A multi-station synchronous machining control method and system

Abstract

The application provides a multi-station synchronous machining control method and system. The method combines the prediction and evaluation capability based on preset characteristic information and a quantitative influence model with the current instantaneous total power, the power upper limit of the current period, and the electricity price of the current period, and introduces quantitative evaluation of the time cost increment and the quality risk cost increment, so as to generate and evaluate multiple candidate process parameter adjustment schemes, and determine an optimal scheme based on a comprehensive adjustment benefit index, thereby achieving the effect of realizing the optimized balance among production efficiency, machining quality, and energy cost while meeting the power constraint.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of numerical control machining, in particular to a multi-station synchronous machining control method and system. BACKGROUND

[0002] Generally, a numerical control machining center is usually configured with multiple stations, such as station one, station two, and station three, for processing different machining tasks in parallel to improve overall production efficiency. These stations share the basic structure and total power supply system of the machine tool. The factory sets an upper limit threshold for the instantaneous total input power of the machining center, which is dynamically adjusted according to different time periods of the day, such as being lowered during peak electricity consumption. At the same time, electricity charges usually adopt a time-sharing pricing strategy, and the electricity price of a specific period is much higher than that of other periods. Therefore, under the premise of ensuring the timely completion of production orders, effectively reducing the total energy consumption cost, and strictly avoiding the instantaneous total power exceeding the current set limit, the core goal of the control system is to control the system.

[0003] However, in actual production, when each station operates according to its own standard process parameters, the instantaneous total power may exceed the current set lower power limit. For example, when station one performs high-hardness material deep milling with large cutting depth, and station two performs continuous deep hole drilling at almost the same time, the total power is likely to exceed the limit. When facing such a situation, the existing control system usually adopts a pre-set and relatively rough coping strategy. One common strategy is "turning off in turn", that is, when the total power approaches or reaches the threshold, the system forcibly suspends the work of one or more stations. Which station to suspend is usually based on simple polling or fixed priority, which does not take into account the relevance and urgency of the tasks of each station, and may cause subsequent processes to be forced to wait, disrupting the production rhythm. Another strategy is "synchronous speed reduction", that is, instructing all running stations to uniformly reduce process parameters (such as spindle speed, feed rate). This "one-size-fits-all" approach ignores the essential differences and specific needs of machining tasks in each station. For rough machining tasks, reducing parameters mainly affects machining time; but for precision machining tasks, unscrupulous parameter adjustment is likely to cause part size to be out of tolerance, surface roughness to be substandard, or even scrap, seriously affecting the delivery and quality of key orders. The final result is that the ideal balance between energy cost control and production efficiency cannot be achieved, which may sacrifice efficiency due to overly conservative power control, or affect the delivery or quality of key tasks due to improper adjustment. SUMMARY

[0004] The present application provides a multi-station synchronous machining control method, which can perform fine evaluation and collaborative adjustment based on multi-dimensional information, thereby optimizing the overall machining efficiency under the premise of meeting the power constraint and quality requirement.

[0005] In one aspect, the application provides a multi-station synchronous machining control method, comprising:

[0006] obtaining preset characteristic information of multiple stations, the preset characteristic information including power consumption, machining time and quality index boundary of each station under standard process parameters;

[0007] constructing a quantitative influence model, wherein the quantitative influence model is used to predict the power consumption change, machining time change and machining quality risk of each station caused by process parameter adjustment;

[0008] obtaining current instantaneous total power of a numerical control machining center, power upper limit of a current period and electricity price of the current period;

[0009] when the current instantaneous total power exceeds the power upper limit of the current period, generating at least one candidate process parameter adjustment scheme for at least one running station;

[0010] based on the preset characteristic information, the quantitative influence model and the electricity price of the current period, evaluating the expected power reduction, the expected time cost increment related to the electricity price of the current period and the expected quality risk cost increment related to the machining quality risk caused by each candidate process parameter adjustment scheme applied to the corresponding station;

[0011] according to the expected power reduction, the expected time cost increment and the expected quality risk cost increment, calculating an adjustment benefit index of each candidate process parameter adjustment scheme to determine an 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 comprises:

[0013] obtaining operation focus information of the numerical control machining center, the operation focus information including emergency degree of a current order, quality grade of a workpiece or electricity price sensitivity of the current period;

[0014] determining a time cost increment influence factor and a quality risk cost influence factor according to the obtained operation focus information;

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

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

[0017] According to the expected power drop, the target expected time cost increment and the target expected quality risk cost increment, an adjustment benefit index of each candidate process parameter adjustment scheme is calculated.

[0018] Optionally, the step of determining the time cost increment influence factor and the quality risk cost influence factor according to the obtained operation focus information comprises:

[0019] Identifying content indicating a target conflict in the obtained operation focus information;

[0020] Obtaining a preset conflict resolution parameter corresponding to the content indicating the target conflict;

[0021] Generating operation focus information with resolved conflicts based on the preset conflict resolution parameter and in combination with the content indicating the target conflict;

[0022] Determining the time cost increment influence factor and the quality risk cost influence factor according to the operation focus information with resolved conflicts.

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

[0024] Monitoring whether the time cost increment influence factor meets a relevant preset change condition;

[0025] When the expected time cost increment influence factor meets the preset change condition, performing adjustment operation on the expected time cost increment according to the time cost increment influence 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 drop, the target expected time cost increment and the target expected quality risk cost increment comprises:

[0027] Obtaining a plurality of operation target information and priority information of each operation target of the numerical control machining center currently;

[0028] According to the obtained operation target information and the priority information, selecting an adjustment benefit index calculation rule corresponding to the operation target information and the priority information from a preset rule set containing a plurality of adjustment benefit index calculation rules;

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

[0030] Optionally, the step of selecting an adjustment benefit index calculation rule corresponding to the operation target information and the priority information from a preset rule set comprising a plurality of adjustment benefit index calculation rules according to the obtained operation target information and the priority information comprises:

[0031] performing feature extraction on the obtained operation target information and the priority information to obtain a current operation feature;

[0032] for each adjustment benefit index calculation rule in the preset rule set, obtaining a preset applicable operation feature corresponding thereto;

[0033] calculating a compliance parameter between the current operation feature and each preset applicable operation feature;

[0034] selecting an adjustment benefit index calculation rule from the preset rule set based on the compliance parameters.

[0035] Optionally, after 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, determining an optimal candidate process parameter adjustment scheme, the method further comprises:

[0036] applying the optimal candidate process parameter adjustment scheme to a target station and setting an observation time window of a preset length for the station;

[0037] collecting actual power consumption, actual processing time, and actual processing quality indicators of the station running under the optimal candidate process parameter adjustment scheme 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 the adjustment scheme is executed to calculate an index deviation value;

[0039] if the index deviation value exceeds a first threshold value of the prediction value, determining that the adjustment is not good and triggering a correction process.

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

[0041] In another aspect, the present application provides a multi-station synchronous processing control system, characterized in that the system comprises:

[0042] a preset characteristic information acquisition module configured to acquire preset characteristic information of the plurality of stations, the preset characteristic information including power consumption, processing time, and quality index boundary of each station under standard process parameters;

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

[0044] an energy constraint state acquisition module configured to acquire current total power of the numerical control machining center, power upper limit of a current period, and electricity price of the current period;

[0045] a process parameter scheme generation module configured to generate at least one candidate process parameter adjustment scheme for at least one running station when the current total power exceeds the power upper limit of the current 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 of the current period, expected power reduction, expected time cost increment related to the electricity price of the current period, and expected quality risk cost increment related to processing quality risk caused by application of each candidate process parameter adjustment scheme to a corresponding station;

[0047] an adjustment scheme determination module configured to calculate an 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 an optimal candidate process parameter adjustment scheme.

[0048] Optionally, the quantitative impact model construction module is further configured to store a power variation model, a processing time variation model, and a quality risk evaluation model.

[0049] The multi-station synchronous machining control method and system provided by the present application can generate and evaluate a plurality of candidate process parameter adjustment schemes by combining the prediction and evaluation capability based on preset characteristic information and a quantitative impact model with current total power, power upper limit of a current period, and electricity price of the current period, and introducing quantitative evaluation of time cost increment and quality risk cost increment, and determine an optimal scheme based on a comprehensive adjustment benefit index, thereby achieving the effect of achieving an optimized balance between production efficiency, processing quality, and energy cost while meeting power constraints. BRIEF DESCRIPTION OF DRAWINGS

[0050] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 The diagram above exemplarily illustrates a multi-station synchronous machining control method according to an embodiment;

[0052] Figure 2 The diagram illustrates a module configuration block diagram of a multi-station synchronous machining control system 100 according to an embodiment. Detailed Implementation

[0053] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0054] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0055] Existing multi-station synchronous machining control methods, facing the dual dynamic energy constraints of the grid's instantaneous total power limit and time-of-use pricing mechanisms, typically employ strategies such as rotating shutdowns or simultaneous speed reductions at all stations when operating each station according to standard process parameters would lead to total power exceeding the limit. These strategies fail to differentiate between task characteristics and thus impact overall efficiency, potentially suspending high-value tasks, affecting the processing quality of critical components, delaying the delivery of urgent orders, or failing to effectively reduce operating costs caused by high electricity prices during peak periods.

[0056] like Figure 1 As shown, an exemplary flowchart of a multi-station synchronous machining control method is illustrated. This application proposes a multi-station synchronous machining control method, comprising:

[0057] S10, obtain preset characteristic information of multiple workstations, the preset characteristic information including power consumption, processing time and quality index boundary of each workstation under standard process parameters.

[0058] The preset characteristic information refers to inherent processing attributes and performance boundaries of each workstation under standard process parameters, which can be stored in the form of a data table, a database or a configuration file, for example, recording typical power of the workstation under a specific material and tool combination, time required to complete a standard part and allowable size tolerance range, which mainly provides basic reference data required for process parameter adjustment decision-making.

[0059] S20, construct a quantitative influence model, wherein the quantitative influence model is used to predict the power consumption change, processing time change and processing quality risk of each workstation caused by process parameter adjustment.

[0060] The quantitative influence model refers to a mathematical or statistical model used to predict the influence of process parameter adjustment on the running state and result of the workstation.

[0061] In some embodiments, the quantitative influence model can be implemented by 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 and power consumption, processing time and surface roughness.

[0062] S30, obtain the current instantaneous total power of the numerical control 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 period, generate at least one candidate process parameter adjustment scheme for at least one running workstation.

[0064] The candidate process parameter adjustment scheme refers to a series of feasible process parameter combinations for one or more running workstations, which are used to reduce power consumption.

[0065] In some embodiments, the candidate process parameter adjustment scheme can be generated by using preset rules, searching based on an optimization algorithm or inputting artificial experience, for example, reducing the cutting depth of a certain workstation, adjusting the feed speed of another workstation or suspending a non-critical workstation, which is used to provide multiple possible coping strategies for system evaluation and selection.

[0066] S50, based on the preset characteristic information, the quantitative influence model and the electricity price of the current period, evaluate the expected power reduction of each candidate process parameter adjustment scheme applied to the corresponding workstation, the expected time cost increment related to the electricity price of the current period, and the expected quality risk cost increment related to the processing quality risk;

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

[0068] The adjustment benefit index is used to measure a quantitative value of the comprehensive effect of a candidate process parameter adjustment scheme. In some embodiments, the adjustment benefit index can be calculated by using weighted summation, a multi-objective optimization function, or a rule-based scoring method, for example, a comprehensive score obtained by converting the benefits brought by the expected power drop, the expected time cost increment, and the expected quality risk cost increment.

[0069] The multi-station synchronous machining control method and system provided by the present application can generate and evaluate multiple candidate process parameter adjustment schemes by combining the prediction and evaluation capabilities based on the preset characteristic information and the quantitative influence model with the current instantaneous total power, the power upper limit of the current period, and the electricity price of the current period, and introducing quantitative evaluation of the time cost increment and the quality risk cost increment, and determine an optimal scheme based on the comprehensive adjustment benefit index, thereby achieving the effect of optimizing the balance between production efficiency, machining quality, and energy cost while meeting the power constraint.

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

[0071] S601, obtaining operation focus information of the numerical control machining center, the operation focus information including an emergency degree of a current order, a quality level of a workpiece, or a price sensitivity of a current period.

[0072] S602, determining a time cost increment influence factor and a quality risk cost influence factor according to the obtained operation focus information.

[0073] The time cost increment influence factor is a value used to adjust the weight of the expected time cost increment, which can be determined according to the operation focus information, such as the order emergency degree, by using a table lookup, formula calculation, or machine learning model output.

[0074] The quality risk cost influence factor is a value used to adjust the weight of the expected quality risk cost increment, which can be determined according to the operation focus information, such as the quality level of the workpiece, by using a table lookup, formula calculation, or machine learning model output.

[0075] S603, adjusting the expected time cost increment based on the time cost increment influence factor to obtain a target expected time cost increment.

[0076] S604, adjusting the expected quality risk cost increment based on the quality risk cost influence factor to obtain a target expected quality risk cost increment.

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

[0078] In some embodiments, it is assumed that the current CNC machining center is processing a key high-precision part of an urgent order, and is currently in a peak period of electricity price. The system detects that the total power is over limit, and generates multiple candidate process parameter adjustment schemes. First, the system obtains the operation focus information, and identifies that the current order is high in urgency, the workpiece quality level is high, and the current period is high in electricity price sensitivity. According to these information, the system determines that the time cost increment influence factor is a relatively high value, for example, 1.8, and the quality risk cost influence factor is a relatively high value, for example, 2.0, and the high electricity price sensitivity may mean that the evaluation weight of the expected power decrement is also increased accordingly. Then, the system adjusts the expected time cost increment of each scheme based on the time cost increment influence factor of 1.8 to obtain a target expected time cost increment, and adjusts the expected quality risk cost increment of each scheme based on the quality risk cost influence factor of 2.0 to obtain a target expected quality risk cost increment. Finally, the system calculates the adjustment benefit index of each scheme using the expected power decrement, the adjusted target expected time cost increment, and the adjusted target expected quality risk cost increment of each scheme. For example, one scheme may have a larger expected power decrement, but also has a larger expected time cost increment and quality risk cost increment; another scheme may have a smaller expected power decrement, but also has a smaller expected time increment and quality risk cost increment. By using the adjusted cost increments for calculation, the system can more accurately evaluate which scheme has higher comprehensive benefit under the current “order urgent, high quality requirement” operation focus, so as to select the optimal scheme.

[0079] Through the above technical solutions, the present application can dynamically adjust the weights of the time cost increment and the quality risk cost increment in the calculation of the adjustment benefit index according to the dynamically changing operation focus of the CNC machining center. This makes the calculation result of the adjustment benefit index more accurately reflect the comprehensive benefit of different process parameter adjustment schemes under the current actual production environment, so that the adjustment scheme that truly meets the current operation goal can be selected, the problem of selecting a suboptimal scheme due to inaccurate evaluation is avoided, the pertinence and effectiveness of the decision are improved, and the production efficiency, machining quality, and energy cost are better balanced.

[0080] In some embodiments, the step of determining the time cost increment influence factor and the quality risk cost influence factor according to the obtained operation focus information comprises:

[0081] identifying content indicating a target conflict in the obtained operation focus information;

[0082] obtaining a preset conflict resolution parameter corresponding to the content indicating the target conflict;

[0083] generating operation focus information with resolved conflicts based on the preset conflict resolution parameter and in combination with the content indicating the target conflict;

[0084] determining the time cost increment influence factor and the quality risk cost influence factor according to the operation focus information with resolved conflicts.

[0085] The content indicating a target conflict refers to requirements in the operation focus information that are contradictory to each other or difficult to satisfy simultaneously, for example, extremely short processing time and extremely high processing quality.

[0086] The preset conflict resolution parameter refers to a rule, weight or model that is preset and used to guide how to handle or coordinate conflicting content in the operation focus information. Specifically, it can be a set of weight coefficients, a priority sorting rule or a decision model, and is used to provide a unified evaluation or compromise standard for conflicting operation targets.

[0087] The operation focus information with resolved conflicts refers to operation focus information that has been processed for conflict resolution and 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 guide parameter.

[0088] The present application introduces a conflict resolution mechanism to solve the problem of accurately determining the time cost increment influence factor and the quality risk cost influence factor when the operation focus information contains conflicts.

[0089] Specifically, first, the system identifies the conflicting parts in the operation focus information, such as the inconsistency between the urgency level and the quality grade requirement. Once these conflict-indicating contents are identified, the system obtains pre-set conflict resolution parameters, which provide strategies or weights for handling specific types of conflicts. Then, based on these pre-set parameters and in combination with the identified conflict contents, the system generates a reconciled operation focus information that no longer contains direct conflicts. This reconciled information takes into account the requirements of all parties in conflict and weighs them according to pre-set rules. Finally, based on this reconciled operation focus information, the system can accurately determine the time cost increment influence factor and the quality risk cost influence factor. This processing method avoids the calculation bias of the influence factors caused by directly using the conflict information, making the subsequent adjustment of the expected time cost increment and the expected quality risk cost increment based on these influence factors more reasonable, thereby improving the accuracy of the adjustment benefit index calculation. In this way, even in the case of internal conflicts in operation goals, the system can provide a stable and effective basis to guide process parameter adjustment, ensuring that the energy constraints are met while maximizing the balance of production efficiency, processing quality, and delivery cycle and other operation goals.

[0090] In some preferred embodiments, when the obtained operation focus information indicates that the current order urgency level is "high" and the workpiece quality grade requirement is "extremely high", the system identifies this as a conflict-indicating content. The system looks up the pre-set conflict resolution parameters, for example, the pre-set parameters may specify that in this "urgent + extremely high quality" conflict situation, the weight of the quality grade is 0.6 and the weight of the urgency level is 0.4. Based on these weights, the system combines the original urgency level and quality grade information to generate a reconciled operation focus information, such as a comprehensive priority score or an adjusted guidance parameter, through weighted averaging or other pre-set algorithms. For example, if the urgency level and the quality grade are quantified as numerical values, a comprehensive numerical value can be obtained by weighted summation. Subsequently, the system determines the time cost increment influence factor for adjusting the expected time cost increment and the high quality risk cost influence factor for adjusting the expected quality risk cost increment based on this comprehensive numerical value or adjusted guidance parameter by consulting the pre-set mapping table or applying the pre-set calculation formula.

[0091] By the technical solution, the application can effectively handle the conflicts in the operation focus information, such as the contradiction between the urgency and the quality level requirement. By identifying the conflicts, obtaining the preset resolution parameters, and generating the information with the resolved conflicts, the system can accurately determine the time cost increment influence factor and the quality risk cost influence factor based on a coordinated and consistent input. Thus, the adjustment accuracy of the expected time cost increment and the expected quality risk cost increment can be improved, and the calculated adjustment benefit index is more accurate, which provides a reliable basis for selecting the optimal process parameter adjustment scheme, so that the production efficiency, processing quality, delivery cycle and other operation targets can be better balanced while meeting the energy constraints.

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

[0093] monitoring whether the time cost increment influence factor meets a related preset change condition;

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

[0095] The preset change condition refers to a rule set for judging whether the time cost increment influence factor has a specific state or change amplitude that needs to trigger adjustment, which can include that the absolute value of the time cost increment influence factor changes by more than a threshold, the relative value changes by more than a percentage, or is associated with a specific external event (such as a power price period switching or an urgent order insertion), and is mainly used to ensure that the expected time cost increment is only re-adjusted when necessary.

[0096] In some embodiments, the application realizes the adjustment operation by a mathematical processing process of the original expected time cost increment according to the current time cost increment influence factor, which can use multiplication, addition or more complex function 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 operation focus.

[0097] In the calculation of the adjustment benefit index, the application introduces a time cost increment influence factor to adjust the expected time cost increment. Unlike directly adjusting using the initially determined influence factor, the application increases dynamic monitoring of the time cost increment influence factor. By monitoring whether the influence factor meets the preset change condition, the system can determine whether the currently determined influence factor is still applicable to the current actual situation. When it is monitored that the influence factor meets the preset change condition, it indicates that the original influence factor may no longer accurately reflect the current operation focus or external environment change. At this time, the system will adjust the expected time cost increment according to the current latest time cost increment influence factor, thereby generating a target expected time cost increment that is more in line with the current actual situation. This target expected time cost increment is then used to calculate the adjustment benefit index. This dynamic adjustment mechanism ensures that even in the case where the operation focus information has not changed significantly but the influence factor itself has changed, the time cost parameter used for benefit calculation can be updated in time, thereby improving the accuracy of the adjustment benefit index calculation. This improvement in accuracy enables the finally determined optimal candidate process parameter adjustment scheme to more accurately balance power reduction, time cost and quality risk, and better adapt to the dynamically changing production environment and operation demand.

[0098] In some embodiments, the application is implemented as follows. The system continuously monitors the current time cost increment influence factor. For example, the time cost increment influence factor initially has a value of 0.5, reflecting that the current order has an average sensitivity to time. The preset change condition can be set as follows: when the absolute difference between the value of the time cost increment influence factor and the value used for the last adjustment operation is greater than 0.1, or when the system detects that the current electricity price period switches from off-peak to peak, it is considered that the preset change condition is met. Suppose at a certain time, due to a new urgent order being inserted, the system recalculates the time cost increment influence factor to be 0.8 according to the updated operation 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, meeting the preset change condition. The system then adjusts the currently calculated expected time cost increment according to this new influence factor 0.8. For example, if the original expected time cost increment is 100 yuan, the adjustment operation can simply use multiplication: target expected time cost increment = expected time cost increment x time cost increment influence factor = 100 yuan x 0.8 = 80 yuan. This 80 yuan target expected time cost increment will be used for subsequent adjustment benefit index calculation. In this way, when the time cost increment influence factor changes significantly, the expected time cost increment can be updated in time, ensuring that the calculation of the adjustment benefit index accurately reflects the current emphasis on time cost.

[0099] By the technical solution, the system can monitor the change of the time cost increment influencing factor in real time. When the influencing factor meets the preset change condition, the system can timely adjust and calculate the expected time cost increment according to the changed influencing factor, and generate a more accurate target expected time cost increment. This ensures that the time cost parameter used for calculating the adjustment benefit index can dynamically adapt to the change of the actual situation, and improves the accuracy of the adjustment benefit index calculation. The improvement of the accuracy of the adjustment benefit index calculation enables the system to more accurately evaluate the pros and cons of different candidate process parameter adjustment schemes, thereby determining an optimal scheme that is more in line with the current operation target and actual situation, and optimizing the decision-making process of the multi-station synchronous machining 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 drop, the target expected time cost increment, and the target expected quality risk cost increment comprises:

[0101] Obtaining a plurality of operation target information of the numerical control machining center and priority information of each operation target;

[0102] According to the obtained operation target information and priority information, selecting an adjustment benefit index calculation rule corresponding to the operation target information and priority information from a preset rule set containing a plurality of adjustment benefit index calculation rules;

[0103] According to the selected adjustment benefit index calculation rule, the expected power drop, the target expected time cost increment, and the target expected quality risk cost increment are used to calculate the adjustment benefit index of each candidate process parameter adjustment scheme.

[0104] The operation target information is the production or management target currently concerned by the numerical control machining center, such as energy saving and consumption reduction, improving production efficiency, ensuring product quality, and reducing operation cost, which can be represented in the form of structured data, text description, or identifier.

[0105] The priority information refers to the importance or weight of each operation target in the current environment, which can be represented in the form of numerical weight, ordering list, or grade division.

[0106] The preset rule set refers to a set of different adjustment benefit index calculation rules pre-stored or defined, each rule can correspond to one or more specific operation target combinations or priority configurations, which can be stored as a rule library, a lookup table, or a model set.

[0107] In some embodiments, the expected power reduction, the target expected time cost increment, the target expected quality risk cost increment and other input quantities are converted into a single or multi-dimensional index that comprehensively evaluates the pros and cons of each scheme by a specific algorithm or formula, so as to realize the adjustment of the benefit index calculation rule.

[0108] The present application obtains the current multiple operation target information and the priority information of each operation target of the numerical control machining center, thereby determining the specific requirements and emphases in the current production environment. Based on these information, the system can dynamically select a rule that best matches the current operation target and priority from the preset multiple adjustment benefit index calculation rules. This dynamic selection mechanism overcomes the limitations of using a fixed calculation rule, so that the subsequent scheme evaluation can more accurately reflect the importance of different operation targets in the current environment. Finally, according to the selected adjustment benefit index calculation rule, the expected power reduction, the target expected time cost increment and the target expected quality risk cost increment that have been evaluated are used to calculate the adjustment benefit index of each candidate process parameter adjustment scheme. This process makes the evaluation of each scheme no longer based on a general and possibly inapplicable standard, but on an evaluation standard tailored to real-time operation requirements. In this way, the present application can more effectively balance energy saving, efficiency, quality, cost and other operation targets, especially when the importance of these targets changes dynamically, the process parameter adjustment scheme that best meets the current overall operation requirements can be selected. Combined with the ability to generate candidate schemes, evaluate expected impacts and adjust cost increments according to specific emphases in claim 2 in the basic scheme, a more perfect scheme evaluation system is formed, so that the finally determined optimal scheme can better adapt to complex production environments and changing operation requirements.

[0109] In some embodiments, the system can first obtain current operation target information, such as the current main focus on energy saving and delivery of emergency orders, and corresponding priority information, such as high priority for energy saving, high priority for delivery of emergency orders, and medium priority for other targets. The system internally maintains a preset rule set containing multiple calculation rules, such as rule A focusing on maximizing energy saving effect, rule B focusing on minimizing delivery time cost of emergency orders, and rule C focusing on balancing energy saving and delivery time. According to the obtained operation target and priority information, the system can determine that the current situation is more suitable for rule B or rule C. Further, the system can select rule C as the current adjustment benefit index calculation rule according to the priority information, for example, by calculating the degree of conformity between the current operation characteristics and the preset applicable characteristics of each rule. Then, for each candidate process parameter adjustment scheme, the system uses the selected rule C to calculate the adjustment benefit index of the scheme based on the expected power reduction, the target expected time cost increment, and the target expected quality risk cost increment of the scheme. For example, rule C can be a weighted sum formula in which the weight of the energy saving term is high, the weight of the time cost term is also high, and the weight of the quality risk cost term is moderate. In this way, the system can use the most suitable evaluation standard to measure the pros and cons of each scheme according to the current specific operation demand.

[0110] Through the above technical solutions, the most suitable adjustment benefit index calculation rule can be dynamically selected according to the current operation target and priority, so that the comprehensive benefits of each candidate process parameter adjustment scheme can be more accurately evaluated. This enables the system to better balance the relationship between different operation targets and avoid one-sidedness, thereby selecting a process parameter adjustment scheme that best meets the current overall operation demand. Ultimately, the overall operation efficiency and benefit of the numerical control machining center are improved.

[0111] In some embodiments, the step of selecting an adjustment benefit index calculation rule corresponding to the operation target information and the priority information from a preset rule set containing multiple adjustment benefit index calculation rules according to the obtained operation target information and the priority information comprises:

[0112] extracting features from the obtained operation target information and the priority information to obtain current operation characteristics;

[0113] obtaining the preset applicable operation characteristics corresponding to each adjustment benefit index calculation rule in the preset rule set;

[0114] calculating the conformity parameters between the current operation characteristics and each preset applicable operation characteristic;

[0115] selecting an adjustment benefit index calculation rule from the preset rule set based on the conformity parameters.

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

[0117] Current operating features refer to the quantified or symbolic representations obtained after feature extraction, which reflect the current operating state of the CNC machining center.

[0118] Pre-set applicable operating features refer to the feature representations associated with each adjustment benefit index calculation rule in the pre-set rule set, describing the operating scenarios or states suitable for the application of the rule.

[0119] The degree of compliance parameter refers to a numerical value that measures the degree of matching or similarity between the current operating features and the pre-set applicable operating features of a certain adjustment benefit index calculation rule.

[0120] In some embodiments, the process of deriving the degree of compliance parameter is achieved by executing a specific algorithm or model. It can use distance calculation, similarity calculation, or classification model methods to achieve the calculation of the degree of compliance parameter.

[0121] In some embodiments, the process of determining and selecting a calculation rule from the pre-set rule set according to the calculated degree of compliance parameter is used to achieve the selection of the adjustment benefit index calculation rule. It can use strategies such as selecting the rule with the highest degree of compliance value, threshold-based filtering, or weighted average to achieve the selection.

[0122] The application obtains current operation characteristics by performing feature extraction on the obtained operation target information and priority information, thereby converting operation requirements into quantitative representation. Meanwhile, for each adjustment benefit index calculation rule in the preset rule set, a corresponding preset applicable operation characteristic is defined in advance, and these characteristics describe the scenarios in which each rule can function. Then, a compliance parameter between the current operation characteristic and each preset applicable operation characteristic is calculated, thereby quantifying the matching degree between the current operation state and the applicable scenario of each rule. Based on these compliance parameters, one adjustment benefit index calculation rule 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 operation scenario and the relationship between multiple targets, thereby selecting a calculation rule suitable for the current working condition. Applying this accurately selected calculation rule to the calculation of the adjustment benefit index enables the calculation result to accurately reflect the benefits of each candidate process parameter adjustment scheme under the current actual operation requirements. This accurate benefit evaluation makes the subsequent process of determining the optimal candidate process parameter adjustment scheme reliable, thereby enabling the selection of a scheme that can meet the power constraint while taking into account production efficiency, quality and cost, thereby solving the problem of inaccurate adjustment benefit index evaluation caused by simple rule selection mechanism in the operation scenario, which in turn affects the determination of the optimal scheme.

[0123] In some embodiments, after 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, and determining the optimal candidate process parameter adjustment scheme, the method further comprises:

[0124] applying the optimal candidate process parameter adjustment scheme to the target station and setting a preset length of running observation time window for the station;

[0125] collecting the actual power consumption, actual processing time and actual processing quality index of the station running under the optimal candidate process parameter adjustment scheme within the observation time window;

[0126] comparing the actual power consumption, actual processing time and actual processing quality index with the expected power reduction, expected time cost increment and expected quality risk cost increment before the adjustment scheme is executed, and calculating an index deviation value;

[0127] if the index deviation value exceeds a first threshold value of the prediction value, determining that the adjustment is not good and triggering a correction process.

[0128] The optimal candidate process parameter adjustment scheme refers to a process parameter adjustment combination scheme that can theoretically bring the best overall benefit according to the foregoing evaluation and calculation, which can be achieved by selecting the scheme with the highest adjustment benefit index.

[0129] The target station refers to a specific processing station that applies the optimal candidate process parameter adjustment scheme, which can be one or more running stations.

[0130] The preset duration of the running observation time window refers to a specific time period set for evaluating the actual effect of the adjustment scheme, during which data is continuously collected after the scheme is applied, which can be set according to the type of the station, the processing task, or the nature of the adjustment scheme.

[0131] The actual power consumption, actual processing time, and actual processing quality indicators refer to the actual running data measured after the target station applies the adjustment scheme within the observation time window, which can be obtained by sensors or monitoring systems.

[0132] The expected power reduction, expected time cost increment, and expected quality risk cost increment refer to the changes in power, time, and quality risk that the adjustment scheme may bring before it is applied, which are predicted based on the quantitative influence model and can be calculated by the evaluation module.

[0133] The index deviation value refers to the quantitative representation 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 value of the predicted value refers to the preset upper limit for determining whether the adjustment effect meets the expectation, which can be set according to experience, historical data, or system requirements.

[0135] The undesirable adjustment refers to an adjustment scheme whose actual running effect deviates from the expectation beyond an acceptable range, and the basis for its determination is whether the index deviation value exceeds the first threshold value of the predicted value.

[0136] The application obtains real effect data of the adjustment scheme in the actual running environment by applying the optimal candidate process parameter adjustment scheme to the target station and setting a running observation time window. It is because that the actual power consumption, actual processing time and actual processing quality indicators are collected within the observation time window that the influence of the adjustment scheme on the running state of the station can be comprehensively reflected. By comparing the actual data with the expected data before the adjustment scheme is executed, the index deviation value is calculated to quantify the difference between the actual running effect and the expected effect. It is because that the deviation is quantified that the effect of the adjustment scheme can be judged based on the preset prediction value first threshold. If the index deviation value exceeds the prediction value first threshold, it is determined that the adjustment is not good and the deviation correction process is triggered to timely find and correct the deviation of the adjustment scheme and avoid negative effects. The closed-loop control mechanism based on actual running feedback effectively makes up for the limitations of the prediction accuracy of the quantitative influence model and improves the robustness and reliability of the control system.

[0137] In some embodiments, the application is implemented as follows, assuming that the system determines that the optimal candidate process parameter adjustment scheme is to reduce the feed speed of station one according to the foregoing steps. The system applies the scheme to station one and sets a running observation time window with a preset duration, for example, 10 minutes. Within the next 10 minutes, the system continuously collects the actual power consumption, actual processing time (for example, the time required to complete a specific processing section) and actual processing quality indicators (for example, surface roughness or dimensional accuracy obtained through online measurement or sampling detection) of station one running at the new feed speed. At the same time, the system has the expected power reduction, expected time cost increase and expected quality risk cost increase predicted based on the quantitative influence model before the adjustment scheme is executed. After the 10-minute observation window ends, the system compares the collected actual data with the corresponding expected data to calculate 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 index deviation value according to these single deviations. The system has a prediction value first threshold, for example, the comprehensive index deviation value threshold is a certain specific value. The system compares the calculated comprehensive index deviation value with the threshold. If the comprehensive index deviation value exceeds the preset prediction value first threshold, the system determines that the adjustment is not good and immediately triggers the preset deviation correction process, for example, an alarm can be sent to the operator to suggest re-evaluating the adjustment scheme, or the system automatically attempts to roll back to the parameters before the adjustment, or starts a more detailed parameter optimization process.

[0138] By the above technical solution, the system can monitor and evaluate the actual running effect of the optimal candidate process parameter adjustment scheme in real time. By collecting actual running data and comparing with the expectation, the deviation between the actual effect of the adjustment scheme and the prediction can be quantified. Based on the set threshold, it can be determined in time and accurately whether the adjustment scheme has achieved the expected effect or there is adverse effect. For the case of being determined as adverse adjustment, the system can timely trigger the rectification process, thereby avoiding potential negative consequences, such as decline of processing quality, serious damage of production efficiency or increase of energy consumption instead of reduction. This mechanism based on actual feedback improves the adaptability and reliability of the control system, ensuring that the balance between energy cost control and production benefit can be achieved more effectively under the dynamic changing processing environment and energy constraints.

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

[0140] The power variation model refers to a model for predicting the impact of process parameter adjustment on the power consumption of the station, which can be specifically constructed by analyzing historical processing data, establishing a physical simulation model or using a machine learning algorithm, and the purpose is to accurately predict the power reduction or increase caused by process parameter adjustment.

[0141] The processing time variation model refers to a model for predicting the impact of process parameter adjustment on the processing time of the station, which can be specifically constructed by analyzing cutting theory formula, establishing an empirical model or using statistical regression method, and the purpose is to predict the time cost increment or decrement caused by process parameter adjustment.

[0142] The quality risk assessment model refers to a model for assessing the potential risk of process parameter adjustment on the processing quality of the station, which can be specifically constructed by analyzing the historical relationship between process parameters and quality indicators, establishing a quality prediction model or using expert system rules, and the purpose is to assess the impact of process parameter adjustment on the stability of processing quality and the qualification rate.

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

[0144] In some embodiments, the quantitative impact model can be specifically implemented as three independent prediction modules. The power change amount model can be a multivariate regression model based on historical power data and process parameters, which predicts the power change relative to the power under standard parameters by inputting the adjusted process parameters (such as spindle speed, feed rate, cutting depth, etc.). The processing time change amount model can be a model based on cutting theory formulas (such as metal cutting principles) and empirical coefficients, which calculates the theoretical change amount of processing path time 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 detection data, which outputs a value representing the quality risk level (such as low, medium, high) or risk probability by inputting the adjusted process parameters and workpiece material, tool state, etc. The three models receive the candidate process parameter adjustment scheme as input in parallel, and output corresponding prediction results, which are then used to calculate the adjustment benefit indicators.

[0145] By the above technical solution, the quantitative impact model is refined into a power change amount model, a processing time change amount model, and a quality risk assessment model, so that the impact of process parameter adjustment on power consumption, processing time, and processing quality risk can be more accurately predicted. This ensures the accuracy and reliability of the prediction results of the quantitative impact model, provides a solid foundation for subsequent evaluation of the comprehensive benefits of candidate process parameter adjustment schemes, and thus enables the optimal scheme to be selected more effectively, while meeting the power constraint, balancing production efficiency, energy cost, and processing quality to the maximum extent.

[0146] In another aspect, as shown in Figure 2 The application further provides a multi-station synchronous machining control system 100, which comprises:

[0147] A preset characteristic information acquisition module 10 is configured to acquire preset characteristic information of the plurality of stations, wherein the preset characteristic information comprises power consumption, machining time and quality index boundary of each station under standard process parameters.

[0148] A quantitative influence model construction module 20 is configured to construct a quantitative influence model, wherein the quantitative influence model is used to predict power consumption variation, machining time variation and machining quality risk of each station caused by process parameter adjustment.

[0149] An energy constraint state acquisition module 30 is configured to acquire current instantaneous total power of the numerical control machining center, power upper limit of a current period and electricity price of the current period.

[0150] A process parameter scheme generation module 40 is configured to generate at least one candidate process parameter adjustment scheme for at least one running station when the current instantaneous total power exceeds the power upper limit of the current period.

[0151] A process parameter scheme evaluation module 50 is configured to evaluate expected power reduction, expected time cost increment related to the electricity price of the current period and expected quality risk cost increment related to machining quality risk caused by application of each candidate process parameter adjustment scheme to the corresponding station based on the preset characteristic information, the quantitative influence model and the electricity price of the current period.

[0152] An adjustment scheme determination module 60 is configured to calculate adjustment benefit indexes 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 an 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 such as power consumption, processing time, and quality index boundary of each station under standard process parameters stored in the database. The quantitative influence model construction module can be specifically a software module loaded with a pre-trained machine learning model, which can be constructed based on historical processing data or simulation data, for predicting the influence of different process parameter adjustments on the power consumption, processing time, and processing quality risk of each station. The energy constraint state acquisition module can be specifically an interface for communicating with a numerical control system or a factory energy management system, for real-time acquisition of the current instantaneous total power of the numerical control machining center, receiving the power upper limit signal of the current period, and obtaining the electricity price information of the current period. The process parameter scheme generation module can be specifically an algorithm library containing a plurality of preset process parameter adjustment strategies (such as reducing the feed rate, reducing the spindle speed, suspending non-critical stations, etc.) and their combination rules. When the total power is over the limit, the module generates a set containing a plurality of adjustment schemes according to the current station running state and the over-limit degree. The process parameter scheme evaluation module can be specifically a calculation engine that calls the quantitative influence model and calculates the expected power reduction, the expected time cost increment due to the extension of processing time, and the expected quality risk cost increment due to the increase of quality risk after the implementation of each candidate scheme, in combination with the electricity price of the current period. The adjustment scheme determination module can be specifically an optimization solver that calculates the adjustment benefit index of each candidate scheme according to a preset benefit function (for example, benefit = expected power reduction - expected time cost increment - expected quality risk cost increment), and selects the scheme with the highest benefit index as the optimal adjustment scheme. The system can send the determined optimal scheme to the numerical control system through the interface, and the numerical control system executes the corresponding process parameter adjustment.

[0154] Through the above technical solutions, the present application provides a multi-station synchronous machining control system, which realizes the automatic control and optimization of the multi-station machining process through modular design. The system can automatically acquire station characteristics and energy constraint states, construct and utilize a quantitative influence model to predict the influence of parameter adjustment, and intelligently generate, evaluate, and determine the optimal process parameter adjustment scheme when the total power is over the limit. This makes the originally complex and experience-dependent control method be efficiently and accurately executed, overcoming the problem of difficulty in landing or the need for a large amount of manual intervention. The system can better balance production efficiency, time cost, and quality risk to improve overall efficiency by quantitatively evaluating and optimizing decisions under the premise of meeting the power constraint.

[0155] In some embodiments, the quantitative influence model construction module is also used to store a power variation model, a processing time variation model, and a quality risk evaluation model.

[0156] In some embodiments, the quantified 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 trained based on a neural network, which inputs the process parameter adjustment amount and outputs the predicted power variation value. The processing time variation model can be a set of formulas for calculating the time variation of each process, according to the process parameter adjustment ratio. The quality risk assessment model can be a model constructed 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 the scheme evaluation is needed, the quantified impact model construction module can load the corresponding model from the storage unit, input the data of the candidate process parameter adjustment scheme to be evaluated, and call the model for prediction calculation.

[0157] Through the above technical solution, the quantified impact model construction module can store specific models for predicting different impact dimensions, thereby providing more detailed and comprehensive prediction capabilities for subsequent process parameter adjustment scheme evaluation, so that the system can more accurately weigh factors such as power, time, and quality, and select a more optimal adjustment scheme.

[0158] The above only describes the embodiments of the present application and is not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A multi-station simultaneous machining control method, characterized by, The method comprises the following steps: acquiring preset characteristic information of a plurality of workstations, the preset characteristic information including power consumption, processing time and quality index boundary of each workstation under standard process parameters; constructing a quantitative influence model, wherein the quantitative influence model is used to predict the power consumption variation, processing time variation and processing quality risk of each workstation caused by process parameter adjustment; acquiring current instantaneous total power of a numerical control machining center, power upper limit of a current period and electricity price of the current period; when the current instantaneous total power exceeds the power upper limit of the current period, generating at least one candidate process parameter adjustment scheme for at least one running workstation; based on the preset characteristic information, the quantitative influence model and the electricity price of the current period, evaluating expected power reduction, expected time cost increment related to the electricity price of the current period and expected quality risk cost increment related to processing quality risk caused by application of each candidate process parameter adjustment scheme to the corresponding workstation; calculating adjustment benefit indicators 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 determining an optimal candidate process parameter adjustment scheme; the step of calculating adjustment benefit indicators 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: acquiring operation focus information of the numerical control machining center, the operation focus information including emergency degree of a current order, quality grade of a workpiece or electricity price sensitivity of the current period; determining a time cost increment influence factor and a quality risk cost influence factor according to the acquired operation focus information; adjusting the expected time cost increment based on the time cost increment influence factor to obtain a target expected time cost increment; adjusting the expected quality risk cost increment based on the quality risk cost influence factor to obtain a target expected quality risk cost increment; calculating adjustment benefit indicators 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.

2. The multi-station simultaneous machining control method according to claim 1, characterized by, the step of determining a time cost increment influence factor and a quality risk cost influence factor according to the acquired operation focus information comprises: identifying content indicating a target conflict in the acquired operation focus information; acquiring preset conflict resolution parameters corresponding to the content indicating the target conflict; generating conflict-resolved operation focus information based on the preset conflict resolution parameters and in combination with the content indicating the target conflict; determining the time cost increment influence factor and the quality risk cost influence factor according to the conflict-resolved operation focus information.

3. The multi-station simultaneous machining control method of claim 1, wherein, the step of adjusting the expected time cost increment based on the time cost increment influence factor to obtain a target expected time cost increment comprises: monitoring whether the time cost increment influence factor meets a related preset variation condition; When the expected time cost increment influence factor meets the preset change condition, the target expected time cost increment is generated by adjusting the expected time cost increment according to the time cost increment influence factor.

4. The multi-station simultaneous machining control method of claim 1, wherein, The step of calculating the adjustment benefit index of each candidate process parameter adjustment scheme according to the expected power drop, the target expected time cost increment, and the target expected quality risk cost increment comprises: Obtain the current operating target information and the priority information of each operating target of the numerical control machining center; According to the obtained operating target information and the priority information, select an adjustment benefit index calculation rule corresponding to the operating target information and the priority information from a preset rule set containing multiple adjustment benefit index calculation rules; According to the selected adjustment benefit index calculation rule, the expected power drop, the target expected time cost increment, and the target expected quality risk cost increment are used to calculate the adjustment benefit index of each candidate process parameter adjustment scheme.

5. The multi-station simultaneous machining control method according to claim 4, wherein The step of selecting an adjustment benefit index calculation rule corresponding to the operating target information and the priority information from a preset rule set containing multiple adjustment benefit index calculation rules according to the obtained operating target information and the priority information comprises: Extract features from the obtained operating target information and the priority information to obtain current operating features; For each adjustment benefit index calculation rule in the preset rule set, obtain its corresponding preset applicable operating feature; Calculate the compliance parameters between the current operating features and each preset applicable operating feature; Based on the compliance parameters, select an adjustment benefit index calculation rule from the preset rule set.

6. The multi-station simultaneous machining control method of claim 1, wherein, After the step of calculating the adjustment benefit index of each candidate process parameter adjustment scheme according to the expected power drop, the expected time cost increment, and the expected quality risk cost increment, and determining the optimal candidate process parameter adjustment scheme, the method further comprises: Apply the optimal candidate process parameter adjustment scheme to the target station and set a preset running observation time window for the station; In the observation time window, collect the actual power consumption, actual machining time, and actual machining quality index of the station running under the optimal candidate process parameter adjustment scheme; Compare the actual power consumption, actual machining time, and actual machining quality index with the expected power drop, expected time cost increment, and expected quality risk cost increment before executing the adjustment scheme to calculate the index deviation value; If the index deviation value exceeds the first threshold value of the predicted value, it is determined as a bad adjustment and a correction process is triggered.

7. The multi-station simultaneous machining control method of claim 1, wherein, The quantitative influence model comprises a power change amount model, a machining time change amount model, and a quality risk assessment model.

8. A multi-station synchronized machining control system, characterized by, The system comprises: A preset characteristic information acquisition module is configured to obtain preset characteristic information of multiple stations, wherein the preset characteristic information comprises power consumption, machining time, and quality index boundaries of each station under standard process parameters; The quantification influence model construction module is configured to construct a quantification influence model, wherein the quantification influence model is configured to predict power consumption variation, processing time variation and processing quality risk caused by the process parameter adjustment on each of the stations; The energy constraint state acquisition module is configured to acquire the current instantaneous total power of the numerical control machining center, the power upper limit of the current period and the electricity price of the current period; The process parameter scheme generation module is configured to generate at least one candidate process parameter adjustment scheme for at least one running station when the current instantaneous total power exceeds the power upper limit of the current period; The process parameter scheme evaluation module is configured to evaluate, based on the preset characteristic information, the quantification influence model and the electricity price of the current period, the expected power reduction caused by each of the candidate process parameter adjustment schemes when applied to the corresponding station, the expected time cost increment related to the electricity price of the current period, and the expected quality risk cost increment related to the processing quality risk; The adjustment scheme determination module is configured to calculate an adjustment benefit index of each 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 determine an optimal candidate process parameter adjustment scheme. The device is also configured to acquire operation focus information of the numerical control machining center, wherein the operation focus information includes the urgency of a current order, the quality level of a workpiece or the electricity price sensitivity of the current period. According to the acquired operation focus information, a time cost increment influence factor and a quality risk cost influence factor are determined. The expected time cost increment is adjusted based on the time cost increment influence factor to obtain a target expected time cost increment. The expected quality risk cost increment is adjusted based on the quality risk cost influence factor to obtain a target expected quality risk cost increment. According to the expected power reduction, the target expected time cost increment and the target expected quality risk cost increment, an adjustment benefit index of each of the candidate process parameter adjustment schemes is calculated.

9. The multi-station synchronized machining control system of claim 8, wherein, The quantification influence model construction module is also configured to store a power variation model, a processing time variation model and a quality risk evaluation model.

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