Method and system for identifying heat stealing behavior based on edge computing and vibration spectrum analysis
By using edge computing and vibration spectrum analysis, the monitoring terminal locally processes the vibration signal of the heating pipeline, and uses time-frequency characteristics and neural network models to identify heat theft behavior, which solves the problems of inaccurate heat theft behavior identification and high energy consumption in the heating system, and achieves low power consumption and high accuracy heat theft identification.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANDONG PUSAI COMM TECH CO LTD
- Filing Date
- 2026-02-10
- Publication Date
- 2026-07-21
AI Technical Summary
In centralized heating systems, existing technologies struggle to accurately identify heat theft, and high-frequency sampling and data transmission result in significant energy consumption. Furthermore, environmental interference is difficult to distinguish, leading to inaccurate identification and a high false alarm rate.
By employing a method based on edge computing and vibration spectrum analysis, vibration signals of heating pipelines are acquired locally through monitoring terminals. Time-domain and frequency-domain feature analysis is used, combined with a neural network model, to identify heat theft behavior, thereby reducing energy consumption and improving accuracy.
Under extremely low power consumption conditions, it can accurately distinguish between heat theft and environmental disturbances, reduce false alarm rate, adapt to different installation environments, and achieve efficient heat theft identification.
Smart Images
Figure CN122432802A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of smart heating monitoring and IoT security technology, and in particular to a method and system for identifying heat theft behavior based on edge computing and vibration spectrum analysis. Background Technology
[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.
[0003] In centralized heating systems, there are instances of illegal activities such as opening valves without authorization and cutting pipes to extract heat, which affect the normal operation of the heating system.
[0004] Currently, when identifying heat theft, heating monitoring terminals are set up at multiple monitoring points in the heating pipeline. The heating monitoring terminals acquire monitoring data from the monitoring points and then upload the data to the cloud or monitoring center, which then determines whether heat theft has occurred.
[0005] The monitoring terminal needs to continuously sample the monitoring data at high frequency and transmit the monitoring data, which consumes a lot of energy. Moreover, when analyzing the monitoring data to determine whether heat theft has occurred, the current method only uses simple thresholds or single features to make judgments, which makes it difficult to distinguish between environmental interference and actual heat theft, resulting in inaccurate identification of heat theft. Summary of the Invention
[0006] To address the aforementioned problems, this invention proposes a method and system for identifying heat theft behavior based on edge computing and vibration spectrum analysis, which improves the accuracy of heat theft behavior identification and reduces energy consumption.
[0007] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, a heat theft behavior identification method based on edge computing and vibration spectrum analysis is proposed, including: Acquire vibration signals from heating pipelines; When the sum vector energy of the vibration signal is greater than the upper limit of the wake-up threshold interval, the temporal features of the vibration signal are extracted. Based on the time-domain characteristics, determine whether the vibration signal is a continuous event signal; When the vibration signal is a continuous event signal, extract the frequency domain features of the vibration signal; Based on the frequency domain characteristics of the vibration signal, determine whether heat theft has occurred.
[0008] Furthermore, when the sum vector energy of the vibration signal is greater than the upper limit of the wake-up threshold range, the vibration signal is subsequently acquired through the first sampling frequency. When the sum vector energy of the vibration signal is less than the lower limit of the wake-up threshold range, the vibration signal is subsequently acquired through a second sampling frequency.
[0009] Furthermore, the first sampling frequency is greater than the second sampling frequency.
[0010] Furthermore, when the vibration signal is determined to have instantaneous impact characteristics based on time-domain characteristics and the energy attenuation of the vibration signal is greater than a set attenuation threshold, the vibration signal is determined to be a short-term physical interference signal. When the duration of the sum vector energy of the vibration signal being greater than the upper limit of the wake-up threshold range is greater than the set time threshold, and the vibration signal is not a short-term physical interference signal, the vibration signal is determined to be a continuous event signal.
[0011] Furthermore, the frequency domain characteristics of the vibration signal are identified through a behavioral feature model to determine whether heat theft has occurred. The behavioral feature model takes the frequency domain characteristics of the vibration signal as input and the occurrence of heat theft as output, and is constructed using a neural network model.
[0012] Furthermore, when heat theft is detected, an alarm will be issued.
[0013] Secondly, a heat theft behavior identification system based on edge computing and vibration spectrum analysis is proposed, including: Vibration signal acquisition unit, used to acquire vibration signals of heating pipelines; The temporal feature extraction unit is used to extract the temporal features of the vibration signal when the sum vector energy of the vibration signal is greater than the upper limit of the wake-up threshold interval. The event determination unit is used to determine whether a vibration signal is a continuous event signal based on its time-domain characteristics. The behavior recognition unit is used to extract the frequency domain features of the vibration signal when the vibration signal is a continuous event signal; and to determine whether heat theft behavior has occurred based on the frequency domain features of the vibration signal.
[0014] Thirdly, a computer device is proposed, the device comprising: A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the heat theft behavior identification method based on edge computing and vibration spectrum analysis proposed in the first aspect.
[0015] Fourthly, a computer-readable storage medium is proposed, wherein the computer-readable storage medium stores a computer program adapted to be loaded and executed by a processor, the method for identifying heat theft behavior based on edge computing and vibration spectrum analysis proposed in the first aspect.
[0016] Fifthly, a computer program product is proposed, which includes a computer program. When the computer program is executed by a processor, it implements the heat theft behavior identification method based on edge computing and vibration spectrum analysis proposed in the first aspect.
[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: The invention proposes a method and system for identifying heat theft behavior based on edge computing and vibration spectrum analysis. When identifying heat theft behavior, the method first determines whether the vibration signal is a continuous event signal based on the time-domain characteristics of the vibration signal to eliminate sudden changes in the vibration signal caused by environmental interference. Then, it only identifies continuous event signals to determine whether heat theft behavior has occurred, thus eliminating environmental interference and improving the accuracy of heat theft behavior identification.
[0018] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description
[0019] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application.
[0020] Figure 1 This is a flowchart of the heat theft behavior identification method based on edge computing and vibration spectrum analysis proposed in an embodiment of the present invention; Figure 2 The time-domain envelope diagram and energy distribution diagram of the vibration signals corresponding to the heat theft behavior and the normal door opening inspection behavior proposed in the embodiments of the present invention are shown. Detailed Implementation The present invention will be further described below with reference to the accompanying drawings and embodiments.
[0021] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.
[0022] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.
[0023] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.
[0024] First, the application scenarios of the heat theft behavior identification method based on edge computing and vibration spectrum analysis proposed in the embodiments of the present invention will be described.
[0025] The heat theft behavior identification method based on edge computing and vibration spectrum analysis proposed in this invention is applied to the application scenario of heat theft behavior identification in heating pipelines.
[0026] Currently, vibration signals are collected using heating monitoring terminals installed at the heating pipelines. The collected vibration signals are then transmitted to the cloud or monitoring center, which determines whether heat theft has occurred.
[0027] First, the monitoring terminal needs to continuously sample the monitoring data at high frequency and transmit the monitoring data, which consumes a lot of energy. Heating monitoring terminals usually rely on battery power and the industry requires maintenance-free operation for 5-8 years. They cannot continuously perform high-frequency sampling or upload raw data to the cloud for processing.
[0028] Secondly, heating pipe wells are mostly located underground or in metal environments, where communication conditions are poor, making it difficult to rely on cloud-based real-time analysis.
[0029] Furthermore, current analysis of monitoring data to determine whether heat theft has occurred relies solely on simple thresholds or single features, making it difficult to distinguish between environmental interference and actual heat theft, ultimately leading to inaccurate identification of heat theft.
[0030] Finally, the background vibration noise varies significantly at different monitoring points, and a fixed threshold is difficult to balance sensitivity and false alarm control, further increasing the false alarm rate of heat theft.
[0031] To achieve accurate identification of heat theft behavior and possess environmental adaptability and ultra-low power consumption characteristics, this invention proposes a heat theft behavior identification method based on edge computing and vibration spectrum analysis. This method can complete the identification locally at the monitoring terminal, adapt to different background noise in different installation environments, operate under extremely low power consumption conditions, and accurately distinguish between heat theft behavior and normal environmental disturbances.
[0032] like Figure 1 As shown in the embodiment of the present invention, the heat theft behavior identification method based on edge computing and vibration spectrum analysis includes: Acquire vibration signals from heating pipelines; When the sum vector energy of the vibration signal is greater than the upper limit of the wake-up threshold interval, the temporal features of the vibration signal are extracted. Based on the time-domain characteristics, determine whether the vibration signal is a continuous event signal; When the vibration signal is a continuous event signal, extract the frequency domain features of the vibration signal; Based on the frequency domain characteristics of the vibration signal, determine whether heat theft has occurred.
[0033] In this embodiment of the invention, vibration signals are acquired by a monitoring terminal installed at a monitoring point on the heating pipeline, and the vibration signals are analyzed at the monitoring terminal to determine whether heat theft has occurred.
[0034] To achieve low-power vibration monitoring, when the monitoring terminal is in a non-alarm state, a triaxial accelerometer is used to collect vibration signals from the environmental background at a low sampling rate.
[0035] The sum of squares of the vibration signal is taken as the square root to obtain the resultant vector energy of the vibration signal, which characterizes the overall intensity of the vibration signal.
[0036] When the sum vector energy of the vibration signal is greater than the upper limit of the wake-up threshold range, the monitoring terminal switches from the low-power listening state to the working state, and then acquires the vibration signal through the first sampling frequency. When the sum vector energy of the vibration signal is less than the lower limit of the wake-up threshold range, the monitoring terminal maintains a low-power listening state and subsequently acquires the vibration signal through the second sampling frequency.
[0037] The first sampling frequency is greater than the second sampling frequency.
[0038] In this embodiment of the invention, a wake-up threshold range is set for each monitoring point on the heating pipeline, and the wake-up threshold range is generated statistically based on the resultant vector energy distribution of the vibration signals collected during the normal operation of the pipeline in the early stage of installation.
[0039] In the actual process of identifying heat theft, after obtaining the vibration signal of the monitoring point, the wake-up threshold range set for the monitoring point is retrieved. Then, the vibration signal of the monitoring point is compared with the wake-up threshold range set for the monitoring point to determine whether to switch the working state of the monitoring terminal at the monitoring point.
[0040] In this embodiment of the invention, time-domain feature analysis of the vibration signal is performed only when the sum vector energy of the vibration signal is greater than the upper limit of the wake-up threshold range. The time-domain features of the vibration signal are extracted, including but not limited to energy attenuation characteristics, zero-crossing rate variation characteristics, and waveform kurtosis characteristics.
[0041] When the vibration signal is determined to have instantaneous impact characteristics based on the time domain characteristics and the energy attenuation of the vibration signal is greater than the set attenuation threshold, the vibration signal is determined to be a short-term physical interference signal. When the duration of the sum vector energy of the vibration signal being greater than the upper limit of the wake-up threshold range is greater than the set time threshold, and the vibration signal is not a short-term physical interference signal, the vibration signal is determined to be a continuous event signal.
[0042] Among them, when the vibration signal is determined to have instantaneous impact characteristics based on the time-domain characteristics of the vibration signal, it is determined that the vibration signal meets the three conditions of high peak value (strong non-Gaussianity), high waveform factor (strong suddenness) and rapid exponential decay.
[0043] This invention embodiment only performs frequency domain analysis on persistent event signals to extract frequency domain features of persistent event signals. The frequency domain features include, but are not limited to, the energy distribution features, spectral centroid features, and spectral stability features of vibration signals in different frequency bands.
[0044] Frequency domain analysis includes performing one or more of the following on the vibration signal: Fast Fourier Transform, Wavelet Transform, or other equivalent frequency domain transformations.
[0045] The frequency domain characteristics of vibration signals are identified by a behavioral feature model to determine whether heat theft has occurred. The behavioral feature model takes the frequency domain characteristics of the vibration signal as input and the presence or absence of heat theft as output, and is constructed using a neural network model.
[0046] In this embodiment of the invention, a behavioral feature model is set in advance in the monitoring terminal. The behavioral feature model is a model trained with training data, which includes the frequency domain features of vibration signals when heat theft occurs.
[0047] When heat theft is detected, an alarm is issued, including an audible and visual alarm from the monitoring terminal and an alarm message sent from the monitoring terminal to the management platform via the communication module.
[0048] The communication module uses narrowband IoT communication.
[0049] When it is determined that no heat theft has occurred, the monitoring terminal returns to the low-power listening state and resumes acquiring vibration signals through the second sampling frequency.
[0050] In addition, when heat theft is detected, the vibration signal and frequency domain characteristics of the vibration signal at the time of heat theft are uploaded to the management platform to update the behavior feature model, thereby realizing model optimization among multiple terminals.
[0051] like Figure 2 The figure shows the time-domain envelope and energy distribution diagram of the vibration signals corresponding to heat theft behavior and normal door opening inspection behavior. Figure 2 The two images on the left in the middle section are related to heat theft, while the two images on the right are related to normal door opening and inspection procedures. Figure 2The two images at the top and bottom are time-domain envelope diagrams of the vibration signal, while the two images at the bottom are energy distribution diagrams of the vibration signal.
[0052] The heat theft identification method based on edge computing and vibration spectrum analysis proposed in this invention involves a local monitoring terminal at the heating pipeline. Based on power consumption constraints, vibration signals are sampled, analyzed, and judged in a tiered manner. High-energy-consuming computations or communication operations are performed only when specific technical conditions are met. The tiered discrimination structure effectively filters environmental interference, significantly reducing the false alarm rate. High-energy-consuming analysis is triggered only when necessary, meeting ultra-low power consumption requirements and suitable for long-term battery power supply. Adaptive noise modeling avoids manual parameter tuning, adapting to complex installation environments. The terminal can complete local alarms in environments without or with weak networks, improving on-site response capabilities.
[0053] This invention also proposes a heat theft behavior identification system based on edge computing and vibration spectrum analysis, including: Vibration signal acquisition unit, used to acquire vibration signals of heating pipelines; The temporal feature extraction unit is used to extract the temporal features of the vibration signal when the sum vector energy of the vibration signal is greater than the upper limit of the wake-up threshold interval. The event determination unit is used to determine whether a vibration signal is a continuous event signal based on its time-domain characteristics. The behavior recognition unit is used to extract the frequency domain features of the vibration signal when the vibration signal is a continuous event signal; and to determine whether heat theft behavior has occurred based on the frequency domain features of the vibration signal.
[0054] It should be noted that the heat theft behavior identification system based on edge computing and vibration spectrum analysis provided in the above embodiments is only illustrated by the division of the above functional modules when identifying heat theft behavior. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the heat theft behavior identification system based on edge computing and vibration spectrum analysis provided in the above embodiments and the heat theft behavior identification method based on edge computing and vibration spectrum analysis belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.
[0055] The present invention also discloses a computer device, the device comprising: A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the heat theft behavior identification method based on edge computing and vibration spectrum analysis disclosed in the embodiments of the present invention.
[0056] The present invention also discloses a computer-readable storage medium storing a computer program adapted for loading and execution by a processor of the heat theft behavior identification method based on edge computing and vibration spectrum analysis disclosed in the embodiments of the present invention.
[0057] The present invention also discloses a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the heat theft behavior identification method based on edge computing and vibration spectrum analysis disclosed in the embodiments of the present invention.
[0058] The method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are omitted here.
[0059] Those skilled in the art will recognize that the units and algorithm steps described in conjunction with the embodiments herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0060] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.
Claims
1. A method for identifying heat theft behavior based on edge computing and vibration spectrum analysis, characterized in that, include: Acquire vibration signals from heating pipelines; When the sum vector energy of the vibration signal is greater than the upper limit of the wake-up threshold interval, the temporal features of the vibration signal are extracted. Based on the time-domain characteristics, determine whether the vibration signal is a continuous event signal; When the vibration signal is a continuous event signal, extract the frequency domain features of the vibration signal; Based on the frequency domain characteristics of the vibration signal, determine whether heat theft has occurred.
2. The heat theft behavior identification method based on edge computing and vibration spectrum analysis as described in claim 1, characterized in that, When the sum vector energy of the vibration signal is greater than the upper limit of the wake-up threshold range, the vibration signal is subsequently acquired by sampling at the first sampling frequency. When the sum vector energy of the vibration signal is less than the lower limit of the wake-up threshold range, the vibration signal is subsequently acquired through a second sampling frequency.
3. The heat theft behavior identification method based on edge computing and vibration spectrum analysis as described in claim 2, characterized in that, The first sampling frequency is greater than the second sampling frequency.
4. The heat theft behavior identification method based on edge computing and vibration spectrum analysis as described in claim 1, characterized in that, When the vibration signal is determined to have instantaneous impact characteristics based on the time domain characteristics and the energy attenuation of the vibration signal is greater than the set attenuation threshold, the vibration signal is determined to be a short-term physical interference signal. When the duration of the sum vector energy of the vibration signal being greater than the upper limit of the wake-up threshold range is greater than the set time threshold, and the vibration signal is not a short-term physical interference signal, the vibration signal is determined to be a continuous event signal.
5. The heat theft behavior identification method based on edge computing and vibration spectrum analysis as described in claim 1, characterized in that, The frequency domain characteristics of vibration signals are identified by a behavioral feature model to determine whether heat theft has occurred. The behavioral feature model takes the frequency domain characteristics of the vibration signal as input and the presence or absence of heat theft as output, and is constructed using a neural network model.
6. The heat theft behavior identification method based on edge computing and vibration spectrum analysis as described in claim 1, characterized in that, An alarm will be issued when heat theft is detected.
7. A heat theft behavior identification system based on edge computing and vibration spectrum analysis, characterized in that, include: Vibration signal acquisition unit, used to acquire vibration signals of heating pipelines; The temporal feature extraction unit is used to extract the temporal features of the vibration signal when the sum vector energy of the vibration signal is greater than the upper limit of the wake-up threshold interval. The event determination unit is used to determine whether a vibration signal is a continuous event signal based on its time-domain characteristics. The behavior recognition unit is used to extract the frequency domain features of the vibration signal when the vibration signal is a continuous event signal; and to determine whether heat theft behavior has occurred based on the frequency domain features of the vibration signal.
8. An electronic device, characterized in that, The device includes: A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the heat theft behavior identification method based on edge computing and vibration spectrum analysis as described in any one of claims 1-6.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted for loading and execution by a processor of the heat theft behavior identification method based on edge computing and vibration spectrum analysis as described in any one of claims 1-6.
10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the heat theft behavior identification method based on edge computing and vibration spectrum analysis as described in any one of claims 1-6.