A device energy consumption optimization scheduling method and system based on big data analysis

Abstract

The application discloses a kind of equipment energy consumption optimization scheduling method and system based on big data analysis, it is related to energy management and intelligent scheduling field.The method includes: collecting the multidimensional state data in the process of equipment operation, constructs unit time energy consumption function and extracts energy consumption disturbance structure index;Introduce input stress dissipation index, space heterostructure adaptation index and behavior impedance difference factor, form equipment operation characteristic parameter system;Combined with actual operation data, construct energy consumption deviation structure and generate disturbance feedback correction factor, establish multi-cycle-oriented scheduling strategy feedback correction and self-adapting evolution mechanism, to build the complete energy consumption optimization scheduling method system covering data acquisition, state modeling, parameter extraction, combined scheduling and closed-loop updating.By constructing multidimensional disturbance structure modeling, difference characteristic parameter extraction and closed-loop feedback correction mechanism, realize the dynamic perception, fine identification and self-adapting optimization of equipment energy consumption scheduling.

Description

Technical Field

[0001] This invention relates to the field of energy management and intelligent scheduling, specifically to a method and system for optimizing equipment energy consumption scheduling based on big data analysis. Background Technology

[0002] With the continuous improvement of industrial automation, complex equipment systems are being deployed more and more widely in various production, manufacturing, and energy management scenarios. Scheduling strategies for multi-device collaborative operation are of great significance for ensuring overall system efficiency, reducing energy waste, and improving dynamic adaptability. Against this backdrop, equipment energy consumption scheduling has become one of the key research areas in energy efficiency management.

[0003] In existing technologies, equipment scheduling mainly relies on static strategies or empirical rules, which often cannot adequately cope with real-time disturbances and energy consumption anomalies during operation. When faced with dynamically changing operating environments and complex coupled behaviors of multiple devices, traditional scheduling methods have significant limitations in identifying the cooperative relationships between devices, their dynamic adaptability, and abnormal response patterns, resulting in limited overall energy consumption optimization effects.

[0004] Furthermore, while some predictive model-based energy scheduling technologies incorporate data-driven analysis, they often remain at the level of energy consumption prediction or simple ranking, lacking comprehensive modeling of the adaptability to heterogeneous states among devices, the structural characteristics of energy consumption disturbances, and system-level collaborative stability. This makes it difficult to support dynamic scheduling decisions for complex multi-device systems. Simultaneously, energy consumption feedback after scheduling execution is often underutilized, and the strategy lacks an effective closed-loop correction mechanism, making it difficult to achieve temporal iteration and adaptive evolution of scheduling parameters.

[0005] Therefore, there is an urgent need to construct a device energy consumption optimization scheduling method that integrates operational status perception, disturbance structure modeling, scheduling potential energy quantification, and feedback-driven updates, which can achieve intelligent, dynamic, and multi-dimensional adaptive optimization of scheduling strategies while ensuring operational safety. Summary of the Invention

[0006] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method and system for optimizing equipment energy consumption scheduling based on big data analysis, so as to solve the above-mentioned technical problems.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing and scheduling equipment energy consumption based on big data analysis, comprising:

[0008] During the scheduling cycle, the operating status data of the set of devices to be scheduled with preset numbers is collected. Based on the collected operating status data, the unit time energy consumption function of each device is constructed, and an energy consumption disturbance structure index characterizing the intensity of operating disturbance is generated.

[0009] Based on the energy consumption disturbance structure index and operating status data, the input stress dissipation index and spatial heterogeneity adaptation index are calculated. For any two devices, a behavioral impedance difference factor is constructed to form a set of equipment operating characteristic parameters.

[0010] Based on the equipment operating characteristic parameters, a scheduling potential function is constructed to measure the cooperative stability level and disturbance risk intensity of the preset candidate equipment within the scheduling cycle. The scheduling potential function is used as the objective function for scheduling combinatorial optimization.

[0011] Based on the scheduling potential energy function, structural adaptability threshold, and behavioral conflict judgment conditions, a set of devices that meet the adaptability requirements and do not constitute operational conflicts is selected from the devices to be scheduled. For each device in the set, its marginal potential energy contribution value in the potential energy function is calculated, and the devices are sorted according to the size of the contribution value to generate a scheduling priority queue and output scheduling instructions.

[0012] After the scheduling task is completed, the actual energy consumption data of the scheduled equipment is collected, and the data is compared with the prediction model results at multiple time points to calculate the energy consumption deviation structure. Based on the energy consumption deviation structure, a disturbance feedback correction factor is generated to update the aforementioned input stress dissipation index and spatial heterogeneity adaptation index. The adaptive update of the scheduling parameters is completed in the next scheduling cycle.

[0013] The present invention is further configured such that the collection of operating status data of the set of devices to be scheduled within the scheduling cycle includes continuous sampling of device voltage, current, control input strength and disturbance response information over multiple time periods;

[0014] Based on the collected operational status data, construct the energy consumption function of the device per unit time;

[0015] By extracting the dynamic characteristics of the energy consumption function and disturbance response information per unit time, an energy consumption disturbance structure index is formed to characterize the intensity of equipment operation disturbance.

[0016] The present invention is further configured such that the set of operating characteristic parameters of the forming device includes:

[0017] Based on the equipment energy consumption disturbance structure index and the equipment operation status data collected during the scheduling cycle, the input stress dissipation index and spatial heterogeneity adaptation index of each piece of equipment are calculated respectively.

[0018] Based on the input stress dissipation index and the spatial heterogeneity adaptation index, a behavioral impedance difference factor is constructed for any two devices to quantitatively describe the differences in operating behavior and mutual influence between devices.

[0019] By comprehensively inputting the stress dissipation index, spatial heterogeneity adaptation index, and inter-equipment behavioral impedance difference factor, a set of equipment operation characteristic parameters is formed, which includes the dynamic characteristics of a single piece of equipment and the coupling relationship between equipment.

[0020] The present invention is further configured such that the construction of the scheduling potential function includes:

[0021] Based on the equipment operation characteristic parameters, a scheduling potential function for the candidate equipment set within the scheduling cycle is constructed. The scheduling potential function comprehensively characterizes the dynamic energy consumption characteristics and adaptability of individual equipment, as well as the mutual coupling relationship between equipment in the operation coordination process.

[0022] By introducing a cooperative stability structure and a disturbance risk structure, the overall operational coordination of the candidate equipment set and its resilience to external disturbances are measured, respectively.

[0023] The scheduling potential function is used as the objective function for equipment combination scheduling optimization. The information on the cooperative potential level of equipment is passed to the downstream scheduling priority generation step to support the judgment basis of equipment selection, marginal contribution assessment and scheduling instruction output.

[0024] The present invention is further configured such that, based on the evaluation results of the scheduling potential function, the judgment conditions of the structural adaptability threshold and the judgment conditions of the behavioral conflict, a set of devices that meet the adaptability criteria and do not have behavioral conflicts in the operating state are selected from the set of devices to be scheduled.

[0025] For each device in the selected set of devices, the marginal potential energy contribution value of the device is determined based on the change in the corresponding scheduling potential energy function after it is included in the current combination.

[0026] The screening devices are sorted according to their marginal potential energy contribution values ​​to form a scheduling priority queue;

[0027] Output the corresponding scheduling instructions based on the scheduling priority queue.

[0028] The present invention is further configured such that the marginal potential energy contribution value is constructed based on the current combination structure of the screened device set, in which the change in the scheduling potential energy function caused by the target device is introduced;

[0029] The marginal potential energy contribution value is combined with the input stress dissipation index, spatial heterogeneity adaptation index and behavioral impedance difference factor of the target equipment to each equipment in the selected equipment set, reflecting the cooperative stability change and disturbance coupling strength change caused by the target equipment in the current scheduling structure.

[0030] The marginal potential energy contribution value is transmitted to the scheduling instruction generation stage as the basis for determining scheduling priority.

[0031] The present invention is further configured such that, after the scheduling task is completed, the actual energy consumption data of all devices that have received scheduling instructions in the previous scheduling cycle are collected in different time periods.

[0032] Actual energy consumption data includes unit-time energy consumption measurements corresponding to consecutive moments within the scheduling cycle;

[0033] The actual energy consumption data is matched with the predicted energy consumption data generated based on the unit time energy consumption function on a time-by-time basis to construct an energy consumption deviation structure covering the entire scheduling cycle;

[0034] Based on the energy consumption change offset trajectory, response abrupt change amplitude and control input coupling relationship presented in the energy consumption deviation structure, a corresponding disturbance feedback correction factor is generated;

[0035] The input stress dissipation index and spatial heterogeneity adaptation index of each device in the previous cycle are adjusted in multiple dimensions based on the adjustment intensity of the disturbance feedback correction factor to form the parameter structure after feedback correction.

[0036] The revised parameter structure is used as the input basis for the equipment operation characteristic parameters in the next scheduling cycle, so as to realize the dynamic correction and time-series iteration of scheduling parameters.

[0037] The present invention is further configured such that the energy consumption deviation structure includes a set of multiple time-series deviation records formed by mapping the actual energy consumption data and predicted energy consumption data of each device at continuous sampling times within the scheduling cycle;

[0038] The multi-time-series deviation record set constitutes a two-layer mapping structure. One layer associates the unique device number with the corresponding sampling time sequence, and the other layer associates the actual energy consumption offset value and control input status at each time point.

[0039] The energy consumption deviation structure is used to identify abnormal energy consumption response segments that occur during actual operation, including deviation accumulation mutation segments, frequent fluctuation segments, and disturbance delay segments. Based on this, multiple types of energy consumption drift tags are provided to trigger the selection of subsequent disturbance feedback correction factor construction strategies and the screening of equipment operation status update paths.

[0040] The present invention is further configured such that the disturbance feedback correction factor is composed of three types of feedback source information:

[0041] The first type of feedback source information includes a label indicating the consistency between the stability of the device's energy consumption deviation and its disturbance change trend within a continuous sampling period;

[0042] The second type of feedback source information includes the synchronous offset trajectory between the history of changes in control input intensity and the difference in energy consumption response;

[0043] The third type of feedback source information includes the degree of offset coupling between the predicted energy consumption decay rate of the energy consumption structure near the sudden disturbance point and the actual response structure.

[0044] After being diverted through the feedback channel, the three types of feedback source information correspond to the response adjustment term of the input stress dissipation index, the adaptation compression term of the spatial heterogeneity adaptation index, and the retention term of the prediction model correction path, respectively. A dynamic update vector is constructed through the parameter transfer structure to control the multi-dimensional time-series correction process of the equipment operation characteristic parameters.

[0045] The present invention also provides a device energy consumption optimization and scheduling system based on big data analysis, the system comprising:

[0046] Data acquisition and energy consumption modeling module: During the scheduling cycle, it collects the operating status data of the set of devices to be scheduled with preset numbers, constructs the unit time energy consumption function of each device based on the collected operating status data, and generates an energy consumption disturbance structure index that characterizes the intensity of operating disturbance.

[0047] Feature parameter extraction module: Based on energy consumption disturbance structure index and operating status data, calculate input stress dissipation index and spatial heterogeneity adaptation index, construct behavioral impedance difference factor between any two devices, and form a set of device operating feature parameters;

[0048] Potential energy function construction module: Constructs a scheduling potential energy function based on equipment operating characteristic parameters, measures the cooperative stability level and disturbance risk intensity of preset candidate equipment within the scheduling cycle, and uses the scheduling potential energy function as the objective function for scheduling combinatorial optimization;

[0049] Scheduling optimization and instruction generation module: Based on the scheduling potential energy function, structural adaptability threshold and behavioral conflict judgment conditions, it selects a set of devices that meet the adaptability requirements and do not constitute operational conflicts from the devices to be scheduled. For each device in the set, it calculates its marginal potential energy contribution value in the potential energy function, sorts them according to the size of the contribution value, generates a scheduling priority queue, and outputs scheduling instructions.

[0050] Feedback correction and parameter update module: After the scheduling task is completed, the actual energy consumption data of the scheduled equipment is collected, and the data is compared with the prediction model results at multiple time points. The energy consumption deviation structure is calculated, and the disturbance feedback correction factor is generated based on the energy consumption deviation structure. This factor is used to update the aforementioned input stress dissipation index and spatial heterogeneity adaptation index, and the adaptive update of the scheduling parameters is completed in the next scheduling cycle.

[0051] This invention provides a method and system for optimizing equipment energy consumption scheduling based on big data analysis. The method involves collecting operational status data from a set of pre-numbered devices to be scheduled within a scheduling cycle; constructing a unit-time energy consumption function for each device based on the collected operational status data; generating an energy consumption disturbance structure index characterizing the intensity of operational disturbances; calculating an input stress dissipation index and a spatial heterogeneity adaptation index based on the energy consumption disturbance structure index and operational status data; constructing a behavioral impedance difference factor between any two devices to form a set of equipment operational characteristic parameters; and constructing a scheduling potential energy function based on the equipment operational characteristic parameters to measure the cooperative stability level and disturbance risk intensity of pre-selected candidate devices within the scheduling cycle, using the scheduling potential energy function as the target for scheduling combinatorial optimization. The function, based on the scheduling potential energy function, structural adaptability threshold, and behavioral conflict judgment conditions, selects a set of devices from the devices to be scheduled that meet the adaptability requirements and do not constitute operational conflicts. For each device in this set, its marginal potential energy contribution value in the potential energy function is calculated, and the devices are sorted according to the contribution value to generate a scheduling priority queue and output scheduling instructions. After the scheduling task is completed, the actual energy consumption data of the scheduled devices is collected, and this data is compared with the prediction model results at multiple time points to calculate the energy consumption deviation structure. Based on the energy consumption deviation structure, a disturbance feedback correction factor is generated to update the aforementioned input stress dissipation index and spatial heterogeneity adaptation index. The adaptive update of scheduling parameters is completed in the next scheduling cycle. The beneficial effects include:

[0052] 1. Achieve detailed modeling of operational disturbance structure: By collecting multi-dimensional operational status data such as voltage, current, control input strength, and disturbance response of the equipment, construct the energy consumption function per unit time, and extract energy consumption disturbance structure indicators, so that the system can fully perceive the dynamic energy consumption change characteristics and disturbance response mode during equipment operation.

[0053] 2. Introduce multidimensional feature indices to enhance the ability to characterize operational differences between equipment: Construct input stress dissipation index, spatial heterogeneity adaptation index and behavioral impedance difference factor. The system can quantify the differences in individual equipment operation performance and the interaction and coupling state between equipment, providing a structured and multi-level behavioral expression basis for scheduling decisions.

[0054] 3. Construct an energy consumption feedback closed-loop correction mechanism to enhance the adaptive capability of the scheduling system: By collecting actual energy consumption data after the completion of scheduling tasks, construct a multi-time series energy consumption deviation structure, extract disturbance feedback correction factors, dynamically correct key parameters and promote the adaptive evolution of equipment operating characteristic parameters, and construct a closed-loop feedback and multi-cycle iterative optimization system for scheduling strategies.

[0055] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0057] Figure 1 A flowchart illustrating an exemplary embodiment of the present invention is provided, showing a method for optimizing and scheduling equipment energy consumption based on big data analysis.

[0058] Figure 2 This is a schematic diagram illustrating the structure of a device energy consumption optimization and scheduling system based on big data analysis, as an exemplary embodiment of the present invention. Detailed Implementation

[0059] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0060] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0061] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0062] Example 1:

[0063] A method for optimizing equipment energy consumption scheduling based on big data analytics, such as Figure 1 As shown, it includes:

[0064] During the scheduling cycle, the operating status data of the set of devices to be scheduled with preset numbers is collected. Based on the collected operating status data, the unit time energy consumption function of each device is constructed, and an energy consumption disturbance structure index characterizing the intensity of operating disturbance is generated.

[0065] Based on the energy consumption disturbance structure index and operating status data, the input stress dissipation index and spatial heterogeneity adaptation index are calculated. For any two devices, a behavioral impedance difference factor is constructed to form a set of equipment operating characteristic parameters.

[0066] Based on the equipment operating characteristic parameters, a scheduling potential function is constructed to measure the cooperative stability level and disturbance risk intensity of the preset candidate equipment within the scheduling cycle. The scheduling potential function is used as the objective function for scheduling combinatorial optimization.

[0067] Based on the scheduling potential energy function, structural adaptability threshold, and behavioral conflict judgment conditions, a set of devices that meet the adaptability requirements and do not constitute operational conflicts is selected from the devices to be scheduled. For each device in the set, its marginal potential energy contribution value in the potential energy function is calculated, and the devices are sorted according to the size of the contribution value to generate a scheduling priority queue and output scheduling instructions.

[0068] After the scheduling task is completed, the actual energy consumption data of the scheduled equipment is collected, and the data is compared with the prediction model results at multiple time points to calculate the energy consumption deviation structure. Based on the energy consumption deviation structure, a disturbance feedback correction factor is generated to update the aforementioned input stress dissipation index and spatial heterogeneity adaptation index. The adaptive update of the scheduling parameters is completed in the next scheduling cycle.

[0069] The present invention is further configured such that the collection of operating status data of the set of devices to be scheduled within the scheduling cycle includes continuous sampling of device voltage, current, control input strength, and disturbance response information over multiple time periods; specifically, control input strength refers to the strength of the input control signal of the device, which is the direct impact of external commands or parameter settings on the operation of the device; disturbance response information reflects the device's response characteristics to external disturbances (such as environmental changes, power fluctuations, etc.), and is used to identify the stability and adaptability of the device; by continuously sampling device voltage, current, control input strength, and disturbance response over multiple time periods, the operating status of the device can be comprehensively and dynamically obtained, thereby providing comprehensive and time-series data support for subsequent energy consumption modeling and scheduling decisions;

[0070] Based on the collected operational status data, a device energy consumption function per unit time is constructed; specifically, if the device's energy consumption per unit time is E(t), then the device energy consumption function per unit time is defined as follows: Where V(t) is the voltage of the device at time t, I(t) is the current of the device at time t, and f ctrl u(t) is a function of the control input strength, describing the impact of the control signal strength on energy consumption. It is defined by the device control algorithm, where u(t) is the control input signal strength. By constructing the unit-time energy consumption function of the device, the energy consumption performance of each device under different operating conditions is accurately quantified, providing basic data support for subsequent scheduling decisions. This energy consumption function can reflect the real-time operating status and load changes of the device, enabling the scheduling system to dynamically adjust the scheduling strategy based on time-series data.

[0071] By extracting the dynamic characteristics of the energy consumption function and disturbance response information per unit time, an energy consumption disturbance structure index characterizing the intensity of equipment operation disturbances is formed. Specifically, the disturbance response characteristics reflect the energy consumption change response of the equipment after being subjected to disturbances (such as load changes, environmental changes, etc.). Dynamic feature extraction extracts the response mode of the equipment when facing disturbances from the energy consumption function, which can be modeled through methods such as amplitude change and frequency response. The energy consumption disturbance structure index is defined as ΔE(t), which is expressed by the relationship between the change of the equipment's energy consumption and control input: ΔE(t)=|E post (t)-E pre (t)|, where E post (t) represents the energy consumption of the equipment at time t after the disturbance, in watt-hours (E). pre (t) represents the energy consumption of the equipment at time t before the disturbance, in watt-hours. This disturbance structure index reflects the intensity of the equipment's response to the disturbance and its stability under external disturbances. By extracting the dynamic characteristics of the disturbance response and generating the energy consumption disturbance structure index, the system can clearly understand the adaptability and stability of the equipment during operation, especially under external disturbances. This provides real-time feedback for the optimization decision-making of the scheduling system, making equipment energy consumption scheduling more intelligent and precise.

[0072] The present invention is further configured such that the set of operating characteristic parameters of the forming device includes:

[0073] Based on the equipment energy consumption disturbance structure index and the equipment operating status data collected during the scheduling cycle, the input stress dissipation index and spatial heterogeneity adaptation index of each piece of equipment are calculated respectively; specifically, the formula for calculating the input stress dissipation index is as follows: The input stress dissipation index measures the energy dissipation capability of equipment during operation caused by control input signals and external disturbances. This index describes how effectively the equipment converts energy into working output rather than ineffective energy loss when faced with internal and external disturbances. The formula for calculating the spatial heterogeneity adaptation index is: in, The external environmental disturbance at time t reflects the impact of the environment on the equipment's operating state. The unit is dimensionless. The spatial heterogeneity adaptation index measures the equipment's ability to adapt to changes in the external environment in a multidimensional space. It examines how the equipment responds to challenges from multidimensional disturbances by adjusting the coupling relationship between its internal state and the external environment, reflecting the equipment's stability in complex environments.

[0074] Based on the input stress dissipation index and the spatial heterogeneity adaptation index, a behavioral impedance difference factor is constructed for any two devices to quantitatively describe the differences in operating behavior and mutual influence between devices. Specifically, the behavioral impedance difference factor describes the behavioral differences that may exist between any two devices during scheduling. This factor quantitatively reflects the energy coupling effect, cooperative efficiency, and mutual influence between devices, providing a basis for subsequent scheduling decisions. This factor quantifies the mutual influence and impedance difference between device i and device j during operation. Specifically, the impedance difference considers energy transfer and mutual regulation between devices, and its calculation formula is as follows: Where, Δη input (i) and Δη space (i) represent the changes in the input stress dissipation index and the spatial heterogeneity adaptation index of device i during operation; Δη input (j) and Δη space (j) represent the changes in the input stress dissipation index and the spatial heterogeneity adaptation index of device j during operation; ΔE i and ΔE j These are the energy consumption disturbance structure indices for devices i and j, respectively, reflecting their energy dissipation characteristics under disturbance response. By introducing a behavioral impedance difference factor, the mutual influence and collaborative working capabilities between devices can be comprehensively quantified, providing a reliable basis for subsequent device selection and scheduling priority ranking, ensuring the efficiency and stability of the scheduling scheme.

[0075] By comprehensively inputting the stress dissipation index, spatial heterogeneity adaptation index, and inter-equipment behavioral impedance difference factor, a set of equipment operation characteristic parameters is formed, which includes the dynamic characteristics of a single piece of equipment and the coupling relationship between equipment.

[0076] The present invention is further configured such that the construction of the scheduling potential function includes:

[0077] Based on the equipment operation characteristic parameters, a scheduling potential function for the candidate equipment set within the scheduling cycle is constructed. The scheduling potential function comprehensively characterizes the dynamic energy consumption characteristics and adaptability of individual equipment, as well as the mutual coupling relationship between equipment in the operation coordination process.

[0078] By introducing a cooperative stability structure and a disturbance risk structure, the overall operational coordination of the candidate device set and its resilience to external disturbances are measured, respectively. Specifically, the cooperative stability structure describes whether the devices can maintain a stable operating state when cooperating, avoiding performance degradation due to interactions between devices. The cooperative stability structure can be modeled based on the mutual influence between devices. Among them, S co (i,j) represents the stability of devices i and j during the collaborative process, γ ij Let d be the coupling strength between device i and device j. ij The relative distance or performance difference between device i and device j represents the disturbance risk structure. This structure describes the ability of a device to withstand external disturbances (such as load changes, environmental factors, etc.), exhibiting a certain degree of resilience. The disturbance risk structure is represented as: Among them, R d (i) represents the risk tolerance of device i under disturbance, β i For the response elasticity of device i, δ i The disturbance intensity;

[0079] The scheduling potential function is used as the objective function for equipment combination scheduling optimization. It transmits equipment collaborative potential energy level information to the downstream scheduling priority generation step, supporting the judgment basis for equipment selection, marginal contribution assessment, and scheduling command output. Specifically, the formula for constructing the scheduling potential function is as follows: Among them, E schedule S represents the overall scheduling potential function, reflecting the overall scheduling effect of the equipment within the current scheduling cycle. i S represents the individual energy consumption dynamic characteristics of device i. co (i,j) represents the cooperative stability between device i and device j, R d (i) represents the disturbance risk carrying capacity of device i, where α, β, and γ are weighting coefficients used to adjust the relative importance of each factor. The scheduling potential function comprehensively characterizes the cooperative stability and disturbance risk of the device. By introducing the aforementioned cooperative stability structure and disturbance risk structure, the state of the device is comprehensively evaluated. The ultimate goal is to construct an objective function for optimizing the device scheduling combination.

[0080] The invention is further configured such that, based on the evaluation results of the scheduling potential function, the determination criteria of the structural adaptability threshold, and the judgment criteria of behavioral conflicts, a set of devices that meet the adaptability criteria and do not exhibit behavioral conflicts in their operating states is selected from the set of devices to be scheduled. Specifically, the structural adaptability threshold refers to the adaptability of a device in the current scheduling environment. This is a threshold parameter representing the device's ability to withstand external disturbances and changes in internal conditions. For each device i, this threshold can be expressed as: θ i =f(S) co (i),R d (i)), where S co (i) represents the cooperative stability of device i, R d (i) represents the disturbance risk tolerance of device i, and function f is an adaptive threshold model calculated based on device characteristics. The behavioral conflict judgment condition determines whether behavioral conflicts exist between devices. Behavioral conflicts between devices typically manifest as a decrease in operating efficiency due to resource competition or poor coordination between devices. The behavioral conflict factor between devices is C. ij A value of 1 indicates a conflict, while a value of 0 indicates no conflict. Where Π is an indicator function, which indicates that a conflict exists when the difference in cooperative stability between devices exceeds the threshold δ;

[0081] For each device in the selected set, its marginal potential energy contribution value is determined based on the change in its corresponding scheduling potential energy function after being included in the current combination; the marginal potential energy contribution value ΔE i It is used to measure the impact of each device on the overall scheduling potential, specifically reflecting the changes in cooperative stability and disturbance coupling strength caused by the device in the current scheduling structure.

[0082] The screened devices are sorted according to their marginal potential energy contribution values ​​to form a scheduling priority queue; among them, the larger the marginal potential energy contribution value of device i, the stronger the effect of the device on improving the overall system scheduling efficiency, and the more priority it should be scheduled.

[0083] Output the corresponding scheduling instructions based on the scheduling priority queue.

[0084] The present invention is further configured such that the marginal potential energy contribution value is constructed based on the current combination structure of the screened device set, in which the change in the scheduling potential energy function caused by the target device is introduced;

[0085] The marginal potential energy contribution value, combined with the target device's input stress dissipation index, spatial heterogeneity adaptation index, and the behavioral impedance difference factor of the target device to each device in the selected set, reflects the changes in cooperative stability and disturbance coupling strength caused by the target device in the current scheduling structure; specifically, the marginal potential energy contribution value ΔE of device i... iIt can be calculated using the following formula: in, It is the scheduling potential function E schedule The derivative of the individual energy consumption dynamic characteristics of device i reflects the contribution of device i to changes in energy consumption. It is the derivative of the scheduling potential function on the cooperative stability between device i and device j. It is the derivative of the scheduling potential energy function with respect to the disturbance risk bearing capacity of device i;

[0086] The marginal potential energy contribution value is transmitted to the scheduling instruction generation stage as the basis for determining scheduling priority.

[0087] The present invention is further configured such that, after the scheduling task is completed, the actual energy consumption data of all devices that have received scheduling instructions in the previous scheduling cycle are collected in different time periods.

[0088] Actual energy consumption data includes unit-time energy consumption measurements corresponding to consecutive moments within the scheduling cycle;

[0089] Actual energy consumption data is matched against predicted energy consumption data generated based on the unit-time energy consumption function on a time-by-time basis to construct an energy consumption deviation structure Π covering the entire scheduling cycle. m (τ), Π m (τ) represents the energy consumption deviation of device m at time point τ;

[0090] Based on the energy consumption change offset trajectory, response abrupt change amplitude, and coupling relationship with the control input presented in the energy consumption deviation structure, a corresponding disturbance feedback correction factor is generated; specifically, the local disturbance change intensity is defined as: Υ m (τ)=|Π m (τ+δ)-Π m (τΛ m (τ+δ)-Λ m (τ)|wherein, Λ m (τ) represents the control input strength of device m at time τ, δ represents the disturbance evaluation window length, and Υ m (τ) represents the synchronous response strength reflecting the combined changes in energy consumption offset and control input, used to locate abrupt change segments and abnormal drift intervals. The tag generation rules are as follows:

[0091] If Υ m If (τ)>η1 and the direction of change remains consistent, it is marked as a "drift delay segment";

[0092] If Υ m (τ) If it suddenly increases and then quickly decreases, it is marked as a "mutation segment";

[0093] If in consecutive time intervals ζ, Υ mIf (τ) fluctuates frequently, it is marked as a "frequent fluctuation segment".

[0094] The set of all labels is denoted as

[0095] The input stress dissipation index and spatial heterogeneity adaptation index of each device in the previous cycle are adjusted in multiple dimensions based on the adjustment intensity of the disturbance feedback correction factor to form the parameter structure after feedback correction.

[0096] The revised parameter structure is used as the input basis for the equipment operation characteristic parameters in the next scheduling cycle, so as to realize the dynamic correction and time-series iteration of scheduling parameters.

[0097] The present invention is further configured such that the energy consumption deviation structure includes a set of multiple time-series deviation records formed by mapping the actual energy consumption data and predicted energy consumption data of each device at continuous sampling times within the scheduling cycle;

[0098] The multi-time-series deviation record set constitutes a two-layer mapping structure. One layer associates the unique device number with the corresponding sampling time sequence, and the other layer associates the actual energy consumption offset value and control input status at each time point.

[0099] The energy consumption deviation structure is used to identify abnormal energy consumption response segments during actual operation, including segments with cumulative deviation mutations, segments with frequent fluctuations, and segments with disturbance delays. Based on this, multiple types of energy consumption drift tags are provided to trigger the selection of subsequent disturbance feedback correction factor construction strategies and the screening of equipment operating status update paths. Specifically, the energy consumption deviation structure refers to the offset mapping sequence formed by the actual energy consumption and predicted value of the equipment on the same time axis, used to quantify the prediction accuracy and actual offset intensity, constructing the energy consumption deviation structure of equipment m: Π m (τ)=Θ m (τ)-Ξ m (τ), Among them, Π m (τ) represents the energy consumption deviation of device m at time point τ, Θ m (τ) represents the actual energy consumption of device m per unit time, Ξ m (τ) represents the energy consumption per unit time of device m as output by the prediction model at the same time point. For the set of continuous sampling moments within the scheduling period, by traversing... Form a device number-time-deviation ternary mapping structure

[0100] The present invention is further configured such that the disturbance feedback correction factor is composed of three types of feedback source information:

[0101] The first type of feedback source information includes the consistency label of the device's energy consumption deviation stability and its disturbance change trend within a continuous sampling period; specifically, the first type of feedback source information extracts a stability consistency label feedback factor, the formula of which is: in, The input response gradient is used as an indicator to reflect the consistency between the direction of the disturbance and the change in the input, and to quantify the controllability of the prediction error.

[0102] The second type of feedback source information includes the synchronous offset trajectory between the history of control input intensity changes and the difference in energy consumption response; specifically, the synchronous offset feedback factor is extracted from the second type of feedback source information, and its formula is: Among them, ΔΛ m (τ)=Λ m (τ)-Λ m (τ-δ), ΔΠ m (τ)=Π m (τ)-Π m (τ-δ) is used to measure the intensity of the synchronous impact of changes in control input on energy consumption deviation;

[0103] The third type of feedback source information includes the offset coupling degree between the predicted energy consumption decay rate of the energy consumption structure near the sudden disturbance point and the actual response structure; specifically, the third type of feedback source information extracts the coupling factor at the sudden disturbance point, and its formula is: Only time points within the mutation tag set are calculated, and the difference in the second-order time derivative is used to express the model coupling error between the predicted response and the actual response under abrupt perturbation.

[0104] After being split through the feedback channel, the three types of feedback source information correspond to the response adjustment term of the input stress dissipation index, the adaptive compression term of the spatial heterogeneity adaptation index, and the retention term of the prediction model correction path, respectively. A dynamic update vector is constructed via the parameter transfer structure to control the multi-dimensional time-series correction process of the equipment's operating characteristic parameters. Specifically, the three factors are combined into an update vector: The system adjusts the input stress dissipation index Φ based on equipment feedback characteristics. m With spatial heterogeneity adaptation index Γ m The following updates will be made: Where ρ1,ρ2∈(0,1) are adaptive adjustment coefficients, Φ m '、Γ m To use the updated parameters for scheduling in the next cycle, three types of disturbance feedback correction factors are constructed, covering multiple dimensions such as input response, control coupling, and prediction offset, forming a high-dimensional dynamic correction logic that supports adaptive evolution of scheduling parameters.

[0105] By dynamically updating the feedback correction vector control parameter structure, the closed-loop regulation capability and long-term steady-state evolution mechanism of the equipment scheduling system in complex operating environments are realized.

[0106] Example 2:

[0107] Please see Figure 2 This exemplary device energy consumption optimization and scheduling system based on big data analysis includes:

[0108] Data acquisition and energy consumption modeling module: During the scheduling cycle, it collects the operating status data of the set of devices to be scheduled with preset numbers, constructs the unit time energy consumption function of each device based on the collected operating status data, and generates an energy consumption disturbance structure index that characterizes the intensity of operating disturbance.

[0109] Feature parameter extraction module: Based on energy consumption disturbance structure index and operating status data, calculate input stress dissipation index and spatial heterogeneity adaptation index, construct behavioral impedance difference factor between any two devices, and form a set of device operating feature parameters;

[0110] Potential energy function construction module: Constructs a scheduling potential energy function based on equipment operating characteristic parameters, measures the cooperative stability level and disturbance risk intensity of preset candidate equipment within the scheduling cycle, and uses the scheduling potential energy function as the objective function for scheduling combinatorial optimization;

[0111] Scheduling optimization and instruction generation module: Based on the scheduling potential energy function, structural adaptability threshold and behavioral conflict judgment conditions, it selects a set of devices that meet the adaptability requirements and do not constitute operational conflicts from the devices to be scheduled. For each device in the set, it calculates its marginal potential energy contribution value in the potential energy function, sorts them according to the size of the contribution value, generates a scheduling priority queue, and outputs scheduling instructions.

[0112] Feedback correction and parameter update module: After the scheduling task is completed, the actual energy consumption data of the scheduled equipment is collected, and the data is compared with the prediction model results at multiple time points. The energy consumption deviation structure is calculated, and the disturbance feedback correction factor is generated based on the energy consumption deviation structure. This factor is used to update the aforementioned input stress dissipation index and spatial heterogeneity adaptation index, and the adaptive update of the scheduling parameters is completed in the next scheduling cycle.

[0113] It should be noted that the equipment energy consumption optimization scheduling system based on big data analysis provided in the above embodiments and the equipment energy consumption optimization scheduling method based on big data analysis provided in the above embodiments belong to the same concept. The specific methods of execution of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the equipment energy consumption optimization scheduling system based on big data analysis provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0114] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0115] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0116] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0117] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0118] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0119] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0120] In the several embodiments provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0121] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0122] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0123] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0124] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for optimizing equipment energy consumption scheduling based on big data analysis, characterized in that, include: Continuous sampling of equipment voltage, current, control input strength, and disturbance response information across multiple time periods; Based on the collected operational status data, construct the energy consumption function of the device per unit time; By extracting the dynamic characteristics of the energy consumption function and disturbance response information per unit time, an energy consumption disturbance structure index is formed to characterize the intensity of equipment operation disturbances. The energy consumption disturbance structure index is defined as follows: It can be represented by the relationship between the energy consumption of the equipment and the changes in control input: , For the equipment after being disturbed Energy consumption, measured in watt-hours. For the equipment at the moment before the disturbance Energy consumption, measured in watt-hours, is the energy consumption disturbance structure index, which reflects the intensity of the equipment's response to disturbances. Based on energy consumption disturbance structural indicators and operating status data, the input stress dissipation index and spatial heterogeneity adaptation index are calculated. For any two devices, a behavioral impedance difference factor is constructed to form a set of device operating characteristic parameters. The formula for calculating the input stress dissipation index is: ,in, Is the equipment in constant voltage, Is the equipment in constant The current, It is a function that controls the input strength, describing the impact of the control signal strength on energy consumption, and is defined by the device control algorithm. The input stress dissipation index measures the energy dissipation capability of equipment during operation caused by the control input signal and external disturbances. The formula for calculating the spatial heterogeneity adaptation index is: ,in, The equipment is in constant time The external environmental disturbance reflects the impact of the environment on the equipment's operating status. The spatial heterogeneity adaptation index is used to measure the equipment's ability to adapt to changes in the external environment in a multidimensional space. The calculation formula for the behavioral impedance difference factor is: ,in, and These are the equipment During operation, the changes in the input stress dissipation index and spatial heterogeneity adaptation index are considered. and respectively equipment and equipment The energy consumption disturbance structure index and the behavioral impedance difference factor are used to describe the behavioral differences that may exist between any two devices during the scheduling process. Based on equipment operating characteristic parameters, a scheduling potential function for the candidate equipment set within a scheduling cycle is constructed. This scheduling potential function comprehensively characterizes the dynamic energy consumption characteristics and adaptability of individual equipment, as well as the mutual coupling relationship between equipment during operational coordination. By introducing a collaborative stability structure and a disturbance risk structure, the overall operational coordination degree and resilience to external disturbances of the candidate equipment set are measured, respectively. The scheduling potential function is used as the objective function for equipment combination scheduling optimization, transmitting equipment collaborative potential energy level information to the downstream scheduling priority generation step. This information serves as the basis for determining equipment selection, marginal contribution assessment, and scheduling command output. The formula for constructing the scheduling potential function is as follows: , The total scheduling potential function reflects the overall scheduling effect of the equipment within the current scheduling cycle. For equipment The dynamic characteristics of individual energy consumption For equipment and equipment Cooperative stability between them For equipment The ability to withstand disturbance risks. , , As the weighting coefficient, the scheduling potential function comprehensively characterizes the cooperative stability and disturbance risk of the equipment. The state of the equipment is comprehensively evaluated by introducing the above-mentioned cooperative stability structure and disturbance risk structure. Based on the evaluation results of the scheduling potential function, the judgment criteria for the structural adaptability threshold, and the judgment criteria for behavioral conflicts, a set of equipment that meets the adaptability criteria and has no behavioral conflicts in its operating state is selected from the set of equipment to be scheduled. For each equipment in the selected set, the marginal potential energy contribution value of the equipment is determined based on the change in its corresponding scheduling potential function after being included in the current combination. The selected equipment is sorted according to the magnitude of the marginal potential energy contribution value to form a scheduling priority queue. The corresponding scheduling instructions are output according to the scheduling priority queue. Here, the structural adaptability threshold refers to the adaptability of the equipment in the current scheduling environment, representing the equipment's ability to withstand external disturbances and changes in internal conditions. For each equipment... This threshold can be expressed as: , It is equipment Cooperative stability, It is equipment The function's tolerance for disturbance risk It is an adaptive threshold model based on equipment characteristics; the behavioral conflict judgment condition determines whether there is a behavioral conflict between devices. Behavioral conflicts between devices typically manifest as a decrease in operating efficiency due to resource competition or poor coordination between devices; the behavioral conflict factor between devices is defined as... A value of 1 indicates a conflict, and a value of 0 indicates no conflict. The formula is: , It is an indicator function that indicates when the difference in cooperative stability between devices exceeds a threshold. When a conflict exists, the marginal potential energy contribution value can be calculated using the following formula: , It is the scheduling potential function For equipment The derivative of the individual energy consumption dynamic characteristics reflects the equipment Contribution to changes in energy consumption It is the scheduling potential function for the device With equipment The derivative of the effect of cooperative stability between them It is the scheduling potential function for the device The derivative of the disturbance risk carrying capacity and the marginal potential energy contribution value are combined with the input stress dissipation index, spatial heterogeneity adaptation index of the target equipment and the behavioral impedance difference factor of the equipment to each equipment in the selected equipment set to reflect the cooperative stability change and disturbance coupling strength change caused by the target equipment in the current scheduling structure. After the scheduling task is completed, the actual energy consumption data of the scheduled equipment is collected. This data is then compared with the prediction model results over multiple time intervals to calculate the energy consumption deviation structure. Based on the energy consumption deviation structure, a disturbance feedback correction factor is generated to update the aforementioned input stress dissipation index and spatial heterogeneity adaptation index. Adaptive updates of the scheduling parameters are then completed in the next scheduling cycle. The energy consumption deviation structure is an offset mapping sequence formed by the actual energy consumption and predicted values ​​of the equipment on the same time axis. It is used to quantify the prediction accuracy and the actual offset intensity. The calculation formula is as follows: , , For equipment At the point of time Energy consumption deviation, For equipment Actual energy consumption per unit time For equipment Energy consumption per unit time output by the prediction model at the same time point. The set of continuous sampling moments within the scheduling period; the disturbance feedback correction factor includes three types of feedback source information: the first type is the stability consistency label feedback factor, with the formula: , The first type is the input response gradient, and the second type is the stability consistency label feedback factor used to adjust the response of the input stress dissipation index; the third type is the synchronization offset feedback factor, with the following formula: , , The synchronous offset feedback factor is used for adaptive compression of the spatial heterogeneity adaptation index; the third type is the sudden disturbance point coupling factor, the formula of which is: The sudden disturbance point coupling factor is used to preserve the path of the prediction model correction.

2. The equipment energy consumption optimization and scheduling method based on big data analysis according to claim 1, characterized in that, The set of equipment operating characteristic parameters includes: Based on the equipment energy consumption disturbance structure index and the equipment operation status data collected during the scheduling cycle, the input stress dissipation index and spatial heterogeneity adaptation index of each piece of equipment are calculated respectively. Based on the input stress dissipation index and the spatial heterogeneity adaptation index, a behavioral impedance difference factor is constructed for any two devices to quantitatively describe the differences in operating behavior and mutual influence between devices. By comprehensively inputting the stress dissipation index, spatial heterogeneity adaptation index, and inter-equipment behavioral impedance difference factor, a set of equipment operation characteristic parameters is formed, which includes the dynamic characteristics of a single piece of equipment and the coupling relationship between equipment.

3. The equipment energy consumption optimization and scheduling method based on big data analysis according to claim 1, characterized in that, After the scheduling task is completed, collect the actual energy consumption data of all devices that have received scheduling instructions in the previous scheduling cycle in different time periods. Actual energy consumption data includes unit-time energy consumption measurements corresponding to consecutive moments within the scheduling cycle; The actual energy consumption data is matched with the predicted energy consumption data generated based on the unit time energy consumption function on a time-by-time basis to construct an energy consumption deviation structure covering the entire scheduling cycle; Based on the energy consumption change offset trajectory, response abrupt change amplitude and control input coupling relationship presented in the energy consumption deviation structure, a corresponding disturbance feedback correction factor is generated; The input stress dissipation index and spatial heterogeneity adaptation index of each device in the previous cycle are adjusted in multiple dimensions based on the adjustment intensity of the disturbance feedback correction factor to form the parameter structure after feedback correction. The revised parameter structure is used as the input basis for the equipment operation characteristic parameters in the next scheduling cycle, so as to realize the dynamic correction and time-series iteration of scheduling parameters.

4. The equipment energy consumption optimization and scheduling method based on big data analysis according to claim 3, characterized in that, The energy consumption deviation structure includes a set of multi-time-series deviation records formed by mapping the actual energy consumption data and predicted energy consumption data of each device at continuous sampling times within the scheduling cycle; The multi-time-series deviation record set constitutes a two-layer mapping structure. One layer associates the unique device number with the corresponding sampling time sequence, and the other layer associates the actual energy consumption offset value and control input status at each time point. The energy consumption deviation structure is used to identify abnormal energy consumption response segments that occur during actual operation, including deviation accumulation mutation segments, frequent fluctuation segments, and disturbance delay segments. Based on this, multiple types of energy consumption drift tags are provided to trigger the selection of subsequent disturbance feedback correction factor construction strategies and the screening of equipment operation status update paths.

5. The equipment energy consumption optimization and scheduling method based on big data analysis according to claim 3, characterized in that, The disturbance feedback correction factor consists of three types of feedback source information: The first type of feedback source information includes a label indicating the consistency between the stability of the device's energy consumption deviation and its disturbance change trend within a continuous sampling period; The second type of feedback source information includes the synchronous offset trajectory between the history of changes in control input intensity and the difference in energy consumption response; The third type of feedback source information includes the degree of offset coupling between the predicted energy consumption decay rate of the energy consumption structure near the sudden disturbance point and the actual response structure. After being diverted through the feedback channel, the three types of feedback source information correspond to the response adjustment term of the input stress dissipation index, the adaptation compression term of the spatial heterogeneity adaptation index, and the retention term of the prediction model correction path, respectively. A dynamic update vector is constructed through the parameter transfer structure to control the multi-dimensional time-series correction process of the equipment operation characteristic parameters.

6. A device energy consumption optimization scheduling system based on big data analysis, used to implement the device energy consumption optimization scheduling method based on big data analysis as described in any one of claims 1-5, characterized in that, include: Data acquisition and energy consumption modeling module: During the scheduling cycle, it collects the operating status data of the set of devices to be scheduled with preset numbers, constructs the unit time energy consumption function of each device based on the collected operating status data, and generates an energy consumption disturbance structure index that characterizes the intensity of operating disturbance. Feature parameter extraction module: Based on energy consumption disturbance structure index and operating status data, calculate input stress dissipation index and spatial heterogeneity adaptation index, construct behavioral impedance difference factor between any two devices, and form a set of device operating feature parameters; Potential energy function construction module: Constructs a scheduling potential energy function based on equipment operating characteristic parameters, measures the cooperative stability level and disturbance risk intensity of preset candidate equipment within the scheduling cycle, and uses the scheduling potential energy function as the objective function for scheduling combinatorial optimization; Scheduling optimization and instruction generation module: Based on the scheduling potential energy function, structural adaptability threshold and behavioral conflict judgment conditions, it selects a set of devices that meet the adaptability requirements and do not constitute operational conflicts from the devices to be scheduled. For each device in the set, it calculates its marginal potential energy contribution value in the potential energy function, sorts them according to the size of the contribution value, generates a scheduling priority queue, and outputs scheduling instructions. Feedback correction and parameter update module: After the scheduling task is completed, the actual energy consumption data of the scheduled equipment is collected, and the data is compared with the prediction model results at multiple time points. The energy consumption deviation structure is calculated, and the disturbance feedback correction factor is generated based on the energy consumption deviation structure. This factor is used to update the aforementioned input stress dissipation index and spatial heterogeneity adaptation index, and the adaptive update of the scheduling parameters is completed in the next scheduling cycle.

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