CN121036325A - Intelligent socket adaptive power supply management system with multi-device collaborative control - Google Patents

Abstract

The invention relates to the technical field of industrial automation control and discloses a multi-equipment cooperative control intelligent socket self-adaptive power supply management system, which comprises at least one intelligent socket for supplying power to industrial equipment and monitoring the running state of the industrial equipment; the central controller comprises a device digital twin model library, an intention perception module, a power supply strategy generation module, a distributed negotiation execution module, a model evolution engine and an intelligent socket, wherein the device digital twin model library stores digital twin models corresponding to devices, the intention perception module analyzes production instructions to form a structured task, the power supply strategy generation module calls model previewing to generate an initial collaborative power supply strategy, the distributed negotiation execution module issues the strategy to the intelligent socket and supports inter-socket negotiation to process unplanned events, and the model evolution engine quantifies execution deviation according to the strategy and reversely updates the digital twin models. The intelligent socket distributed negotiation and self-optimization capability is endowed, so that the safety, flexibility and efficiency of the cooperative control of multiple devices are greatly improved.

Description

Multi-device cooperative control intelligent socket self-adaptive power supply management system

Technical Field

The invention relates to the technical field of industrial automation control, in particular to an intelligent socket self-adaptive power supply management system for cooperative control of multiple devices.

Background

In modern industrial manufacturing, multi-equipment co-production places higher demands on shop power management. The smart socket is used as terminal hardware capable of monitoring the state of industrial equipment and executing control instructions, and the application of the smart socket provides a physical basis for realizing fine multi-equipment cooperative control and adaptive power supply management, but how to construct an effective management method is still a key issue facing the current moment.

Conventional power management often employs fixed timing control, or centralized monitoring based on a total power threshold. Such methods typically detect that the total power approaches the upper safety limit, and force the shut-down of the part of the device preset to a low priority to avoid overload by a simple switching means by a central controller.

However, existing schemes for multi-device power management lack prospective predictions of future loads, power conflicts under dynamic conditions are difficult to predict, and only passive responses are possible. And when unplanned power fluctuation occurs, the response delay of the centralized control mode is too stiff, and quick self-adaptive adjustment of the intelligent socket layer is difficult to realize. The existing mode also generally lacks a closed-loop learning mechanism, experiences are difficult to draw from historical operation to automatically optimize a control model and a strategy, and management accuracy is difficult to improve.

Therefore, the invention provides an intelligent socket self-adaptive power supply management system with cooperative control of multiple devices, which solves the defects in the prior art.

Disclosure of Invention

Aiming at the defects of the prior art, the invention provides the intelligent socket self-adaptive power supply management system for the multi-device cooperative control, which solves the problems that the intelligent socket is difficult to develop the quick response capability due to lack of prospective prediction and rigidification of a control mode and has no passive power supply management, slow response and strategy solidification caused by a closed-loop learning mechanism in the multi-device cooperative control in the prior art.

In order to achieve the purpose, the intelligent socket self-adaptive power supply management system with the multi-device cooperative control is realized by the following technical scheme that the intelligent socket self-adaptive power supply management system comprises:

at least one smart jack for powering and monitoring the operating status of the industrial equipment;

a central controller in communication with the at least one smart jack, the central controller comprising:

An equipment-level digital twin model library for storing equipment-level digital twin models corresponding to each of the industrial equipment;

the intention perception module is used for acquiring production instructions from an external information system and analyzing the production instructions into a structured process intention task comprising a task equipment list and process logic;

the power supply strategy generation module is used for calling the equipment-level digital twin model to conduct power supply influence previewing according to the structured process intention task so as to generate an initial collaborative power supply strategy;

The distributed negotiation execution module is used for issuing the initial collaborative power supply strategy to the corresponding intelligent sockets for execution, and supporting distributed negotiation between the intelligent sockets based on preset rules to process unplanned events in the execution process so as to form an actual execution strategy;

And the model evolution engine is used for quantitatively calculating the negotiation result deviation between the initial cooperative power supply strategy and the actual execution strategy, and reversely updating the corresponding equipment-level digital twin model in the equipment-level digital twin model library according to the negotiation result deviation.

Preferably, the device-level digital twin model includes:

Static electrical representation of industrial equipment;

describing a dynamic working condition power consumption model of power consumption change of industrial equipment in different operation modes;

a process role definition defining the importance of the industrial equipment in the production process;

an initial negotiation intent parameter that characterizes the industrial equipment's propensity for resource conflict.

Preferably, the intention perception module is specifically configured to:

Interfacing with a manufacturing execution system or an enterprise resource planning system, capturing in real-time production orders or process instructions as the production instructions;

parsing the production instruction into the structured process intent task, the structured process intent task further comprising a global priority of tasks.

Preferably, the power supply policy generation module is specifically configured to:

simulating the dynamic working condition power consumption model of the related industrial equipment on a virtual time axis based on the structured process intention task so as to obtain a predicted power supply management system total power curve;

and comparing the predicted total power curve of the power supply management system with a preset power threshold, and if the conflict exists, generating the initial collaborative power supply strategy by adjusting the operation time sequence or the operation mode of each device.

Preferably, the distributed negotiation execution module is specifically configured to:

triggering any intelligent socket to initiate a negotiation request to other associated intelligent sockets when the intelligent socket monitors that the actual power and the predicted value of the dynamic working condition power consumption model have preset deviation;

And the intelligent socket receiving the negotiation request autonomously decides whether to temporarily adjust the self power supply strategy according to the current state of the intelligent socket, the process role definition and the negotiation willingness parameter.

Preferably, the negotiation result bias is a data vector comprising at least one of the following dimensions:

in a task period, energy deviation between actual power consumption of equipment and predicted power consumption of the dynamic working condition power consumption model is generated;

Time deviation formed by the difference between the actual completion time and the planning time of the key subtasks of the industrial equipment;

The industrial equipment initiates the negotiation frequency of the negotiation request in the task;

the industrial equipment accepts the request in a negotiation and yields a compromise rate for the resource.

Preferably, the model evolution engine is specifically configured to:

mapping the negotiation result deviation vector into a specific adjustment quantity of parameters in the equipment-level digital twin model based on a preset updating rule;

and iteratively updating the dynamic working condition power consumption model or the negotiation wish parameter in the equipment-level digital twin model by using the specific adjustment quantity.

Preferably, the smart socket receiving the negotiation request, when making an autonomous decision, further determines a global priority of the task corresponding to the task of the smart socket and the task of the requester according to the global priority of the task included in the task of the intention of the structuring process.

Preferably, the power supply management system further comprises a power supply strategy template library associated with the model evolution engine;

The power supply strategy template library is used for storing a combined record formed by a structural process intention task with successful history, a corresponding initial cooperative power supply strategy and a corresponding actual execution strategy, and using the combined record as an initial solution or guide scheme of the power supply strategy generation module when generating a new initial cooperative power supply strategy.

The invention also provides a multi-device cooperative control intelligent socket self-adaptive power supply management method, which comprises the following steps:

Establishing and storing an equipment-level digital twin model corresponding to each industrial equipment;

Acquiring a production instruction from an external information system and analyzing the production instruction into a structured process intention task comprising a task equipment list and process logic;

According to the structured process intention task, invoking the equipment-level digital twin model to conduct power supply influence previewing so as to generate an initial collaborative power supply strategy;

issuing the initial collaborative power supply strategy to a corresponding intelligent socket for execution, and supporting distributed negotiation between the intelligent sockets based on preset rules to process unplanned events in the execution process to form an actual execution strategy;

quantitatively calculating the negotiation result deviation between the initial cooperative power supply strategy and the actual execution strategy;

And reversely updating the corresponding equipment-level digital twin model in the equipment-level digital twin model library according to the deviation of the negotiation result.

The invention provides an intelligent socket self-adaptive power supply management system with cooperative control of multiple devices.

The beneficial effects are as follows:

1. According to the invention, the power supply strategy generation module is arranged, the device-level digital twin model is utilized to conduct power supply influence previewing, and the total power conflict possibly generated when the multiple devices work cooperatively can be predicted and identified in the virtual space before the physical devices actually run. Furthermore, by pre-adjusting the operation time sequence or the operation mode of the equipment, a conflict-free initial cooperative power supply strategy is generated, the traditional passive responsive power supply management is converted into a prospective and preventive active planning, and the safety and the production stability of the power grid for cooperative operation of the multiple equipment are ensured from the source.

2. The distributed negotiation execution module is constructed, the intelligent socket is endowed with the capability of making an autonomous decision based on the preset rule, and when the intelligent socket faces an unplanned event caused by the uncertainty of the physical world, the production is not required to be interrupted and the central controller is not required to wait for recalculating the global strategy. The related intelligent socket groups can automatically achieve temporary power supply adjustment consensus through quick and local distributed negotiation, so that a more practical execution strategy is formed, and the flexibility, robustness and real-time response capability to the sudden situation of the cooperative control of multiple devices are greatly enhanced.

3. The invention establishes a set of closed-loop feedback and self-optimization paths from a physical execution result to a virtual digital model by setting up a model evolution engine. The engine quantitatively calculates multidimensional deviation between the initial planning and the actual execution, and reversely maps the deviation into a specific adjustment quantity of the equipment-level digital twin model parameters, so that the model serving as a decision basis can continuously learn and evolve from the actual running experience. The self-adaptive mechanism ensures that the accuracy of the model to the physical entity description is continuously improved along with the time, and further realizes the long-term and automatic iterative optimization of the power supply management strategy.

Drawings

FIG. 1 is a system architecture diagram of the present invention;

FIG. 2 is a schematic diagram of an equipment-level digital twin model library of the present invention;

FIG. 3 is a schematic diagram of an intent perception module according to the present invention;

FIG. 4 is a schematic diagram of a power policy generation module according to the present invention;

FIG. 5 is a schematic diagram of a distributed negotiation execution module according to the present invention;

FIG. 6 is a schematic diagram of a model evolution engine of the present invention;

Fig. 7 is a flow chart of the method of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Referring to fig. 1 to fig. 6, an embodiment of the present invention provides an adaptive power management system for a smart jack with cooperative control of multiple devices, including:

at least one smart jack for powering and monitoring the operating status of the industrial equipment;

In this embodiment, at least one smart jack is a key edge execution unit and a data aware tip in the adaptive power management system of the present invention. The main function is to execute the power supply strategy, collect the operation data accurately, and carry out local and real-time negotiation adjustment under the rule frame authorized by the central controller.

In particular, each smart jack preferably includes a power control module, an electrical energy metering module, and a two-way communication module. The communication module ensures that the communication module can stably receive instructions from the central controller and report local state and monitoring data.

When the power management system is running, the smart socket first receives a specific sub-policy for the industrial equipment to which it is connected from the central controller. The core of this sub-strategy is to include a predicted power curve P _predicted (t) that is a refined description of the ideal power consumption of the device during the task, derived by the central controller after power impact prediction based on its internal device-level digital twin model.

One of the core functions of the intelligent socket is to use a built-in high-frequency electric energy metering module to monitor the actual running power P _actual (t) of the connected industrial equipment in real time and continuously compare the actual running power P _actual (t) with a received predicted power curve P _predicted (t).

When the deviation between the actual power and the predicted power, i.e., |P _actual(t)-P_predicted (t) |, is detected to exceed a preset threshold, the intelligent socket determines that an unplanned event occurs. At this time, it does not directly take rigid measures such as power-off, but is given the ability to initiate "distributed negotiations" according to the cooperative control mechanism of the present invention. It will broadcast a negotiation request to other associated smart sockets performing the same production task.

Likewise, when a smart jack receives a negotiation request, it will exhibit its edge intelligence. The method can carry out autonomous decision according to parameters preset by the central controller and stored in the equipment-level digital twin model. These decisions include the "process role definition" by which the industrial equipment is defined (i.e., its importance in the process flow), and the "negotiation intent parameter" that characterizes its inherent compromises. In addition, it references the "global priority of task" for that task. Through comprehensive judgment of the multidimensional information, the intelligent socket autonomously decides whether to temporarily adjust the self-power supply strategy to respond to negotiation, so that flexible adaptation of the system in the face of local disturbance is realized.

The smart jack is not a single device and all its high-level functions are implemented in a depth dependent manner on the digital twin model, predictive strategy and negotiation rules generated by the central controller. The monitored actual power data, the initiated negotiation behavior and the final negotiation result form a part of an actual execution strategy together and are fed back to a model evolution engine of a central controller, so that the most basic and key data input is provided for closed loop learning and self-adaptive evolution of the whole industrial system.

The central controller, with at least one smart jack communication connection, the central controller includes:

A device-level digital twin model library storing device-level digital twin models corresponding to each industrial device;

the intention perception module is used for acquiring production instructions from an external information system and analyzing the production instructions into a structured process intention task comprising a task equipment list and process logic;

the power supply strategy generation module is used for calling the equipment-level digital twin model to conduct power supply influence previewing according to the intention task of the structuring process so as to generate an initial collaborative power supply strategy;

the distributed negotiation execution module is used for issuing an initial cooperative power supply strategy to the corresponding intelligent sockets for execution, and supporting distributed negotiation between the intelligent sockets based on preset rules to process unplanned events in the execution process so as to form an actual execution strategy;

The model evolution engine is used for quantitatively calculating the negotiation result deviation between the initial collaborative power supply strategy and the actual execution strategy, and reversely updating the corresponding equipment-level digital twin model in the equipment-level digital twin model library according to the negotiation result deviation;

in this embodiment, the central controller is a computing center and decision core of the intelligent socket adaptive power supply management system cooperatively controlled by the whole multi-device, and can be regarded as an "intelligent brain" of the power supply management system. The intelligent socket establishes stable and reliable two-way communication connection with a communication module arranged inside an intelligent socket at the front end of each industrial device through a built-in communication interface. The implementation mode of the connection can be flexibly configured according to specific industrial field environments, and preferably, a wired mode such as industrial Ethernet can be adopted to ensure anti-interference performance and stability, or a wireless mode such as Wi-Fi, 5G, zigbee and the like can be adopted to improve flexibility and convenience of deployment.

The central controller has the core function of executing global, complex top-level planning and cognitive learning tasks which require a large amount of computing resources. The method integrates originally dispersed and isolated industrial equipment in the physical world into an organic whole with cooperative capability and self-evolution capability through an intelligent socket network under the jurisdiction of the industrial equipment. In hardware, the central controller may be one or more high-performance servers, industrial control computers (IPCs), or clusters of edge computing gateways, within which the core software functional modules (device-level digital twin model library; intent awareness module; power policy generation module; distributed negotiation execution module; model evolution engine) of the present invention are deployed.

In this embodiment, the device-level digital twin model library provides basic data and model basis for the prediction, simulation and decision functions of the central controller. It is not a simple static parameter database, but a dynamic system for storing, managing and continuously evolving a device-level digital twin model corresponding to each industrial device. The equipment-level digital twin model library provides a complete, accurate and continuous iterative optimized digital mirror image as an operation basis for other functional modules in the central controller, such as a power supply strategy generation module, a distributed negotiation execution module and a model evolution engine.

Specifically, each device-level digital twinning model stored in the library is a refined, multi-dimensional composite data structure. The model realizes the comprehensive description of the physical industrial equipment from static attribute to dynamic behavior and then to the game role played by the physical industrial equipment in the production system through the following core components:

first, the plant-level digital twin model includes a static electrical representation of the industrial plant.

This part forms the static basis of the digital twin model, being the electrical "identity fingerprint" of the device. The method mainly comprises quasi-static parameters obtained from a nameplate, a factory manual or initial calibration of the equipment, such as rated voltage, rated current, rated power and power factor of the equipment, and typical starting current curve characteristics, idle and full power consumption ranges and the like provided by manufacturers. These data are present to provide a reliable boundary and initial reference for the power management system with respect to the basic electrical capabilities of the device prior to any simulation and calculation.

Second, the plant-level digital twin model includes a dynamic operating mode power consumption model.

This is the most central dynamic part of the digital twin model, which aims to accurately describe the power consumption variations of industrial equipment in different modes of operation. Preferably, the plant-level digital twin model may be constructed as a state machine model or time series function closely associated with the plant's actual process flow. For example, for a CNC machine, the dynamic operating mode power consumption model may be defined as a set of states such as { standby, spindle acceleration, X/Y axis fast feed, Z axis heavy load cutting, tool change }, etc. Each state corresponds to a specific, time-varying power consumption function or power curve P _state (t). The digital twin model is a direct calculation basis for the power supply strategy generation module to conduct power supply influence previewing, so that the digital twin model can predict the total power demand of a future task cycle in a virtual space by simulating the combination of different working conditions.

In addition, the plant-level digital twinning model also includes process role definitions that define the importance of the industrial plant in the production process.

The definition associates physical equipment with the service roles of the physical equipment in the production process, and is one of key inputs for realizing intelligent coordination. It classifies the equipment into different importance classes according to its role and role in a typical production process chain. For example, it may be defined as "core processing unit", "key auxiliary unit", "general auxiliary unit", and the like. Among a group of cooperating devices, the device in the process of a production bottleneck may be defined as a "core", while the industrial robot for feeding and discharging it may be defined as a "critical assistance". The process role parameter is one of important rules issued to the intelligent socket by the distributed negotiation execution module, so that the intelligent socket can know the identity of the intelligent socket and the opposite party when making an autonomous negotiation decision, thereby making a judgment more in accordance with the global process target.

Finally, the device-level digital twin model also includes an initial negotiation intent parameter that characterizes the industrial device's propensity to conflict with resources.

The parameter provides a quantized game theory basis for autonomous decision-making in the distributed negotiation process. It may preferably be a quantifiable parameter (e.g., a floating point number between 0 and 1) that characterizes whether the device tends to maintain its own operating policy or to adjust the policy in response to a co-request in the face of a power resource conflict. This parameter is not fixed, it is an "initial" value, which is one of the main objectives of the model evolution engine for feedback learning and iterative optimization. When the device-level digital twin model evolution engine carries out reverse updating according to the actual negotiation result deviation, the parameter is dynamically adjusted, so that the digital twin model of the industrial device can learn better cooperative tendency in continuous operation in game behavior.

The device-level digital twin model library provides necessary data base and model support for the 'prediction-negotiation-evolution' closed loop of the whole system by storing and managing the structured and multidimensional digital twin model.

In this embodiment, the core task of the intent awareness module is to convert the service instruction, which is usually in a high level, from the external information system into a technical task schema that can be accurately understood and executed by the power management system, so as to ensure that all the following power policies serve the real production targets.

Specifically, the intent awareness module is first configured to interface with a Manufacturing Execution System (MES) or an enterprise resource planning system (ERP) of a factory. Such interfacing is preferably implemented by standard industrial communication protocols (e.g., OPC-UA, MQTT) or Application Programming Interfaces (APIs) to ensure compatibility and reliability of data exchange. Through this connection, the intent awareness module is able to automatically capture in real-time the latest production orders or process instructions that constitute the production instructions in the present invention. For example, a typical production instruction might be "produce 100 parts model PN-XYZ for order SO_789".

After such production instructions are obtained, the intent perception module parses the instructions. The relative macroscopic business instruction is converted into a structuring process intention task with strict internal data structure and complete information. The process is a key step for realizing the linkage of the business target and the power supply technology management. To accomplish this, a process knowledge base may be maintained within the module that stores mappings between different product models and desired processing processes and equipment.

The structured process intent task generated by the parsing process is a data object, preferably represented by a tetrad T _intent:

T_intent＝(ID,E,L,P_global);

Where ID is a unique task identifier. The method provides a unique identity ID for the production task, and is convenient for subsequent tracking, recording and analysis.

E is a task device list. It is a set containing unique identifiers of all industrial devices required to execute the present production instruction, for example, e= { E _{CNC_01},e_{Robot_02},e_{Conveyor_03} }, and each element in the set, for example, E _{CNC_01}、e_{Robot_02} and E _{Conveyor_03}, is a unique identifier of a device. The identifiers are in one-to-one correspondence with specific industrial devices deployed in the physical world. For example:

e _{CNC_01} represents a numerical control machine tool numbered 01 in the system.

E _{Robot_02} represents an industrial robot numbered 02 in the system.

E _{Conveyor_03} represents the conveyor belt numbered 03 in the system.

The list is obtained by matching the product model in the production instruction with the process knowledge base, so that the power supply management system can accurately lock and call all relevant models required by subsequent simulation from the equipment-level digital twin model library.

L is process logic. It defines the process constraints and operation timing that must be followed between the devices in the task device list, and is the key to describing the cooperative relationship between the devices.

Preferably, L may be represented as a directed acyclic graph, or a set of preferential constraints, such as l= { (e _{CNC_01},e_{Robot_02}),(e_{Robot_02},e_{Conveyor_03}) }. The ordered pair (e _i,e_j) here indicates that core operations on the predecessor device e _i must complete before core operations of the successor device e _j begin. The process logic provides a basic logic framework and constraint boundaries for the subsequent power policy generation module to perform simulation previews.

P _global is the global priority of the task. This information is also parsed from the upper MES or ERP system. Production instructions are typically accompanied by a degree of urgency in their business, such as "urgent orders", "normal scheduling", or "low priority spare part production". The intent awareness module translates this traffic priority into a system recognizable, standardized value (e.g., a floating point number between 0 and 1) as the task global priority parameter. This parameter is critical because it provides a quantized input of the business value level for the technical decision process. In the subsequent operation process, especially when the distributed negotiation execution module manages resource conflict, the global priority will become a high-weight decision basis, so that the limited power resource can be ensured to be preferentially served for the production activity with the highest overall value to the enterprise.

The intention perception module ensures that all actions of the power supply management system can be circulated and the target is clear through the complete process of 'butt joint-capture-analysis'. The generated structured process intention task is directly transmitted to a power supply strategy generation module as a basis of subsequent power supply planning and simulation, so that specific power supply planning and simulation work is started.

In this embodiment, the power policy generation module is responsible for performing prospective, model-based simulation calculations to generate a safe, efficient and collision-free top-level power execution plan before any physical device actually powers on.

The workflow of the power policy generation module begins with receiving a structured process intent task analytically generated by the intent awareness module. Upon receiving this task, the power policy generation module performs a power impact preview.

Firstly, the power supply strategy generation module queries and calls the corresponding device-level digital twin model in batches from the device-level digital twin model library according to each device identifier in the task device list E. In the process, the power supply strategy generation module extracts a dynamic working condition power consumption model of each device.

Next, the power policy generation module simulates running these invoked models on a virtual timeline. It will arrange the starting and running order of the device models according to the process logic L defined in the structured process intention task. For example, for a constraint relationship (e _i,e_j) ε L, the power policy generation module may ensure that in simulation, the power consumption model of the subsequent device e _j is activated after the power consumption model of the preceding device e _i is running.

During the simulation, the module calculates the superposition of the power consumption of all the devices running concurrently at each discrete time point of the virtual time axis t, so as to obtain a predicted power supply management system total power curve P _{total_predicted} (t). The calculation method of the curve is as follows:

P_{total_predicted}(t)＝∑_i∈EP_i(t);

Where P _i (t) is the predicted power consumption of device i at virtual time t, which is given by its dynamic operating mode power consumption model in the simulated operating state.

After the predicted power management system total power curve is obtained, the module compares it point by point with a system preset power threshold P _threshold. The threshold may be set according to the physical capacity of the power line, an agreement with the power department, or an energy consumption management goal of the enterprise itself.

If P _{total_predicted}(t)≤P_threshold is satisfied for all time points t, it indicates that the current optimal scheduling scheme (e.g., let the device complete tasks as soon as possible on the premise of satisfying the process logic) is feasible, which together with its power curve can be used as the initial collaborative power policy.

Conversely, if P _{total_predicted}(t_conflict)>P_threshold is found at any point in time t _conflict, then a power conflict is determined to exist. At this time, the power supply strategy generation module starts an optimization algorithm, and iteratively corrects the scheduling scheme by adjusting the operation time sequence or the operation mode of each device until the conflict is eliminated. Preferably, the adjustment means includes:

the operation timing is adjusted by introducing a start-up delay for devices on the non-critical path without violating the process logic L, with their peak power consumption staggered in time from the peak power consumption of other devices.

Adjusting the operating mode if adjusting the timing alone is difficult to resolve the conflict, the module queries the digital twin model of the device to see if it has a standby operating mode with lower power consumption (e.g., from "high-speed cut" mode to "economy cut" mode). The module may attempt to switch the operation mode of the partial devices during the collision period to reduce the total power locally.

Through the simulation, comparison and optimization adjustment, the power supply strategy generation module finally generates a conflict-free and refined initial collaborative power supply strategy. The strategy is a composite data structure that specifies the start-up time, mode of operation, and predicted power profile of each device in the task device list during the task period. This policy is then issued to the distributed negotiation execution module as an initial basis for guiding the smart jack to perform the power operation.

In this embodiment, the distributed negotiation execution module is responsible for executing and dynamically adjusting the power supply policy to cope with uncertainty in actual operation. The distributed negotiation execution module has the functions of issuing an initial cooperative power supply strategy and supporting distributed negotiation among intelligent sockets in the execution process to process unplanned events.

Firstly, the distributed negotiation executing module accurately transmits the initial collaborative power supply strategy formulated by the power supply strategy generating module to each corresponding intelligent socket related to the strategy. The content of the issuing is not just a simple start-stop instruction, but a data packet containing a detailed execution plan of the smart socket in a future task cycle. Preferably, the data packet includes a time-by-time predicted power curve planned for the outlet, a predetermined operating mode switching point, and more importantly, a set of preset negotiation rules for handling unplanned events. These rules originate from the digital twin model of the device itself (including its process role definition and initial negotiation willingness parameters) and the global priority of the task to which it belongs.

The distributed negotiation execution module supports the ability to conduct distributed negotiations between smart sockets to handle unplanned events. In industrial sites, the actual power consumption of the equipment is difficult to be completely consistent with the model prediction due to equipment wear, material differences, environmental fluctuation and the like. When such a deviation occurs, if the global policy is recalculated entirely in dependence of the central controller, a response delay will result. According to the scheme, a part of decision weights are lowered to an execution layer through a distributed negotiation mechanism, so that quick and local dynamic adjustment is realized.

Specifically, the triggering and executing process of the negotiation mechanism is as follows:

Triggering negotiation:

During task execution, each smart jack is continuously monitoring, at high frequency, the actual power consumption P _actual (t) of its connected device. At the same time, it will compare the actual value with the predicted value P _predicted (t) for which it was planned in the initial co-power strategy in real time. When the module detects that the absolute value of the deviation between the two exceeds a preset deviation threshold Δp _threshold, i.e., the following condition is met, it considers that an unplanned event requiring coordination occurs:

|P_actual(t)-P_predicted(t)|>ΔP_threshold;

at this point, the smart jack (hereinafter "requestor") immediately initiates a negotiation request to the other associated smart jacks (hereinafter "responder") in the task device list.

Autonomous decision making of negotiations:

After receiving the negotiation request, the responder smart socket does not blindly accept or reject, but can make a quick autonomous decision based on the multidimensional information grasped by the responder smart socket itself. The autonomous decision-making is particularly important in terms of global priorities of tasks contained in the structured process intent tasks, corresponding to the tasks themselves and the requesting task, respectively. The decision process may preferably be modeled as a utility function evaluation process for quantifying its propensity to make collaborative adjustments. A typical utility function U _cooperate may be defined as a weighted sum of a number of decision factors:

U_cooperate＝w_s·f_s(state)+w_r·f_r(role)+w_w·P_willingness+w_p·f_p(ΔP_global);

Wherein w _s,w_r,w_w,w_p is a preset weight coefficient for adjusting the importance of different decision factors.

F _s (state) is a function of the current state of itself. If the responder device is in a non-critical phase of standby or low power consumption, the function outputs a higher value, and if it is in a critical process of high power, uninterrupted, the function outputs a lower value.

F _r (role) is a function defined in relation to the process role. It compares the role importance of the requesting party to the responding party. If the role of the requesting party (e.g. "core processing unit") is more important than the responding party (e.g. "general auxiliary unit"), the function outputs a positive value, and conversely outputs a negative value or zero.

P _willingness is the negotiation intent parameter defined in the digital twin model of the responder device, directly as decision input. The higher this value, the stronger the inherent propensity for collaboration.

F _p(ΔP_global) is a function of global priority of tasks.

After the responder's smart jack calculates the utility value U _cooperate, it is compared with a decision threshold. If the value is above the threshold, the responder decides to agree to make temporary adjustments to the self-power policy (e.g., delay the initiation of non-critical actions of itself or reduce operating power) and reply to the confirmation.

Finally, through one or more such distributed negotiation interactions, the associated smart jack group achieves a new, localized, temporary power supply consensus around unplanned events without the intervention of a central controller. All of these dynamic fine-tuning on the basis of the initial strategy together constitute the final actual execution strategy.

In this embodiment, the model evolution engine is a core hub for implementing closed-loop learning and adaptive optimization of the power supply management system of the present invention. By performing deep analysis on the historical execution data, the digital twin model serving as a system decision basis can be continuously and automatically learned from real operation experience, so that the mapping accuracy of the digital twin model to the physical world is continuously improved.

The model evolution engine has the function of establishing a reverse feedback and correction path from a physical execution result to virtual model parameters. The workflow mainly comprises two stages of deviation quantization calculation and model reverse updating.

Firstly, after each production task is executed, a model evolution engine is started, and the primary task is to quantitatively calculate the negotiation result deviation between the initial cooperative power supply strategy and the actual execution strategy. The "deviation" here is not a single value, but a data vector that enables a complete assessment of the gap between the plan and the reality from multiple dimensions. Preferably, the negotiation result bias vector D _dev includes at least the following key dimensions:

The energy deviation (D _E) is used for measuring the accuracy of the power consumption model of the dynamic working condition of the industrial equipment. It is derived by calculating the area enclosed between the actual power consumption curve of the industrial plant and the model predicted power consumption curve over the whole mission period, i.e. the difference in energy.

Where T _start and T _end represent the actual start time and the actual end time, respectively, of the production task being evaluated, and ∈dt represents integrating the power difference over the entire task time interval [ T _start,T_end ].

A continuously positive energy deviation indicates that the model power consumption prediction is low, and conversely, high.

Time bias (D _T) this dimension concerns task execution efficiency. It quantifies the difference between the actual completion time T _actual of the critical subtasks of the industrial equipment and the planned completion time T _planned in the initial strategy.

D_T＝T_actual-T_planned;

The deviation reflects the accuracy of the power consumption model in predicting the working efficiency of the industrial equipment under different working conditions.

Negotiation frequency (D _F) this dimension reflects the robustness of the initial policy. The method counts the total number of times that the specific industrial equipment actively initiates the negotiation request due to the overrun of the power deviation in the task execution process. A higher negotiation frequency implies a lower feasibility of the initial strategy or an unstable operation of the industrial plant.

Compromise rate (D _C) this dimension is used to characterize the actual behavior of the industrial equipment in distributed collaboration. The method calculates the proportion of the times of receiving negotiation requests of other industrial equipment and adjusting own strategies by the industrial equipment when the industrial equipment is used as a response party to the total times of receiving the negotiation requests.

After quantization yields the negotiated result bias vector D _dev, the model evolution engine enters the reverse update phase of its core. The purpose of this stage is to correct the corresponding model parameters in the plant-level digital twin model library to more closely approximate the true characteristics of the physical entity based on the discovered deviations.

Specifically, the model evolution engine maps the multidimensional deviation vector into a specific adjustment quantity of a specific parameter in the digital twin model according to a set of preset updating rules. These rules may be a series of logical decisions or a more complex mapping function. For example, the update procedure of one parameter Param may be expressed as:

Param_new＝Param_old+α·f(D_dev);

Where Param _old is the current value of the parameter, param _new is the updated value, α is a preset learning rate for controlling the update step size, and f (D _dev) is a function of calculating the adjustment based on the bias vector.

If the energy deviation D _E is obviously positive, the engine judges that the dynamic working condition power consumption model of the industrial equipment needs to be corrected, and correspondingly adjusts the basic power consumption or the peak power consumption coefficient in the model through the updating rule. If the negotiation frequency D _F is high while the compromise rate D _C is very low, and the industrial device is not a core industrial device on the mission critical path, the engine may determine that the negotiation intent parameter preset in the digital twin model is too low, resulting in lack of negotiation flexibility in cooperation. At this point, the engine will moderately raise the parameter by updating the rules.

In this way, the engine iteratively updates the dynamic operating mode power consumption model or the negotiation wish parameter in the device-level digital twin model by using the calculated specific adjustment quantity. This process makes the digital twin model no longer static, one-time set data, but rather a dynamic model with vitality capable of self-evolution.

In addition, in this embodiment, the system associated with the model evolution engine further includes a power policy template library. The function of the power policy template library is to archive and store historically successful task execution cases. Each case is a complete combined record consisting of the structured process intent task, the initial coordinated power strategy corresponding to it, and the actual execution strategy that is ultimately formed.

When the power policy generation module is planning a new production task, it may first query the template library using the features of the task. If one or more similar historical successful cases are found, the actual execution strategy of the cases which is verified by actual operation can be used as a high-quality initial solution or guiding scheme for the simulation deduction.

The multi-device cooperative control intelligent socket adaptive power supply management method and the multi-device cooperative control intelligent socket adaptive power supply management system described above can be referred to correspondingly.

Referring to fig. 7, the invention further provides a multi-device cooperative control intelligent socket adaptive power supply management method, which comprises the following steps:

S1, establishing and storing an equipment-level digital twin model corresponding to each industrial equipment;

S2, acquiring a production instruction from an external information system, and analyzing the production instruction into a structured process intention task comprising a task equipment list and process logic;

s3, according to the intention task of the structuring process, invoking a device-level digital twin model to conduct power supply influence previewing so as to generate an initial collaborative power supply strategy;

S4, issuing an initial cooperative power supply strategy to a corresponding intelligent socket for execution, and supporting distributed negotiation between the intelligent sockets based on preset rules to process unplanned events in the execution process to form an actual execution strategy;

s5, quantitatively calculating the deviation of the negotiation result between the initial cooperative power supply strategy and the actual execution strategy;

and S6, reversely updating the corresponding equipment-level digital twin model in the equipment-level digital twin model library according to the deviation of the negotiation result.

The method of this embodiment may be used to execute the system embodiment described above, and its principle and technical effects are similar, and will not be described herein again.

Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (10)

1. Multi-device cooperative control's smart jack self-adaptation power management system, its characterized in that includes:

at least one smart jack for powering and monitoring the operating status of the industrial equipment;

a central controller in communication with the at least one smart jack, the central controller comprising:

An equipment-level digital twin model library for storing equipment-level digital twin models corresponding to each of the industrial equipment;

the intention perception module is used for acquiring production instructions from an external information system and analyzing the production instructions into a structured process intention task comprising a task equipment list and process logic;

the power supply strategy generation module is used for calling the equipment-level digital twin model to conduct power supply influence previewing according to the structured process intention task so as to generate an initial collaborative power supply strategy;

The distributed negotiation execution module is used for issuing the initial collaborative power supply strategy to the corresponding intelligent sockets for execution, and supporting distributed negotiation between the intelligent sockets based on preset rules to process unplanned events in the execution process so as to form an actual execution strategy;

And the model evolution engine is used for quantitatively calculating the negotiation result deviation between the initial cooperative power supply strategy and the actual execution strategy, and reversely updating the corresponding equipment-level digital twin model in the equipment-level digital twin model library according to the negotiation result deviation.

2. The multi-device co-controlled smart jack adaptive power management system of claim 1, wherein the device-level digital twinning model comprises:

Static electrical representation of industrial equipment;

describing a dynamic working condition power consumption model of power consumption change of industrial equipment in different operation modes;

a process role definition defining the importance of the industrial equipment in the production process;

an initial negotiation intent parameter that characterizes the industrial equipment's propensity for resource conflict.

3. The multi-device co-controlled smart jack adaptive power management system of claim 1, wherein the intent awareness module is specifically configured to:

Interfacing with a manufacturing execution system or an enterprise resource planning system, capturing in real-time production orders or process instructions as the production instructions;

parsing the production instruction into the structured process intent task, the structured process intent task further comprising a global priority of tasks.

4. The multi-device cooperative control smart jack adaptive power supply management system according to claim 2, wherein the power supply policy generation module is specifically configured to:

simulating the dynamic working condition power consumption model of the related industrial equipment on a virtual time axis based on the structured process intention task so as to obtain a predicted power supply management system total power curve;

and comparing the predicted total power curve of the power supply management system with a preset power threshold, and if the conflict exists, generating the initial collaborative power supply strategy by adjusting the operation time sequence or the operation mode of each device.

5. The multi-device cooperatively controlled smart jack adaptive power management system of claim 2, wherein the distributed negotiation execution module is specifically configured to:

triggering any intelligent socket to initiate a negotiation request to other associated intelligent sockets when the intelligent socket monitors that the actual power and the predicted value of the dynamic working condition power consumption model have preset deviation;

And the intelligent socket receiving the negotiation request autonomously decides whether to temporarily adjust the self power supply strategy according to the current state of the intelligent socket, the process role definition and the negotiation willingness parameter.

6. The multi-device co-controlled smart jack adaptive power management system of claim 2, wherein the negotiation result bias is a data vector comprising at least one of the following dimensions:

in a task period, energy deviation between actual power consumption of equipment and predicted power consumption of the dynamic working condition power consumption model is generated;

Time deviation formed by the difference between the actual completion time and the planning time of the key subtasks of the industrial equipment;

The industrial equipment initiates the negotiation frequency of the negotiation request in the task;

the industrial equipment accepts the request in a negotiation and yields a compromise rate for the resource.

7. The multi-device co-controlled smart jack adaptive power management system of claim 2, wherein the model evolution engine is specifically configured to:

mapping the negotiation result deviation vector into a specific adjustment quantity of parameters in the equipment-level digital twin model based on a preset updating rule;

and iteratively updating the dynamic working condition power consumption model or the negotiation wish parameter in the equipment-level digital twin model by using the specific adjustment quantity.

8. The multi-device co-controlled intelligent socket adaptive power management system according to claim 5, wherein the intelligent socket receiving the negotiation request, when making an autonomous decision, further depends on the global priorities of tasks included in the structured process intention task and corresponding to the task of the intelligent socket and the task of the requester, respectively.

9. The multi-device co-controlled smart jack adaptive power management system of claim 1, further comprising a power policy template library associated with the model evolution engine;

The power supply strategy template library is used for storing a combined record formed by a structural process intention task with successful history, a corresponding initial cooperative power supply strategy and a corresponding actual execution strategy, and using the combined record as an initial solution or guide scheme of the power supply strategy generation module when generating a new initial cooperative power supply strategy.

10. The multi-device cooperative control intelligent socket self-adaptive power supply management method is applied to the multi-device cooperative control intelligent socket self-adaptive power supply management system as claimed in any one of claims 1 to 9, and is characterized by comprising the following steps:

Establishing and storing an equipment-level digital twin model corresponding to each industrial equipment;

Acquiring a production instruction from an external information system and analyzing the production instruction into a structured process intention task comprising a task equipment list and process logic;

According to the structured process intention task, invoking the equipment-level digital twin model to conduct power supply influence previewing so as to generate an initial collaborative power supply strategy;

issuing the initial collaborative power supply strategy to a corresponding intelligent socket for execution, and supporting distributed negotiation between the intelligent sockets based on preset rules to process unplanned events in the execution process to form an actual execution strategy;

quantitatively calculating the negotiation result deviation between the initial cooperative power supply strategy and the actual execution strategy;

And reversely updating the corresponding equipment-level digital twin model in the equipment-level digital twin model library according to the deviation of the negotiation result.