Cut tobacco production energy consumption data acquisition method and system based on adaptive sampling frequency

Abstract

The invention discloses a cut tobacco production energy consumption data acquisition method and system based on adaptive sampling frequency. The method comprises the following steps: initializing sampling strategy parameters; energy consumption data is collected in real time, and the data change rate, the equipment operation state and the network load are monitored synchronously; dynamically deciding the optimal sampling frequency of the next period based on multi-dimensional information fusion; executing an acquisition task according to a new frequency, and adaptively adjusting a data packaging and caching strategy; when the network is abnormal or low-frequency, data are locally cached and compressed, and breakpoint resuming is carried out after recovery; and finally, uploading the standardized data to a central platform. The system comprises a data acquisition layer, an edge processing layer and a platform service layer, wherein the edge processing layer is integrated with a multi-source sensing and intelligent scheduling module. The problems of resource waste and information loss caused by unreasonable energy consumption data acquisition frequency in industrial scenes such as cut tobacco manufacturing workshops can be solved, and the efficiency and quality of energy consumption data acquisition can be improved.

Description

Technical Field

[0001] This invention relates to the field of industrial automation and energy management technology, and in particular to a method and system for collecting energy consumption data in silk production based on adaptive sampling frequency. It is mainly used to solve the problems of resource waste and information loss caused by unreasonable energy consumption data collection frequency in industrial scenarios such as silk production workshops. Background Technology

[0002] In existing silk-making workshops, energy consumption data collection commonly employs a fixed sampling frequency. This "one-size-fits-all" approach has significant drawbacks: for slowly changing parameters (such as ambient temperature), continuous high-frequency sampling generates a large amount of redundant data, consuming valuable network bandwidth and storage resources; while for critical parameters with frequent abrupt changes (such as motor start-stop power), fixed low-frequency sampling may lose important information such as transient peak values, leading to distorted energy efficiency analysis. Furthermore, the fixed-frequency mechanism cannot adapt to dynamically changing network loads, easily causing data delays or loss during network congestion. In summary, the current technical problems mainly include: Low data collection efficiency: Fixed sampling frequency cannot distinguish the actual value of data, resulting in "not collecting what should be collected, and collecting too much what should not be collected", and low data utility ratio.

[0003] Waste of network and storage resources: The generation and transmission of a large amount of redundant data unnecessarily consumes network bandwidth and increases the storage and computing pressure on the central platform.

[0004] Insufficient system intelligence: The existing system lacks the ability to perceive and respond to the characteristics of the data itself, the operating conditions of the equipment, and the network environment, and the data collection strategy is rigid.

[0005] Incomplete capture of key information: For critical events such as sudden changes in energy consumption and equipment start-up and shutdown, a fixed sampling period may not be effective in capturing them, affecting the accuracy of fault diagnosis and precise analysis.

[0006] Therefore, there is an urgent need to provide a technical solution that can intelligently sense and adaptively adjust the sampling strategy, which is the key to improving the efficiency and quality of energy consumption data collection. Summary of the Invention

[0007] The purpose of this invention is to provide a method and system for collecting energy consumption data in silk production based on adaptive sampling frequency, which aims to solve the problems of resource waste and information loss caused by unreasonable energy consumption data collection frequency in industrial scenarios such as silk production workshops, so as to improve the efficiency and quality of energy consumption data collection.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for acquiring silk-making energy consumption data based on adaptive sampling frequency, the method comprising the following steps: S1. Preset the baseline parameters related to the acquisition of silk production energy consumption data in the edge gateway, establish a frequency adjustment rule base, and set the sampling frequency protection range; S2. Collect energy consumption data of each monitoring point in the silk-making workshop at the currently set frequency, acquire the operating status data of the execution equipment, and monitor the network communication quality data simultaneously. S3. Based on the energy consumption data, operating status data, and network communication quality data, the sampling frequency for the next cycle is output through the rule engine or lightweight machine learning model built into the edge gateway. S4. Perform data acquisition according to the output sampling frequency, and dynamically adjust the data packet size and compression strategy.

[0009] Furthermore, the method also includes: S5. When the network is abnormal or the sampling frequency decreases, the collected data will be cached in the local storage and compressed without loss; after the network is restored, the interrupted transmission will be resumed automatically.

[0010] Furthermore, the method also includes: S6. Standardize the collected data, encrypt it, and then upload it to the central energy management platform.

[0011] Furthermore, in S1, the reference parameters include the default sampling frequency of each monitoring point, the frequency adjustment trigger threshold, and the mapping rules between the device status and the acquisition strategy.

[0012] Furthermore, in S2, the collected energy consumption data includes: data on electrical energy, water consumption, steam, and compressed air; the acquired operating status data includes: PLC status signals, current waveforms, and data on changes in switching quantities; and the monitored network communication quality data includes: data on network latency, bandwidth utilization, and data packet loss rate.

[0013] Furthermore, in step S3, a sliding window algorithm is used to analyze the collected data in real time and fuse data from different dimensions.

[0014] Furthermore, in S3, the lightweight machine learning model includes decision trees and lightweight neural networks, which predict the optimal sampling frequency by learning from historical data.

[0015] Secondly, the present invention also provides a silk-making energy consumption data acquisition system based on adaptive sampling frequency. The system includes a data acquisition layer, an edge processing layer, and a platform service layer, and uses the method described above to acquire silk-making energy consumption data.

[0016] Thirdly, the present invention also provides an electronic device, including a processor and a memory, the memory storing machine-executable instructions executable by the processor, the processor executing the machine-executable instructions to perform the above-described method.

[0017] As can be seen from the above technical solution, compared with the prior art, the beneficial effects of the present invention include: 1. This invention can solve the problem of resource waste and information loss caused by unreasonable energy consumption data collection frequency in industrial scenarios such as silk-making workshops, and helps to improve the efficiency and quality of energy consumption data collection.

[0018] 2. This invention enables "on-demand collection" of various energy consumption data such as electricity, water, steam, and compressed air in the silk-making workshop, greatly improving collection efficiency while ensuring data integrity.

[0019] 3. This invention can intelligently adapt to equipment status and network fluctuations based on the rate of change of energy consumption data, equipment operating status, and network load, and dynamically adjust the sampling frequency to build an efficient, low-power, and reliable energy consumption data acquisition channel.

[0020] 4. This invention can optimize network bandwidth usage and improve the efficiency and effectiveness of data collection.

[0021] 5. This invention provides higher quality and more representative energy consumption data for upper-level energy management systems, supporting refined energy efficiency analysis and optimization decisions.

[0022] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0023] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0026] Figure 1 This is a schematic diagram of the process for acquiring silk-making energy consumption data based on adaptive sampling frequency, provided in an embodiment of the present invention.

[0027] Figure 2 This is a schematic diagram illustrating the working principle of silk-making energy consumption data acquisition based on adaptive sampling frequency, provided in an embodiment of the present invention.

[0028] Figure 3 This is a schematic diagram of the electronic device structure provided in an embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 some embodiments of the present invention, but not all embodiments.

[0030] In the description of this invention, it should be noted that some processes described in this application specification and drawings include multiple operations that appear in a specific order. However, it should be clearly understood that these operations may be performed in any order or in parallel. Furthermore, various numbers are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0031] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0032] See Figure 1 and Figure 2 As shown in the figure, this invention discloses a method for collecting silk production energy consumption data based on adaptive sampling frequency. It mainly adopts an adaptive sampling mechanism of "state perception - frequency decision - dynamic execution - feedback optimization" to upgrade data collection from "passive fixed" mode to "active adaptive" mode.

[0033] The technical solution process is as follows: Step 01: Initialization Configuration Phase ①Preset baseline parameters in the edge gateway: including the default sampling frequency of each monitoring point, the frequency adjustment trigger threshold, and the mapping rules between device status and acquisition strategy; ② Establish a frequency adjustment rule base and set minimum / maximum sampling frequency protection intervals.

[0034] Step 02: Real-time monitoring and status recognition: ① Continuously collect energy consumption data (electricity, water, steam, compressed air, etc.) at the currently set frequency; ② Parallel execution of equipment operation status diagnosis: Identify equipment operation status by analyzing features such as PLC status signals, current waveforms, and changes in switching quantities; ③ Synchronous monitoring of network communication quality: Real-time assessment of network latency, bandwidth utilization, and packet loss rate.

[0035] Step 03: Intelligent Frequency Decision Engine: ① Dynamically calculate data change characteristics: Use the sliding window algorithm to analyze data slope, variance and other change indicators in real time; ② Multi-dimensional strategy integration: Integrating real-time information from three dimensions: data volatility, equipment status, and network load; ③ Generate the optimal sampling scheme: Output the sampling frequency for the next cycle through a rule engine or a lightweight machine learning model.

[0036] In one specific implementation, rule-based adaptive sampling involves embedding a rule engine in the edge gateway and adjusting the frequency using preset thresholds (e.g., increasing the frequency if the rate of change exceeds ±5%). This approach is suitable for scenarios with high real-time requirements and well-defined logic.

[0037] In one specific implementation, adaptive sampling based on a lightweight machine learning model involves embedding a lightweight machine learning model (such as a decision tree or lightweight neural network) into the gateway to predict the optimal sampling frequency by learning from historical data. This approach is suitable for scenarios with complex and irregular data patterns.

[0038] Difference Analysis: Rule-based adaptive sampling is simple to implement and fast to respond, making it suitable for most industrial scenarios; adaptive sampling based on a lightweight machine learning model is more intelligent and predictive, making it suitable for demanding energy efficiency optimization scenarios. Both can be flexibly configured according to actual needs, enhancing the system's applicability and scalability.

[0039] Step 04: Dynamic Execution and Policy Update: ① Adjust the data acquisition task scheduling cycle in real time and update the data acquisition sequence; ② Adaptive optimization data packaging strategy: Adjust the data packaging size and compression algorithm according to frequency changes; ③ Synchronously update the edge cache management strategy to ensure data integrity.

[0040] Step 05: Intelligent caching and fault tolerance: ① Establish a tiered caching mechanism: During periods of low-frequency sampling or network anomalies, data is temporarily stored in local solid-state storage; ② Ensure breakpoint resume transmission: When the network recovers or the sampling frequency increases, the cached data is automatically retransmitted; ③ Employ data compression technology: Perform lossless compression on cached data to optimize storage space utilization.

[0041] Step 06: Data Upload and Platform Integration ① Package the data and upload it to the central energy management platform via an encrypted channel; ② The platform performs data parsing, storage, and quality verification; ③ Provide high-quality energy consumption analysis, energy efficiency reports, and anomaly warnings for upper-layer applications.

[0042] From the description of the above embodiments, those skilled in the art will understand that the present invention provides a method for acquiring silk-making energy consumption data based on adaptive sampling frequency. The technical problems that this solution can solve and its advantages include the following: Technical problems to be solved: It solves the problems of data redundancy, low value density, and resource waste caused by fixed sampling frequency; It eliminates the risk of missing key event information due to rigid data collection strategies; This alleviated the pressure on network bandwidth and the processing capacity of the central system; It enhances the system's ability to adapt to device status and network environment.

[0043] Advantages: High level of intelligence: It automatically adjusts the sampling strategy based on data characteristics and environmental conditions to achieve "on-demand data collection"; Excellent resource utilization: Significantly reduces redundant data, saves network traffic and storage costs, and improves the efficiency of the entire data link; The system is highly adaptable: it has achieved a leap from "human setting" to "self-adjustment", and the data collection strategy follows the environment and the data itself. It is applicable to different device types and energy consumption modes and has good versatility. Data quality improvement: Avoid invalid data collection and ensure that uploaded data is highly representative and timely.

[0044] Furthermore, this invention also provides a silk-making energy consumption data acquisition system based on adaptive sampling frequency. This system, through the collaborative operation of an edge gateway and a central platform, constructs a closed-loop control system of "perception-decision-execution-optimization," upgrading data acquisition from a "passive fixed" mode to an "active adaptive" mode. This system mainly consists of a data acquisition layer, an edge processing layer, and a platform service layer, forming a layered intelligent processing architecture. Its core technical points include: 1. Data Change Rate Monitoring Module: Monitors the numerical change trend of each energy consumption data point in real time and calculates its change rate (such as slope, variance, etc.). For data points with slow changes, the sampling frequency is automatically reduced; for data points with drastic changes, the sampling frequency is increased.

[0045] 2. Equipment Status Identification Module: By analyzing equipment operating signals (such as PLC status words, current values, and switch signals), this module identifies the equipment's different states, such as "running," "standby," and "stopping," and adjusts the data acquisition strategy accordingly. For example, it significantly reduces the data acquisition frequency during equipment downtime.

[0046] 3. Network load awareness module: Real-time monitoring of network latency and bandwidth utilization between the edge gateway and the central platform, dynamically adjusting data upload frequency and packaging strategy according to network conditions to avoid network congestion.

[0047] 4. Adaptive Frequency Scheduling Engine: Based on the aforementioned multi-dimensional information, the engine dynamically generates and executes the optimal sampling strategy through preset rules or lightweight machine learning algorithms. It supports minimum and maximum frequency limits to prevent frequencies from being too high or too low.

[0048] 5. Edge-side data caching and compression: During periods of frequency reduction, the edge gateway caches and lightly compresses the data, and uploads it in batches when the frequency recovers or the network is idle, ensuring that no data is lost.

[0049] The system provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the system embodiment can be referred to the corresponding content in the aforementioned method embodiment, and will not be repeated here.

[0050] Additionally, refer to Figure 3 As shown, this embodiment of the invention also provides an electronic device, which may include a processor, a memory, a communication bus and a communication interface, and may also include a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the above-described method.

[0051] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, software systems, electronic devices, or computer program products, etc. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0052] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0053] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for acquiring silk-making energy consumption data based on adaptive sampling frequency, characterized in that, The method includes the following steps: S1. Preset the baseline parameters related to the acquisition of silk production energy consumption data in the edge gateway, establish a frequency adjustment rule base, and set the sampling frequency protection range; S2. Collect energy consumption data of each monitoring point in the silk-making workshop at the currently set frequency, acquire the operating status data of the execution equipment, and monitor the network communication quality data simultaneously. S3. Based on the energy consumption data, operating status data, and network communication quality data, the sampling frequency for the next cycle is output through the rule engine or lightweight machine learning model built into the edge gateway. S4. Perform data acquisition according to the output sampling frequency, and dynamically adjust the data packet size and compression strategy.

2. The method according to claim 1, characterized in that, The method also includes: S5. When the network is abnormal or the sampling frequency decreases, the collected data will be cached in the local storage and compressed without loss; after the network is restored, the interrupted transmission will be resumed automatically.

3. The method according to claim 2, characterized in that, The method also includes: S6. Standardize the collected data, encrypt it, and then upload it to the central energy management platform.

4. The method according to claim 1, characterized in that, In S1, the reference parameters include the default sampling frequency of each monitoring point, the frequency adjustment trigger threshold, and the mapping rules between the device status and the acquisition strategy.

5. The method according to claim 1, characterized in that, In S2, the collected energy consumption data includes: data on electrical energy, water consumption, steam, and compressed air; the acquired operating status data includes: PLC status signals, current waveforms, and data on changes in switching quantities; and the monitored network communication quality data includes: data on network latency, bandwidth utilization, and data packet loss rate.

6. The method according to claim 1, characterized in that, In step S3, a sliding window algorithm is used to analyze the collected data in real time and fuse data from different dimensions.

7. The method according to claim 1, characterized in that, In S3, the lightweight machine learning model includes decision trees and lightweight neural networks, which predict the optimal sampling frequency by learning from historical data.

8. A data acquisition system for silk-making energy consumption based on adaptive sampling frequency, characterized in that, The system includes a data acquisition layer, an edge processing layer, and a platform service layer, and uses the method described in any one of claims 1–7 to collect silk production energy consumption data.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing machine-executable instructions that can be executed by the processor, the processor executing the machine-executable instructions to perform the method as described in any one of claims 1-7.

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