. First, the system calculates the evaluation metric score for each production plan. Next, the plans are ranked based on the scores to determine which plans perform best. Furthermore, based on specific production constraints (such as minimizing cost and maximizing output), plans that meet these constraints are selected for further consideration. Interactively updating operation times involves treating the operation time of each piece of equipment in the selected production plan as an adjustable update factor. By exchanging these update factors between different plans, a new production operation plan is generated that may be more optimal. This step involves dynamically adjusting and optimizing operation times to adapt to production demand and resource allocation. For example, if a piece of equipment has low efficiency in one plan, the operating time configuration in another plan can be applied to see if this improves efficiency. This approach relies on algorithmic simulation and predictive techniques to ensure that time adjustments lead to overall performance improvements. Calibrating operational accuracy involves using the inspection dataset from the previous moment to calibrate the operational accuracy of all production operation plans at the current moment. This ensures the reliability and accuracy of each plan in terms of quality control. The expected operational accuracy of each plan is calibrated based on data collected from actual production (such as error rate, pass rate, etc.). This calibration process may involve statistical analysis and machine learning techniques to learn from historical data and predict future performance. In this way, production strategies can be adjusted in a timely manner to respond to quality fluctuations or changes in the production environment.

[0100] Taken together, this forms a continuous optimization cycle, constantly improving the efficiency of the production process and the quality of output through data analysis and technology application. In this way, production management can not only respond to rapidly changing market demands, but also continuously improve production efficiency and product quality.

[0101] Specifically, the steps to optimize the operating time or operating accuracy of operating equipment are:

[0102] Under the same impact mode, compare the operating time of the operating equipment with the operating time of the output production operation plan. If the operating time of the production operation plan is shorter, update the power or operating speed of the operating equipment to achieve the required operating time.

[0103] The operating time of the operating equipment on the production line is updated according to the comparison results.

[0104] Regarding the specific description above, the system compares the current actual operating time of the operating equipment with the operating time set in the production operation plan. The purpose of this step is to identify opportunities to shorten operating time and improve efficiency. For example, if the actual operating time of a machine is longer than the scheduled time in the production plan, the system will identify this difference. When it is found that the operating time in the production operation plan is shorter than the actual time, the system will guide the adjustment of the operating equipment to achieve the planned operating time. This may involve adjusting the power setting or operating speed of the machine. This adjustment ensures that the equipment operates in an optimal state to achieve the predetermined production goals and reduce energy consumption and material waste.

[0105] In summary, sensors and data acquisition systems installed on the production line monitor operating times in real time and compare them with target operating times in the database. Real-time data monitoring technology and big data analysis are used to quickly identify deviations in operating times. Based on the results of the data analysis, the operating equipment settings (such as power and speed) are automatically or manually adjusted. This adjustment is based on pre-set algorithms that ensure the accuracy and efficiency of equipment adjustments. It relies on control systems and machine learning algorithms that can automatically adjust equipment parameters based on historical data and predictive models to optimize the production process. After adjustments, the system continues to monitor the changed operating times to verify the effectiveness of the adjustments. If the adjustments fail to achieve the desired results, the system may make further adjustments or propose alternatives. A continuous feedback loop is key in this process, relying on sensor feedback and real-time data processing to continuously optimize operations.

[0106] The present invention provides a production detection method for automated cluster operation equipment. By constructing a production operation plan that takes into account the operation time and operation accuracy of the operation equipment and establishing a corresponding evaluation model, the present invention can optimize the production process in the solution design stage, ensure that each step is operated under the optimal conditions allowed by the equipment performance, and improve overall production efficiency and product quality; by generating multiple production operation plans and using a production evaluation model to evaluate each plan, the most appropriate production plan can be selected, thereby maintaining efficient and high-quality production under different production conditions; real-time monitoring of the operation time and accuracy of multiple devices on the production line, and using these data as a detection set, helps to quickly discover and solve problems in production, reduce downtime, and improve production continuity and product consistency; by continuously optimizing the production operation plan according to the evaluation index and outputting the plan with the highest evaluation index before each optimization, the present invention allows the production process to maintain dynamic adjustment to ensure that it always operates in the optimal way. In addition, by optimizing the operation time and accuracy of the equipment, efficiency can be further improved and resource waste can be reduced.

[0107] The second aspect of the present invention provides a system for implementing the production detection method described in any of the above embodiments. In some embodiments of the present invention, such as Figure 3 As shown, the production detection system 2 includes:

[0108] The plan generation module 201 is used to construct multiple production operation plans according to the performance parameters of the operation equipment and the production expectations.

[0109] The evaluation and optimization module 202 evaluates all the production operation plans through the production evaluation model and obtains the corresponding evaluation indicators; and selects and optimizes the production operation plans according to the evaluation indicators.

[0110] The real-time monitoring module 203 is used to monitor all operating equipment on the production line and obtain the operating time and the operating accuracy.

[0111] The present invention provides a system that customizes and generates suitable production operation plans based on specific equipment performance parameters and production goals (production expectations). This high degree of customization ensures that each plan can maximize the performance advantages of the equipment while meeting the specific needs of production, thereby improving overall production efficiency and product quality; by conducting a detailed evaluation of all generated production operation plans, it is ensured that each plan can achieve optimal performance in actual applications. This process not only relies on the initial plan evaluation, but also includes continuous performance tracking and iterative optimization to ensure that the production process adapts to environmental changes or equipment performance changes at any time; it can accurately monitor the operating status of all operating equipment on the production line, including operating time and accuracy. The acquisition of such real-time data helps to adjust production parameters in a timely manner, prevent equipment failures, reduce downtime, and ensure production quality.

[0112] Embodiments of the third aspect of the present invention provide electronic devices. In some embodiments of the present invention, such as Figure 4 As shown, an electronic device is provided, which includes: electronic devices such as desktop computers, notebooks, handheld computers and cloud servers. The electronic device 3 may include but is not limited to a processor 301 and a memory 302. Those skilled in the art will understand that Figure 4 This is merely an example of the electronic device 3 and does not limit the electronic device 3 . The electronic device 3 may include more or fewer components than shown in the figure, or different components.

[0113] The processor 301 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0114] The memory 302 can be an internal storage unit of the electronic device 3, such as a hard disk or memory of the electronic device 3. The memory 302 can also be an external storage device of the electronic device 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. The memory 302 can also include both an internal storage unit of the electronic device 3 and an external storage device. The memory 302 is used to store computer programs and other programs and data required by the electronic device.

[0115] Embodiments of the fourth aspect of the present invention provide a computer-readable storage medium. In some embodiments of the present invention, a computer-readable storage medium is provided that, when executed by processor 301, implements the steps of the above-described method. Therefore, the computer-readable storage medium provided in the fourth aspect of the present invention has all the technical effects of the above-described steps and will not be further described here.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0117] Those skilled in the art will appreciate that the units 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. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0118] In the embodiments provided in the present disclosure, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of modules or units is merely a logical function division. In actual implementation, there may be other division methods. Multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0119] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present disclosure implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. The computer program may include computer program code, which may be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0120] The above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present disclosure, and should all be included in the scope of protection of the present disclosure.

Claims

1. A production detection method for automated cluster operation equipment, characterized in that: The steps include: Construct a production operation plan that takes into account the operation time of the operation equipment and the processing production yield rate, and a production evaluation model for evaluating the production operation plan; the production evaluation model is the following formula: or or Wherein, E is the comprehensive index parameter; S is the time evaluation parameter; P is the accuracy evaluation parameter; Used to characterize the influence mode; the α1 and α2 together constitute the first weight factor; the β1 and β2 together constitute the second weight factor; the S -1 and the S -2 The calculation formula for the time evaluation parameter configured according to the constraint type; the P -1 and the P -2 The calculation formula for the accuracy evaluation parameter configured according to the constraint type; the T a is the operation time; a is the processing production yield; A={A1, A2, ..., A a } is the number of the operating equipment, and a is a positive integer; When time and accuracy are independent of each other in production expectations, the Dynamic adjustment is an additive operation; when there is a significant interaction effect between time and accuracy in production expectations, the Dynamically adjust to multiplication operation; During the planning and operation phases of the production process, the first weighting factor and the second weighting factor are dynamically adjusted according to current market demand, cost conditions, and production capacity; When production expectations change, dynamically adjust the time evaluation parameter to the S -1 and the S -2 and dynamically adjust the accuracy evaluation parameter to the P -1 and the P -2 one of the; updating the production evaluation model under the currently set production expectations according to the results of the dynamic adjustment; Obtaining performance parameters of each of the operating equipment, and generating a plurality of the production operation plans according to the performance parameters and the currently set production expectations, and inputting each of the production operation plans into the updated production evaluation model to obtain evaluation indicators corresponding to the current production expectations; Real-time detection of the operating time of multiple operating devices on the same production line, as well as the processing and production yield rate under the current operating time, and the two are collectively used as a detection set; All the production operation plans are optimized according to the evaluation index, and the production operation plan with the highest evaluation index is output before each optimization; the highest evaluation index and the detection set are used as the detection results and the operation time or processing production yield of the operation equipment is optimized.

2. The production detection method according to claim 1, characterized in that: The production evaluation model is constructed through the following steps: configuring a time evaluation parameter and an accuracy evaluation parameter on the independent variables of the production evaluation model respectively, and configuring a comprehensive index parameter on the dependent variable of the production evaluation model; configuring a first weighting factor adjusted according to the production expectation on the time evaluation parameter, and configuring a second weighting factor adjusted according to the production expectation on the accuracy evaluation parameter; Determine whether the production evaluation model and the time evaluation parameter have an influence relationship on the current production expectation, and configure the influence mode of the accuracy evaluation parameter and the time evaluation parameter on the comprehensive index parameter according to the result of the determination.

3. The production detection method according to claim 2, characterized in that: The first weighting factor is used to characterize the efficiency expectation set in the production expectation, and the second weighting factor is used to characterize the economic expectation set in the production expectation.

4. The production detection method according to claim 3, characterized in that: When there is no influence relationship, the value of α2 is set to 1 and the first weight factor is adjusted by the value of α1, and the value of β2 is set to 1 and the second weight factor is adjusted by the value of β1; When an influence relationship exists, the α1 is assigned a value of 1 and the first weight factor is adjusted by the α2, and the β1 is assigned a value of 1 and the second weight factor is adjusted by the β2.

5. The production detection method according to claim 4, characterized in that: The step of generating a plurality of production operation plans according to the performance parameters and the currently set production expectations specifically includes: Determine the type of constraints set in the current production expectations; Configuring a calculation method for the time evaluation parameter and the accuracy evaluation parameter according to the constraint type; Defining the operating time range of the operating equipment within the performance parameters based on the production expectations; A plurality of the production operation plans are generated within the operation time range.

6. The production detection method according to claim 5, characterized in that: The step of optimizing all the production operation plans according to the evaluation indicators specifically includes: Sorting all the production operation plans generated at the current moment according to the evaluation index, and filtering the production operation plans according to the constraint type; Using the operation time of each of the operation equipment in the production operation plan as an update factor, and exchanging the update factors between the production operation plans to generate a new production operation plan; The processing and production yield of all the production operation plans at the current moment is calibrated by using the detection set at the previous moment.

7. The production detection method according to claim 6, characterized in that: The steps of optimizing the operation time or processing production yield of the operation equipment are as follows: Under the same influencing mode, comparing the operating time of the operating equipment with the operating time of the output production operation plan; The operating time of the operating equipment on the production line is updated according to the comparison result.

8. A system for implementing the production detection method according to any one of claims 1 to 7, characterized in that: include: A plan generation module, configured to construct a plurality of production operation plans according to the performance parameters of the operation equipment and the production expectations; An evaluation and optimization module, which evaluates all the production operation plans through the production evaluation model and obtains the corresponding evaluation indicators; and selecting and optimizing the production operation plan according to the evaluation index; The real-time monitoring module is used to monitor all operating equipment on the production line and obtain the operating time and the processing production yield.

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