A temporary power supply intelligent management system

Abstract

This invention discloses a temporary power supply intelligent management system, specifically relating to the field of data link management technology. It includes a power anomaly detection module, a local control module, a multi-mode communication module, a communication status detection module, a self-healing control module, and a centralized management platform. The system monitors power supply status in real time, identifies overload, short circuit, and leakage anomalies, and executes local control. Through the multi-mode communication module, it dynamically switches between wired and wireless links, and implements self-healing control and transmission power regulation based on link status assessment to ensure communication stability. The centralized management platform enables data interaction and log recording, improving the intelligent management level of the power supply system. This invention employs a comprehensive link quality assessment model combining dynamic stability index and channel health index to achieve refined analysis and dynamic assessment of communication link status, accurately identifying the current health status of the communication link and ensuring the continuity of the communication link and the real-time nature of data transmission.

Description

Technical Field

[0001] This invention relates to the field of data link management technology, and more specifically, to a temporary power supply intelligent management system. Background Technology

[0002] With the acceleration of infrastructure construction, the increase in emergency power supply demand, and the widespread use of temporary power supply scenarios such as large-scale exhibitions and construction projects, the demand for intelligent management and safety control of temporary power supply systems is growing. Existing temporary power supply management systems typically rely on wireless communication to achieve centralized monitoring and control of power terminals. In temporary power supply applications such as temporary construction, power exhibitions, and emergency power supply, temporary power supply systems usually use wireless communication technology to achieve remote monitoring and centralized control of each power terminal. However, due to the presence of numerous high-frequency electromagnetic interference devices in these environments, wireless communication links often experience problems such as signal attenuation, frequent interference, or complete interruption. In this situation, traditional temporary power supply management systems often rely on a single communication link and fixed communication strategies, lacking real-time assessment of link status and intelligent self-healing control capabilities. Once the communication link fails, the system will be unable to effectively monitor the status of power terminals, making it difficult to promptly detect and handle abnormal power supply conditions such as overload, short circuit, and leakage, thereby increasing the risk of equipment damage, electrical fires, and even personal injury accidents. Furthermore, existing temporary power supply systems have limited means of adjusting the transmission power of the communication link, failing to dynamically adapt to environmental changes, resulting in insufficient communication link stability and low energy utilization efficiency.

[0003] Therefore, there is an urgent need to develop a temporary power intelligent management system with the capabilities of dynamic switching of multi-mode communication links, comprehensive evaluation of link status, and self-healing regulation of communication links, so as to effectively improve communication stability and power supply security in temporary power environments. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A temporary power supply intelligent management system includes a power consumption anomaly detection module, a local control module, a multi-mode communication module, a communication status detection module, a self-healing control module, and a centralized management platform;

[0006] The power consumption anomaly detection module monitors the operating power parameters of each power terminal node in real time, detects abnormal states based on the built-in fault detection model, and transmits the abnormal state information to the local control unit and the centralized management platform.

[0007] The local control module consists of local control units deployed at each power terminal node. During communication anomalies, the local control units autonomously execute the corresponding local control strategies.

[0008] The multi-mode communication module is used to realize dynamic switching between wired communication and various wireless communication. It judges the feasibility of the current communication mode based on the link status, and when a communication anomaly is detected, it executes the communication link switching operation of the self-healing control module to automatically switch to the link with the best quality or the wired link to ensure the continuity and stability of the communication link.

[0009] The communication status detection module is used to collect real-time data of the communication link, evaluate the status of the communication link, and transmit the evaluation results to the self-healing control module.

[0010] The self-healing control module is used to perform a communication link switching operation when the current communication link status is abnormal, and to start a dynamic control mechanism for the transmission power of the abnormal link to improve its communication quality.

[0011] The centralized management platform interacts with each power terminal node through a multi-mode communication module and logs the interaction data.

[0012] In a preferred embodiment, wireless communication includes Wi-Fi, LoRa, and ZigBee communication.

[0013] In a preferred embodiment, abnormal state detection is divided into three categories: overload, short circuit, and leakage current. Each category corresponds to a preset control strategy in the local control strategy, which respectively realizes local detection and protection against overload, short circuit, and leakage current.

[0014] In a preferred embodiment, the link state is determined through the following steps:

[0015] Within a preset time window, a method combining acceleration rate of change analysis and Fourier frequency fluctuation energy is used to perform dual analysis in both time and frequency domains, generating a dynamic stability index for evaluating the timing stability of the link.

[0016] A method combining nonlinear power-law averaging and Gaussian function harmonics is employed to emphasize overall communication quality performance and generate a channel health index for evaluating the overall quality and health of the link.

[0017] The dynamic stability index and the channel health index are input together into the pre-trained comprehensive link quality evaluation model, and the comprehensive index value of the communication link status is output.

[0018] The current link status is determined based on the comprehensive index value of the communication link status to determine whether the current link status is in an abnormal communication state.

[0019] In a preferred embodiment, the logic for obtaining the dynamic stability index is as follows:

[0020] Within a preset time window, acquire the received signal strength at time t. Calculate the first-order difference of the received signal strength, i.e., the rate of change: ; The sampling time interval, This represents the rate of change of the received signal strength at time point i. and These represent the received signal strength at time points i and i-1, respectively;

[0021] Calculate the second-order difference of the received signal strength, i.e., the changing acceleration: ; This represents the rate of change of the received signal strength at time point i-1. This represents the acceleration of the change in received signal strength at time point i;

[0022] Calculate the root mean square value of the changing acceleration : N represents the number of sampling points;

[0023] Perform a Fast Fourier Transform on the time series of the received signal strength:

[0024] ; ; Indicates the time point as The signal frequency domain components at that time;

[0025] Calculate the energy components of the high-frequency part, i.e., the energy of frequency fluctuations. : Where K is the frequency boundary point, usually N / 2, retaining only the high-frequency fluctuation part;

[0026] Calculate the dynamic stability index: ; It is a dynamic stability index. and All are preset non-zero scaling factors.

[0027] In a preferred embodiment, the channel health index acquisition logic is as follows:

[0028] Within a preset time window, the bit error rate is... Data latency and packet loss rate All were normalized: ; Let X be the value of parameter X after normalization. Let X be the initial value of parameter X. and These are the minimum and maximum values ​​of the standardized range corresponding to parameter X, where parameter X is the bit error rate, data latency, or packet loss rate.

[0029] Construct a Gaussian function for health status to suppress the influence of outliers:

[0030] ; This represents the Gaussian function output value corresponding to the health status of parameter X. This represents the median within the time window corresponding to parameter X. This represents the average value within the time window corresponding to parameter X. This represents the standard deviation within the time window corresponding to parameter X;

[0031] Calculated using the power-law harmonic mean:

[0032] ; To preset the sensitivity factor used to control the power factor, This is the channel health index.

[0033] The comprehensive link quality assessment model refers to:

[0034] ; and All are preset non-zero weight coefficients, and their sum is one. This is a comprehensive indicator value for the communication link status.

[0035] In a preferred embodiment, determining the feasibility of the current communication method based on the link status means:

[0036] The comprehensive indicator value of the communication link status is compared with the preset usage threshold. When the comprehensive indicator value of the communication link status is greater than or equal to the preset usage threshold, the current communication mode is normal. When the comprehensive indicator value of the communication link status is less than the preset usage threshold, the current communication mode is abnormal.

[0037] In a preferred embodiment, when the communication type of wireless communication is abnormal, it is removed from the candidate set for the next round of link switching. After recovery, it becomes a candidate for the next round of link switching and enters the candidate set for the next round of link switching. The candidate set for the next round of link switching is composed of wireless links built by wireless communication types with normal communication and wired links built by wired communication.

[0038] In a preferred embodiment, initiating a dynamic transmit power control mechanism for abnormal links refers to:

[0039] ; This represents the actual transmit power of the node. This represents the node's minimum transmit power. This represents the node's maximum transmit power. The distance between the node and the receiver. To the maximum permissible communication distance, The path loss coefficient, which is dynamically calculated, satisfies the following calculation formula:

[0040] ;

[0041] This is the theoretical minimum path loss coefficient. This is the theoretical maximum path loss coefficient. The minimum acceptable link state metrics, This represents the highest link status indicator under ideal conditions.

[0042] The technical effects and advantages of this invention are as follows:

[0043] This invention introduces a multi-mode communication module, combining wired communication with various wireless communication methods (including Wi-Fi, LoRa, and ZigBee), to achieve dynamic switching and intelligent scheduling of communication links, significantly improving the reliability and stability of system communication links in temporary power supply environments. Through a communication status detection module that monitors link status in real time, the system can accurately identify the current health status of the communication link. In the event of a link anomaly, a self-healing control module automatically switches to the best-quality alternative wireless or wired link, ensuring the continuity of communication and the real-time nature of data transmission. This design solves the problem of insufficient stability of wireless communication links in existing temporary power supply systems, effectively avoiding the risk of uncontrolled power terminals due to communication interruptions, and improving the communication stability and overall reliability of temporary power supply systems in complex electromagnetic environments.

[0044] This invention employs a comprehensive link quality assessment model combining a dynamic stability index and a channel health index to achieve refined analysis and dynamic evaluation of communication link status. By combining the acceleration change rate from time-domain analysis with the Fourier frequency fluctuation energy from frequency-domain analysis, the system can comprehensively measure the temporal volatility and stability of the link. Simultaneously, the channel health index, based on a nonlinear power-law averaging and Gaussian function harmonic method, assesses the overall link quality performance and effectively identifies potential link vulnerabilities. This model outputs a comprehensive communication link status index (QLS), providing precise data for link switching decisions, self-healing control, and dynamic transmission power adjustment. This significantly improves the system's proactive response capability to link anomalies, enabling intelligent and efficient management of communication links in temporary power supply systems.

[0045] This invention utilizes a dynamic transmission power adjustment mechanism within a self-healing control module, based on dynamically calculated path loss coefficients, to achieve intelligent optimization and energy balance of wireless communication link transmission power. The system dynamically adjusts the path loss coefficient according to the comprehensive link status index value and accurately calculates the actual transmission power of nodes, effectively reducing communication energy consumption while ensuring link communication quality. The transmission power adjustment algorithm avoids electromagnetic interference and energy waste caused by unreasonable power configuration, improving the stability of wireless communication and the overall energy utilization efficiency of the system. This mechanism enhances the self-healing capability and dynamic adaptability of wireless communication links, enabling temporary power systems to achieve dual optimization of communication and energy management in complex environments, improving the system's sustainable operation and economic efficiency. Attached Figure Description

[0046] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0047] Figure 1 This is a schematic diagram of a temporary power intelligent management system according to the present invention. Detailed Implementation

[0048] 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 some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0049] Reference Figure 1 The following examples were obtained:

[0050] Example 1: With the accelerated development of urban infrastructure and the frequent occurrence of temporary large-scale exhibitions and emergency disaster relief activities, the demand for the construction and management of temporary power systems is constantly increasing. Existing temporary power systems generally rely on wireless communication technologies (such as Wi-Fi, LoRa, ZigBee, etc.) to achieve status monitoring and centralized control of each power terminal node. However, in complex environments such as construction sites, power exhibitions, and emergency power supply, there are usually many sources of electromagnetic interference, such as high-power welding machines, frequency converters, and high-frequency wireless transmitters. These interference sources cause frequent interruptions of wireless communication links or a severe degradation in communication quality. Furthermore, existing temporary power systems mostly rely on a single communication link or static communication strategy, lacking real-time assessment and self-healing capabilities for the communication link status. Once a communication anomaly occurs, the system cannot switch or repair the communication link in a timely and effective manner, affecting real-time monitoring of power status and anomaly handling, thus leading to significant safety hazards.

[0051] Existing temporary power management systems rely on centralized control commands to handle power anomalies (such as overload, short circuit, and leakage). If the communication link malfunctions or is interrupted, these anomalies may not be controlled or addressed in a timely and effective manner. Furthermore, in systems with multiple communication modes, a robust dynamic assessment and switching mechanism for link quality has not been established, and intelligent control of communication link transmission power is lacking. Consequently, the overall communication reliability and energy efficiency management of the system are limited, failing to meet the demands of high-reliability and high-stability temporary power environments.

[0052] This invention aims to solve the technical problems of existing temporary power management systems, such as unstable communication links, lack of self-healing capabilities, delayed response to power anomalies, and unreasonable energy management in complex electromagnetic environments. By constructing a temporary power intelligent management system that integrates power anomaly detection, local intelligent control, multi-mode communication, dynamic communication status assessment, self-healing control, and centralized management, efficient management and real-time protection of power terminal nodes in temporary power environments are achieved.

[0053] This invention collects link status data in real time through a communication status detection module, combines dynamic stability index and channel health index, and dynamically evaluates the communication link status based on a comprehensive link quality assessment model to ensure timely detection of communication anomalies. The system uses a self-healing control module to quickly switch to the best-quality wireless or wired link when a communication anomaly occurs, and dynamically adjusts the transmit power and path loss coefficient of the abnormal link to improve communication link recovery efficiency and stability. Furthermore, this invention enables real-time local detection and response to power anomalies such as overload, short circuit, and leakage through the collaborative work of a power consumption anomaly detection module and a local control module, ensuring that the power system retains local protection capabilities even when communication links are faulty. A centralized management platform provides real-time monitoring and optimized scheduling of the entire system status, further optimizing the overall communication links and energy consumption of the temporary power system, improving the system's intelligent management level and operational reliability.

[0054] A temporary power supply intelligent management system includes a power consumption anomaly detection module, a local control module, a multi-mode communication module, a communication status detection module, a self-healing control module, and a centralized management platform;

[0055] The power consumption anomaly detection module monitors the operating power parameters of each power terminal node in real time. Based on a built-in fault detection model, it detects abnormal states and transmits the abnormal state information to the local control unit and the centralized management platform. This module serves as the first line of defense for system safety, with its core function being the real-time and continuous monitoring of electrical parameters for each power terminal node. By collecting and analyzing key power parameters such as current, voltage, and power, it quickly identifies abnormal operating conditions based on the built-in fault detection model. It promptly detects and judges electrical anomalies such as overload, short circuit, and leakage, and reports the anomaly information to the local control module and the centralized management platform immediately, ensuring the power supply system's safety and providing real-time basic data for local control decisions and centralized management scheduling.

[0056] The local control module consists of local control units deployed at each power-consuming terminal node. These local control units autonomously execute corresponding local control strategies during communication anomalies. The local control module is the core emergency self-protection unit of the system in the event of communication link failure or instability, ensuring that terminal nodes possess "autonomous decision-making" capabilities and can handle power anomalies without relying on external network communication commands. When communication is interrupted, terminal nodes autonomously execute corresponding protection measures according to pre-set local control strategies, achieving localized power-off protection or current limiting operations for overload, short circuit, and leakage, reducing safety hazards caused by communication failures, enhancing the system's fault self-healing and robustness, and ensuring continuous and safe power supply capabilities in temporary power environments.

[0057] The multi-mode communication module enables dynamic switching between wired and various wireless communication methods. It assesses the feasibility of the current communication mode based on link status and, upon detecting a communication anomaly, executes a self-healing control module to automatically switch to the best-quality link or a wired link, ensuring the continuity and stability of the communication link. The multi-mode communication module achieves seamless switching and collaborative operation between different communication modes (wired + wireless), serving as a crucial hub for system information transmission. It addresses the vulnerability of wireless links to interference and signal interruptions in temporary power supply environments, providing multi-path redundancy and communication link reliability guarantees. By constructing a flexible communication network through wired and wireless communication (Wi-Fi, LoRa, ZigBee), it dynamically selects the optimal communication path based on the communication link status, ensuring continuous and stable data transmission. Upon detecting a communication anomaly, it automatically executes a link switching operation, improving the system's adaptive communication capabilities and establishing a highly reliable, low-latency data interaction guarantee between key nodes and devices.

[0058] The communication status detection module collects real-time data from the communication link and assesses its status, transmitting the results to the self-healing control module. This module serves as the central hub for monitoring the system's communication link quality, focusing on real-time analysis of the link's operational status and comprehensively measuring the stability and reliability of the communication network. It collects real-time communication link status data, such as signal strength index (RSSI), bit error rate (BER), and delay (DLY), generating dynamic stability and channel health indices. These indices are then input into the comprehensive link quality assessment model, providing the self-healing control module with a basis for communication status assessment and determining whether link switching or adjustment is necessary. Through precise monitoring, the module improves the communication system's ability to perceive and respond to environmental changes.

[0059] The self-healing control module is used to perform communication link switching operations when the current communication link status is abnormal, and to initiate a dynamic transmission power adjustment mechanism for the abnormal link to improve its communication quality. The self-healing control module is the system's self-repair decision-making and execution unit, responsible for quickly making link switching and optimization decisions after detecting a communication link abnormality, and is the core intelligent scheduling part of the system. When the current communication link status is abnormal, it quickly executes communication link switching to ensure link continuity; it performs dynamic transmission power adjustment for the abnormal link, dynamically adjusting the path loss coefficient and transmission power based on the comprehensive link status index value to improve link recovery capability; it manages the link switching candidate set, dynamically adding and deleting communication links to ensure the real-time optimality of the candidate link set, guaranteeing the high availability and high reliability of the temporary power supply system in complex environments.

[0060] The centralized management platform interacts with each power-consuming terminal node through a multi-mode communication module and logs the interaction data. As the system's global control center and data management hub, the platform communicates with all power-consuming terminal nodes to achieve unified monitoring, command issuance, and historical data management of the temporary power system. It uniformly receives and manages the operational status data and communication link status of each terminal node, performs system-level power dispatching, alarm management, and fault analysis, stores and logs the interaction data, providing data support for system optimization and fault tracing, and enhancing the system's centralized visual management capabilities and intelligent collaborative capabilities for large-scale temporary power systems.

[0061] Wireless communication includes Wi-Fi, LoRa, and ZigBee. Wi-Fi is a wireless local area network (WLAN) communication technology based on the IEEE 802.11 standard, primarily used for short-range, high-speed data transmission. Wi-Fi boasts high bandwidth and is commonly used for high-speed communication between terminal devices, with typical transmission rates reaching hundreds of Mbps and communication distances typically within tens of meters, depending on the environment. Wi-Fi devices support adjusting transmit power to alter signal coverage and penetration. Increasing transmit power expands the effective coverage area and improves the receiver's signal strength (RSSI), thereby reducing packet loss and bit error rates during data transmission and improving the quality and stability of the communication link. Furthermore, in environments with multiple devices coexisting, dynamically reducing transmit power can reduce channel interference and collisions, improving overall system communication efficiency and stability. The common range for dynamic Wi-Fi transmit power adjustment is typically between 1 dBm and 20 dBm, and the system can flexibly adjust based on real-time link status.

[0062] LoRa is a long-range, low-power wireless communication technology, belonging to the LPWAN (Low Power Wide Area Network) category, and employs spread spectrum modulation. LoRa communication boasts strong anti-interference capabilities and a transmission distance of several kilometers, making it suitable for scenarios requiring low power consumption and low data rates, such as IoT devices, remote monitoring, and control applications. In LoRa communication systems, transmit power directly affects signal coverage and link quality. Increasing transmit power enhances signal strength for long-distance communication, improves obstacle penetration, increases the signal-to-noise ratio (SNR) at the receiver, reduces the data error rate, and thus improves link stability and reliability. LoRa devices typically support a transmit power adjustment range from -4dBm to +20dBm.

[0063] ZigBee is a short-range, low-power wireless communication technology based on the IEEE 802.15.4 standard, widely used in wireless sensor networks, smart homes, industrial control, and other fields. ZigBee has a relatively low communication rate, typically 250kbps, and a communication distance generally between 10 and 100 meters, but it features excellent low power consumption and mesh networking capabilities, making it suitable for large-scale collaborative communication between devices. ZigBee devices can optimize network topology and communication quality by adjusting their transmit power. Increasing transmit power can extend the communication range of nodes, enhance the reliability of links between long-distance nodes, increase the success rate of data transmission, and reduce the number of retransmissions, thereby improving the overall network performance. The typical transmit power adjustment range for ZigBee is -25dBm to +5dBm.

[0064] Abnormal state detection is divided into three categories: overload, short circuit, and leakage current. Each category corresponds to a preset control strategy in the local control strategy, realizing local detection and protection against overload, short circuit, and leakage current respectively. In the temporary power intelligent management system of this invention, the power anomaly detection module realizes real-time detection of the three types of abnormal states—overload, short circuit, and leakage current—through a built-in fault detection model. This model achieves accurate identification of abnormal states by comprehensively analyzing and extracting features from real-time power parameters (such as current, voltage, power, leakage current, etc.) collected from power terminal nodes, combined with multi-dimensional threshold judgment algorithms and trend prediction algorithms.

[0065] Overload detection is implemented as follows: The system collects load current, voltage, and active power values ​​in real time at the power terminal node and compares and analyzes them against preset load rated parameters. The fault detection model identifies overload through the following steps: Comparing the current value with the rated current value. If the current value continuously exceeds a preset percentage of the rated current (e.g., 110%) for a certain time window (e.g., more than 3 seconds), it is determined to be an overload risk. Voltage data is combined to determine whether there is a voltage drop in the power grid, eliminating false judgments caused by unstable power supply voltage. The current change trend within the sampling time window is utilized, and short-term fluctuations caused by instantaneous load changes are eliminated through moving average filtering and abrupt change detection algorithms, ensuring the stability and accuracy of the detection results.

[0066] Short-circuit detection is implemented by the system acquiring real-time loop current and voltage data and performing detection based on a current surge analysis model. The specific process is as follows: When a sudden, large-amplitude current surge signal (several times the rated value) occurs and its duration is extremely short (milliseconds), the system determines it to be a short circuit. Simultaneously, it detects whether the voltage drops sharply or falls to near zero to aid in confirming the authenticity of the short circuit. The detection model uses a current slope calculation algorithm to determine the rate of change of the current rise edge, screening for short-circuit signals caused by rapid current increases.

[0067] The leakage current detection system works by monitoring the residual current (the deviation between the sum of the phase current and the neutral current) in real time using a leakage current detection module, and then making a judgment based on the residual current protection model. The specific process includes: real-time measurement of the three-phase current and neutral current; calculation of the residual current value using a residual current vector synthesis algorithm; and identification of a leakage current phenomenon when the residual current value exceeds a set threshold (e.g., 30mA or less) and the duration exceeds the set action time (e.g., 0.03 seconds). Simultaneously, the dynamic trend of leakage current changes is evaluated to avoid misjudgments caused by transient electromagnetic interference or sudden grounding interference.

[0068] Taking overload detection as an example, after detecting an abnormal overload state at the power terminal, the system executes the following preset control strategy through the local control unit to ensure terminal safety and system stability. Control strategy flow: Local early warning: When an initial overload state is detected (the current value exceeds the rated value but has not yet reached the severe overload standard), the system alerts on-site personnel to the load status through the local audible and visual alarm module.

[0069] Tiered protection response: First stage: When the overload condition lasts for no more than a preset value (e.g., within 3 seconds), the system enters a delayed protection state, waiting for the load fluctuation to return to normal, avoiding false triggering due to instantaneous load changes. Second stage: If the overload condition continues to exceed the timeout, the system immediately executes local control commands to cut off the power supply to the corresponding circuit, preventing cable temperature rise, insulation aging, or even fire hazards caused by prolonged overload.

[0070] Automatic recording and reporting: The local control unit simultaneously records parameters such as the time of overload event occurrence, current, and voltage. After the communication link is restored, the event log is synchronously uploaded to the centralized management platform, enabling post-event traceability and system optimization analysis. The system can restart the power circuit after local manual reset or remote command reset, avoiding unnecessary prolonged power outages.

[0071] Link status is determined through the following steps:

[0072] Within a preset time window, a method combining acceleration rate of change analysis and Fourier frequency fluctuation energy is used to perform dual analysis in both time and frequency domains, generating a dynamic stability index for evaluating the timing stability of the link.

[0073] A method combining nonlinear power-law averaging and Gaussian function harmonics is employed to emphasize overall communication quality performance and generate a channel health index for evaluating the overall quality and health of the link.

[0074] The dynamic stability index and the channel health index are input together into the pre-trained comprehensive link quality evaluation model, and the comprehensive index value of the communication link status is output.

[0075] The current link status is determined based on the comprehensive index value of the communication link status to determine whether the current link status is in an abnormal communication state.

[0076] In temporary power intelligent management systems, the stability of communication links directly affects the system's real-time monitoring and anomaly response capabilities for power-consuming terminals. Due to the complex and variable nature of temporary power environments, communication links are highly susceptible to electromagnetic interference, obstructions, and other factors, leading to link instability. Therefore, relying solely on a single indicator (such as RSSI signal strength) or sampling data at a specific moment for link quality assessment is prone to misjudgment or response delays due to the volatility and randomness of link status. To address this, this invention proposes a dual analysis method combining acceleration rate of change analysis and Fourier frequency fluctuation energy. This method comprehensively evaluates link status from both time and frequency domains, generating a Link Dynamic Stability Index (DSI) for more accurate and dynamic link status assessment.

[0077] By receiving RSSI data at sampling nodes, the first-order difference of RSSI (i.e., the rate of change) is calculated, reflecting how quickly the signal strength changes over time. Then, the second-order difference of RSSI (i.e., the acceleration of change) is calculated, reflecting the drastic nature of the rate of change in signal strength. Finally, the root mean square (RMS) value of the acceleration is calculated to quantify the fluctuation amplitude of the link over time. If the communication link is subjected to temporary interference, although the average RSSI may not necessarily decrease, the "speed" and "acceleration" of signal change will significantly increase. High acceleration indicates drastic signal changes and high link instability. Compared to simple signal strength mean analysis, acceleration RMS can more sensitively detect sudden interference or fluctuations, providing early warning of potential link anomalies.

[0078] The time-series data of the acquired RSSI signal strength are subjected to Fast Fourier Transform (FFT) to convert the time-domain signal into a frequency-domain signal. The high-frequency components are analyzed in detail (typically with the frequency boundary K being half or more of the total number of sampling points N), calculating the fluctuation energy in the high-frequency portion. Higher high-frequency energy indicates more severe signal fluctuations within a short period, and poorer link stability. Frequency domain analysis reveals the inherent periodicity and frequent changes in link state fluctuations; low-frequency components represent signal trends, while high-frequency components highlight frequent jitter. High high-frequency energy usually indicates that the link is in a disturbed or unstable state. Time-domain analysis mainly reflects the overall amplitude of signal changes, while frequency domain analysis reveals signal jitter and periodic noise. Combining the two provides a more complete assessment of link health.

[0079] The detailed logic for obtaining the dynamic stability index is as follows:

[0080] Within a preset time window, acquire the received signal strength at time t. Calculate the first-order difference of the received signal strength, i.e., the rate of change: ; The sampling time interval, This represents the rate of change of the received signal strength at time point i. and These represent the received signal strength at time points i and i-1, respectively;

[0081] Calculate the second-order difference of the received signal strength, i.e., the changing acceleration: ; This represents the rate of change of the received signal strength at time point i-1. This represents the acceleration of the change in received signal strength at time point i;

[0082] Calculate the root mean square value of the changing acceleration : N represents the number of sampling points;

[0083] Perform a Fast Fourier Transform on the time series of the received signal strength:

[0084] ; ; Indicates the time point as The signal frequency domain components at that time;

[0085] Calculate the energy components of the high-frequency part, i.e., the energy of frequency fluctuations. : Where K is the frequency boundary point, usually N / 2, retaining only the high-frequency fluctuation part;

[0086] Calculate the dynamic stability index: ; It is a dynamic stability index. and All are preset non-zero scaling factors, used to flexibly balance the importance of the time domain and frequency domain under different environments. The dynamic stability index is calculated based on the root mean square value of acceleration and the high-frequency fluctuation energy in the frequency domain. It can effectively measure the severity of link state fluctuations and provide a reliable link state evaluation basis for communication link self-healing control and dynamic control of transmission power, thereby improving the communication stability and system response capability of the system in complex temporary power supply environments.

[0087] In temporary power intelligent management systems, the health status of communication links directly affects the system's real-time control of power-consuming terminals and its ability to respond quickly to power outages. However, link status is often influenced by a combination of factors, such as bit error rate (BER), delay (DLY), and packet loss rate (PLR). These three indicators represent the reliability, timeliness, and stability of link communication. Existing solutions typically use a linear weighted average method to comprehensively evaluate these parameters, but this method is sensitive to outliers and cannot effectively eliminate the interference of individual abnormal indicators on the overall link status evaluation results. Therefore, this invention designs a channel health index generation method based on nonlinear power-law averaging and Gaussian function harmonics, which more comprehensively, stably, and accurately reflects the overall health status of the communication link.

[0088] First, the various communication link state parameters (BER, DLY, PLR) are normalized to map data of different dimensions and magnitudes to a unified interval (0-1), avoiding the impact of magnitude differences between different parameters on subsequent calculations. The unified data is more convenient for subsequent processing with Gaussian functions and power-law averages, improving the model's computational efficiency and consistency.

[0089] Applying a Gaussian function to the normalized parameters, the Gaussian function, a bell-shaped curve, ensures that the output of outliers deviates further from the expected value, thus reducing the interference of extreme values ​​on the overall system evaluation. Even if a single indicator experiences short-term drastic fluctuations, the Gaussian function prevents this anomaly from dominating the overall health assessment, improving the system's tolerance to occasional disturbances. The Gaussian function's inherent smoothness prevents abrupt changes and enhances the continuity of link status evaluation.

[0090] The Gaussian-processed indicators are then subjected to a nonlinear power-law harmonic average. A preset sensitivity factor determines the system's sensitivity to poorer indicators. When the value is greater than 1, the overall result is more sensitive to components with smaller values ​​(i.e., poorer individual indicators), highlighting weak links in the link. When the value is less than 1, the overall result focuses more on high-scoring items, emphasizing the overall system performance. Flexible adjustment of the sensitivity factor value allows for personalized optimization of link health assessment according to application requirements. Traditional weighted average methods are easily "masked" by some good indicators, while power-law averaging ensures that deterioration in individual indicators is significantly reflected in the overall health assessment, improving the system's early warning capabilities.

[0091] The combination of Gaussian function and power-law averaging optimizes link evaluation results. Gaussian function smooths and suppresses outliers, preventing noise interference from affecting the stability of link evaluation. Power-law averaging highlights link weaknesses, preventing weak links from being masked by overall performance. This dual mechanism ensures that the system can accurately reflect link health even in complex environments with large fluctuations in communication status. Through dynamic adjustment of sensitivity factor and Gaussian distribution parameters, the system can adaptively adjust detection sensitivity according to different environments and link types (Wi-Fi, LoRa, ZigBee). It has good scalability and adaptability when applied to various complex temporary power environments such as construction sites (strong electromagnetic interference), power exhibitions (dense equipment), and emergency power supply (variable links).

[0092] The specific logic for obtaining the channel health index is as follows:

[0093] Within a preset time window, the bit error rate is... Data latency and packet loss rate All were normalized: ; Let X be the value of parameter X after normalization. Let X be the initial value of parameter X. and These are the minimum and maximum values ​​of the standardized range corresponding to parameter X, where parameter X is the bit error rate, data latency, or packet loss rate.

[0094] Construct a Gaussian function for health status to suppress the influence of outliers:

[0095] ; This represents the Gaussian function output value corresponding to the health status of parameter X. This represents the median within the time window corresponding to parameter X. This represents the average value within the time window corresponding to parameter X. This represents the standard deviation within the time window corresponding to parameter X;

[0096] Calculated using the power-law harmonic mean:

[0097] ; To preset the sensitivity factor used to control the power factor, As a channel health index, it comprehensively measures the health status of the link in complex environments. It can not only accurately reveal potential communication quality hazards of the link, but also has the functions of suppressing abnormal fluctuations and strengthening the identification of weak links. It provides a reliable basis for the system to perform link switching, transmission power regulation and self-healing optimization, thereby ensuring the communication continuity and security of temporary power supply systems in harsh working conditions.

[0098] The comprehensive link quality assessment model refers to:

[0099] ; and All are preset non-zero weight coefficients, and their sum is one. The Link Quality Index (QLS) is a comprehensive indicator of the communication link status. The link quality comprehensive assessment model analyzes the dynamic stability index and channel health index to output the QLS, which comprehensively reflects the overall performance and stability of the current communication link. This index accurately assesses the communication reliability and service quality of the link in complex environments, serving as a core basis for the system to perform link switching, self-healing control, and dynamic transmission power adjustment, ensuring that temporary power systems maintain efficient and stable communication capabilities even in high-interference and high-risk environments.

[0100] Judging the feasibility of the current communication method based on link status refers to comparing the comprehensive index value of the communication link status with a preset usage threshold. When the comprehensive index value is greater than or equal to the preset threshold, the current communication method is normal; when the comprehensive index value is less than the preset threshold, the current communication method is abnormal. Judging the feasibility of the current communication method based on link status dynamically assesses whether the current communication method has stable and reliable operating capabilities by comparing the comprehensive index value of the communication link status (QLS) with the system's preset usage threshold. This judgment mechanism ensures that the system can perceive the health status of the communication link in real time, promptly detect and determine communication anomalies, and trigger measures such as link switching and self-healing optimization. This ensures the continuity of communication and the real-time effective transmission of control commands in the temporary power supply system under changing and complex environments, improving the overall system stability and security.

[0101] When a wireless communication type is deemed unavailable, it is removed from the candidate set for the next round of link switching. Upon recovery, it becomes a candidate for the next round of link switching and enters the candidate set. The candidate set for the next round of link switching consists of wireless links established by wireless communication types with normal communication and wired links established by wired communication. By dynamically maintaining and managing the candidate set for link switching, the system achieves real-time optimization and filtering of communication link health status. When a wireless communication type is identified as unavailable due to link anomalies, the system immediately removes it from the candidate set for the next round of link switching to avoid exacerbating communication failures or link recovery failures due to repeated selection of abnormal links. When the link status of that communication type recovers, it can be reinstated into the candidate set as an alternative path for subsequent link switching. Through this mechanism, the system ensures that each link switching decision is based solely on currently functioning wireless and wired links, effectively improving the accuracy of link switching and the reliability of system communication, achieving efficient link management and communication assurance for temporary power systems in complex and dynamic environments.

[0102] It should be noted that wired communication links (such as Ethernet and fiber optics) typically possess characteristics such as high bandwidth, low latency, and strong anti-interference capabilities. In temporary power systems, wired links are usually deployed on relatively stable and core communication nodes, making them less susceptible to environmental interference. Due to their high stability, frequent fluctuations and interruptions in wireless links are generally avoided. Wired links serve as backup and core backbone links in the system; when wireless links experience widespread failures due to environmental interference, wired links ensure that the system's basic communication and control capabilities remain unaffected. Wired links offer high stability and availability, thus eliminating the need for frequent link quality assessments and dynamic adjustments. The system adopts a "wireless first, wired backup" communication strategy, improving overall communication flexibility and efficiency. Wired links do not require link assessment and control mechanisms, avoiding waste of system resources. Focusing on optimizing wireless links and enhancing the self-healing and adaptability of the wireless network is key to resolving communication bottlenecks in temporary power environments. Wired links only need to maintain physical connectivity and basic link status monitoring, without requiring complex state calculations and dynamic optimization.

[0103] The mechanism for dynamically adjusting transmit power on abnormal links refers to:

[0104] ; This represents the actual transmit power of the node. This represents the node's minimum transmit power. This represents the node's maximum transmit power. The distance between the node and the receiver. To the maximum permissible communication distance, The path loss coefficient is dynamically calculated. The dynamic calculation formula for the actual transmit power of a node is based on key parameters such as the path loss coefficient, the distance between the node and the receiver, the maximum allowable communication distance, and the minimum and maximum allowable transmit power of the system. This formula enables adaptive control of the wireless link, optimizing the stability and energy efficiency management of the communication link by dynamically adjusting the transmit power of the node.

[0105] By proportionally converting the node's current communication distance to its maximum communication distance, and combining this with the path loss coefficient to calculate the theoretical signal attenuation, the transmission power is adjusted accordingly, achieving precise power allocation. This method avoids electromagnetic interference and increased system energy consumption caused by excessively high transmission power, as well as communication failures or reduced link quality due to insufficient transmission power. The dynamic adjustment mechanism of node transmission power, closely coordinated with the dynamic calculation of the path loss coefficient, enables the system to automatically adjust communication parameters based on link status, improving overall system communication efficiency and extending the battery life of node devices. It is particularly suitable for scenarios with frequent fluctuations in wireless communication under temporary power conditions, ensuring continuous and stable system operation. Dynamic optimization of node transmission power improves the communication reliability of wireless links, balances communication quality and energy consumption, reduces overall system energy consumption, avoids channel congestion and interference from neighboring nodes caused by excessive communication power, increases overall system communication capacity, and, in conjunction with a real-time link status feedback mechanism, enhances the system's self-healing capabilities and dynamic adaptability.

[0106] The path loss coefficient is calculated according to the following formula:

[0107] ;

[0108] This is the theoretical minimum path loss coefficient. This is the theoretical maximum path loss coefficient. The minimum acceptable link state metrics, This represents the highest link state index under ideal conditions. Path loss coefficient is an important parameter that measures the rate attenuation of a wireless signal due to the propagation environment during transmission. It reflects the degree to which the signal weakens with increasing distance and is typically limited by the type of environment (such as free space, urban buildings, construction sites, etc.). The path loss coefficient varies significantly under different environments. Therefore, accurate calculation and dynamic adjustment of the path loss coefficient are crucial for ensuring the communication quality of wireless links.

[0109] In the temporary power intelligent management system of this invention, the Link Quality Index (QLS) is dynamically acquired through real-time link status monitoring, and the path loss coefficient is dynamically calculated based on this index. This design enables rapid adaptation to complex and changing environments, allowing the system to adjust communication model parameters according to the actual link status and improve the accuracy of transmit power control. This dynamic calculation method avoids errors caused by using a fixed path loss coefficient, effectively improving the system's link self-healing capability and overall communication reliability in high-interference and strong obstruction environments. The path loss coefficient reflects changes in the wireless link environment in real time, dynamically adapting to different communication scenarios; it provides a scientific basis for transmit power control, ensuring signal coverage and communication quality; it avoids power configuration failures caused by fixed path loss parameters, improving system robustness.

[0110] Example 2: Implementation of the Intelligent Power Management System for Temporary Construction Sites:

[0111] Construction sites present complex electrical environments with frequent starts and stops of numerous electric machinery and equipment, leading to severe fluctuations in grid load and strong electromagnetic interference. This implementation method is based on a temporary power intelligent management system, which uses a power anomaly detection module to collect real-time operating parameters such as current, voltage, and leakage current of various construction equipment. The system's built-in fault detection model can accurately distinguish between overload, short circuit, and leakage anomalies, promptly issuing anomaly warnings to the local control module. When the communication link is normal, the local control module is controlled by the centralized management platform to execute corresponding emergency handling commands. If an anomaly is detected in the current link status, the communication status detection module activates the link quality comprehensive evaluation model, analyzes the dynamic stability index and channel health index, and transmits the analysis results to the self-healing control module. If the current link status is determined to be abnormal, the self-healing control module immediately performs a link switching operation, prioritizing switching to a wired link or switching to the wireless link with the best quality, while simultaneously activating a dynamic transmission power adjustment mechanism for the abnormal wireless link. Through the adjustment mechanism, the transmission power and path loss coefficient of the wireless nodes are automatically adjusted to improve the link communication quality, and the system maintains stable and efficient management of temporary power supply at the construction site.

[0112] Example 3: Implementation method of emergency disaster relief power supply system:

[0113] In emergency disaster relief power supply scenarios, power systems need to deploy temporary power supply networks within a short period of time, often in remote areas or post-disaster areas with high electromagnetic interference. This implementation method employs a temporary power intelligent management system, with a centralized management platform dynamically scheduling power supply equipment based on the disaster area's power grid status. Each power terminal node is equipped with a power anomaly detection module to continuously monitor power parameters and detect and classify faults such as overload, short circuit, and leakage in real time. During communication interruptions, the local control module automatically executes power-off or current-limiting operations according to preset local control strategies to ensure the safety of personnel and equipment. A multi-mode communication module constructs redundant wireless communication links through Wi-Fi, LoRa, and ZigBee networks. In complex terrain and high-interference environments in disaster areas, the communication status detection module dynamically analyzes link volatility and health, determining the communication status by inputting both dynamic stability index and channel health index into a comprehensive link quality evaluation model. When the comprehensive index value of the link status is lower than the preset usage threshold, the self-healing control module will prioritize switching to the wired link or other wireless links with normal communication, and start dynamic control of the transmission power of the abnormal link. Based on the current link status index value, the path loss coefficient will be dynamically adjusted, the transmission power output will be optimized in real time, the communication link recovery efficiency will be improved, and the real-time response and reliability of the emergency power supply system will be guaranteed.

[0114] Example 4: Implementation of a Temporary Energy Management System for Power Exhibitions:

[0115] In large-scale power equipment exhibitions, the demand for temporary power management is complex, involving electrical equipment from multiple exhibitors and a dense crowd, placing high demands on power supply security and communication stability. This implementation method achieves centralized monitoring and intelligent control of the entire exhibition hall's temporary power supply system through a temporary power intelligent management system. The system collects the operating status of various electrical equipment within the exhibition area through a power anomaly detection module. For different anomaly types such as short circuits, overloads, and leakage, it automatically selects matching local control strategies to quickly disconnect abnormal lines or implement isolation protection, ensuring the stable operation of the power system. The multi-mode communication module dynamically switches communication modes in the complex electromagnetic environment of the exhibition. The communication status detection module collects real-time link data and analyzes the link communication status based on two indicators: dynamic stability index and channel health index. The link quality comprehensive evaluation model compares the output comprehensive communication status index value with a preset threshold. The self-healing control module executes link switching and link self-healing strategies based on the evaluation results. For abnormal communication links, the self-healing control module activates a dynamic transmission power adjustment mechanism, dynamically adjusting the path loss coefficient based on the comprehensive communication link status index value to optimize transmission power output, reduce interference and energy consumption, and ensure the high efficiency and stability of the temporary power system and intelligent energy management during the exhibition.

[0116] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

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

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

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

[0120] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A temporary power smart management system, characterized in that, The power utilization anomaly detection module, the local control module, the multi-mode communication module, the communication state detection module, the self-healing control module and the centralized management platform are included. The power utilization anomaly detection module detects the running power parameters of each power utilization terminal node in real time, detects the abnormal state based on the built-in fault detection model, and transmits the abnormal state information to the local control unit and the centralized management platform. The local control module is composed of local control units deployed at each power utilization terminal node. The multi-mode communication module is used to realize dynamic switching of wired communication and multiple wireless communication. The communication state detection module is used to collect real-time data of the communication link and evaluate the communication link state. The self-healing control module is used to perform communication link switching operation when the current communication link state is abnormal, and start the dynamic transmission power regulation mechanism for the abnormal link to improve its communication quality. The centralized management platform interacts with each power utilization terminal node through the multi-mode communication module and records the interaction data.

2. The intelligent management system for temporary power according to claim 1, wherein, The wireless communication includes Wi-Fi, LoRa and ZigBee communication.

3. The intelligent management system for temporary power according to claim 2, wherein, The abnormal state detection includes overload, short circuit and leakage, each of which corresponds to a preset control strategy in the local control strategy, respectively realizing local detection and protection of overload, short circuit and leakage.

4. The intelligent management system for temporary power according to claim 3, wherein, The link state is determined by the following steps: In the preset time window, the method of combining acceleration change rate analysis and Fourier frequency fluctuation energy is used for dual analysis in time and frequency domain to generate a dynamic stability index for evaluating the stability of the link in time sequence. The method of nonlinear power law average and Gaussian function harmonic is used to strengthen the overall communication quality performance to generate a channel health index for evaluating the quality and health degree of the link in the overall state. The dynamic stability index and the channel health index are input into the pre-trained link quality comprehensive evaluation model to output a communication link state comprehensive index value. According to the communication link state comprehensive index value, it is judged whether the current link state is in a communication abnormal state.

5. The intelligent management system for temporary power according to claim 4, wherein, The dynamic stability index acquisition logic is: within a preset time window, the received signal strength at time t is acquired , the first order difference of the received signal strength, i.e. the change speed, is calculated: ; is the sampling time interval, represents the change speed of the received signal strength at time point i, and represent the received signal strengths at time points i and i-1, respectively. The second-order difference of the received signal strength, i.e. the change acceleration, is calculated: ; denotes the change velocity of the received signal strength at the time point i-1, denotes the change acceleration of the received signal strength at the time point i; Computing a root mean square value of the change acceleration : ; N represents the number of sampling points; The time series of received signal strength is subjected to fast Fourier transform: ; ; denotes the signal frequency domain component at the time point ; calculating an energy component of the high frequency portion, i.e. the frequency fluctuation energy : ; wherein K is a frequency demarcation point; The dynamic stability index is calculated as: ; is the dynamic stability index, and are preset non-zero scaling factors.

6. The intelligent management system for temporary power according to claim 5, wherein, The channel health index acquisition logic is: In a preset time window, the bit error rate , data delay and packet loss rate are normalized: ; is the value of parameter X after normalization, is the initial value of parameter X, and are the minimum and maximum values of the standardized range corresponding to parameter X, and parameter X is the bit error rate, data delay or packet loss rate. A health degree Gaussian function is constructed to suppress the influence of abnormal values: ; represents the output value of the health Gaussian function corresponding to the parameter X, represents the median within the time window corresponding to the parameter X, represents the mean within the time window corresponding to the parameter X, represents the standard deviation within the time window corresponding to the parameter X; Power law harmonic average is used: ; is a predetermined sensitivity factor for controlling the power, is a channel health index.

7. The intelligent management system for temporary power according to claim 6, wherein, The link quality comprehensive evaluation model refers to: ; and are preset non-zero weight coefficients and their sum is one, is a communication link state comprehensive index value.

8. The intelligent management system for temporary power according to claim 7, wherein, According to the link state, the feasibility of the current communication mode refers to: The communication link state comprehensive index value is compared with the preset use threshold value. When the communication link state comprehensive index value is greater than or equal to the preset use threshold value, the current communication mode is normal. When the communication link state comprehensive index value is less than the preset use threshold value, the current communication mode is abnormal.

9. The intelligent management system for temporary power according to claim 8, wherein, When the communication type of the wireless communication is abnormal, the wireless communication is removed from the candidate set of the next round of link switching, becomes a candidate of the next round of link switching after recovery, enters the candidate set of the next round of link switching, and the candidate set of the next round of link switching is composed of the wireless link built by the communication type of the wireless communication which is normal and the wired link built by the wired communication.

10. The intelligent management system for temporary power according to claim 9, wherein, The dynamic control mechanism for starting the transmission power of the abnormal link refers to: ; is the actual transmitting power of the node, is the minimum transmitting power of the node, is the maximum transmitting power of the node, is the distance between the node and the receiving end, is the maximum allowed communication distance, is the dynamically calculated path loss coefficient, satisfying the following calculation formula: ； theoretical minimum path loss coefficient, theoretical maximum path loss coefficient, the lowest link status indicator acceptable, the highest link status indicator in the ideal state.

Images