Energy efficiency optimization method and system based on multi-device coupling relationship

Abstract

The invention provides an energy efficiency optimization method and system based on a multi-device coupling relationship, and is applied to the field of data processing. According to the method, the change rate of the collection frequency is dynamically judged by combining various working parameters related to the energy consumption equipment and the coupling relation, accurate regulation and control are performed based on the frequency peak value, the minimum holding time period and the change coefficient, quick response can be performed when the frequency fluctuates abnormally, data over-collection or data missing is avoided, and the accuracy of data collection is improved. And furthermore, by combining emergency process matching and state flag bit identification, the acquisition tasks are layered according to safety influence degrees, and high-frequency monitoring in a high-risk state and low-frequency acquisition in a low-risk state are realized, so that data integrity and resource saving are both considered.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to an energy efficiency optimization method and system based on the coupling relationship of multiple devices. Background Art

[0002] Existing data acquisition frequencies are usually fixed and do not take into account the different rates of change in the coupling relationship of devices. For example, due to the large thermal inertia, the coupling state of heat exchange equipment may not change significantly for several minutes or even tens of minutes. In this case, high-frequency acquisition will waste communication bandwidth and computing resources. On the other hand, the coupling relationship of high-speed rotating equipment may change in seconds or even milliseconds. Fixed low-frequency acquisition will miss key instantaneous information, causing optimization strategies to lag or even fail. Summary of the Invention

[0003] This invention aims to solve the problem of how to dynamically adjust the acquisition frequency of each device so that data acquisition is neither overloaded nor missing key information, thereby reducing resource consumption and improving optimization accuracy. It provides an energy efficiency optimization method and system based on the coupling relationship of multiple devices.

[0004] The present invention employs the following technical means to solve the technical problem: This invention provides an energy efficiency optimization method based on multi-device coupling relationships, comprising: Based on the operating parameters related to the energy-consuming equipment and the coupling relationship, the energy consumption acquisition frequency of the energy-consuming equipment is read. Specifically, the operating parameters include temperature, pressure, flow rate, rotation speed, current, and power. Determine whether the rate of change of the energy consumption sampling frequency exceeds a preset rate threshold; If so, then divide the frequency peak from the energy consumption sampling frequency, obtain the minimum holding period after the energy consumption sampling frequency change, generate the maximum change ratio of the energy consumption sampling frequency based on the frequency peak and the minimum holding period, and calculate the change coefficient of the energy consumption sampling frequency based on the maximum change ratio. The frequency peak specifically includes the highest sampling frequency and the lowest sampling frequency, and the change coefficient specifically includes the instantaneous change rate and the average change rate. Determine whether the change coefficient matches the preset emergency process of the energy-consuming device; If not, the status flag of the energy-consuming device is identified, and based on the status flag, the collection priority of the energy consumption collection frequency is generated. According to the collection priority, the non-critical energy consumption data of the energy-consuming device during energy consumption collection is dynamically compressed. Based on the non-critical energy consumption data, a hierarchical multi-frequency collection strategy for the energy-consuming device is constructed. Specifically, the status flag includes start-up, shutdown, and fault, and the hierarchical multi-frequency collection strategy includes the highest frequency affecting device safety, the medium frequency indirectly affecting the device, and the lower frequency monitoring device indicators.

[0005] Furthermore, the step of dividing the frequency peak from the energy consumption sampling frequency and obtaining the minimum retention period after the energy consumption sampling frequency change also includes: Based on the sampling period of the energy consumption sampling frequency, obtain the pre-allocated capacity cache queue of the energy consumption device; Determine whether the data in the capacity cache queue is saved in chronological order; If so, the earliest recorded first capacity data and the latest recorded second capacity data are read from the capacity cache queue, the time difference between the first capacity data and the second capacity data is calculated, and the number of samples to be collected in the capacity cache queue is dynamically adjusted according to the time difference.

[0006] Furthermore, after the step of generating the maximum change ratio of the energy consumption sampling frequency based on the frequency peak and the minimum hold period, the method further includes: Based on the operating characteristics of the energy-consuming equipment, the variation law of the energy consumption acquisition frequency is constructed. Specifically, the operating characteristics include frequency variation distribution and energy consumption response delay, and the variation law includes seasonality, periodic fluctuation pattern and stable range. Determine whether the described pattern of change indicates data loss or missing data. If so, the location where the time interval of the missing data packet is exceeded is obtained. Based on the location where the time interval is exceeded, the missing information of the missing data packet is collected. Based on the missing information, the missing type of the missing data packet is identified. The missing information specifically includes the start and end times of the missing data packet, the number of missing data points, and the devices involved. The missing type specifically includes continuous missing data packet, intermittent missing data packet, and single-point missing data packet.

[0007] Furthermore, the step of dynamically compressing the non-critical energy consumption data of the energy-consuming device during energy consumption collection according to the collection priority also includes: Based on the preset triggering conditions of the energy-consuming device, a preset compression strategy for the energy consumption collection frequency is selected. The triggering conditions specifically include network bandwidth utilization exceeding a threshold, storage space shortage, memory usage exceeding a set value, and the need to temporarily increase the collection frequency of key data. The compression strategy specifically includes sampling rate compression, data precision compression, time period aggregation compression, and event-driven storage. Determine whether the triggering condition matches the compression strategy; If so, the energy consumption parameters before and after compression are obtained, and the compression effect of the compression strategy is generated based on the energy consumption parameters. The compression ratio of the compression strategy is dynamically adjusted according to the operating status of the energy-consuming device and the compression effect. Specifically, the energy consumption parameters include data volume, bandwidth utilization, and storage usage.

[0008] Furthermore, after the step of determining whether the rate of change of the energy consumption sampling frequency exceeds a preset rate threshold, the method further includes: Identify the extent of exceeding the limit of the energy consumption sampling frequency; Determine whether the exceeded amplitude is an isolated phenomenon; If so, the excess range is divided into excess levels. Based on the excess level, the collection rate of the energy consumption collection frequency is limited, and the preset protection mechanism of the energy consumption device is dynamically triggered. The excess levels specifically include mild, moderate and severe, and the protection mechanism specifically includes temporarily freezing frequency adjustment, reducing the frequency of devices with low collection priority and sending alarms to the operation and maintenance center.

[0009] Furthermore, the step of determining whether the change coefficient matches the preset emergency process of the energy-consuming device further includes: Based on the preset energy consumption change threshold of the energy-consuming device, the change series strength of the change coefficient is obtained, and the change series strength is combined to obtain the intensity trajectory; Determine whether the intensity trajectory matches the expected characteristics of the emergency process; If not, the emergency type of the emergency process is identified, and the allowable value of the energy consumption collection frequency is dynamically adjusted according to the emergency type. The emergency type specifically includes start / stop, over-temperature, overload, and external fast-cycle command. The allowable value specifically includes collection frequency, upper limit of change range, and adjustment interval.

[0010] Furthermore, before the step of reading the energy consumption sampling frequency of the energy-consuming device based on the operating parameters related to the coupling relationship between the energy-consuming device and the device, the method further includes: Based on the preset energy consumption mapping rules of the energy-consuming devices, the coupling relationship between the energy-consuming devices is identified. Specifically, the energy consumption mapping rules include normal operating conditions, different load ranges, and emergency operating conditions, and the coupling relationship specifically includes direct coupling and indirect coupling. Determine whether the coupling relationship matches the acquisition priority of the energy-consuming device; If not, the operating parameter values ​​of the energy-consuming device are matched with the preset definition range of the coupling relationship, the sampling frequency range of the energy-consuming device is dynamically corrected, and the operating status of the energy-consuming device and its coupled object is updated in real time according to the sampling priority. The operating status specifically includes the working condition status and the load status.

[0011] The present invention also provides an energy efficiency optimization system based on multi-device coupling relationships, comprising: The reading module is used to read the energy consumption acquisition frequency of the energy-consuming device based on the operating parameters related to the coupling relationship between the energy-consuming device and the device. The operating parameters specifically include temperature, pressure, flow rate, rotation speed, current, and power. The judgment module is used to determine whether the rate of change of the energy consumption acquisition frequency exceeds a preset rate threshold. An execution module is configured to, if so, divide the frequency peak from the energy consumption sampling frequency, obtain the minimum holding period after the energy consumption sampling frequency change, generate the maximum change ratio of the energy consumption sampling frequency based on the frequency peak and the minimum holding period, and calculate the change coefficient of the energy consumption sampling frequency based on the maximum change ratio, wherein the frequency peak specifically includes the highest sampling frequency and the lowest sampling frequency, and the change coefficient specifically includes the instantaneous change rate and the average change rate; The second judgment module is used to determine whether the change coefficient matches the preset emergency process of the energy consumption device; The second execution module is used to identify the status flag of the energy-consuming device if no, generate a collection priority for the energy consumption collection frequency based on the status flag, dynamically compress non-critical energy consumption data of the energy-consuming device when collecting energy consumption data according to the collection priority, and construct a hierarchical multi-frequency collection strategy for the energy-consuming device based on the non-critical energy consumption data. The status flag specifically includes start-up, shutdown, and fault, and the hierarchical multi-frequency collection strategy specifically includes the highest frequency that affects device safety, the medium frequency that indirectly affects the device, and the lower frequency that monitors device indicators.

[0012] Furthermore, the execution module also includes: The acquisition unit is used to acquire the pre-allocated capacity cache queue of the energy-consuming device based on the acquisition cycle of the energy consumption acquisition frequency. The judgment unit is used to determine whether the data in the capacity cache queue is saved in chronological order. An execution unit is configured to, if so, read the earliest recorded first capacity data and the latest recorded second capacity data from the capacity cache queue, calculate the time difference between the first capacity data and the second capacity data, and dynamically adjust the number of samples to be collected in the capacity cache queue based on the time difference.

[0013] Furthermore, it also includes: The construction module is used to construct the variation law of the energy consumption acquisition frequency based on the operating characteristics of the energy consumption equipment. Specifically, the operating characteristics include frequency variation distribution and energy consumption response delay, and the variation law includes seasonality, periodic fluctuation pattern and stable range. The third judgment module is used to determine whether the change pattern detects data packet loss or missing data. The third execution module is used to, if so, obtain the location where the time interval of the missing data packet is exceeded, collect the missing information of the missing data packet based on the location where the time interval is exceeded, and identify the missing type of the missing data packet based on the missing information. The missing information specifically includes the start and end times of the missing data, the number of missing data points and the devices involved, and the missing type specifically includes continuous missing data, intermittent missing data and single-point missing data.

[0014] This invention provides an energy efficiency optimization method and system based on the coupling relationship of multiple devices, which has the following beneficial effects: This invention dynamically judges the rate of change of the acquisition frequency by combining multiple operating parameters related to energy-consuming equipment and coupling relationships, and makes precise adjustments based on the frequency peak, minimum hold time, and change coefficient. It can respond quickly when the frequency fluctuates abnormally, avoiding over-collection or under-collection of data. Furthermore, by combining emergency process matching and status flag identification, the acquisition tasks are layered according to the degree of safety impact, realizing high-frequency monitoring in high-risk states and low-frequency acquisition in low-risk states, thereby balancing data integrity and resource conservation. This method effectively prevents storage and transmission pressure caused by acquisition overload, while reducing the bandwidth and computing resources occupied by invalid data, improving the accuracy and real-time performance of energy consumption optimization decisions, and improving the overall energy efficiency management level of the system. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating an embodiment of the energy efficiency optimization method based on multi-device coupling relationships of the present invention; Figure 2 This is a structural block diagram of an embodiment of the energy efficiency optimization system based on multi-device coupling relationship of the present invention. DETAILED DESCRIPTION

[0016] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The realization of the purpose, functional features, and advantages of the invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings.

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Reference Appendix Figure 1 The present invention provides an energy efficiency optimization method based on multi-device coupling relationships, comprising: S1: Based on the operating parameters related to the energy-consuming device and the coupling relationship, read the energy consumption acquisition frequency of the energy-consuming device, wherein the operating parameters specifically include temperature, pressure, flow rate, rotation speed, current and power; S2: Determine whether the rate of change of the energy consumption sampling frequency exceeds a preset rate threshold; S3: If so, then divide the frequency peak from the energy consumption sampling frequency, obtain the minimum holding period after the energy consumption sampling frequency change, generate the maximum change ratio of the energy consumption sampling frequency based on the frequency peak and the minimum holding period, and calculate the change coefficient of the energy consumption sampling frequency based on the maximum change ratio. The frequency peak specifically includes the highest sampling frequency and the lowest sampling frequency, and the change coefficient specifically includes the instantaneous change rate and the average change rate. S4: Determine whether the change coefficient matches the preset emergency process of the energy-consuming device; S5: If not, identify the status flag bit of the energy-consuming device, generate the collection priority of the energy consumption collection frequency based on the status flag bit, dynamically compress the non-critical energy consumption data of the energy-consuming device when collecting energy consumption according to the collection priority, and construct the hierarchical multi-frequency collection strategy of the energy-consuming device based on the non-critical energy consumption data. The status flag bit specifically includes start-up, shutdown, and fault, and the hierarchical multi-frequency collection strategy specifically includes the highest frequency that affects the safety of the device, the medium frequency that indirectly affects the device, and the lower frequency that monitors the device indicators.

[0019] In this embodiment, the system reads the energy consumption sampling frequency of each energy-consuming device based on the operating parameters related to the coupling relationship of the energy-consuming devices. These operating parameters specifically include temperature, pressure, flow rate, rotational speed, current, and power. The system then determines whether the rate of change of these energy consumption sampling frequencies exceeds a preset rate threshold to execute corresponding steps. For example, if the system determines that the energy consumption sampling frequency of a certain energy-consuming device does not exceed the preset rate threshold, the system considers the current sampling frequency variation of that device to be within an acceptable stable range, without sudden fluctuations or abnormal sampling demands. The system will continue to acquire data according to the existing sampling frequency to avoid the additional resource consumption caused by frequent adjustments. The device is marked as "frequency stable," reducing its priority for triggering frequency adjustments within a short period. It will only be re-evaluated in the next detection cycle or monitoring window. Furthermore, the computing and communication resources originally used for frequency adjustments by this device will be allocated to other devices experiencing high frequency fluctuations or critical operating conditions to improve overall system energy efficiency. For example, when the system determines that the energy consumption sampling frequency of a certain energy-consuming device exceeds a preset rate threshold, the system considers the fluctuation range of the current device's sampling frequency to be abnormal. The system will then divide the energy consumption sampling frequency into frequency peaks, specifically including the highest and lowest sampling frequencies, and obtain the minimum hold-up period after the energy consumption sampling frequency change. Based on different... The system generates the maximum percentage change in energy consumption sampling frequency by analyzing the peak frequency and minimum hold time. Based on this maximum percentage change, it calculates the frequency change coefficient, which includes both instantaneous and average change rates. When the energy consumption sampling frequency exceeds a preset rate threshold, the system immediately identifies it as an abnormal change and further extracts the highest and lowest sampling frequencies as frequency peak indicators. This approach quickly captures extreme frequency changes, avoiding missed anomalies due to relying solely on a single average value. This more accurately reflects the device's fluctuation characteristics within a moment or period. Furthermore, by combining different frequency peaks and minimum hold times to generate the maximum percentage change in sampling frequency, the system can effectively limit... The excessive amplitude of frequency adjustment prevents frequent and large-scale frequency changes due to sudden fluctuations. This proportional adjustment mechanism enables the acquisition strategy to maintain stability while responding to fluctuations, which helps reduce system resource consumption and data jitter. Furthermore, when calculating the change coefficient, both instantaneous change rate and average change rate are considered simultaneously. This not only reflects rapid changes in a short period of time but also captures long-term trend deviations. This dual-indicator judgment method allows the system to balance short-term sensitivity and long-term robustness when adjusting the acquisition frequency, thereby ensuring the integrity of key data while improving the accuracy of energy consumption optimization and equipment status diagnosis. Then, the system determines whether these change coefficients match the pre-set emergency processes of the energy-consuming equipment and executes the corresponding steps.For example, when the system determines that the change coefficient of the energy consumption sampling frequency of a certain energy-consuming device matches a pre-set emergency process, the system will consider that the current operating state of the device has entered or is approaching a critical operating condition. This condition may be highly related to safety risks, production continuity, and equipment protection. The system will adjust the sampling frequency of the device to the highest frequency level affecting equipment safety to ensure the real-time and completeness of key parameters. Simultaneously with the sampling frequency adjustment, additional data monitoring will be initiated to record high-precision changes in key operating parameters (temperature, pressure, flow rate, etc.) for rapid analysis of the emergency cause. Furthermore, if the device is coupled with other devices, status signals will be sent to the relevant coupled devices, if necessary... To prevent cascading risks, the system synchronously increases the sampling frequency of energy-consuming devices, and monitors the change coefficient in real time during the high-frequency sampling phase. Once the coefficient falls out of the emergency process matching range and stabilizes for a period of time, the sampling frequency is gradually reduced to save resources. For example, if the system determines that the change coefficient of the energy consumption sampling frequency of a certain energy-consuming device does not match the pre-set emergency process, the system will consider that the current operating state of the device cannot enter the critical operating condition. The system will identify the status flag of the energy-consuming device, which specifically includes start-up, shutdown, and fault. Based on different status flags, the system generates the sampling priority of the energy consumption sampling frequency, and dynamically compresses the non-critical energy consumption of the energy-consuming device during energy consumption sampling according to different sampling priorities. Based on this non-critical energy consumption data, a hierarchical multi-frequency acquisition strategy is constructed for the energy-consuming equipment. This strategy specifically includes the highest frequency affecting equipment safety, the medium frequency indirectly affecting the equipment, and the lower frequency monitoring equipment indicators. When the change coefficient does not match an emergency process, the system automatically determines that the equipment has not entered a critical operating condition, thus avoiding the waste of bandwidth, storage, and computing resources caused by maintaining a high sampling rate. By identifying status flags such as startup, shutdown, and fault, the system can proactively compress non-critical energy consumption data in stable or low-risk states, retaining only necessary operational information, making data acquisition more streamlined and efficient. Simultaneously, based on different status flags, acquisition priorities are generated, allowing for adjustments to the acquisition frequency. The system allocates data to the most critical monitoring stages, maintaining a high sampling frequency in high-priority states to ensure real-time performance, while reducing the sampling frequency in low-priority states to ensure overall system load control. This priority-driven acquisition mode allows for more precise resource allocation and improves the system's ability to handle the parallel operation of multiple devices. Furthermore, by constructing a hierarchical multi-frequency acquisition strategy, the acquisition frequency is divided into three levels: highest, medium, and lowest, corresponding to device safety, indirect impact, and indicator monitoring needs, respectively. This enables differentiated acquisition of information of varying importance. This hierarchical strategy can quickly increase the acquisition frequency to ensure safety when needed, and reduce the frequency to reduce energy consumption under normal conditions, thus balancing safety, real-time performance, and economy.

[0020] It should be noted that the operating parameters of the energy-consuming equipment related to the coupling relationship are as follows: Energy-consuming equipment refers to equipment that consumes energy such as electricity, heat, and gas during production, operation, or service. Examples include pumps, compressors, fans, motors, heating furnaces, and cooling units. Coupling relationships refer to the mutual influence of multiple energy-consuming devices in terms of functionality, energy flow, or control during operation. For example, a change in the flow rate of a pump can directly affect the workload of a downstream heat exchanger; the operating status of a cooling unit can affect the temperature control requirements of production equipment. In other words, coupling relationships are the "energy and operational dependency chains" between devices, which can be direct or indirect.

[0021] The energy consumption sampling frequency is divided into frequency peaks, and the minimum holding period after the energy consumption sampling frequency change is obtained. Based on the frequency peaks and the minimum holding period, the maximum change ratio of the energy consumption sampling frequency is generated. Based on the maximum change ratio, the change coefficient of the energy consumption sampling frequency is calculated. A specific example is as follows: Suppose that the sampling frequency of a wind turbine needs to be increased to capture rapid fluctuations; we use the most recent 10 minutes as the analysis window for evaluation. Known / read data: The highest sampling frequency in the last 10 minutes was 20 Hz and the lowest was 2 Hz. The current effective sampling frequency of the device is 10 Hz. The minimum retention period after the change is set to 30 seconds (that is, after one adjustment, it will not be changed again for at least 30 seconds). Strategy requirements: The entire dynamic range (from lowest to highest) must be traversed in at least 5 steps; and the maximum percentage of each step should not exceed 30% and should not be less than 5% (to prevent the effect from being too small to be noticeable). Therefore, the steps are as follows: Step 1: Divide the frequency peaks, with the highest sampling frequency = 20Hz and the lowest sampling frequency = 2Hz; Step 2, read the minimum hold period, minimum hold period = 30 seconds; Step 3, generate "Maximum Change Ratio". a. Dynamic range = highest (20) − lowest (2) = 18Hz, b. It stipulates that the entire range must be traversed in at least 5 steps. c. Single-step absolute step size = dynamic range (18) ÷ 5 = 3.6Hz, d. The proportion converted to "relative to the current frequency", The current frequency is 10Hz, and a single step of 3.6Hz is equivalent to 36%, but the strategy upper limit is 30%, so the previous step needs to be tightened to 30%; at the same time, it is 5% higher than the lower limit, so there is no need to raise it further, resulting in a maximum change ratio of 30%. e. Boundary protection: If adjusted upwards by 30%, the target frequency = 10 Hz × (1 + 30%) = 13 Hz. 13 Hz is between the minimum 2 Hz and the maximum 20 Hz, which does not exceed the boundary and can be used. Step 4, calculate the coefficient of change and the instantaneous rate of change (for this instance): Frequency change = 13 − 10 = 3 Hz; Time = 30 seconds; Instantaneous rate of change = 3Hz / 30s = 0.1Hz / s; Average rate of change (see the overall trend of the last two holding periods, for example): Assuming the previous cycle was 8Hz→10Hz (a change of 2Hz, taking 30 seconds), and the current cycle is 10Hz→13Hz (a change of 3Hz, taking 30 seconds), the rates of change for the two cycles are 2 / 30=0.0667Hz / s and 3 / 30=0.1Hz / s respectively. The average rate of change (equal duration) is (0.0667+0.1) / 2=0.0833Hz / s. In summary, the system ensures that no adjustment exceeds the upper / lower limits of the device and system's safety and resource capabilities by defining the boundaries with the highest / lowest frequencies. The minimum hold period ensures that each adjustment has an observable period, avoiding "reversal after adjustment," which is beneficial for stability and evaluation. By evenly distributing the dynamic range and clamping it proportionally, the "total distance to be traveled" (the difference between the highest and lowest frequencies) is first divided into several small steps to ensure smooth completion within several cycles. Then, the small steps are relative to the current frequency to become a "proportion," which facilitates maintaining a consistent relative adjustment intensity at different starting points (high / low frequencies). Finally, the upper and lower limits of the strategy are used for clamping, balancing response speed and system stability.

[0022] It should be added that the status flags of the energy-consuming device are identified, and based on these status flags, a collection priority for the energy consumption collection frequency is generated. According to the collection priority, non-critical energy consumption data of the energy-consuming device during energy consumption collection is dynamically compressed. Based on this non-critical energy consumption data, a hierarchical multi-frequency collection strategy for the energy-consuming device is constructed. Specifically, the status flags include start-up, shutdown, and fault states. The hierarchical multi-frequency collection strategy specifically includes the highest frequency affecting device safety, the medium frequency indirectly affecting the device, and the lower frequency monitoring device indicators. A specific example is as follows: Assume the coupled object is the upstream heat exchanger HX-7 (whose load changes directly affect the pump's flow rate and head). Monitoring parameters (6 items): temperature T, pressure P, flow rate Q, speed N, current I, power Pwr; System resource constraints: Maximum available bandwidth: 1,200 samples / second (total of all parameters); Inbound throughput limit: 600 records / second; Common parameters for the strategy: Minimum duration of retention: 30 seconds (after each policy is issued, it must be maintained for at least 30 seconds before evaluation); Event-triggered write: If a parameter deviates from the statistical mean of the previous hold period by more than 10% (during startup / failure) or 20% (during shutdown), an event record is immediately written; Compression strategy library: downsampling, time aggregation (mean / extreme value / variance), precision compression (32→16 bits), event-driven (over-threshold write); Step 1, identify status flags → generate acquisition priority. Current status flag: Start (P-101 has issued a start command, and the speed is gradually increasing from 0); Priority mapping: Fault → P3 (highest), Startup → P2 (higher), Shutdown → P1 (lower); Conclusion: The priority for this cycle is P2; Step 2, critical / non-critical data classification (based on startup conditions). Key data (requires close monitoring): Q, P, N, I, Pwr (directly related to startup transients, overload, and protection); Secondary key data: T (due to its large thermal inertia, coupled monitoring is used as a supplement during the startup phase); Examples of non-critical data: ambient temperature T_env, secondary vibration statistics V_rms (used for health trends); Note: This example strictly follows the six core parameters of the problem statement for layering, but in order to demonstrate "non-critical compression", two common slow variables (T_env, V_rms) are added as non-critical sets; Step 3: Develop a layered multi-frequency acquisition strategy (first provide the "ideal frequency", then perform resource verification and compression). 3.1 Ideal hierarchical frequency (startup = priority P2), Highest frequency layer (security / protection layer): Q, P, N, I, Pwr 20 Hz each; Mid-frequency layer (coupling / scheduling layer): T 5 Hz; Lower frequency layer (indicator / trend layer): T_env and V_rms are each 1 Hz; 3.2 Calculate the ideal bandwidth usage (add items one by one, without skipping steps). Top layer: 5 parameters × 20 Hz = 100 samples / second; Medium layer: T 5 Hz = 5 samples / second; Low-frequency layer: 2 parameters × 1 Hz = 2 samples / second; Total ideal requirement: 100 + 5 + 2 = 107 samples / second; Compared to the bandwidth limit of 1,200 samples / second, this is far below the limit. Inbound data is estimated based on "1 sample point ≈ 1 record": 107 records / second < 600 records / second → Pass; Conclusion: The ideal tiered frequency can be directly approved in terms of resources, without the need for resource downgrading; Step 4: Dynamically compress "non-critical data" according to priority P2. 4.1 Objective: During the initial phase, resources should be allocated as much as possible to critical transients; non-critical data only needs to "show trends and be reported immediately when issues arise"; 4.2 Compression settings (determine each item according to the strategy in the library). T_env (1 Hz): Time aggregation: Maintain 1 Hz acquisition, but generate an aggregated record (mean / maximum / minimum) every 10 seconds; Event-driven: If the instantaneous value measured in a certain second deviates from the average value of the previous 10 seconds by more than 10%, an event record is immediately added; Precision: Reduced from 32 bits to 16 bits (does not affect trend judgment); V_rms (1 Hz): Time aggregation: Same as above (one statistic every 10 seconds); Event-driven: Immediately report deviations of more than 10% from the 10-second average; Precision: 32-bit → 16-bit; 4.3 Estimation of warehousing after compression (without skipping steps). Standard statistics: T_env 1 record every 10 seconds, V_rms 1 record every 10 seconds → 0.1 records / second each, totaling 0.2 records / second; Event Triggering: If there are slight environmental changes or vibration fluctuations during the startup period, it is assumed that an event will be triggered once every 30 seconds (total of both). Conversion: 1 message every 30 seconds ≈ 0.033 messages / second; Low-frequency layer total (after compression): 0.2 + 0.033 ≈ 0.233 records / second; Because the top and middle layers continue to input data at the original frequency (crucially, without compression), the total input is approximately: Top level: 100 records / second; Middle layer: 5 records / second; Lower layer: ≈0.233 records / second; Total ≈ 105.233 records / second (approximately 107 in step 3, satisfying the condition of no skipping steps and below the 600 upper limit); Step 5: Strategy distribution and minimum hold period. The three-layer frequency and compression strategy are distributed to the acquisition terminal at once, and a 30-second minimum hold period is started. During this period, the frequency and compression parameters are not changed to ensure stable observation of the startup transient and coupling feedback. Step 6: Observe the actual operation segments during the hold period (segment by segment, without skipping steps). Demonstrate how the strategy works through several real-world phenomena during the 30-second hold period. 6.1 Seconds 0–10: Speed ​​increases, flow rate changes rapidly The highest level (20 Hz) promptly captured the continuous rise of N, the synchronous rise of Q and Pwr, and the short-term peak of I; The middle layer (5 Hz) recorded a slight upward movement of T (due to high thermal inertia, 5 Hz is sufficient); The lower layer only accumulates 10 seconds of aggregated statistics, and no events are triggered (small environmental changes, V_rms is stable). 6.2 Seconds 10–20: Coupled with upstream load fluctuations, The HX-7 experiences short-term load jitter, and Q and P show a clear "valley → peak" structure at 20 Hz sampling. I and Pwr respond synchronously, helping to determine if there is an overshoot trend; Low-level events: V_rms corresponds to the pump body micro-vibration, which deviates by 12% from the average of the previous 10 seconds at the 18th second, triggering one event record, so as not to be missed due to low frequency; 6.3 Seconds 20–30: Gradually stabilizing. The fluctuations of Q, P, N, I, and Pwr decreased; T continued to rise slowly. No new events are occurring at the lower level; statistics are only output at 10-second intervals. Observations: The high-frequency layer ensures clear capture of startup transients and coupled disturbances; the low-level layer uses "sparse statistics + event patching" to ensure no anomalies are missed; resource consumption is stable at the level of 10⁵–10⁷ records / second, far below the system limit. Step 7, evaluation and fine-tuning after the maintenance period ends (still following the principle of small steps). Key evaluation points: Whether the fluctuation range of the highest-level parameters (Q, P, N, I, Pwr) has decreased significantly; whether the event triggering frequency has decreased significantly; whether there is still sufficient margin for data entry and bandwidth; In this example, we observed that the fluctuations significantly converged and the number of events decreased. Fine-tuning (small steps): Slightly reduce Q, P, I, and Pwr from 20 Hz to 15 Hz; N is kept at 20 Hz for another holding period (because the speed closed loop is still stabilizing). T remains at 5 Hz; The underlying strategy remains unchanged (continue with 10-second aggregation + event triggering). New bandwidth estimate (without skipping steps): Top layer: Q, P, I, Pwr 4 items × 15 Hz = 60; N 1 item × 20 Hz = 20; Total 80 samples / second; Middle layer: T 5 samples / second; Lower layer: ≈0.233; Total ≈ 85.233 records / second (further decrease, higher stability); In summary, the examples above demonstrate how prioritizing high-frequency sampling of transient-related parameters Q, P, N, I, and Pwr during the startup state (P2) ensures no missing critical information. T operates at a mid-frequency level, saving resources while capturing thermal processes. A combination of low-frequency sampling, aggregation, event patching, and precision compression is used for T_env and V_rms to ensure the scientific principle of "recording any anomalies" while maintaining resource efficiency. All compression actions are executed under explicit rules of "10-second aggregation + threshold events," ensuring transparency and auditability. Furthermore, item-by-item summation and threshold comparison are performed before and after policy issuance, and a minimum hold period is set to avoid high-frequency jitter and unnecessary switching. After stabilization, the frequency is gradually reduced (from 20 to 15). (Hz) to ensure that critical transients are not lost due to "sudden drops"; this singleton completes the chain of "status flag (startup) → priority (P2) → dynamic compression of non-critical data → hierarchical multi-frequency strategy (high / medium / low frequency) → resource verification → retention period observation → small-step fine-tuning", so as not to overload or lose critical information.

[0023] In this embodiment, step S3, which involves dividing the energy consumption sampling frequency into frequency peaks and obtaining the minimum retention period after the energy consumption sampling frequency change, further includes: S31: Based on the collection cycle of the energy consumption collection frequency, obtain the capacity cache queue pre-allocated by the energy consumption device; S32: Determine whether the data in the capacity cache queue is saved in chronological order; S33: If so, read the earliest recorded first capacity data and the latest recorded second capacity data from the capacity cache queue, calculate the time difference between the first capacity data and the second capacity data, and dynamically adjust the number of samples to be collected in the capacity cache queue based on the time difference.

[0024] In this embodiment, the system obtains the pre-allocated capacity buffer queues of the energy-consuming devices based on the energy consumption sampling frequency sampling period. The system then determines whether the data in these capacity buffer queues is saved in chronological order to execute corresponding steps. For example, if the system determines that the pre-allocated capacity buffer queues of the energy-consuming devices are not saved in chronological order, the system assumes that there is a delay, packet loss, or out-of-order retransmission at the sampling end or transmission link, causing the data to arrive at the buffer queues at a different time than the sampling time. The system will temporarily block new data writing to prevent the out-of-order problem from escalating, read all data items in the buffer into a temporary working area, and rearrange them according to the sampling timestamp from earliest to latest. If there is... If timestamps are duplicated or missing, they should be marked and written to the exception log. Simultaneously, the time intervals between adjacent records after sorting should be compared to see if they fall within the sampling period tolerance. If an interval exceeds the tolerance, the time range of the missing segment should be recorded for subsequent supplementary sampling. Furthermore, if multi-threaded sampling is used, a queue write lock should be added or a concurrent safe data structure should be used. If there is network transmission delay or out-of-order data, frame encapsulation with sequence numbers should be used, and sequence number verification and sorting should be performed before enqueuing. For example, when the system determines that the pre-allocated capacity buffer queue of energy consumption devices can save data in chronological order, the system will assume that there are no anomalies at the sampling end or in the transmission link, and that the order in which data arrives at the buffer queue is consistent with the sampling time. The system will then proceed smoothly. The earliest recorded first-capacity data and the latest recorded second-capacity data are read from the capacity buffer queue. By calculating the time difference between the first and second-capacity data, the number of samples to be collected in the capacity buffer queue is dynamically adjusted according to different time differences. When the capacity buffer queue can save data in chronological order, it indicates that the acquisition end and the transmission link are working normally, and the arrival order of the data is completely consistent with the actual acquisition time. Such time sequence consistency provides a reliable foundation for subsequent analysis, modeling, and energy consumption prediction, avoiding energy consumption data distortion caused by out-of-order or delay, and ensuring the accuracy of optimization decisions from the source. At the same time, by reading the earliest first-capacity data and the latest second-capacity data, the calculation... By understanding the time difference between the two, the system can monitor the current collection cycle in real time and dynamically adjust the number of samples to be collected based on this time difference. This balances sampling density and data storage costs, ensuring that data redundancy is avoided while retaining sufficient key feature information, thus improving collection efficiency. Furthermore, by dynamically adjusting the number of samples based on the time difference, the capacity buffer queue can adaptively adjust its load under different operating conditions. For example, it can reduce the sampling amount when the equipment is running smoothly to save storage and processing resources, and increase the sampling density to improve monitoring accuracy during load fluctuations or critical operating conditions. This intelligent scheduling method helps reduce the overall resource consumption of the system and improves the response speed and precision of energy consumption monitoring and optimization.

[0025] It should be noted that the earliest recorded first capacity data and the latest recorded second capacity data are read from the capacity cache queue, the time difference between the first and second capacity data is calculated, and the number of samples to be collected in the capacity cache queue is dynamically adjusted based on the time difference. A specific example is as follows: Assume the equipment is a circulating pump A (the buffer is measured in units of "time series records," meaning each sample is a record containing all monitored parameters). Current cache queue information (read). The current number of samples M = 50 (the number of records in the queue); The first time stamp in the queue is t_first=10:00:00.000; The last timestamp of the queue is t_last = 10:00:25.000; Objectives and constraints: We want the cache to cover a target time window of T_target = 120 seconds (e.g., for a 2-minute sliding window analysis). The minimum allowable sampling interval S_min = 0.05 s (i.e., the maximum sampling frequency is 20 Hz). The average size of a single sample is 200 bytes (a record contains several parameters); The system's available uplink bandwidth budget is 2000 bytes / second (for additional sample reporting by this device). Storage write throughput limitations (acceptable for a single device) are much greater than the expected sampling rate (which is not a bottleneck in this case); The maximum expandable cache size is N_max = 300 entries (it can be expanded to this value if needed). Step 1, calculate the current time span ΔT, read the timestamps of the first and last queues and subtract them: ΔT=t_last−t_first=25.000 seconds; (Explanation: The queue covers a total of 25 seconds, from 10:00:00 to 10:00:25). Step 2, calculate the current average sampling interval S_current. The sample interval is calculated using (M−1) intervals: M−1=50−1=49; S_current=ΔT÷(M−1)=25÷49; Step-by-step calculation: 49 × 0.5 = 24.5, remainder 0.5; 49 × 0.01 = 0.49, remainder 0.01; combined, the result is approximately 0.51020408 seconds; Explanation: The average interval between samples in the current queue is approximately 0.5102 s, corresponding to an average sampling frequency of approximately 1.96 Hz (1 ÷ 0.5102). Step 3: Calculate the number of samples N_needed required to cover the target duration. To cover at the current density within T_target=120s, the required number of intervals I_needed=T_target÷S_current; Substitute: I_needed = 120 ÷ 0.51020408; Using fractional relationships provides greater precision: S_current = 25 / 49 → I_needed = 120 × (49 / 25) = 4.8 × 49 = 235.2; The required number of samples N_needed = ceil(I_needed) + 1 = ceil(235.2) + 1 = 235 + 1 = 236; Explanation: 236 samples ensure that the time window covers at least 120 seconds (including the boundaries). Step 4: Compare the current sample size with the demand, calculate the gap ΔN and the waiting time T_wait. The current sample size M=50, N_needed=236 → difference ΔN=236−50=186; If the sampling rate is not adjusted, and we only wait for the existing sampling frequency to continue sampling ΔN lines, the estimated waiting time is: T_wait≈ΔN×S_current=186×0.51020408; Calculate: 186 × (25 / 49) = (186 × 25) ÷ 49 = 4650 ÷ 49 = 94.89795918 seconds (approximately 94.9 seconds); Conclusion: If a wait of approximately 95 seconds is allowed, the cache can be naturally filled to cover 120 seconds; however, if faster filling is desired (e.g., within 10–30 seconds), the sampling rate needs to be increased or the cache size expanded and the frequency increased. Step 5: Calculate the new sampling interval S_new and frequency f_new required for "fast padding" (example of two expected padding times). Objective A (Fast): Fill ΔN=186 entries within T_fill=30 seconds; The required new average interval is: S_new = T_fill ÷ ΔN = 30 ÷ 186; Step by step: 30 / 186 = (divide numerator and denominator by 6) = 5 / 31 ≈ 0.1612903226 seconds; The corresponding sampling frequency f_new = 1 ÷ S_new ≈ 1 ÷ 0.16129 = 6.2 Hz (precise value 31 / 5 = 6.2). Target B (Medium Speed): Fill within T_fill=60 seconds; S_new = 60 ÷ 186 = 2 × (30 / 186) = 2 × 5 / 31 = 10 / 31 ≈ 0.3225806452 seconds; f_new≈1÷0.3225806≈3.1 Hz (exactly 31 / 10=3.1); Step 6, bandwidth and storage verification (step by step to determine feasibility). First, calculate the current average sampling frequency: f_current≈1÷S_current=1÷0.51020408≈1.96Hz; Target A (30s fill): New frequency f_new = 6.2 Hz, Additional sampling rate = f_new − f_current = 6.2 − 1.96 = 4.24 times / second; Additional bandwidth requirement = Additional sampling rate × Single sample size = 4.24 × 200 bytes ≈ 848 bytes / second; Compared to the available additional bandwidth of 2000 bytes / second: 848 < 2000 → Bandwidth allowed; Storage throughput: Target frequency 6.2 records / second ≪ System inbound limit (assuming it is much greater than 600 records / second) → Storage allowed; Target B (60s fill): New frequency f_new = 3.1 Hz, extra rate = 3.1 − 1.96 = 1.14 times / second; Additional bandwidth = 1.14 × 200 ≈ 228 bytes / second, which is significantly lower and the bandwidth is sufficient; Conclusion: In this example, either 30-second fast patching (requiring an additional ~848 B / s) or 60-second deferred patching (requiring an additional ~228 B / s) can be selected; both are within the available bandwidth. Step 7, verify the cache capacity N_max. Calculate N_needed = 236; check N_needed ≤ N_max (300) → 236 ≤ 300, which can be accommodated by expanding the capacity or using existing capacity; If N_needed > N_max (other than in this example), then data compression or T_fill needs to be combined. Step 8, Decision-making and issuing recommendations (based on the quantitative results above). If the business requires coverage to be completed within 30 seconds (e.g., a 2-minute window needs to be quickly restored for real-time judgment), a temporary sampling frequency adjustment command is issued to increase the sampling frequency to 6.2 Hz (or round down to 6.0 Hz to align with the device's capabilities). At the same time, confirm that the cache is expanded to ≥236 entries (if the current cache limit is lower than this); Resource alert: Record the bandwidth increment as approximately 848 B / s and register the total bandwidth usage; Set a minimum hold period (e.g., 30 seconds) after completion to observe the system's response to high frequencies and whether packet loss occurs; If the service can tolerate a slower completion time (e.g., 60 seconds): Similarly, increase the frequency to 3.1 Hz or use 3.0 Hz, and reduce the bandwidth usage accordingly; If bandwidth or cache is limited and frequency cannot be increased, an alternative solution will be adopted: Temporarily refrain from increasing the frequency, expand the buffer and wait for natural filling (T_wait≈94.9 seconds); or temporarily compress / reduce the sampling of non-critical parameters (to release bandwidth) in order to make room for increasing the frequency of critical samples; or extend T_fill (relax the time requirement) as a compromise. Step 9: Post-execution monitoring and rollback strategy. After execution, continuously reread the timestamps of the beginning and end of the queue every 5-10 seconds to ensure that ΔT is increasing and reaches or exceeds T_target within the expected T_fill. If packet loss or bandwidth overrun occurs during frequency increase (e.g., the instantaneous peak of additional bandwidth exceeds 2000 B / s), the fallback strategy is immediately triggered: the frequency is reduced back to a lower f_new (e.g., from 6.2 → 4.0 Hz) and T_fill is extended, or non-critical data is compressed to free up bandwidth; After completing and verifying that ΔT≥T_target: gradually restore the sampling frequency to the normal operating value according to the principle of "small step reduction" (e.g., from 6.2→3.1→1.96 Hz, maintaining the shortest holding period in each step before adjusting down) to avoid information loss caused by sudden drop; In summary, by using the time difference ΔT between the beginning and end of the cycle and the number of samples M to obtain S_current, N_needed (236 entries) can be quantified, rather than relying solely on intuition to decide whether to increase the frequency. Rapid frequency replenishment (30s) requires f_new≈6.2 Hz and an additional bandwidth of approximately 848 B / s; delayed replenishment (60s) requires f_new≈3.1 Hz and an additional bandwidth of approximately 228 B / s; without replenishment, it takes approximately 95 seconds for natural replenishment. In this example, N_needed≤N_max and the additional bandwidth is within the available budget. Therefore, it is recommended to prioritize a 30s or 60s frequency increase scheme based on the service time limit, and to gradually reduce the frequency after replenishment to ensure system stability.

[0026] In this embodiment, after step S3 of generating the maximum change ratio of the energy consumption sampling frequency based on the frequency peak and the minimum holding period, the method further includes: S301: Based on the operating characteristics of the energy-consuming equipment, construct the variation law of the energy consumption acquisition frequency, wherein the operating characteristics specifically include frequency variation distribution and energy consumption response delay, and the variation law specifically includes seasonality, periodic fluctuation pattern and stable range; S302: Determine whether the change pattern detects data packet loss or missing data; S303: If so, obtain the location where the time interval of the missing data packet exceeds the limit, collect the missing information of the missing data packet according to the location where the time interval exceeds the limit, and identify the missing type of the missing data packet according to the missing information. The missing information specifically includes the start and end times of the missing data, the number of missing data points and the devices involved, and the missing type specifically includes continuous missing data, intermittent missing data and single-point missing data.

[0027] In this embodiment, the system constructs a pattern of energy consumption sampling frequency variation based on the operating characteristics of the energy-consuming device, specifically including frequency variation distribution and energy consumption response delay. This pattern includes seasonality, periodic fluctuations, and stable intervals. The system then determines whether these patterns detect data loss or missing data, and executes corresponding steps accordingly. For example, if the system determines that the energy consumption sampling frequency variation pattern does not detect data loss or missing data, it considers the data stream to be complete and reliable in the time / sample dimension. The system generates a "Completeness Verification Passed" record containing the device ID, detection window time range, detection rule version, detection time, and verification statistics (e.g., sampling rate stability). The system performs a small-scale self-check after passing the initial test (compare the first and last timestamps, verify sequence number continuity, and check ACK / retransmission counts) to ensure the judgment is not an instantaneous misjudgment. The current complete window is then marked as a "reliable sample" to adjust subsequent cache allocation strategies (e.g., temporarily reducing the cache priority for this device and allocating buffer resources to noisier links). For example, if the system detects data packet loss due to changes in the energy consumption sampling frequency, it considers the data stream unreliable in terms of time / sample dimension. The system will obtain the time interval exceeding the limit for data packet loss and, based on different time interval exceeding limits, collect missing information about the missing data packets. The loss information specifically includes the start and end times of the loss, the number of missing data points, and the affected devices. Based on this information, the loss type of data packet loss is identified, including continuous loss, intermittent loss, and single-point loss. By obtaining the location where the time interval of the data packet loss exceeds the limit, the system can quickly locate the specific time period and location of the anomaly, rather than simply knowing that "there is a loss." This precise location helps narrow down the investigation scope, avoids global scanning and ineffective analysis, and thus significantly improves the efficiency of maintenance personnel in the fault detection and tracing process. Furthermore, the loss information collected by the system not only includes the start and end times of the loss but also records the number of missing data points and the specific affected devices, making abnormal data... The context is more complete, and this completeness of information helps to conduct in-depth analysis of factors such as equipment operation mode, data link stability, and network latency. It provides reliable basic data for establishing packet loss prediction models and optimizing data transmission links. Furthermore, by subdividing the missing types into continuous missing, intermittent missing, and single-point missing, the system can take differentiated remedial measures for different missing characteristics. For example, continuous missing may require retrospective sampling or resynchronization, intermittent missing may require optimization of the transmission buffer mechanism, and single-point missing can be filled by interpolation. This typified processing not only improves the repair efficiency but also reduces interference with normal data flow, ultimately improving the stability and data reliability of the entire energy consumption monitoring system.

[0028] It should be noted that the location where the time interval for the missing data packets exceeds the limit is obtained. Based on the location where the time interval exceeds the limit, the missing information of the missing data packets is collected. Based on the missing information, the missing type of the missing data packets is identified. A specific example is as follows: Suppose we need to identify packet loss or missing data in a single energy-consuming device: Prerequisite configuration, Expected sampling interval for the device: 1 second / sample; Tolerance factor: 1.5 → A delay exceeding 1.5 seconds is considered a loss; Minimum number of points required for consecutive missing data: 2 points; The input time series (device A) is shown in Table 1 below. Table 1:

[0029] Step 1: Calculate the difference between adjacent timestamps and detect any exceeding limits. Calculate Δ for each pair of adjacent timestamps, as shown in Table 2 below. Table 2:

[0030] Results: Exceeding limits were found at positions A (5→6) and B (8→9); Step 2: Determine the start and end points and duration of the missing segment. Section A, Start: 10:00:04.000 (after sample 5); End: 10:00:07:500 (Arrival time of sample #6); Duration T_gap_A = 3.5 seconds; Section B, Start: 10:00:09:500 (after sample number 8); End: 10:00:12.600 (Arrival time of sample #9); Duration T_gap_B = 3.1 seconds; Step 3: Estimate the number of missing data points. Section A: N_missing_A≈round(T_gap_A / S_expected)−1=round(3.5 / 1)−1=3−1=2; Note that samples from 10:00:05 and 10:00:06 may be missing; Segment B: N_missing_B≈round(3.1 / 1)−1=3−1=2; Note that samples at 10:00:10 and 10:00:11 may be missing; Affected device: Device A (This example only demonstrates a single device); Step 4, identify the missing type. Section A: Number of consecutive out-of-limit points ≥ 2 → consecutive missing points; Section B: Number of consecutive out-of-limit points ≥ 2 → consecutive missing points; If Δ has only one out-of-limit value and the surrounding area is normal, it is determined to be a single-point missing value; if the out-of-limit values ​​are scattered and discontinuous, it is determined to be an intermittent missing value. Step 5, Record and Processing Suggestions Segment A: 10:00:04–10:00:07.5, lasting 3.5 seconds, estimated missing 2 points, consecutive missing, affected device A → can trigger backtracking or check transmission link; Segment B: 10:00:09.5–10:00:12.6, lasting 3.1 seconds, estimated missing 2 points, consecutive missing, affected device A → same processing; The results are written to the missing event log for easy analysis and remediation later. In summary, the example above, starting from time series, achieves scientific and quantifiable data loss and missing data analysis through time difference detection, missing segment location, missing point estimation, and missing type identification.

[0031] In this embodiment, step S5, which dynamically compresses non-critical energy consumption data of the energy-consuming device during energy consumption collection based on the collection priority, further includes: S51: Based on the preset triggering conditions of the energy consumption device, select a preset compression strategy for the energy consumption collection frequency. The triggering conditions specifically include network bandwidth utilization exceeding a threshold, storage space shortage, memory usage exceeding a set value, and the need to temporarily increase the collection frequency of key data. The compression strategy specifically includes sampling rate compression, data precision compression, time period aggregation compression, and event-driven storage. S52: Determine whether the triggering condition matches the compression strategy; S53: If so, obtain the energy consumption parameters before and after compression, generate the compression effect of the compression strategy based on the energy consumption parameters, and dynamically adjust the compression ratio of the compression strategy according to the operating status of the energy-consuming device and the compression effect. The energy consumption parameters specifically include data volume, bandwidth utilization, and storage usage.

[0032] In this embodiment, the system selects a pre-set compression strategy for the energy consumption acquisition frequency based on pre-defined trigger conditions of the energy consumption device. These trigger conditions specifically include network bandwidth utilization exceeding a threshold, storage space shortage, memory usage exceeding a set value, and the need to temporarily increase the frequency of critical data acquisition. The compression strategies specifically include sampling rate compression, data precision compression, time-period aggregation compression, and event-driven storage. The system then determines whether these trigger conditions match the compression strategy to execute the corresponding steps. For example, if the system determines that the pre-defined trigger conditions of the energy consumption device cannot match the pre-defined compression strategy for the energy consumption acquisition frequency, the system will consider that there is a conflict between the current system resource pressure or device operating status and the existing compression strategy. If a mismatch or conflict occurs, the system will identify the specific reason for the conflict between the triggering conditions and the compression strategy, such as insufficient network bandwidth, limited storage space, or incompatibility between the sampling rate of critical data and the compression strategy. Based on the type of conflict, it will dynamically select or combine other available compression strategies. For example, if sampling rate compression is not feasible, it can try time-based aggregation compression or event-driven storage, prioritizing the collection of critical data while temporarily reducing the collection frequency or accuracy of non-critical data. The system will also record events where the triggering conditions and compression strategy do not match, generating alarms to prompt maintenance personnel or upper-level management systems to intervene and ensure that critical data is not lost. For instance, when the system determines that the pre-set triggering conditions of the energy consumption device match the pre-set compression strategy for the energy consumption collection frequency... At this point, the system assumes that the current system resource pressure or device operating status is compatible with the existing compression strategy. The system obtains energy consumption parameters before and after compression, specifically including data volume, bandwidth utilization, and storage usage. Based on these parameters, it generates the compression effect of the compression strategy and dynamically adjusts the compression ratio according to the device's operating status and the compression effect. By determining that the triggering conditions match the preset compression strategy, the system confirms that the current network bandwidth, storage space, and memory usage are compatible with the compression strategy. This means that data transmission and storage will not experience congestion or data loss due to insufficient resources, thus achieving efficient utilization of system resources while reducing the burden on devices and the network. The system simultaneously acquires energy consumption parameters before and after compression, including data volume, bandwidth utilization, and storage usage, and calculates the compression effect by comparison. This method can not only quantify the resource savings brought by compression, but also provide a scientific basis for subsequent dynamic adjustments. It enables the compression strategy to not only rely on preset rules, but also to be optimized based on real-time operating data. Furthermore, the system can dynamically adjust the compression ratio according to the device operating status and compression effect, ensuring the accuracy of key energy consumption data collection and reducing resource consumption of non-critical data through reasonable compression. This adaptive adjustment mechanism improves the accuracy and reliability of the energy consumption acquisition and optimization system, and achieves the goal of minimizing resource consumption while ensuring data integrity.

[0033] In this embodiment, after step S2 of determining whether the rate of change of the energy consumption sampling frequency exceeds a preset rate threshold, the method further includes: S201: Identify the extent of exceeding the limit of the energy consumption sampling frequency; S202: Determine whether the exceeded amplitude is an occasional phenomenon; S203: If so, then classify the over-limit level of the over-limit amplitude, and based on the over-limit level, limit the collection rate of the energy consumption collection frequency, and dynamically trigger the preset protection mechanism of the energy consumption device. The over-limit level specifically includes mild, moderate and severe, and the protection mechanism specifically includes temporarily freezing frequency adjustment, reducing the frequency of devices with low collection priority and sending alarms to the operation and maintenance center.

[0034] In this embodiment, the system identifies the extent of the energy consumption sampling frequency exceeding the limit, and then determines whether the extent of the exceedance is an occasional phenomenon in order to execute the corresponding steps. For example, when the system determines that the extent of the energy consumption sampling frequency exceeding the limit is not an occasional phenomenon, the system will consider that the current sampling frequency change of the device has a continuous or regular anomaly, which may reflect potential problems in the device's operating status, energy consumption behavior, or external environment, rather than an occasional fluctuation. The system will record the time period of the exceedance, the device involved, and the specific values, distinguishing between the continuity and fluctuation pattern of the abnormal amplitude, providing a basis for subsequent analysis, and at the same time, based on the device's operating parameters (such as temperature, pressure, flow rate, speed, current)... The system analyzes the energy consumption data (including power) and its relationship with coupled devices to determine whether abnormal frequency changes are related to critical operating conditions or urgent processes. If the abnormal frequency does not match the equipment's operating status, there may be a fault, control strategy failure, or sensor malfunction. Based on the identified anomalies, the system adjusts the energy consumption data acquisition frequency and priority, increases the monitoring frequency of critical parameters, or temporarily compresses or delays the acquisition of non-critical data to ensure the integrity of critical data and the accuracy of optimization analysis. For example, if the system determines that the excessive energy consumption data acquisition frequency is an occasional phenomenon, it will consider the current equipment's acquisition frequency change to be normal. The system will then classify the excessive frequency into different levels. The limit levels are specifically categorized as mild, moderate, and severe. Based on different exceedance levels, the energy consumption sampling rate is restricted, and pre-set protection mechanisms on energy-consuming devices are dynamically triggered. These protection mechanisms include temporarily freezing frequency adjustments, reducing the frequency of devices with low sampling priority, and sending alarms to the operation and maintenance center. By determining that the exceedance amplitude is an intermittent phenomenon, the system can avoid misjudging short-term or random fluctuations as equipment malfunctions. This differentiation mechanism reduces the false alarm rate, making the energy consumption sampling system more accurate when monitoring frequency anomalies, and only handling truly potential risks, thereby improving the reliability of monitoring and optimization strategies. The system also classifies exceedance amplitudes into mild, moderate, and severe levels, and... Based on the classification-based limit on the data acquisition rate, differentiated measures can be taken under varying degrees of occasional fluctuations. For example, the acquisition frequency can be slightly reduced for mild occasional fluctuations, while the frequency adjustment may be temporarily frozen for severe occasional fluctuations. This hierarchical control not only ensures the continuity of critical data but also avoids excessive resource consumption. Furthermore, through dynamic triggering protection mechanisms, such as reducing the acquisition frequency of low-priority devices or sending alarms to the operation and maintenance center, the system can proactively protect itself under occasional fluctuations, avoiding potential chain reactions caused by the fluctuations. This mechanism ensures the continued stable operation of critical equipment, maintains the overall security and reliability of the system, and improves the stability and long-term operation and maintenance efficiency of the energy consumption acquisition and optimization system.

[0035] In this embodiment, step S4, which determines whether the change coefficient matches the preset emergency process of the energy-consuming device, further includes: S41: Based on the preset energy consumption change threshold of the energy-consuming device, obtain the change series strength of the change coefficient, and combine the change series strength to obtain the strength trajectory; S42: Determine whether the intensity trajectory matches the expected characteristics of the emergency process; S43: If not, identify the emergency type of the emergency process, and dynamically adjust the allowable value of the energy consumption acquisition frequency according to the emergency type. The emergency type specifically includes start / stop, over-temperature, overload and external fast-cycle command. The allowable acquisition value specifically includes acquisition frequency, upper limit of change range and adjustment interval.

[0036] In this embodiment, the system obtains the series intensity of changes in the coefficients of energy consumption based on a pre-set energy consumption change threshold for the energy-consuming device. These series intensities are combined to obtain an intensity trajectory. The system then determines whether the intensity trajectory matches the expected characteristics of an emergency process to execute corresponding steps. For example, if the system determines that the intensity trajectory matches the expected characteristics of an emergency process, it considers the change pattern of the energy-consuming device's coefficients to be highly consistent with the characteristics of an emergency process, indicating that the device may be entering a critical operating condition or experiencing potentially high load or abnormal energy consumption. The system then increases the frequency of collecting relevant energy consumption parameters for the device, including temperature, pressure, flow rate, speed, current, and power. Key indicators are used to ensure the completeness, continuity, and absence of critical data. Simultaneously, based on the characteristics of emergency processes, the system dynamically adjusts the energy consumption data collection frequency and priority of equipment, triggering protection mechanisms when necessary, such as limiting the collection frequency of non-critical equipment, freezing adjustable frequency equipment parameters, or issuing alarms to the operations and maintenance center to prevent potential risks. Furthermore, the matched intensity trajectory and collected data are recorded for subsequent analysis, improving the system's responsiveness and predictive accuracy for similar emergency processes. For example, if the system determines that the intensity trajectory does not match the expected characteristics of an emergency process, it will consider the change pattern of the current energy consumption equipment's coefficients to be inconsistent with the operational characteristics of the emergency process, indicating that the equipment has not entered a critical operating state. The system identifies the emergency type of an emergency process, including start / stop, over-temperature, overload, and external fast-cycle commands. Based on the different emergency types, it dynamically adjusts the allowable values ​​for energy consumption data collection frequency, including the collection frequency, the upper limit of the change range, and the adjustment interval. By determining that the intensity trajectory cannot match the expected characteristics of the emergency process, the system can clearly distinguish that the current equipment operating state is not a critical condition, thus avoiding over-response or unnecessary resource scheduling for non-critical states. This identification mechanism improves the accuracy of the energy consumption monitoring system, reduces false alarms and misoperations, and ensures stable operation of the system in non-emergency situations. Simultaneously, the system identifies different emergency types, such as start / stop, over-temperature, and overload. The system dynamically adjusts the allowable acquisition values ​​based on the specific type of the load and external fast-cycle commands, including the acquisition frequency, the upper limit of the change range, and the adjustment interval. Through this differentiated adjustment, the system can ensure optimal resource utilization under non-critical operating conditions while reserving response space for possible emergencies, achieving flexibility and adaptability in energy consumption acquisition. Furthermore, the dynamic adjustment of the allowable acquisition values ​​not only ensures priority acquisition of critical data but also avoids non-critical data from consuming excessive computing, storage, and transmission resources, thereby improving the overall energy consumption monitoring efficiency. This mechanism provides conditions for rapid response when equipment enters potentially emergency operating conditions, enabling the system to optimize resource consumption, improve energy consumption acquisition, and optimize the accuracy and reliability of the system while ensuring data integrity.

[0037] It should be noted that, based on the preset energy consumption change threshold of the energy-consuming device, the change series strength of the change coefficient is obtained, and the change series strength is combined to obtain the intensity trajectory, as shown in the following example: Key parameters of energy-consuming equipment (such as power, current, temperature, etc.) are continuously collected, and the coefficient of change (Δ) at each moment is calculated. This coefficient is typically defined as the ratio of the changes in adjacent sampled values, and the formula is:

[0038] Among them, among them, Here is the parameter value of the i-th sampling point; each change coefficient is compared with the preset energy consumption change threshold of the device. If the change exceeds the threshold, it is considered that there is a significant energy consumption fluctuation at that point. Points with consecutive change coefficients exceeding the threshold are connected in series, and their amplitudes are accumulated to form the series intensity. The series intensity can be understood as the cumulative impact of energy consumption change over a period of time, used to describe the continuity and intensity of change in a short period of time. The series intensities are combined in chronological order to obtain the intensity trajectory of the device over the entire monitoring period. Suppose a certain energy-consuming device collected power data (unit: kW) within 10 seconds, as shown in Table 3 below. Table 3:

[0039] Step 1, calculate the coefficient of variation.

[0040]

[0041]

[0042] ...and so on, to obtain the coefficient of change at each time point; Step 2, determine if the threshold is exceeded. Assuming the threshold is set to 0.03 (i.e., a 3% change), then: Δ1=0.02 → Not exceeding the threshold; Δ2 = 0.0588 → Exceeds the threshold; Δ3 = 0.037 → Exceeds the threshold; Δ4 = 0.009 → Not exceeding the threshold; ...judging sequentially; Step 3, calculate the varying series strength. Accumulate the values ​​of Δ2 and Δ3 that continuously exceed the threshold: 0.0588 + 0.037 = 0.0958 → the first series strength; The next consecutive threshold-exceeding sequence is Δ5=0.036, Δ6=0.026→0.036+0.026=0.062→the second series intensity; ...and so on; Step 4: Generate intensity trajectories. Plot or record all series intensities in chronological order to obtain an intensity trajectory sequence: [0.0958, 0.062, …]. This trajectory can be used to match the expected characteristics of the emergency process to identify whether a critical condition has been entered. In summary, the examples above, starting from the collected data, gradually calculate the variation coefficient and series strength, and combine them to form an intensity trajectory, which can be used for equipment operation status monitoring and emergency condition identification.

[0043] In this embodiment, before step S1 of reading the energy consumption sampling frequency of the energy-consuming device based on the operating parameters related to the coupling relationship of the energy-consuming device, the method further includes: S101: Based on the preset energy consumption mapping rules of the energy-consuming devices, identify the coupling relationship between the energy-consuming devices, wherein the energy consumption mapping rules specifically include normal operating conditions, different load ranges and emergency operating conditions, and the coupling relationship specifically includes direct coupling and indirect coupling; S102: Determine whether the coupling relationship matches the acquisition priority of the energy consumption device; S103: If not, the operating parameter value of the energy-consuming device is matched with the preset definition range of the coupling relationship, the sampling frequency range of the energy-consuming device is dynamically corrected, and the operating status of the energy-consuming device and its coupled object is updated in real time according to the sampling priority. The operating status specifically includes the working condition status and the load status.

[0044] In this embodiment, the system identifies the coupling relationships between energy-consuming devices based on pre-set energy consumption mapping rules, which specifically include normal operating conditions, different load ranges, and emergency operating conditions. These coupling relationships include direct and indirect coupling. The system then determines whether these coupling relationships match the data collection priorities of the energy-consuming devices to execute corresponding steps. For example, when the system determines that the coupling relationships between energy-consuming devices match their data collection priorities, it considers that the energy consumption impact and correlation characteristics between the devices are highly consistent with the preset data collection strategy. This means that the data collection order, sampling frequency, and priority arrangement of key and coupled devices are reasonable and can effectively reflect the overall energy consumption behavior of the system. Based on the matched coupling relationships and data collection priorities, the system confirms the data collection frequency of key devices and their coupled devices to ensure that key energy consumption data is collected continuously and completely. For non-critical devices, the data collection frequency can be appropriately reduced or the collection can be delayed. To save storage and transmission resources, and through matching coupling relationships, the system can more accurately analyze the energy consumption correlation between devices and the overall system load, providing a reliable data foundation for energy consumption optimization. It can also perform linked monitoring of energy consumption changes between key coupled devices, quickly identifying potential anomalies. Furthermore, based on the current collection priority and coupling relationship, the system can dynamically adjust the collection strategy and frequency under different loads or emergency conditions to ensure priority collection of critical data. For example, when the system determines that the coupling relationship between energy-consuming devices cannot match the collection priority of the energy-consuming devices, the system will consider that the energy consumption impact and correlation characteristics between the devices are inconsistent with the preset collection strategy. The system will then match the operating parameter values ​​of the energy-consuming devices with the pre-defined range of the coupling relationship, dynamically correct the collection frequency range of the energy-consuming devices, and update the operating status of the energy-consuming devices and coupled objects in real time according to different collection priorities. The operating status specifically includes the operating condition status and load status.When the coupling relationship and the acquisition priority do not match, directly acquiring data according to the original strategy will lead to delayed acquisition of critical data or waste of resources. By matching the operating parameter values ​​of energy-consuming devices (such as temperature, pressure, flow rate, speed, etc.) with the preset definition range of the coupling relationship, the system can promptly detect deviations between the acquisition strategy and the actual situation and dynamically adjust the acquisition frequency range. This ensures that the acquisition frequency allocation is updated synchronously with the actual energy consumption impact, thereby avoiding the omission or delay of critical data. Simultaneously, the system updates the operating status (operating condition and load status) of energy-consuming devices and coupled objects in real time according to different acquisition priorities. This allows for accurate understanding of the current operating load, load change trends, and operating characteristics of the devices. In actual acquisition, devices of different importance can obtain acquisition frequencies and data update cycles that match their operating status, ensuring that critical operating condition information enters the monitoring and analysis stage in a timely and complete manner. Furthermore, by dynamically adjusting the acquisition frequency range, the system can acquire data more frequently from high-priority devices or devices with large load fluctuations, and reduce the acquisition frequency from low-priority devices or devices with stable loads. This reduces the consumption of network bandwidth and storage resources. This differentiated strategy not only reduces system operating costs but also maintains high-quality monitoring of critical devices under limited resources.

[0045] It should be noted that, based on the preset energy consumption mapping rules of the energy-consuming devices, the coupling relationship between the energy-consuming devices is identified, specifically as follows: Direct coupling refers to the fact that the operating status of one energy-consuming device directly affects the energy consumption change of another device, and this effect can be directly observed through energy consumption data; Identification method: Monitor the synchronous change trend of the operating parameters (such as current, power, flow rate, etc.) of two devices in the time dimension, and determine whether the fluctuation of the parameter of one device will cause the energy consumption change of the other device in a short period of time (with minimal delay); example: Water pumps and cooling towers: As the speed of the water pump increases, the power of the cooling tower fan increases, and the energy consumption curves change almost synchronously. Air compressors and dryers: Increased air compressor load → increased outlet air temperature → immediate increase in dryer energy consumption; Indirect coupling refers to a situation where the energy consumption changes between two energy-consuming devices are not directly caused by each other, but are transmitted through third-party devices or system environmental variables. Identification method: By using mapping rules to analyze indirect links between devices (which may be transmitted through media, control systems, pipelines, etc.), we can detect that there is a time delay in the energy consumption changes of two devices, and that there are changes in the operating status of other devices in between as a medium. example: Boiler and production line motors: Changes in boiler steam output → affect steam pipeline pressure → indirectly affect the motor load of the heat treatment equipment in the production line; Refrigeration units and office air conditioning terminals: Adjusting the outlet water temperature of the refrigeration unit → affects the heat exchange efficiency of the air conditioning terminal → indirectly changes the operating frequency and energy consumption of the terminal fans.

[0046] It should be added that the operating parameter values ​​of the energy-consuming device are matched with the preset definition range of the coupling relationship, the sampling frequency range of the energy-consuming device is dynamically adjusted, and the operating status of the energy-consuming device and its coupled object is updated in real time according to the sampling priority. A specific example is as follows: Suppose there are two devices: a circulating water pump (device A) and a cooling tower fan (device B), and there is a coupling relationship between them: the higher the water pump flow rate, the greater the load on the cooling tower fan; The known coupling relationships are defined within a predefined range: Strong coupling range: pump flow rate ≥ 80 m³ / h and pressure 0.9–1.1 MPa Weak coupling range: pump flow rate 50–80 m³ / h or pressure 0.7–0.9 MPa / 1.1–1.3 MPa No significant coupling range: pump flow rate <50 m³ / h The steps include: real-time acquisition of working parameters, Current measured flow rate: water pump flow rate = 85 m³ / h; Pressure = 1.0 MPa; Power = 15 kW; If the flow rate is ≥80 and the pressure is 1.0 MPa within the range of 0.9–1.1, then it falls into the strong coupling range. Dynamically correct the acquisition frequency range and strengthen coupling → The system increases the acquisition frequency of the water pump and cooling tower to 2 seconds / time (originally 10 seconds / time), and at the same time performs high-frequency monitoring of parameters such as flow rate, pressure, and fan speed to capture subtle changes in energy consumption; The system updates its running status based on the collection priority. If the current collection priority is high (due to strong coupling and heavy load), the system updates in real time. Operating condition: The water pump is operating under high load; Load status: Cooling tower is running at full speed; The data will then be marked as "critical operating conditions" for the scheduling system to optimize operating strategies; In summary, the examples above demonstrate that high-frequency data acquisition under strong coupling conditions can accurately capture instantaneous energy consumption changes, providing high-quality data for energy efficiency optimization. This avoids wasting acquisition and transmission resources under weak or no coupling conditions, ensures that the real-time status reflects the actual operating conditions of the equipment, and supports coordinated control decisions.

[0047] Reference Appendix Figure 2 An energy efficiency optimization system based on multi-device coupling relationships in one embodiment of the present invention includes: The reading module 10 is used to read the energy consumption acquisition frequency of the energy-consuming device based on the working parameters related to the coupling relationship between the energy-consuming device and the device. The working parameters specifically include temperature, pressure, flow rate, rotation speed, current and power. The judgment module 20 is used to determine whether the rate of change of the energy consumption acquisition frequency exceeds a preset rate threshold. The execution module 30 is configured to, if so, divide the frequency peak from the energy consumption sampling frequency, obtain the minimum holding period after the energy consumption sampling frequency change, generate the maximum change ratio of the energy consumption sampling frequency based on the frequency peak and the minimum holding period, and calculate the change coefficient of the energy consumption sampling frequency based on the maximum change ratio, wherein the frequency peak specifically includes the highest sampling frequency and the lowest sampling frequency, and the change coefficient specifically includes the instantaneous change rate and the average change rate; The second judgment module 40 is used to determine whether the change coefficient matches the preset emergency process of the energy consumption device; The second execution module 50 is used to identify the status flag bit of the energy-consuming device if no, generate the collection priority of the energy consumption collection frequency based on the status flag bit, dynamically compress the non-critical energy consumption data of the energy-consuming device when collecting energy consumption according to the collection priority, and construct the hierarchical multi-frequency collection strategy of the energy-consuming device based on the non-critical energy consumption data. The status flag bit specifically includes start-up, shutdown and fault, and the hierarchical multi-frequency collection strategy specifically includes the highest frequency that affects the safety of the device, the medium frequency that indirectly affects the device and the lower frequency that monitors the device indicators.

[0048] In this embodiment, the reading module 10 reads the energy consumption sampling frequency of each energy-consuming device based on the operating parameters related to the energy-consuming device and its coupling relationship. These operating parameters specifically include temperature, pressure, flow rate, rotational speed, current, and power. Then, the judgment module 20 determines whether the rate of change of these energy consumption sampling frequencies exceeds a preset rate threshold, and executes corresponding steps accordingly. For example, when the system determines that the energy consumption sampling frequency of a certain energy-consuming device does not exceed the preset rate threshold, the system considers the current sampling frequency variation of that device to be within an acceptable stable range, without sudden fluctuations or abnormal sampling demands. The system will continue to acquire data according to the existing sampling frequency, avoiding the additional resource consumption caused by frequent adjustments. Simultaneously, the device is marked as "frequency stable," reducing its priority for triggering frequency adjustments within a short period. It will only be re-evaluated in the next detection cycle or monitoring window. Furthermore, the computing and communication resources originally used for frequency adjustments by this device are allocated to other devices experiencing high frequency fluctuations or critical operating conditions to improve overall system energy efficiency. For example, when the system determines that the energy consumption sampling frequency of a certain energy-consuming device exceeds a preset rate threshold, the execution module 30 will consider the current device's sampling frequency change to be abnormal. The system will then divide the energy consumption sampling frequency into frequency peaks, specifically including the highest and lowest sampling frequencies, and obtain the minimum hold-up period after the energy consumption sampling frequency change. Based on different frequency peaks and minimum hold periods, the maximum percentage change in energy consumption sampling frequency is generated. Based on this maximum percentage change, the change coefficient of the energy consumption sampling frequency is calculated, specifically including the instantaneous change rate and the average change rate. When the energy consumption sampling frequency exceeds a preset rate threshold, the system immediately determines it as an abnormal change amplitude and further extracts the highest and lowest sampling frequencies as frequency peak indicators. This approach can quickly capture extreme change points in the sampling frequency, avoiding missed anomalies caused by relying solely on a single average value. This more accurately reflects the fluctuation characteristics of the device in an instant or period. Furthermore, by combining different frequency peaks and minimum hold periods to generate the maximum percentage change in sampling frequency, frequency limitation can be effectively achieved. The excessive adjustment range prevents frequent and large-scale frequency changes due to sudden fluctuations. This proportional adjustment mechanism enables the acquisition strategy to maintain stability while responding to fluctuations, which helps reduce system resource consumption and data jitter. Furthermore, when calculating the change coefficient, both instantaneous change rate and average change rate are considered simultaneously. This not only reflects rapid changes in a short period of time but also captures long-term trend deviations. This dual-indicator judgment method allows the system to balance short-term sensitivity and long-term robustness when adjusting the acquisition frequency, thereby ensuring the integrity of key data while improving the accuracy of energy consumption optimization and equipment status diagnosis. Then, the second judgment module 40 judges whether these change coefficients match the emergency process pre-set by the energy consumption equipment to execute the corresponding steps.For example, when the system determines that the change coefficient of the energy consumption sampling frequency of a certain energy-consuming device matches a pre-set emergency process, the system will consider that the current operating state of the device has entered or is close to a critical operating condition. This operating condition may be highly related to safety risks, production continuity, equipment protection, etc. The system will adjust the sampling frequency of the device to the highest frequency level that affects equipment safety, ensuring the real-time and completeness of key parameters. While adjusting the sampling frequency, additional data monitoring will be initiated to record high-precision changes in key operating parameters (temperature, pressure, flow, etc.) for rapid analysis of the cause of the emergency. At the same time, if the device is coupled with other devices, status signals will be sent to the relevant coupled devices, and their sampling frequencies will be synchronously increased if necessary to prevent cascading risks. During the high-frequency sampling phase, the change coefficient will be monitored in real time. When it leaves the emergency process matching range and stabilizes for a period of time, the sampling frequency will be gradually reduced to save resources. For example, when the system determines that the change coefficient of the energy consumption sampling frequency of a certain energy-consuming device does not match a pre-set emergency process, the second execution module 50 will recognize... If the current operating status of a device prevents it from entering a critical operating condition, the system will identify the status flags of the energy-consuming device. These status flags include start-up, shutdown, and fault. Based on different status flags, a collection priority for energy consumption sampling frequency is generated. According to different collection priorities, non-critical energy consumption data of the device during energy consumption sampling is dynamically compressed. Based on this non-critical energy consumption data, a hierarchical multi-frequency sampling strategy for the device is constructed. This strategy includes the highest frequency affecting device safety, the medium frequency indirectly affecting the device, and the lower frequency monitoring device indicators. When the change coefficient does not match the emergency process, the system will automatically determine that the device has not entered a critical operating condition, thereby avoiding the waste of bandwidth, storage, and computing resources caused by maintaining a high sampling rate. By identifying status flags such as start-up, shutdown, and fault, the system can proactively compress non-critical energy consumption data in stable or low-risk states, retaining only necessary operating information, making data collection more concise and efficient. Simultaneously, by generating collection priorities based on different status flags, the sampling frequency can be dynamically allocated to the more critical monitoring stages. In high-priority states, a high sampling frequency is maintained to ensure real-time performance, while in low-priority states, the sampling frequency is reduced to ensure overall system load remains controllable. This priority-driven sampling mode allows for more precise resource allocation and improves the system's ability to handle the parallel operation of multiple devices. Furthermore, by constructing a hierarchical multi-frequency sampling strategy, the sampling frequency is divided into three levels: highest, medium, and lowest, corresponding to device safety, indirect impact, and indicator monitoring needs, respectively. This enables differentiated sampling of information of varying importance. This hierarchical strategy can quickly increase the sampling frequency to ensure safety when needed, and reduce the frequency to reduce energy consumption under normal conditions, thus balancing safety, real-time performance, and economy.

[0049] In this embodiment, the execution module further includes: The acquisition unit is used to acquire the pre-allocated capacity cache queue of the energy-consuming device based on the acquisition cycle of the energy consumption acquisition frequency. The judgment unit is used to determine whether the data in the capacity cache queue is saved in chronological order. An execution unit is configured to, if so, read the earliest recorded first capacity data and the latest recorded second capacity data from the capacity cache queue, calculate the time difference between the first capacity data and the second capacity data, and dynamically adjust the number of samples to be collected in the capacity cache queue based on the time difference.

[0050] In this embodiment, the system obtains the pre-allocated capacity buffer queues of the energy-consuming devices based on the energy consumption sampling frequency sampling period. The system then determines whether the data in these capacity buffer queues is saved in chronological order to execute corresponding steps. For example, if the system determines that the pre-allocated capacity buffer queues of the energy-consuming devices are not saved in chronological order, the system assumes that there is a delay, packet loss, or out-of-order retransmission at the sampling end or transmission link, causing the data to arrive at the buffer queues at a different time than the sampling time. The system will temporarily block new data writing to prevent the out-of-order problem from escalating, read all data items in the buffer into a temporary working area, and rearrange them according to the sampling timestamp from earliest to latest. If there is... If timestamps are duplicated or missing, they should be marked and written to the exception log. Simultaneously, the time intervals between adjacent records after sorting should be compared to see if they fall within the sampling period tolerance. If an interval exceeds the tolerance, the time range of the missing segment should be recorded for subsequent supplementary sampling. Furthermore, if multi-threaded sampling is used, a queue write lock should be added or a concurrent safe data structure should be used. If there is network transmission delay or out-of-order data, frame encapsulation with sequence numbers should be used, and sequence number verification and sorting should be performed before enqueuing. For example, when the system determines that the pre-allocated capacity buffer queue of energy consumption devices can save data in chronological order, the system will assume that there are no anomalies at the sampling end or in the transmission link, and that the order in which data arrives at the buffer queue is consistent with the sampling time. The system will then proceed smoothly. The earliest recorded first-capacity data and the latest recorded second-capacity data are read from the capacity buffer queue. By calculating the time difference between the first and second-capacity data, the number of samples to be collected in the capacity buffer queue is dynamically adjusted according to different time differences. When the capacity buffer queue can save data in chronological order, it indicates that the acquisition end and the transmission link are working normally, and the arrival order of the data is completely consistent with the actual acquisition time. Such time sequence consistency provides a reliable foundation for subsequent analysis, modeling, and energy consumption prediction, avoiding energy consumption data distortion caused by out-of-order or delay, and ensuring the accuracy of optimization decisions from the source. At the same time, by reading the earliest first-capacity data and the latest second-capacity data, the calculation... By understanding the time difference between the two, the system can monitor the current collection cycle in real time and dynamically adjust the number of samples to be collected based on this time difference. This balances sampling density and data storage costs, ensuring that data redundancy is avoided while retaining sufficient key feature information, thus improving collection efficiency. Furthermore, by dynamically adjusting the number of samples based on the time difference, the capacity buffer queue can adaptively adjust its load under different operating conditions. For example, it can reduce the sampling amount when the equipment is running smoothly to save storage and processing resources, and increase the sampling density to improve monitoring accuracy during load fluctuations or critical operating conditions. This intelligent scheduling method helps reduce the overall resource consumption of the system and improves the response speed and precision of energy consumption monitoring and optimization.

[0051] In this embodiment, it also includes: The construction module is used to construct the variation law of the energy consumption acquisition frequency based on the operating characteristics of the energy consumption equipment. Specifically, the operating characteristics include frequency variation distribution and energy consumption response delay, and the variation law includes seasonality, periodic fluctuation pattern and stable range. The third judgment module is used to determine whether the change pattern detects data packet loss or missing data. The third execution module is used to, if so, obtain the location where the time interval of the missing data packet is exceeded, collect the missing information of the missing data packet based on the location where the time interval is exceeded, and identify the missing type of the missing data packet based on the missing information. The missing information specifically includes the start and end times of the missing data, the number of missing data points and the devices involved, and the missing type specifically includes continuous missing data, intermittent missing data and single-point missing data.

[0052] In this embodiment, the system constructs a pattern of energy consumption sampling frequency variation based on the operating characteristics of the energy-consuming device, specifically including frequency variation distribution and energy consumption response delay. This pattern includes seasonality, periodic fluctuations, and stable intervals. The system then determines whether these patterns detect data loss or missing data, and executes corresponding steps accordingly. For example, if the system determines that the energy consumption sampling frequency variation pattern does not detect data loss or missing data, it considers the data stream to be complete and reliable in the time / sample dimension. The system generates a "Completeness Verification Passed" record containing the device ID, detection window time range, detection rule version, detection time, and verification statistics (e.g., sampling rate stability). The system performs a small-scale self-check after passing the initial test (compare the first and last timestamps, verify sequence number continuity, and check ACK / retransmission counts) to ensure the judgment is not an instantaneous misjudgment. The current complete window is then marked as a "reliable sample" to adjust subsequent cache allocation strategies (e.g., temporarily reducing the cache priority for this device and allocating buffer resources to noisier links). For example, if the system detects data packet loss due to changes in the energy consumption sampling frequency, it considers the data stream unreliable in terms of time / sample dimension. The system will obtain the time interval exceeding the limit for data packet loss and, based on different time interval exceeding limits, collect missing information about the missing data packets. The loss information specifically includes the start and end times of the loss, the number of missing data points, and the affected devices. Based on this information, the loss type of data packet loss is identified, including continuous loss, intermittent loss, and single-point loss. By obtaining the location where the time interval of the data packet loss exceeds the limit, the system can quickly locate the specific time period and location of the anomaly, rather than simply knowing that "there is a loss." This precise location helps narrow down the investigation scope, avoids global scanning and ineffective analysis, and thus significantly improves the efficiency of maintenance personnel in the fault detection and tracing process. Furthermore, the loss information collected by the system not only includes the start and end times of the loss but also records the number of missing data points and the specific affected devices, making abnormal data... The context is more complete, and this completeness of information helps to conduct in-depth analysis of factors such as equipment operation mode, data link stability, and network latency. It provides reliable basic data for establishing packet loss prediction models and optimizing data transmission links. Furthermore, by subdividing the missing types into continuous missing, intermittent missing, and single-point missing, the system can take differentiated remedial measures for different missing characteristics. For example, continuous missing may require retrospective sampling or resynchronization, intermittent missing may require optimization of the transmission buffer mechanism, and single-point missing can be filled by interpolation. This typified processing not only improves the repair efficiency but also reduces interference with normal data flow, ultimately improving the stability and data reliability of the entire energy consumption monitoring system.

[0053] In this embodiment, the second execution module further includes: The selection unit is used to select a preset compression strategy for the energy consumption collection frequency based on the preset triggering conditions of the energy consumption device. The triggering conditions specifically include network bandwidth utilization exceeding a threshold, storage space shortage, memory usage exceeding a set value, and the need to temporarily increase the collection frequency of key data. The compression strategy specifically includes sampling rate compression, data precision compression, time period aggregation compression, and event-driven storage. The second judgment unit is used to determine whether the triggering condition matches the compression strategy; The second execution unit is configured to, if so, obtain energy consumption parameters before and after compression, generate the compression effect of the compression strategy based on the energy consumption parameters, and dynamically adjust the compression ratio of the compression strategy according to the operating status of the energy-consuming device and the compression effect. Specifically, the energy consumption parameters include data volume, bandwidth utilization, and storage usage.

[0054] In this embodiment, the system selects a pre-set compression strategy for the energy consumption acquisition frequency based on pre-defined trigger conditions of the energy consumption device. These trigger conditions specifically include network bandwidth utilization exceeding a threshold, storage space shortage, memory usage exceeding a set value, and the need to temporarily increase the frequency of critical data acquisition. The compression strategies specifically include sampling rate compression, data precision compression, time-period aggregation compression, and event-driven storage. The system then determines whether these trigger conditions match the compression strategy to execute the corresponding steps. For example, if the system determines that the pre-defined trigger conditions of the energy consumption device cannot match the pre-defined compression strategy for the energy consumption acquisition frequency, the system will consider that there is a conflict between the current system resource pressure or device operating status and the existing compression strategy. If a mismatch or conflict occurs, the system will identify the specific reason for the conflict between the triggering conditions and the compression strategy, such as insufficient network bandwidth, limited storage space, or incompatibility between the sampling rate of critical data and the compression strategy. Based on the type of conflict, it will dynamically select or combine other available compression strategies. For example, if sampling rate compression is not feasible, it can try time-based aggregation compression or event-driven storage, prioritizing the collection of critical data while temporarily reducing the collection frequency or accuracy of non-critical data. The system will also record events where the triggering conditions and compression strategy do not match, generating alarms to prompt maintenance personnel or upper-level management systems to intervene and ensure that critical data is not lost. For instance, when the system determines that the pre-set triggering conditions of the energy consumption device match the pre-set compression strategy for the energy consumption collection frequency... At this point, the system assumes that the current system resource pressure or device operating status is compatible with the existing compression strategy. The system obtains energy consumption parameters before and after compression, specifically including data volume, bandwidth utilization, and storage usage. Based on these parameters, it generates the compression effect of the compression strategy and dynamically adjusts the compression ratio according to the device's operating status and the compression effect. By determining that the triggering conditions match the preset compression strategy, the system confirms that the current network bandwidth, storage space, and memory usage are compatible with the compression strategy. This means that data transmission and storage will not experience congestion or data loss due to insufficient resources, thus achieving efficient utilization of system resources while reducing the burden on devices and the network. The system simultaneously acquires energy consumption parameters before and after compression, including data volume, bandwidth utilization, and storage usage, and calculates the compression effect by comparison. This method can not only quantify the resource savings brought by compression, but also provide a scientific basis for subsequent dynamic adjustments. It enables the compression strategy to not only rely on preset rules, but also to be optimized based on real-time operating data. Furthermore, the system can dynamically adjust the compression ratio according to the device operating status and compression effect, ensuring the accuracy of key energy consumption data collection and reducing resource consumption of non-critical data through reasonable compression. This adaptive adjustment mechanism improves the accuracy and reliability of the energy consumption acquisition and optimization system, and achieves the goal of minimizing resource consumption while ensuring data integrity.

[0055] In this embodiment, it also includes: An identification module is used to identify the extent of exceeding the limit of the energy consumption sampling frequency; The fourth judgment module is used to determine whether the excessive amplitude is an occasional phenomenon; The fourth execution module is used to classify the over-limit level of the over-limit amplitude if the over-limit is true, limit the acquisition rate of the energy consumption acquisition frequency based on the over-limit level, and dynamically trigger the preset protection mechanism of the energy consumption device. The over-limit level specifically includes mild, moderate and severe, and the protection mechanism specifically includes temporarily freezing frequency adjustment, reducing the frequency of devices with low acquisition priority and sending alarms to the operation and maintenance center.

[0056] In this embodiment, the system identifies the extent of the energy consumption sampling frequency exceeding the limit, and then determines whether the extent of the exceedance is an occasional phenomenon in order to execute the corresponding steps. For example, when the system determines that the extent of the energy consumption sampling frequency exceeding the limit is not an occasional phenomenon, the system will consider that the current sampling frequency change of the device has a continuous or regular anomaly, which may reflect potential problems in the device's operating status, energy consumption behavior, or external environment, rather than an occasional fluctuation. The system will record the time period of the exceedance, the device involved, and the specific values, distinguishing between the continuity and fluctuation pattern of the abnormal amplitude, providing a basis for subsequent analysis, and at the same time, based on the device's operating parameters (such as temperature, pressure, flow rate, speed, current)... The system analyzes the energy consumption data (including power) and its relationship with coupled devices to determine whether abnormal frequency changes are related to critical operating conditions or urgent processes. If the abnormal frequency does not match the equipment's operating status, there may be a fault, control strategy failure, or sensor malfunction. Based on the identified anomalies, the system adjusts the energy consumption data acquisition frequency and priority, increases the monitoring frequency of critical parameters, or temporarily compresses or delays the acquisition of non-critical data to ensure the integrity of critical data and the accuracy of optimization analysis. For example, if the system determines that the excessive energy consumption data acquisition frequency is an occasional phenomenon, it will consider the current equipment's acquisition frequency change to be normal. The system will then classify the excessive frequency into different levels. The limit levels are specifically categorized as mild, moderate, and severe. Based on different exceedance levels, the energy consumption sampling rate is restricted, and pre-set protection mechanisms on energy-consuming devices are dynamically triggered. These protection mechanisms include temporarily freezing frequency adjustments, reducing the frequency of devices with low sampling priority, and sending alarms to the operation and maintenance center. By determining that the exceedance amplitude is an intermittent phenomenon, the system can avoid misjudging short-term or random fluctuations as equipment malfunctions. This differentiation mechanism reduces the false alarm rate, making the energy consumption sampling system more accurate when monitoring frequency anomalies, and only handling truly potential risks, thereby improving the reliability of monitoring and optimization strategies. The system also classifies exceedance amplitudes into mild, moderate, and severe levels, and... Based on the classification-based limit on the data acquisition rate, differentiated measures can be taken under varying degrees of occasional fluctuations. For example, the acquisition frequency can be slightly reduced for mild occasional fluctuations, while the frequency adjustment may be temporarily frozen for severe occasional fluctuations. This hierarchical control not only ensures the continuity of critical data but also avoids excessive resource consumption. Furthermore, through dynamic triggering protection mechanisms, such as reducing the acquisition frequency of low-priority devices or sending alarms to the operation and maintenance center, the system can proactively protect itself under occasional fluctuations, avoiding potential chain reactions caused by the fluctuations. This mechanism ensures the continued stable operation of critical equipment, maintains the overall security and reliability of the system, and improves the stability and long-term operation and maintenance efficiency of the energy consumption acquisition and optimization system.

[0057] In this embodiment, the second determination module further includes: The combination unit is used to obtain the change series strength of the change coefficient based on the preset energy consumption change threshold of the energy consumption device, and combine the change series strength to obtain the strength trajectory; The third judgment unit is used to determine whether the intensity trajectory matches the expected characteristics of the emergency process; The third execution unit is used to identify the emergency type of the emergency process if no, and dynamically adjust the allowable value of the energy consumption acquisition frequency according to the emergency type. The emergency type specifically includes start / stop, over-temperature, over-load and external fast-cycle command, and the allowable acquisition value specifically includes acquisition frequency, upper limit of change range and adjustment interval.

[0058] In this embodiment, the system obtains the series intensity of changes in the coefficients of energy consumption based on a pre-set energy consumption change threshold for the energy-consuming device. These series intensities are combined to obtain an intensity trajectory. The system then determines whether the intensity trajectory matches the expected characteristics of an emergency process to execute corresponding steps. For example, if the system determines that the intensity trajectory matches the expected characteristics of an emergency process, it considers the change pattern of the energy-consuming device's coefficients to be highly consistent with the characteristics of an emergency process, indicating that the device may be entering a critical operating condition or experiencing potentially high load or abnormal energy consumption. The system then increases the frequency of collecting relevant energy consumption parameters for the device, including temperature, pressure, flow rate, speed, current, and power. Key indicators are used to ensure the completeness, continuity, and absence of critical data. Simultaneously, based on the characteristics of emergency processes, the system dynamically adjusts the energy consumption data collection frequency and priority of equipment, triggering protection mechanisms when necessary, such as limiting the collection frequency of non-critical equipment, freezing adjustable frequency equipment parameters, or issuing alarms to the operations and maintenance center to prevent potential risks. Furthermore, the matched intensity trajectory and collected data are recorded for subsequent analysis, improving the system's responsiveness and predictive accuracy for similar emergency processes. For example, if the system determines that the intensity trajectory does not match the expected characteristics of an emergency process, it will consider the change pattern of the current energy consumption equipment's coefficients to be inconsistent with the operational characteristics of the emergency process, indicating that the equipment has not entered a critical operating state. The system identifies the emergency type of an emergency process, including start / stop, over-temperature, overload, and external fast-cycle commands. Based on the different emergency types, it dynamically adjusts the allowable values ​​for energy consumption data collection frequency, including the collection frequency, the upper limit of the change range, and the adjustment interval. By determining that the intensity trajectory cannot match the expected characteristics of the emergency process, the system can clearly distinguish that the current equipment operating state is not a critical condition, thus avoiding over-response or unnecessary resource scheduling for non-critical states. This identification mechanism improves the accuracy of the energy consumption monitoring system, reduces false alarms and misoperations, and ensures stable operation of the system in non-emergency situations. Simultaneously, the system identifies different emergency types, such as start / stop, over-temperature, and overload. The system dynamically adjusts the allowable acquisition values ​​based on the specific type of the load and external fast-cycle commands, including the acquisition frequency, the upper limit of the change range, and the adjustment interval. Through this differentiated adjustment, the system can ensure optimal resource utilization under non-critical operating conditions while reserving response space for possible emergencies, achieving flexibility and adaptability in energy consumption acquisition. Furthermore, the dynamic adjustment of the allowable acquisition values ​​not only ensures priority acquisition of critical data but also avoids non-critical data from consuming excessive computing, storage, and transmission resources, thereby improving the overall energy consumption monitoring efficiency. This mechanism provides conditions for rapid response when equipment enters potentially emergency operating conditions, enabling the system to optimize resource consumption, improve energy consumption acquisition, and optimize the accuracy and reliability of the system while ensuring data integrity.

[0059] In this embodiment, it also includes: The second identification module is used to identify the coupling relationship between the energy-consuming devices based on the preset energy consumption mapping rules of the energy-consuming devices. The energy consumption mapping rules specifically include normal operating conditions, different load ranges and emergency operating conditions, and the coupling relationship specifically includes direct coupling and indirect coupling. The fifth judgment module is used to determine whether the coupling relationship matches the collection priority of the energy consumption device; The fifth execution module is used to, if not, match the operating parameter values ​​of the energy-consuming device with the preset definition range of the coupling relationship, dynamically correct the acquisition frequency range of the energy-consuming device, and update the operating status of the energy-consuming device and its coupled object in real time according to the acquisition priority, wherein the operating status specifically includes the working condition status and the load status.

[0060] In this embodiment, the system identifies the coupling relationships between energy-consuming devices based on pre-set energy consumption mapping rules, which specifically include normal operating conditions, different load ranges, and emergency operating conditions. These coupling relationships include direct and indirect coupling. The system then determines whether these coupling relationships match the data collection priorities of the energy-consuming devices to execute corresponding steps. For example, when the system determines that the coupling relationships between energy-consuming devices match their data collection priorities, it considers that the energy consumption impact and correlation characteristics between the devices are highly consistent with the preset data collection strategy. This means that the data collection order, sampling frequency, and priority arrangement of key and coupled devices are reasonable and can effectively reflect the overall energy consumption behavior of the system. Based on the matched coupling relationships and data collection priorities, the system confirms the data collection frequency of key devices and their coupled devices to ensure that key energy consumption data is collected continuously and completely. For non-critical devices, the data collection frequency can be appropriately reduced or the collection can be delayed. To save storage and transmission resources, and through matching coupling relationships, the system can more accurately analyze the energy consumption correlation between devices and the overall system load, providing a reliable data foundation for energy consumption optimization. It can also perform linked monitoring of energy consumption changes between key coupled devices, quickly identifying potential anomalies. Furthermore, based on the current collection priority and coupling relationship, the system can dynamically adjust the collection strategy and frequency under different loads or emergency conditions to ensure priority collection of critical data. For example, when the system determines that the coupling relationship between energy-consuming devices cannot match the collection priority of the energy-consuming devices, the system will consider that the energy consumption impact and correlation characteristics between the devices are inconsistent with the preset collection strategy. The system will then match the operating parameter values ​​of the energy-consuming devices with the pre-defined range of the coupling relationship, dynamically correct the collection frequency range of the energy-consuming devices, and update the operating status of the energy-consuming devices and coupled objects in real time according to different collection priorities. The operating status specifically includes the operating condition status and load status.When the coupling relationship and the acquisition priority do not match, directly acquiring data according to the original strategy will lead to delayed acquisition of critical data or waste of resources. By matching the operating parameter values ​​of energy-consuming devices (such as temperature, pressure, flow rate, speed, etc.) with the preset definition range of the coupling relationship, the system can promptly detect deviations between the acquisition strategy and the actual situation and dynamically adjust the acquisition frequency range. This ensures that the acquisition frequency allocation is updated synchronously with the actual energy consumption impact, thereby avoiding the omission or delay of critical data. Simultaneously, the system updates the operating status (operating condition and load status) of energy-consuming devices and coupled objects in real time according to different acquisition priorities. This allows for accurate understanding of the current operating load, load change trends, and operating characteristics of the devices. In actual acquisition, devices of different importance can obtain acquisition frequencies and data update cycles that match their operating status, ensuring that critical operating condition information enters the monitoring and analysis stage in a timely and complete manner. Furthermore, by dynamically adjusting the acquisition frequency range, the system can acquire data more frequently from high-priority devices or devices with large load fluctuations, and reduce the acquisition frequency from low-priority devices or devices with stable loads. This reduces the consumption of network bandwidth and storage resources. This differentiated strategy not only reduces system operating costs but also maintains high-quality monitoring of critical devices under limited resources.

[0061] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An energy efficiency optimization method based on multi-device coupling relationships, characterized in that, The following steps are involved: Based on the operating parameters related to the energy-consuming equipment and the coupling relationship, the energy consumption acquisition frequency of the energy-consuming equipment is read. Specifically, the operating parameters include temperature, pressure, flow rate, rotation speed, current, and power. Determine whether the rate of change of the energy consumption sampling frequency exceeds a preset rate threshold; If so, then divide the frequency peak from the energy consumption sampling frequency, obtain the minimum holding period after the energy consumption sampling frequency change, generate the maximum change ratio of the energy consumption sampling frequency based on the frequency peak and the minimum holding period, and calculate the change coefficient of the energy consumption sampling frequency based on the maximum change ratio. The frequency peak specifically includes the highest sampling frequency and the lowest sampling frequency, and the change coefficient specifically includes the instantaneous change rate and the average change rate. Determine whether the change coefficient matches the preset emergency process of the energy-consuming device; If not, the status flag of the energy-consuming device is identified, and based on the status flag, the collection priority of the energy consumption collection frequency is generated. According to the collection priority, the non-critical energy consumption data of the energy-consuming device during energy consumption collection is dynamically compressed. Based on the non-critical energy consumption data, a hierarchical multi-frequency collection strategy for the energy-consuming device is constructed. Specifically, the status flag includes start-up, shutdown, and fault, and the hierarchical multi-frequency collection strategy includes the highest frequency affecting device safety, the medium frequency indirectly affecting the device, and the lower frequency monitoring device indicators.

2. The energy efficiency optimization method based on multi-device coupling relationship according to claim 1, characterized in that, The step of dividing the frequency peak from the energy consumption sampling frequency and obtaining the minimum retention period after the energy consumption sampling frequency change further includes: Based on the sampling period of the energy consumption sampling frequency, obtain the pre-allocated capacity cache queue of the energy consumption device; Determine whether the data in the capacity cache queue is saved in chronological order; If so, the earliest recorded first capacity data and the latest recorded second capacity data are read from the capacity cache queue, the time difference between the first capacity data and the second capacity data is calculated, and the number of samples to be collected in the capacity cache queue is dynamically adjusted according to the time difference.

3. The energy efficiency optimization method based on multi-device coupling relationship according to claim 1, characterized in that, After the step of generating the maximum change ratio of the energy consumption sampling frequency based on the frequency peak and the minimum hold period, the method further includes: Based on the operating characteristics of the energy-consuming equipment, the variation law of the energy consumption acquisition frequency is constructed. Specifically, the operating characteristics include frequency variation distribution and energy consumption response delay, and the variation law includes seasonality, periodic fluctuation pattern and stable range. Determine whether the described pattern of change indicates data loss or missing packets. If so, the location where the time interval of the missing data packet is exceeded is obtained. Based on the location where the time interval is exceeded, the missing information of the missing data packet is collected. Based on the missing information, the missing type of the missing data packet is identified. The missing information specifically includes the start and end times of the missing data packet, the number of missing data points, and the devices involved. The missing type specifically includes continuous missing data packet, intermittent missing data packet, and single-point missing data packet.

4. The energy efficiency optimization method based on multi-device coupling relationship according to claim 1, characterized in that, The step of dynamically compressing non-critical energy consumption data collected by the energy-consuming device according to the collection priority further includes: Based on the preset triggering conditions of the energy-consuming device, a preset compression strategy for the energy consumption collection frequency is selected. The triggering conditions specifically include network bandwidth utilization exceeding a threshold, storage space shortage, memory usage exceeding a set value, and the need to temporarily increase the collection frequency of key data. The compression strategy specifically includes sampling rate compression, data precision compression, time period aggregation compression, and event-driven storage. Determine whether the triggering condition matches the compression strategy; If so, the energy consumption parameters before and after compression are obtained, and the compression effect of the compression strategy is generated based on the energy consumption parameters. The compression ratio of the compression strategy is dynamically adjusted according to the operating status of the energy-consuming device and the compression effect. Specifically, the energy consumption parameters include data volume, bandwidth utilization, and storage usage.

5. The energy efficiency optimization method based on multi-device coupling relationship according to claim 1, characterized in that, After the step of determining whether the rate of change of the energy consumption sampling frequency exceeds a preset rate threshold, the method further includes: Identify the extent of exceeding the limit of the energy consumption sampling frequency; Determine whether the exceeded amplitude is an isolated phenomenon; If so, the excess range is divided into excess levels. Based on the excess level, the collection rate of the energy consumption collection frequency is limited, and the preset protection mechanism of the energy consumption device is dynamically triggered. The excess levels specifically include mild, moderate and severe, and the protection mechanism specifically includes temporarily freezing frequency adjustment, reducing the frequency of devices with low collection priority and sending alarms to the operation and maintenance center.

6. The energy efficiency optimization method based on multi-device coupling relationship according to claim 1, characterized in that, The step of determining whether the change coefficient matches the preset emergency process of the energy-consuming device further includes: Based on the preset energy consumption change threshold of the energy-consuming device, the change series strength of the change coefficient is obtained, and the change series strength is combined to obtain the intensity trajectory; Determine whether the intensity trajectory matches the expected characteristics of the emergency process; If not, the emergency type of the emergency process is identified, and the allowable value of the energy consumption collection frequency is dynamically adjusted according to the emergency type. The emergency type specifically includes start / stop, over-temperature, overload, and external fast-cycle command. The allowable value specifically includes collection frequency, upper limit of change range, and adjustment interval.

7. The energy efficiency optimization method based on multi-device coupling relationship according to claim 1, characterized in that, Before the step of reading the energy consumption acquisition frequency of the energy-consuming device based on the operating parameters related to the coupling relationship between the energy-consuming device and the device, the method further includes: Based on the preset energy consumption mapping rules of the energy-consuming devices, the coupling relationship between the energy-consuming devices is identified. Specifically, the energy consumption mapping rules include normal operating conditions, different load ranges, and emergency operating conditions, and the coupling relationship specifically includes direct coupling and indirect coupling. Determine whether the coupling relationship matches the acquisition priority of the energy-consuming device; If not, the operating parameter values ​​of the energy-consuming device are matched with the preset definition range of the coupling relationship, the sampling frequency range of the energy-consuming device is dynamically corrected, and the operating status of the energy-consuming device and its coupled object is updated in real time according to the sampling priority. The operating status specifically includes the working condition status and the load status.

8. An energy efficiency optimization system based on multi-device coupling relationships, characterized in that, include: The reading module is used to read the energy consumption acquisition frequency of the energy-consuming device based on the operating parameters related to the coupling relationship between the energy-consuming device and the device. The operating parameters specifically include temperature, pressure, flow rate, rotation speed, current, and power. The judgment module is used to determine whether the rate of change of the energy consumption acquisition frequency exceeds a preset rate threshold. An execution module is configured to, if so, divide the frequency peak from the energy consumption sampling frequency, obtain the minimum holding period after the energy consumption sampling frequency change, generate the maximum change ratio of the energy consumption sampling frequency based on the frequency peak and the minimum holding period, and calculate the change coefficient of the energy consumption sampling frequency based on the maximum change ratio, wherein the frequency peak specifically includes the highest sampling frequency and the lowest sampling frequency, and the change coefficient specifically includes the instantaneous change rate and the average change rate; The second judgment module is used to determine whether the change coefficient matches the preset emergency process of the energy consumption device; The second execution module is used to identify the status flag of the energy-consuming device if no, generate a collection priority for the energy consumption collection frequency based on the status flag, dynamically compress non-critical energy consumption data of the energy-consuming device when collecting energy consumption data according to the collection priority, and construct a hierarchical multi-frequency collection strategy for the energy-consuming device based on the non-critical energy consumption data. The status flag specifically includes start-up, shutdown, and fault, and the hierarchical multi-frequency collection strategy specifically includes the highest frequency that affects device safety, the medium frequency that indirectly affects the device, and the lower frequency that monitors device indicators.

9. The energy efficiency optimization system based on multi-device coupling relationship according to claim 8, characterized in that, The execution module further includes: The acquisition unit is used to acquire the pre-allocated capacity cache queue of the energy-consuming device based on the acquisition cycle of the energy consumption acquisition frequency. The judgment unit is used to determine whether the data in the capacity cache queue is saved in chronological order. An execution unit is configured to, if so, read the earliest recorded first capacity data and the latest recorded second capacity data from the capacity cache queue, calculate the time difference between the first capacity data and the second capacity data, and dynamically adjust the number of samples to be collected in the capacity cache queue based on the time difference.

10. The energy efficiency optimization system based on multi-device coupling relationship according to claim 8, characterized in that, Also includes: The construction module is used to construct the variation law of the energy consumption acquisition frequency based on the operating characteristics of the energy consumption equipment. Specifically, the operating characteristics include frequency variation distribution and energy consumption response delay, and the variation law includes seasonality, periodic fluctuation pattern and stable range. The third judgment module is used to determine whether the change pattern detects data packet loss or missing data. The third execution module is used to, if so, obtain the location where the time interval of the missing data packet is exceeded, collect the missing information of the missing data packet based on the location where the time interval is exceeded, and identify the missing type of the missing data packet based on the missing information. The missing information specifically includes the start and end times of the missing data, the number of missing data points and the devices involved, and the missing type specifically includes continuous missing data, intermittent missing data and single-point missing data.

Images