Multi-station synchronous machining control method and system

Abstract

According to the multi-station synchronous processing control method and system provided by the invention, the prediction evaluation capability based on the preset characteristic information and the quantitative influence model is combined with the current instantaneous total power, the power upper limit of the current time period and the electricity price of the current time period; and quantitative evaluation on time cost increment and quality risk cost increment is introduced, so that a plurality of candidate process parameter adjustment schemes can be generated and evaluated, and the optimal scheme is determined based on the comprehensive adjustment benefit index, thereby achieving the purposes of satisfying the power constraint and improving the efficiency. And the effect of optimal balance among the production efficiency, the machining quality and the energy cost is achieved.

Description

Technical Field

[0001] The present application relates to the field of CNC machining technology, and in particular to a multi-station synchronous machining control method and system. Background Art

[0002] Typically, a CNC machining center is equipped with multiple workstations, such as Workstation 1, Workstation 2, and Workstation 3, which handle different machining tasks in parallel to improve overall production efficiency. These workstations share the machine tool's infrastructure and main power supply system. The factory sets an upper threshold for the center's instantaneous total input power, which is dynamically adjusted based on the time of day, for example, lowered during peak hours. Furthermore, electricity rates are typically priced using a time-of-use pricing strategy, with prices significantly higher during certain periods than during other times. Therefore, while ensuring timely completion of production orders, effectively reducing overall energy consumption costs and strictly preventing the instantaneous total power from exceeding the currently set limit are the core objectives of the control system.

[0003] However, in actual production, when each workstation operates according to its own standard process parameters, the instantaneous total power may exceed the currently set lower power limit. For example, when workstation one performs high-energy operations such as deep-cut milling of high-hardness materials and continuous deep-hole drilling at workstation two, the total power limit can easily be exceeded. Existing control systems typically use preset, relatively crude strategies to deal with this situation. One common strategy is "rotating pause," where the system forcibly pauses one or more workstations when the total power approaches or reaches a threshold. The decision on which workstation to pause is usually based on simple polling or fixed priority. This approach fails to consider the relevance and urgency of tasks at each workstation, potentially forcing subsequent processes to wait and disrupting the production cycle. Another strategy is "synchronous speed reduction," which instructs all operating workstations to uniformly reduce process parameters (such as spindle speed and feed rate). This "one-size-fits-all" approach ignores the essential differences and specific requirements of the processing tasks at each workstation. For roughing tasks, reducing parameters primarily affects machining time. However, for precision machining tasks, adjusting parameters without careful evaluation is highly likely to result in part dimensional deviations, substandard surface finish, or even scrap, seriously impacting the delivery and quality of critical orders. The end result is often a failure to achieve the ideal balance between energy cost control and production efficiency. This can lead to efficiency sacrificed due to overly conservative power control, or the delivery or quality of critical tasks impacted by improper adjustments. Summary of the Invention

[0004] The present application provides a multi-station synchronous processing control method that can perform refined evaluation and coordinated adjustment based on multi-dimensional information, thereby optimizing the overall processing efficiency while meeting power constraints and quality requirements.

[0005] On the one hand, the present application provides a multi-station synchronous processing control method, comprising:

[0006] Obtaining preset characteristic information of a plurality of workstations, wherein the preset characteristic information includes power consumption, processing time, and quality index boundaries of each workstation under standard process parameters;

[0007] Constructing a quantitative impact model, wherein the quantitative impact model is used to predict the change in power consumption, the change in processing time, and the processing quality risk of each of the workstations caused by the process parameter adjustment;

[0008] Obtain the current instantaneous total power of the CNC machining center, the power upper limit of the current period, and the electricity price of the current period;

[0009] When the current instantaneous total power exceeds the power upper limit of the current time period, generating at least one candidate process parameter adjustment plan for at least one operating station;

[0010] Based on the preset characteristic information, the quantitative impact model, and the electricity price in the current time period, evaluating the expected power reduction resulting from the application of each candidate process parameter adjustment solution to the corresponding workstation, the expected time cost increment associated with the electricity price in the current time period, and the expected quality risk cost increment associated with the processing quality risk;

[0011] According to the expected power reduction, the expected time cost increment and the expected quality risk cost increment, the adjustment benefit index of each candidate process parameter adjustment scheme is calculated to determine the optimal candidate process parameter adjustment scheme.

[0012] Optionally, the step of calculating the adjustment benefit index of each candidate process parameter adjustment scheme according to the expected power reduction, the expected time cost increment, and the expected quality risk cost increment includes:

[0013] Obtaining operational focus information of the CNC machining center, wherein the operational focus information includes the urgency of a current order, the quality level of a workpiece, or the sensitivity of electricity prices in a current period;

[0014] Determine the time cost increment impact factor and the quality risk cost impact factor based on the acquired operational focus information;

[0015] Adjusting the expected time cost increment based on the time cost increment influencing factor to obtain a target expected time cost increment;

[0016] Adjusting the expected quality risk cost increment based on the quality risk cost impact factor to obtain a target expected quality risk cost increment;

[0017] An adjustment benefit index of each of the candidate process parameter adjustment schemes is calculated according to the expected power reduction, the target expected time cost increment, and the target expected quality risk cost increment.

[0018] Optionally, the step of determining the time cost increment impact factor and the quality risk cost impact factor based on the acquired operational focus information includes:

[0019] Identifying content in the acquired operational focus information that indicates a conflict of objectives;

[0020] Acquire preset conflict resolution parameters corresponding to the content that conflicts with the indicated target;

[0021] Based on the preset conflict resolution parameters and in combination with the content indicating the target conflict, generating conflict-resolved operational focus information;

[0022] The time cost increment impact factor and the quality risk cost impact factor are determined based on the conflict-resolved operational focus information.

[0023] Optionally, the step of adjusting the expected time cost increment based on the time cost increment influencing factor to obtain a target expected time cost increment includes:

[0024] Monitoring whether the time cost increment influencing factor satisfies relevant preset change conditions;

[0025] When the expected time cost increment influencing factor satisfies the preset change condition, the expected time cost increment is adjusted according to the time cost increment influencing factor to generate a target expected time cost increment.

[0026] Optionally, the step of calculating the adjustment benefit index of each candidate process parameter adjustment scheme according to the expected power reduction, the target expected time cost increment, and the target expected quality risk cost increment includes:

[0027] Obtaining multiple current operation target information of the CNC machining center and priority information of each operation target;

[0028] selecting, based on the acquired operation target information and the priority information, an adjustment benefit index calculation rule corresponding to the operation target information and the priority information from a preset rule set including multiple adjustment benefit index calculation rules;

[0029] According to the selected adjustment benefit index calculation rule, the adjustment benefit index of each candidate process parameter adjustment scheme is calculated using the expected power reduction, the target expected time cost increment, and the target expected quality risk cost increment.

[0030] Optionally, the step of selecting, based on the acquired operation target information and the priority information, an adjustment benefit index calculation rule corresponding to the operation target information and the priority information from a preset rule set including multiple adjustment benefit index calculation rules includes:

[0031] Performing feature extraction on the acquired operation target information and the priority information to obtain current operation features;

[0032] For each adjustment benefit indicator calculation rule in the preset rule set, obtaining its corresponding preset applicable operating characteristics;

[0033] Calculating a compliance parameter between the current operating characteristic and each of the preset applicable operating characteristics;

[0034] Based on each of the compliance parameters, an adjustment benefit index calculation rule is selected from the preset rule set.

[0035] Optionally, after calculating the adjustment benefit index of each candidate process parameter adjustment scheme based on the expected power reduction, the expected time cost increment, and the expected quality risk cost increment, and determining the optimal candidate process parameter adjustment scheme, the method further includes:

[0036] Applying the optimal candidate process parameter adjustment plan to a target workstation, and setting an operation observation time window of a preset length for the workstation;

[0037] Collecting actual power consumption, actual processing time, and actual processing quality indicators of the workstation under the optimal candidate process parameter adjustment solution within the observation time window;

[0038] Comparing the actual power consumption, actual processing time, and actual processing quality indicators with the expected power reduction, expected time cost increment, and expected quality risk cost increment before executing the adjustment plan, and calculating an indicator deviation value;

[0039] If the indicator deviation value exceeds the first threshold of the predicted value, it is determined to be a bad adjustment and the correction process is triggered.

[0040] Optionally, the quantitative impact model includes: a power variation model, a processing time variation model and a quality risk assessment model.

[0041] On the other hand, the present application provides a multi-station synchronous processing control system, characterized in that the system includes:

[0042] A preset characteristic information acquisition module is used to obtain preset characteristic information of multiple workstations, wherein the preset characteristic information includes power consumption, processing time and quality index boundaries of each workstation under standard process parameters;

[0043] A quantitative impact model construction module is used to construct a quantitative impact model, wherein the quantitative impact model is used to predict the power consumption change, processing time change and processing quality risk of each workstation caused by the process parameter adjustment;

[0044] Energy constraint status acquisition module, used to obtain the current instantaneous total power of the CNC machining center, the power upper limit of the current period, and the electricity price of the current period;

[0045] a process parameter solution generating module, for generating at least one candidate process parameter adjustment solution for at least one operating station when the current instantaneous total power exceeds the power upper limit of the current time period;

[0046] a process parameter scheme evaluation module, configured to evaluate, based on the preset characteristic information, the quantitative impact model, and the electricity price in the current time period, an expected power reduction resulting from the application of each candidate process parameter adjustment scheme to the corresponding workstation, an expected time cost increment associated with the electricity price in the current time period, and an expected quality risk cost increment associated with the processing quality risk;

[0047] The adjustment scheme determination module is used to calculate the adjustment benefit index of each candidate process parameter adjustment scheme according to the expected power reduction, the expected time cost increment and the expected quality risk cost increment, and determine the optimal candidate process parameter adjustment scheme.

[0048] Optionally, the quantitative impact model building module is further used to store a power variation model, a processing time variation model and a quality risk assessment model.

[0049] The present application provides a multi-station synchronous processing control method and system, which combines the predictive evaluation capability based on preset characteristic information and quantitative influence models with the current instantaneous total power, the power upper limit of the current time period and the electricity price of the current time period, and introduces a quantitative evaluation of the time cost increment and the quality risk cost increment, thereby being able to generate and evaluate multiple candidate process parameter adjustment schemes, and determine the optimal scheme based on comprehensive adjustment benefit indicators, thereby achieving the effect of optimizing the balance between production efficiency, processing quality and energy cost while meeting power constraints. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0051] Figure 1 : A schematic diagram of a multi-station synchronous processing control method according to an embodiment is exemplarily shown;

[0052] Figure 2 Schematically shows a module configuration block diagram of a multi-station synchronous processing control system 100 according to an embodiment. DETAILED DESCRIPTION

[0053] The technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0054] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0055] Existing multi-station synchronous machining control methods face the dual dynamic energy constraints of the grid's instantaneous total power cap and time-of-use electricity pricing. When operating each station according to standard process parameters would result in a total power limit exceeding the limit, they typically employ strategies such as rotating pauses or simultaneous speed reductions for all stations. These strategies fail to distinguish between task characteristics, impacting overall efficiency. These strategies can potentially suspend high-value tasks, affect the machining quality of critical parts, delay the delivery of urgent orders, and fail to effectively reduce operating costs caused by high electricity prices during peak periods.

[0056] like Figure 1 As shown, a flowchart of a multi-station synchronous processing control method is exemplarily shown. This application proposes a multi-station synchronous processing control method, including:

[0057] S10 , obtaining preset characteristic information of a plurality of workstations, wherein the preset characteristic information includes power consumption, processing time, and quality index boundaries of each workstation under standard process parameters.

[0058] Among them, preset characteristic information refers to the inherent processing properties and performance boundaries of each workstation under standard process parameters. It can be stored in the form of data tables, databases or configuration files. For example, it records the typical power of the workstation under a specific material and tool combination, the time required to complete a standard part, and the allowable dimensional tolerance range. Its main purpose is to provide basic reference data required for making decisions on process parameter adjustments.

[0059] S20, constructing a quantitative impact model, wherein the quantitative impact model is used to predict the power consumption change, processing time change and processing quality risk of each of the workstations affected by the process parameter adjustment.

[0060] Among them, the quantitative impact model refers to a mathematical or statistical model used to predict the impact of process parameter adjustments on the operating status and results of the workstation.

[0061] In some embodiments, the quantitative impact model can be implemented using a regression model, a neural network model, a lookup table, or a rule set, for example, to establish a functional relationship between spindle speed, feed rate, power consumption, machining time, and surface roughness.

[0062] S30, obtaining the current instantaneous total power of the CNC machining center, the power upper limit of the current period, and the electricity price of the current period.

[0063] S40: When the current instantaneous total power exceeds the power upper limit of the current time period, generating at least one candidate process parameter adjustment plan for at least one operating station.

[0064] The candidate process parameter adjustment scheme refers to a series of feasible process parameter combinations proposed for one or more operating stations to reduce power consumption.

[0065] In some embodiments, candidate process parameter adjustment plans can be generated by preset rules, optimization algorithm search, or manual experience input, such as reducing the cutting depth of a certain workstation, adjusting the feed speed of another workstation, or pausing a non-critical workstation, to provide a variety of possible response strategies for system evaluation and selection.

[0066] S50, based on the preset characteristic information, the quantitative impact model, and the electricity price in the current time period, evaluating the expected power reduction resulting from application of each candidate process parameter adjustment solution to the corresponding workstation, the expected time cost increment associated with the electricity price in the current time period, and the expected quality risk cost increment associated with the processing quality risk;

[0067] S60 , calculating adjustment benefit indicators of the candidate process parameter adjustment schemes according to the expected power reduction, the expected time cost increment, and the expected quality risk cost increment, and determining the optimal candidate process parameter adjustment scheme.

[0068] Among them, the adjustment benefit index is used to measure the quantitative value of the comprehensive effect of a candidate process parameter adjustment plan. In some embodiments, it can be calculated by weighted summation, multi-objective optimization function or rule-based scoring, such as the comprehensive score obtained by converting the benefits brought by the expected power reduction with the expected time cost increase and quality risk cost increase.

[0069] The present application provides a multi-station synchronous processing control method and system, which combines the predictive evaluation capability based on preset characteristic information and quantitative influence models with the current instantaneous total power, the power upper limit of the current time period and the electricity price of the current time period, and introduces a quantitative evaluation of the time cost increment and the quality risk cost increment, thereby being able to generate and evaluate multiple candidate process parameter adjustment schemes, and determine the optimal scheme based on comprehensive adjustment benefit indicators, thereby achieving the effect of optimizing the balance between production efficiency, processing quality and energy cost while meeting power constraints.

[0070] In some embodiments, the step of calculating the adjustment benefit index of each candidate process parameter adjustment solution according to the expected power reduction, the expected time cost increment, and the expected quality risk cost increment includes:

[0071] S601 , obtaining operation focus information of the CNC machining center, where the operation focus information includes the urgency of a current order, the quality level of a workpiece, or the sensitivity of electricity prices in a current period.

[0072] S602: Determine a time cost increment influencing factor and a quality risk cost influencing factor based on the acquired operation focus information.

[0073] Among them, the time cost increment impact factor is a numerical value used to adjust the expected time cost increment weight, which can be determined based on operational focus information, such as order urgency, through table lookup, formula calculation or machine learning model output.

[0074] The quality risk cost impact factor refers to a numerical value used to adjust the incremental weight of the expected quality risk cost. It can be determined based on operational focus information, such as workpiece quality level, through table lookup, formula calculation, or machine learning model output.

[0075] S603: Adjust the expected time cost increment based on the time cost increment influencing factor to obtain a target expected time cost increment.

[0076] S604: Adjust the expected quality risk cost increment based on the quality risk cost impact factor to obtain a target expected quality risk cost increment.

[0077] S605 , calculating an adjustment benefit index of each of the candidate process parameter adjustment solutions according to the expected power reduction, the target expected time cost increment, and the target expected quality risk cost increment.

[0078] In some embodiments, assume that a CNC machining center is currently processing a critical, high-precision part for an urgent order during a peak electricity price period. The system detects that the total power is exceeding the limit and generates multiple candidate process parameter adjustment scenarios. First, the system obtains operational focus information, identifying the high urgency of the current order, the high quality level of the workpiece, and the high sensitivity to electricity prices during the current period. Based on this information, the system determines a high value for the time cost increment impact factor, such as 1.8, and a high value for the quality risk cost impact factor, such as 2.0. Furthermore, high electricity price sensitivity may also mean a higher weighting for the expected power reduction. Next, the system adjusts the expected time cost increment for each scenario based on the time cost increment impact factor of 1.8 to obtain a target expected time cost increment. It also adjusts the expected quality risk cost increment for each scenario based on the quality risk cost impact factor of 2.0 to obtain a target expected quality risk cost increment. Finally, the system uses the expected power reduction, the adjusted target expected time cost increment, and the adjusted target expected quality risk cost increment for each scenario to calculate the adjustment benefit indicator for each scenario. For example, one option might have a larger projected power reduction, but also a larger projected increase in time and quality risk costs. Another option might have a smaller projected power reduction, but also a smaller projected increase in time and quality risk costs. By using these adjusted cost increments for calculations, the system can more accurately assess which option offers the highest overall benefits given the current operational priorities of "urgent orders and high quality requirements," thereby selecting the optimal option.

[0079] Through the above technical solution, this application can dynamically adjust the weights of incremental time cost and incremental quality risk cost in the calculation of the adjustment benefit index based on the dynamically changing operational priorities of the CNC machining center. This allows the calculation results of the adjustment benefit index to more accurately reflect the comprehensive benefits of different process parameter adjustment plans in the current actual production environment, thereby selecting the adjustment plan that truly meets the current operational goals, avoiding the problem of selecting a suboptimal plan due to inaccurate evaluation, improving the pertinence and effectiveness of decision-making, and better balancing production efficiency, processing quality, and energy costs.

[0080] In some embodiments, the step of determining the time cost increment impact factor and the quality risk cost impact factor based on the acquired operational focus information includes:

[0081] Identifying content in the acquired operational focus information that indicates a conflict of objectives;

[0082] Acquire preset conflict resolution parameters corresponding to the content that conflicts with the indicated target;

[0083] Based on the preset conflict resolution parameters and in combination with the content indicating the target conflict, generating conflict-resolved operational focus information;

[0084] The time cost increment impact factor and the quality risk cost impact factor are determined based on the conflict-resolved operational focus information.

[0085] Among them, the content indicating goal conflict refers to the existence of contradictory or difficult to fully meet requirements in the operational focus information, for example, requiring extremely short processing time while requiring extremely high processing quality.

[0086] Preset conflict resolution parameters refer to pre-set rules, weights or models used to guide how to handle or coordinate conflicting content in operational focus information. Specifically, they can be a set of weight coefficients, a priority sorting rule or a decision model, which is used to provide a unified evaluation or compromise standard for conflicting operational goals.

[0087] Resolved operational focus information refers to operational focus information obtained after conflict resolution that no longer contains direct contradictions or has been coordinated according to preset rules. Specifically, it can be a comprehensive score, an adjusted priority list, or a single guiding parameter.

[0088] This application solves the problem of difficulty in accurately determining the time cost increment influencing factor and the quality risk cost influencing factor when there is a conflict in the operational focus information by introducing a conflict resolution mechanism.

[0089] Specifically, the system first identifies conflicting elements within the operational priority information, such as inconsistencies between urgency and quality level requirements. Once these conflicting elements are identified, the system retrieves pre-defined conflict resolution parameters, which provide strategies or weights for handling specific conflict types. Next, based on these pre-defined parameters and the identified conflicting elements, the system generates a reconciled, conflict-resolved operational priority information that no longer contains direct conflicts. This resolved information comprehensively considers the requirements of all conflicting parties and balances them according to pre-defined rules. Ultimately, based on this resolved operational priority information, the system accurately determines the factors influencing the incremental time cost and the incremental quality risk cost. This approach avoids the bias in factor calculations that would result from directly using conflicting information. It makes subsequent adjustments to the expected incremental time cost and the expected incremental quality risk cost based on these factors more reasonable, thereby improving the accuracy of the calculated adjusted benefit indicators. In this way, even when operational objectives are inherently conflicting, the system provides a stable and effective basis for guiding process parameter adjustments, ensuring that multiple operational objectives, such as production efficiency, processing quality, and lead time, are optimally balanced while meeting energy constraints.

[0090] In some preferred embodiments, when the acquired operational priority information indicates that the current order's urgency is "high" and the workpiece quality requirement is "very high," the system identifies this as an indication of a conflicting objective. The system then searches for preset conflict resolution parameters. For example, the preset parameters may specify that, in this "urgent + very high quality" conflict scenario, the quality level is weighted 0.6 and the urgency is weighted 0.4. Based on these weights, the system combines the original urgency and quality level information and, through weighted averaging or other preset algorithms, generates resolved operational priority information, such as a composite priority score or an adjusted guidance parameter. For example, if urgency and quality level are quantified as numerical values, a weighted sum can be used to generate a composite value. Based on this composite value or adjusted guidance parameter, the system then consults a preset mapping table or applies a preset calculation formula to determine a time cost increment impact factor for adjusting the expected time cost increment and a high quality risk cost impact factor for adjusting the expected quality risk cost increment.

[0091] Through the above technical solution, the present application can effectively deal with conflicts in operational focus information, such as the contradiction between urgency and quality level requirements. By identifying conflicts, obtaining preset resolution parameters and generating information on resolved conflicts, the system can accurately determine the time cost increment influencing factors and quality risk cost influencing factors based on a coordinated and consistent input. As a result, the adjustment accuracy of the expected time cost increment and the expected quality risk cost increment can be improved, thereby making the calculated adjustment benefit index more accurate, providing a reliable basis for selecting the optimal process parameter adjustment plan, thereby better balancing operational goals such as production efficiency, processing quality and delivery cycle while meeting energy constraints.

[0092] In some embodiments, the step of adjusting the expected time cost increment based on the time cost increment influencing factor to obtain a target expected time cost increment includes:

[0093] Monitoring whether the time cost increment influencing factor satisfies relevant preset change conditions;

[0094] When the expected time cost increment influencing factor satisfies the preset change condition, the expected time cost increment is adjusted according to the time cost increment influencing factor to generate a target expected time cost increment.

[0095] Among them, the preset change condition refers to a set of rules used to determine whether the time cost increment influencing factor has reached a specific state or change range that requires triggering adjustment. It may include the absolute value change of the time cost increment influencing factor exceeding a threshold, the relative value change exceeding a percentage, or being associated with a specific external event (such as electricity price period switching, emergency order insertion), which is mainly used to ensure that the expected time cost increment is only triggered to be readjusted when necessary.

[0096] In some embodiments, the present application implements adjustment operations through a mathematical processing process of the original expected time cost increment based on the current time cost increment influencing factor, which can be implemented by multiplication, addition or more complex functional relationships, and is mainly used to convert the original expected time cost increment into a target expected time cost increment that can accurately reflect the current operational focus.

[0097] This application introduces a time cost increment impact factor to adjust the expected time cost increment when calculating the adjusted benefit index. Unlike directly using the initially determined impact factor for adjustment, this application adds dynamic monitoring of the time cost increment impact factor. By monitoring whether the impact factor meets the preset change conditions, the system can determine whether the currently determined impact factor is still applicable to the current actual situation. If the impact factor meets the preset change conditions, it indicates that the original impact factor may no longer accurately reflect the current operational focus or changes in the external environment. In this case, the system adjusts the expected time cost increment based on the latest time cost increment impact factor to generate a target expected time cost increment that better reflects the current actual situation. This target expected time cost increment is then used to calculate the adjusted benefit index. This dynamic adjustment mechanism ensures that the time cost parameters used for benefit calculation are updated in a timely manner even when the operational focus information has not changed explicitly but the impact factor itself has changed, thereby improving the accuracy of the adjusted benefit index calculation. This improved accuracy enables the final optimal candidate process parameter adjustment solution to more accurately balance power reduction, time cost, and quality risk, better adapting to the dynamically changing production environment and operational needs.

[0098] In some embodiments, the present application is implemented as follows. The system continuously monitors the current time cost increment impact factor. For example, the initial value of the time cost increment impact factor is 0.5, reflecting that the current order is generally time-sensitive. The preset change condition can be set as follows: when the absolute difference between the value of the time cost increment impact factor and the value last used for adjustment calculation is greater than 0.1, or when the system detects that the current electricity price period switches from off-peak to peak period, the preset change condition is considered to be met. Suppose at a certain moment, due to the insertion of a new emergency order, the system recalculates the time cost increment impact factor to 0.8 based on the updated operational focus information. At this time, the difference between 0.8 and the last used 0.5 is 0.3, which is greater than the preset 0.1, and the preset change condition is met. The system then adjusts the currently calculated expected time cost increment based on this new impact factor of 0.8. For example, if the original expected time cost increment is 100 yuan, the adjustment calculation can be simply multiplied: target expected time cost increment = expected time cost increment × time cost increment impact factor = 100 yuan × 0.8 = 80 yuan. This target expected time cost increment of 80 yuan will be used in the subsequent calculation of the adjusted benefit index. In this way, when the factors affecting the time cost increment change significantly, the expected time cost increment can be updated in a timely manner, ensuring that the calculation of the adjusted benefit index accurately reflects the current emphasis on time cost.

[0099] Through the above technical solution, the system can monitor the changes in the time cost increment influencing factors in real time. When the influencing factor meets the preset change conditions, the system can promptly adjust the expected time cost increment based on the changed influencing factor to generate a more accurate target expected time cost increment. This ensures that the time cost parameters used to calculate the adjustment benefit index can dynamically adapt to changes in actual conditions and improve the accuracy of the adjustment benefit index calculation. The improved accuracy of the adjustment benefit index calculation enables the system to more accurately evaluate the pros and cons of different candidate process parameter adjustment plans, thereby determining the optimal plan that better meets the current operating goals and actual conditions, and optimizing the decision-making process of multi-station synchronous processing control.

[0100] In some embodiments, the step of calculating the adjustment benefit index of each candidate process parameter adjustment scheme according to the expected power reduction, the target expected time cost increment, and the target expected quality risk cost increment includes:

[0101] Obtaining multiple current operation target information of the CNC machining center and priority information of each operation target;

[0102] selecting, based on the acquired operation target information and the priority information, an adjustment benefit index calculation rule corresponding to the operation target information and the priority information from a preset rule set including multiple adjustment benefit index calculation rules;

[0103] According to the selected adjustment benefit index calculation rule, the adjustment benefit index of each candidate process parameter adjustment scheme is calculated using the expected power reduction, the target expected time cost increment, and the target expected quality risk cost increment.

[0104] Among them, the operational target information refers to the production or management goals that the CNC machining center is currently focusing on, such as energy saving and consumption reduction, improving production efficiency, ensuring product quality, reducing operating costs, etc., which can be expressed in the form of structured data, text description or identifier.

[0105] Priority information refers to the importance or weight of each operational objective in the current environment, which can be expressed in the form of numerical weights, ranked lists, or hierarchical divisions.

[0106] A preset rule set refers to a set of different pre-stored or defined adjustment benefit indicator calculation rules. Each rule may correspond to one or more specific operational goal combinations or priority configurations, which can be stored as a rule base, lookup table or model set, etc.

[0107] In some embodiments, input quantities such as expected power drop, target expected time cost increment, target expected quality risk cost increment, etc. are converted into a single or multi-dimensional indicator for comprehensively evaluating the pros and cons of each scheme through a specific algorithm or formula, so as to adjust the benefit indicator calculation rules.

[0108] This application obtains information about multiple current operational objectives and the priorities of each operational objective for a CNC machining center, thereby clarifying the specific needs and priorities within the current production environment. Based on this information, the system dynamically selects a rule from a set of pre-set adjustment benefit indicator calculation rules that best matches the current operational objectives and priorities. This dynamic selection mechanism overcomes the limitations of using fixed calculation rules, allowing subsequent solution evaluations to more accurately reflect the importance of different operational objectives within the current environment. Finally, based on the selected adjustment benefit indicator calculation rule, the adjustment benefit indicators for each candidate process parameter adjustment solution are calculated using the already evaluated expected power reduction, target expected time cost increment, and target expected quality risk cost increment. This process eliminates the need to base the evaluation of each solution on a single, potentially inappropriate standard, but rather on a customized evaluation criterion tailored to real-time operational needs. In this way, the application can more effectively balance multiple operational objectives, such as energy conservation, efficiency, quality, and cost. This allows the application to select the process parameter adjustment solution that best meets the current overall operational needs, especially when the importance of these objectives changes dynamically. This, combined with the ability to generate candidate solutions in the basic solution, evaluate expected impacts, and adjust cost increments based on specific priorities in Claim 2, forms a more complete solution evaluation system, enabling the final optimal solution to better adapt to complex production environments and changing operational needs.

[0109] In some embodiments, the system may first obtain current operational objective information, such as the current primary focus on energy conservation and urgent order delivery, and corresponding priority information, e.g., energy conservation has a high priority, urgent order delivery also has a high priority, and other objectives have a medium priority. The system maintains a preset rule set containing multiple calculation rules, such as Rule A, which focuses on maximizing energy conservation, Rule B, which focuses on minimizing the time cost of urgent order delivery, and Rule C, which focuses on balancing energy conservation and delivery time. Based on the obtained operational objectives and priority information, the system can determine whether the current situation is more compatible with Rule B or Rule C. Furthermore, based on the priority information, the system can ultimately select Rule C as the current adjustment benefit index calculation rule by, for example, calculating the degree of conformity between current operational characteristics and the pre-set applicable characteristics of each rule. Then, for each candidate process parameter adjustment solution, the system uses selected Rule C, combined with the solution's expected power reduction, target expected time cost increment, and target expected quality risk cost increment, to calculate the solution's adjustment benefit index. For example, Rule C may be a weighted summation formula, where energy conservation is weighted highly, time cost is weighted highly, and quality risk cost is weighted moderately. In this way, the system can use the most appropriate evaluation criteria to measure the pros and cons of each solution based on the current specific operational needs.

[0110] This technical solution dynamically selects the most appropriate adjustment benefit indicator calculation rules based on current operational objectives and priorities, allowing for a more accurate assessment of the comprehensive benefits of each candidate process parameter adjustment plan. This enables the system to better balance the relationships between different operational objectives, avoiding compromises and ultimately selecting the process parameter adjustment plan that best meets current overall operational needs. This ultimately improves the overall operational efficiency and profitability of the CNC machining center.

[0111] In some embodiments, the step of selecting, based on the acquired operation target information and the priority information, an adjustment benefit index calculation rule corresponding to the operation target information and the priority information from a preset rule set including multiple adjustment benefit index calculation rules includes:

[0112] Performing feature extraction on the acquired operation target information and the acquired priority information to obtain current operation features;

[0113] For each adjustment benefit indicator calculation rule in the preset rule set, obtaining its corresponding preset applicable operating characteristics;

[0114] Calculating a compliance parameter between the current operating characteristic and each of the preset applicable operating characteristics;

[0115] Based on each of the compliance parameters, an adjustment benefit index calculation rule is selected from the preset rule set.

[0116] Feature extraction is the process of processing raw information to identify, quantify, and extract numerical or symbolic representations that represent its core attributes or states. This can be achieved using techniques such as statistical analysis, machine learning algorithms, or rule matching.

[0117] The current operation characteristics refer to the quantitative or symbolic representation obtained after feature extraction that can reflect the current operation status of the CNC machining center.

[0118] The preset applicable operating characteristics refer to the characteristic representations associated with each adjustment benefit indicator calculation rule in the preset rule set, which describe the operating scenarios or states to which the rule is suitable for application.

[0119] The conformity parameter refers to a numerical value that measures the degree of match or similarity between the current operating characteristics and the preset applicable operating characteristics of a certain adjustment benefit indicator calculation rule.

[0120] In some embodiments, the process of deriving the conformity parameter by executing a specific algorithm or model may employ methods such as distance calculation, similarity calculation, or classification model to calculate the conformity parameter.

[0121] In some embodiments, the selection of a calculation rule for the adjusted benefit indicator is achieved by determining and selecting a calculation rule from a set of preset rules based on the calculated compliance parameter. This can be achieved by selecting the rule with the highest compliance value, screening based on a threshold, or weighted averaging.

[0122] This application obtains the current operating characteristics by extracting features from the acquired operating target information and priority information, thereby converting the operating requirements into quantitative representations. At the same time, for each adjustment benefit index calculation rule in the preset rule set, its corresponding preset applicable operating characteristics are pre-defined. These characteristics describe the scenarios in which each rule can play a role. Then, the compliance parameters between the current operating characteristics and each preset applicable operating characteristic are calculated to quantify the degree of match between the current operating status and the applicable scenario of each rule. Based on these compliance parameters, a rule for calculating the adjustment benefit index is selected from the preset rule set. This rule selection mechanism based on feature matching and compliance calculation overcomes the limitations of simple mapping or fixed rules, and can identify the differences in the current operating scenarios and the relationship between multiple objectives, thereby selecting a calculation rule suitable for the current working conditions. This accurately selected calculation rule is applied to the calculation of the adjustment benefit index, so that the calculation results can accurately reflect the benefits of each candidate process parameter adjustment scheme under the current actual operating needs. This accurate benefit assessment makes the subsequent process of determining the optimal candidate process parameter adjustment plan reliable, thereby selecting a solution that can meet power constraints while taking into account production efficiency, quality, and cost. This solves the problem of inaccurate adjustment benefit indicator assessment caused by simple rule selection mechanisms in operational scenarios, which in turn affects the determination of the optimal solution.

[0123] In some embodiments, after calculating the adjustment benefit index of each candidate process parameter adjustment scheme based on the expected power reduction, the expected time cost increment, and the expected quality risk cost increment, and determining the optimal candidate process parameter adjustment scheme, the method further includes:

[0124] Applying the optimal candidate process parameter adjustment plan to a target workstation, and setting an operation observation time window of a preset length for the workstation;

[0125] Collecting actual power consumption, actual processing time, and actual processing quality indicators of the workstation under the optimal candidate process parameter adjustment solution within the observation time window;

[0126] Comparing the actual power consumption, actual processing time, and actual processing quality indicators with the expected power reduction, expected time cost increment, and expected quality risk cost increment before executing the adjustment plan, and calculating an indicator deviation value;

[0127] If the indicator deviation value exceeds the first threshold of the predicted value, it is determined to be a bad adjustment and the correction process is triggered.

[0128] Among them, the optimal candidate process parameter adjustment plan refers to the process parameter adjustment combination plan obtained based on the above evaluation and calculation, which can theoretically bring the best comprehensive benefits. It can be achieved by selecting the plan with the highest adjustment benefit index.

[0129] The target station refers to a specific processing station to which the optimal candidate process parameter adjustment solution is applied, and can be one or more operating stations.

[0130] The preset operation observation time window refers to a specific time period set to evaluate the actual effect of the adjustment plan and continuously collect data after the plan is applied. It can be set according to the workstation type, processing task or the nature of the adjustment plan.

[0131] The actual power consumption, actual processing time, and actual processing quality indicators refer to the operating data actually measured at the target workstation after the adjustment plan is applied within the observation time window, which can be collected by sensors or monitoring systems.

[0132] The expected power reduction, expected time cost increment, and expected quality risk cost increment refer to the power changes, time changes, and quality risk changes that may be brought about by the adjustment plan, predicted based on the quantitative impact model before the adjustment plan is applied. They can be calculated by the evaluation module.

[0133] The indicator deviation value refers to the quantitative expression of the difference between the actual collected data and the expected data, which can be achieved by calculating the difference or percentage difference between the actual value and the expected value.

[0134] The first threshold of the predicted value refers to the upper limit of the preset deviation used to determine whether the adjustment effect reaches the expected level, which can be set based on experience, historical data or system requirements.

[0135] An adverse adjustment refers to an adjustment plan in which the deviation between the actual operating effect and the expected effect exceeds the acceptable range. The basis for judgment is whether the indicator deviation value exceeds the first threshold of the predicted value.

[0136] This application applies the optimal candidate process parameter adjustment plan to the target workstation and sets an operation observation time window to obtain the real effect data of the adjustment plan in the actual operation environment. It is precisely because the actual power consumption, actual processing time and actual processing quality indicators are collected within the observation time window that the impact of the adjustment plan on the operation status of the workstation can be fully reflected. By comparing these actual data with the expected data before the adjustment plan is executed, the indicator deviation value is calculated to quantify the degree of difference between the actual operation effect and the expected effect. It is precisely because of the quantification of this deviation that the effect of the adjustment plan can be judged based on the preset predicted value first threshold. If the indicator deviation value exceeds the predicted value first threshold, it is determined to be a bad adjustment, and the correction process is triggered, so as to promptly discover and correct the deviation of the adjustment plan to avoid negative effects. This closed-loop control mechanism based on actual operation feedback effectively compensates for the possible limitations of the prediction accuracy of the quantitative impact model and improves the robustness and reliability of the control system.

[0137] In some embodiments, the present application is implemented as follows. Assume that the system, based on the aforementioned steps, determines that the optimal candidate process parameter adjustment solution is to reduce the feed rate of station one. The system applies this solution to station one and sets a preset operation observation time window for it, for example, 10 minutes. Over the next 10 minutes, the system continuously collects the actual power consumption, actual processing time (e.g., the time required to complete a specific processing segment), and actual processing quality indicators (e.g., surface roughness or dimensional accuracy obtained through online measurement or sampling testing) of station one running at the new feed rate. Simultaneously, the system stores the expected power reduction, expected time cost increment, and expected quality risk cost increment predicted by the quantitative impact model before executing the adjustment solution. After the 10-minute observation window expires, the system compares the collected actual data with the corresponding expected data and calculates the deviation value of each indicator. For example, if the expected power reduction is 100W and the actual reduction is only 50W, the power deviation is 50W; if the expected time increase is 5 minutes and the actual increase is 6 minutes, the time deviation is 1 minute; if the expected quality risk increase is 5% and the actual increase is 8%, the quality risk deviation is 3%. The system can calculate a comprehensive indicator deviation value based on these individual deviations. The system presets a first threshold value for the predicted value, for example, the threshold value for the comprehensive indicator deviation value is a specific value. The system compares the calculated comprehensive indicator deviation value with the threshold value. If the comprehensive indicator deviation value exceeds the preset first threshold value for the predicted value, the system determines that the adjustment is a bad adjustment and immediately triggers the preset correction process. For example, an alarm can be issued to the operator, suggesting that the adjustment plan be re-evaluated, or the system can automatically try to roll back to the parameters before the adjustment, or initiate a more sophisticated parameter optimization process.

[0138] Through the above technical solution, the system can monitor and evaluate the actual operating effects of the optimal candidate process parameter adjustment plan in real time. By collecting actual operating data and comparing it with expectations, the deviation between the actual effect of the adjustment plan and the prediction can be quantified. Based on the set threshold, it can be timely and accurately determined whether the adjustment plan has achieved the expected effect or whether there are any adverse effects. For situations that are judged to be poor adjustments, the system can trigger the correction process in time to avoid potential negative consequences, such as reduced processing quality, serious damage to production efficiency, or increased energy consumption. This mechanism based on actual feedback improves the adaptability and reliability of the control system, ensuring that under dynamically changing processing environments and energy constraints, a more effective balance between energy cost control and production benefits can be achieved.

[0139] In some embodiments, the quantitative impact model includes: a power variation model, a processing time variation model, and a quality risk assessment model.

[0140] Among them, the power change model refers to a model used to predict the impact of process parameter adjustments on workstation power consumption. Specifically, it can be constructed by analyzing historical processing data, establishing a physical simulation model, or using a machine learning algorithm. Its purpose is to accurately predict the power decrease or increase caused by process parameter adjustments.

[0141] Among them, the processing time variation model refers to a model used to predict the impact of process parameter adjustments on the workstation processing time. Specifically, it can be constructed by analyzing cutting theory formulas, establishing empirical models, or using statistical regression methods. Its purpose is to predict the increase or decrease in time cost caused by process parameter adjustments.

[0142] Among them, the quality risk assessment model refers to a model used to evaluate the potential risks of process parameter adjustments to the processing quality of the workstation. Specifically, it can be constructed by analyzing the historical relationship between process parameters and quality indicators, establishing a quality prediction model, or using expert system rules. Its purpose is to evaluate the impact of process parameter adjustments on the stability and pass rate of processing quality.

[0143] This application makes the prediction of the impact of process parameter adjustment more precise and accurate by refining the quantitative impact model into a power change model, a processing time change model, and a quality risk assessment model. Specifically, when a candidate process parameter adjustment plan needs to be evaluated, the plan will be input into these three sub-models. The power change model predicts the change in power consumption that the plan will bring based on the adjustment plan, the processing time change model predicts the change in processing time that the plan will cause, and the quality risk assessment model assesses the processing quality risk that the plan may cause. It is precisely because the impact of these three key dimensions is independently and accurately predicted that a reliable data basis can be provided for the subsequent calculation of the adjustment benefit index based on the expected power reduction, the expected time cost increment, and the expected quality risk cost increment related to the processing quality risk. This refined model structure enables the system to more comprehensively understand the comprehensive impact of process parameter adjustment, avoiding the prediction bias that may be caused by using a single or coarse model, thereby ensuring that the optimal candidate process parameter adjustment plan finally selected can more effectively balance production efficiency, energy cost, and processing quality while meeting the power constraint, solving the problem of inaccurate prediction caused by the unclear structure of the quantitative impact model.

[0144] In some embodiments, the quantitative impact model can be specifically implemented as three independent prediction modules. The power change model can be a multivariate regression model based on historical power data and process parameters, which predicts the power change relative to the standard parameters by inputting adjusted process parameters (such as spindle speed, feed rate, cutting depth, etc.). The processing time change model can be a model based on cutting theory formulas (such as metal cutting principles) and empirical coefficients, which calculates the theoretical change in the time required for the processing path according to the adjusted cutting parameters. The quality risk assessment model can be a classification model based on an expert experience rule base and historical quality inspection data, which outputs a value representing the quality risk level (such as low, medium, high) or risk probability by inputting adjusted process parameters and workpiece material, tool status and other information. The three models receive candidate process parameter adjustment plans as input in parallel and output corresponding prediction results respectively, which are then used to calculate the adjustment benefit index.

[0145] Through this technical solution, the quantitative impact model is refined into a power variation model, a processing time variation model, and a quality risk assessment model, enabling more accurate predictions of the impact of process parameter adjustments on power consumption, processing time, and processing quality risks. This ensures the accuracy and reliability of the quantitative impact model's predictions, providing a solid foundation for subsequent evaluation of the comprehensive benefits of candidate process parameter adjustment schemes. This enables more effective selection of the optimal solution, maximizing the balance between production efficiency, energy costs, and processing quality while meeting power constraints.

[0146] On the other hand, Figure 2 As shown, the present application further proposes a multi-station synchronous processing control system 100, which includes:

[0147] The preset characteristic information acquisition module 10 is used to acquire preset characteristic information of a plurality of workstations, wherein the preset characteristic information includes power consumption, processing time and quality index boundary of each workstation under standard process parameters.

[0148] The quantitative impact model construction module 20 is used to construct a quantitative impact model, wherein the quantitative impact model is used to predict the power consumption change, processing time change and processing quality risk of each workstation caused by the process parameter adjustment.

[0149] The energy constraint state acquisition module 30 is used to obtain the current instantaneous total power of the CNC machining center, the power upper limit of the current period, and the electricity price of the current period.

[0150] The process parameter solution generating module 40 generates at least one candidate process parameter adjustment solution for at least one operating station when the current instantaneous total power exceeds the power upper limit of the current time period.

[0151] The process parameter scheme evaluation module 50 is used to evaluate the expected power reduction generated after each candidate process parameter adjustment scheme is applied to the corresponding workstation based on the preset characteristic information, the quantitative impact model and the electricity price in the current time period, the expected time cost increment related to the electricity price in the current time period, and the expected quality risk cost increment related to the processing quality risk.

[0152] The adjustment scheme determination module 60 is used to calculate the adjustment benefit index of each candidate process parameter adjustment scheme according to the expected power reduction, the expected time cost increment and the expected quality risk cost increment, and determine the optimal candidate process parameter adjustment scheme.

[0153] In some embodiments, the multi-station synchronous machining control system can be deployed on an industrial control computer. The preset characteristic information acquisition module can be specifically a database interface for reading information stored in the database, such as power consumption, machining time, and quality indicator boundaries for each station under standard process parameters. The quantitative impact model construction module can be specifically a software module that loads a pre-trained machine learning model. This model can be constructed based on historical machining data or simulation data and is used to predict the impact of different process parameter adjustments on the power consumption, machining time, and machining quality risks of each station. The energy constraint status acquisition module can be specifically an interface that communicates with the CNC system or factory energy management system, and is used to collect the current instantaneous total power of the CNC machining center in real time, receive the power upper limit signal for the current time period, and obtain electricity price information for the current time period. The process parameter solution generation module can be specifically an algorithm library that contains multiple preset process parameter adjustment strategies (e.g., reducing feed rate, reducing spindle speed, pausing non-critical stations, etc.) and their combination rules. When the total power exceeds the limit, the module generates a set of multiple adjustment solutions based on the current station operating status and the degree of the limit. The process parameter scheme evaluation module can be specifically a computing engine that calls the quantitative impact model and, in combination with the electricity price of the current period, calculates the expected power reduction after the implementation of each candidate scheme, the expected time cost increase due to the extension of processing time, and the expected quality risk cost increase due to the increase in quality risk. The adjustment scheme determination module can be specifically an optimization solver that calculates the adjustment benefit index of each candidate scheme based on a preset benefit function (for example, benefit = expected power reduction - expected time cost increase - expected quality risk cost increase) and selects the scheme with the highest benefit index as the optimal adjustment scheme. The system can send the determined optimal scheme to the CNC system through the interface, and the CNC system will perform the corresponding process parameter adjustments.

[0154] Through the above technical solution, the present application provides a multi-station synchronous processing control system, which realizes the automatic control and optimization of the multi-station processing process through modular design. The system can automatically obtain the station characteristics and energy constraint status, build and use quantitative influence models to predict the impact of parameter adjustment, and intelligently generate, evaluate and determine the optimal process parameter adjustment plan when the total power exceeds the limit. This enables the control method that was originally complex and dependent on manual experience to be executed efficiently and accurately, overcoming the problem that the method is difficult to implement or requires a lot of manual intervention. Under the premise of meeting the power constraints, the system can better balance production efficiency, time cost and quality risks through quantitative evaluation and optimization decision-making, thereby achieving overall efficiency improvement.

[0155] In some embodiments, the quantitative impact model building module is further used to store a power variation model, a processing time variation model, and a quality risk assessment model.

[0156] In some embodiments, the quantitative impact model construction module can be configured with a storage unit for storing model files of the power variation model, the processing time variation model, and the quality risk assessment model. These model files can be data structures or algorithm codes stored in a specific format. For example, the power variation model can be a model file obtained based on neural network training, which inputs the process parameter adjustment amount and outputs the predicted power change value. The processing time variation model can be a set of formulas containing multiple process time calculations, which calculates the time change of each process according to the process parameter adjustment ratio. The quality risk assessment model can be a model built based on a rule engine or a decision tree, which inputs the process parameter adjustment range and the workpiece type and outputs the corresponding quality risk level or probability. When a scheme evaluation is required, the quantitative impact model construction module can load the corresponding model from the storage unit, input data according to the candidate process parameter adjustment scheme to be evaluated, and call the model for predictive calculations.

[0157] Through the above technical solution, the quantitative impact model construction module can store specific models for predicting different impact dimensions, thereby providing more refined and comprehensive prediction capabilities for subsequent process parameter adjustment plan evaluations, enabling the system to more accurately weigh factors such as power, time, and quality and select a better adjustment plan.

[0158] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A multi-station synchronous processing control method, characterized in that: include: Obtaining preset characteristic information of a plurality of workstations, wherein the preset characteristic information includes power consumption, processing time, and quality index boundaries of each workstation under standard process parameters; Constructing a quantitative impact model, wherein the quantitative impact model is used to predict the change in power consumption, the change in processing time, and the processing quality risk of each of the workstations caused by the process parameter adjustment; Obtain the current instantaneous total power of the CNC machining center, the power upper limit of the current period, and the electricity price of the current period; When the current instantaneous total power exceeds the power upper limit of the current time period, generating at least one candidate process parameter adjustment plan for at least one operating station; Based on the preset characteristic information, the quantitative impact model, and the electricity price in the current time period, evaluating the expected power reduction resulting from the application of each candidate process parameter adjustment solution to the corresponding workstation, the expected time cost increment associated with the electricity price in the current time period, and the expected quality risk cost increment associated with the processing quality risk; According to the expected power reduction, the expected time cost increment and the expected quality risk cost increment, the adjustment benefit index of each candidate process parameter adjustment scheme is calculated to determine the optimal candidate process parameter adjustment scheme.

2. The multi-station synchronous processing control method according to claim 1, characterized in that: The step of calculating the adjustment benefit index of each candidate process parameter adjustment scheme according to the expected power reduction, the expected time cost increment, and the expected quality risk cost increment comprises: Obtaining operational focus information of the CNC machining center, wherein the operational focus information includes the urgency of a current order, the quality level of a workpiece, or the sensitivity of electricity prices in a current period; Determine the time cost increment impact factor and the quality risk cost impact factor based on the acquired operational focus information; Adjusting the expected time cost increment based on the time cost increment influencing factor to obtain a target expected time cost increment; Adjusting the expected quality risk cost increment based on the quality risk cost impact factor to obtain a target expected quality risk cost increment; An adjustment benefit index of each of the candidate process parameter adjustment schemes is calculated according to the expected power reduction, the target expected time cost increment, and the target expected quality risk cost increment.

3. The multi-station synchronous processing control method according to claim 2, characterized in that: The step of determining the time cost increment influencing factor and the quality risk cost influencing factor based on the acquired operational focus information includes: Identifying content in the acquired operational focus information that indicates a conflict of objectives; Acquire preset conflict resolution parameters corresponding to the content that conflicts with the indicated target; Based on the preset conflict resolution parameters and in combination with the content indicating the target conflict, generating conflict-resolved operational focus information; The time cost increment impact factor and the quality risk cost impact factor are determined based on the conflict-resolved operational focus information.

4. The multi-station synchronous processing control method according to claim 2, characterized in that: The step of adjusting the expected time cost increment based on the time cost increment influencing factor to obtain a target expected time cost increment includes: Monitoring whether the time cost increment influencing factor satisfies relevant preset change conditions; When the expected time cost increment influencing factor satisfies the preset change condition, the expected time cost increment is adjusted according to the time cost increment influencing factor to generate a target expected time cost increment.

5. The multi-station synchronous processing control method according to claim 2, characterized in that: The step of calculating the adjustment benefit index of each candidate process parameter adjustment scheme according to the expected power reduction, the target expected time cost increment, and the target expected quality risk cost increment comprises: Obtaining multiple current operation target information of the CNC machining center and priority information of each operation target; selecting, based on the acquired operation target information and the priority information, an adjustment benefit index calculation rule corresponding to the operation target information and the priority information from a preset rule set including multiple adjustment benefit index calculation rules; According to the selected adjustment benefit index calculation rule, the adjustment benefit index of each candidate process parameter adjustment scheme is calculated using the expected power reduction, the target expected time cost increment, and the target expected quality risk cost increment.

6. The multi-station synchronous processing control method according to claim 5, characterized in that: The step of selecting, based on the acquired operation target information and the priority information, an adjustment benefit index calculation rule corresponding to the operation target information and the priority information from a preset rule set including multiple adjustment benefit index calculation rules comprises: Performing feature extraction on the acquired operation target information and the priority information to obtain current operation features; For each adjustment benefit indicator calculation rule in the preset rule set, obtaining its corresponding preset applicable operating characteristics; Calculating a compliance parameter between the current operating characteristic and each of the preset applicable operating characteristics; Based on each of the compliance parameters, an adjustment benefit index calculation rule is selected from the preset rule set.

7. The multi-station synchronous processing control method according to claim 1, characterized in that: After calculating the adjustment benefit index of each candidate process parameter adjustment scheme based on the expected power reduction, the expected time cost increment, and the expected quality risk cost increment, and determining the optimal candidate process parameter adjustment scheme, the method further includes: Applying the optimal candidate process parameter adjustment plan to a target workstation, and setting an operation observation time window of a preset length for the workstation; Collecting actual power consumption, actual processing time, and actual processing quality indicators of the workstation under the optimal candidate process parameter adjustment solution within the observation time window; Comparing the actual power consumption, actual processing time, and actual processing quality indicators with the expected power reduction, expected time cost increment, and expected quality risk cost increment before executing the adjustment plan, and calculating an indicator deviation value; If the indicator deviation value exceeds the first threshold of the predicted value, it is determined to be a bad adjustment and the correction process is triggered.

8. The multi-station synchronous processing control method according to claim 1, characterized in that: The quantitative impact model includes: a power variation model, a processing time variation model and a quality risk assessment model.

9. A multi-station synchronous processing control system, characterized in that: The system includes: A preset characteristic information acquisition module is used to obtain preset characteristic information of multiple workstations, wherein the preset characteristic information includes power consumption, processing time and quality index boundaries of each workstation under standard process parameters; A quantitative impact model construction module is used to construct a quantitative impact model, wherein the quantitative impact model is used to predict the power consumption change, processing time change and processing quality risk of each workstation caused by the process parameter adjustment; Energy constraint status acquisition module, used to obtain the current instantaneous total power of the CNC machining center, the power upper limit of the current period, and the electricity price of the current period; a process parameter solution generating module, for generating at least one candidate process parameter adjustment solution for at least one operating station when the current instantaneous total power exceeds the power upper limit of the current time period; a process parameter scheme evaluation module, configured to evaluate, based on the preset characteristic information, the quantitative impact model, and the electricity price in the current time period, an expected power reduction resulting from the application of each candidate process parameter adjustment scheme to the corresponding workstation, an expected time cost increment associated with the electricity price in the current time period, and an expected quality risk cost increment associated with the processing quality risk; The adjustment scheme determination module is used to calculate the adjustment benefit index of each candidate process parameter adjustment scheme according to the expected power reduction, the expected time cost increment and the expected quality risk cost increment, and determine the optimal candidate process parameter adjustment scheme.

10. The multi-station synchronous processing control system according to claim 9, characterized in that: The quantitative impact model building module is also used to store a power variation model, a processing time variation model and a quality risk assessment model.

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