Electric power forest fire early warning method and device based on quantum measurement technology

Abstract

The invention discloses an electric power forest fire early warning method and device based on a quantum measurement technology. The method comprises the steps that multiple pieces of sensing data of a target position in a power line are acquired through a quantum sensor, and the multiple pieces of sensing data comprise temperature data, gas concentration data and electric field intensity data; based on the multiple pieces of sensing data, determining a forest fire risk level of the target position, the forest fire risk level including a low risk, a medium risk, a high risk or an extremely high risk; and performing early warning operation on the target position based on the forest fire risk level. According to the invention, the technical problems of low monitoring precision, slow response, easy interference and unsafe data transmission in a traditional electric power forest fire early warning method are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power safety monitoring and early warning, in particular to a power wildfire early warning method and device based on quantum measurement technology. BACKGROUND

[0002] The current power industry is facing the threat of wildfires caused by high summer temperatures, winter droughts, and vegetation self-ignition, especially when power lines pass through mountainous and forest areas. The existing power wildfire early warning system mainly includes infrared temperature measurement, video monitoring, and manual inspection, but these methods have exposed a series of unsolved technical problems in practical application.

[0003] Firstly, insufficient monitoring accuracy is a common problem of existing systems. For example, infrared temperature measurement is highly sensitive to environmental light and smoke, resulting in large temperature detection errors and difficulty in capturing small temperature changes in the initial stage of wildfires, which often indicate potential fire risks. Secondly, slow response speed is another key defect. Video monitoring systems usually rely on manual or AI image recognition to determine the fire situation, and it often takes several minutes or even longer from discovering the fire source to generating an early warning signal, which greatly reduces the effectiveness of the warning in the case of rapid fire spread. Thirdly, the complex electromagnetic environment in mountainous areas poses a great challenge to traditional electronic sensors, leading to data distortion and high false alarm rate, especially false alarms at critical moments, which may mislead emergency decisions. Finally, data security issues cannot be ignored. Early warning data usually uses traditional encryption methods during transmission, but these methods have limited defense capabilities against high-level attacks and are at risk of interception and tampering, thereby affecting the reliability and execution effect of the warning instructions.

[0004] Currently, there is no effective solution to the above problems. SUMMARY

[0005] The embodiments of the present application provide a power wildfire early warning method and device based on quantum measurement technology to at least solve the technical problems of low monitoring accuracy, slow response, susceptibility to interference, and insecure data transmission in traditional power wildfire early warning methods.

[0006] According to an aspect of the embodiments of the present application, a power wildfire early warning method based on quantum measurement technology is provided, comprising: acquiring a plurality of sensing data of a target position in a power line by a quantum sensor, wherein the plurality of sensing data includes temperature data, gas concentration data, and electric field intensity data; determining a wildfire risk level of the target position based on the plurality of sensing data, wherein the wildfire risk level includes low risk, medium risk, high risk, or extremely high risk; and performing a warning operation on the target position based on the wildfire risk level.

[0007] Optionally, the multiple sensing data of the target position in the power line are acquired by the quantum sensor, including: collecting multiple original data by the quantum sensor according to a preset acquisition frequency; performing noise filtering on the multiple original data based on adaptive wavelet transform on the edge computing node to obtain multiple initial data; obtaining encrypted data from the edge computing node, wherein the encrypted data is obtained by encrypting the multiple initial data based on a quantum key, and the quantum key is generated based on a quantum key distribution protocol; and decrypting the encrypted data to obtain the multiple sensing data.

[0008] Optionally, the forest fire risk level of the target position is determined based on the multiple sensing data, including: obtaining multiple environment data corresponding to the target position, line topology data, wherein the multiple environment data include wind speed data, vegetation type, humidity data, temperature data and slope data; and inputting the multiple environment data, the line topology data and the multiple sensing data into a preset level evaluation model to obtain the forest fire risk level, wherein the level evaluation model is a random forest model.

[0009] Optionally, the method further includes: recording the forest fire risk level and the early warning operation; and updating parameters of the level evaluation model based on the forest fire risk level and the early warning operation.

[0010] Optionally, the target position is subjected to the early warning operation based on the forest fire risk level, including: in the case that the forest fire risk level is low risk, recording the forest fire risk level and not performing early warning; or in the case that the forest fire risk level is medium risk, sending early warning information to a user end; or in the case that the forest fire risk level is high risk, triggering an early warning device and sending early warning information to the user end; or in the case that the forest fire risk level is extremely high risk, transferring line load and sending early warning information to the user end.

[0011] Optionally, the method further includes: determining a temperature mutation amplitude based on the temperature data; determining a gas concentration growth rate based on the gas concentration data; and determining whether the temperature mutation amplitude exceeds a first preset threshold, whether the gas concentration growth rate exceeds a second preset threshold, and whether the electric field intensity data exceeds a third preset threshold to obtain preliminary early warning information, wherein the preliminary early warning information includes single parameter anomaly or multiple parameter anomaly; and in the case that the preliminary early warning information is multiple parameter anomaly, issuing an alarm instruction to an alarm device corresponding to the target position.

[0012] According to another aspect of the embodiments of the present application, there is also provided an electric power wildfire early warning device based on quantum measurement technology, comprising: an acquisition module configured to acquire, by a quantum sensor, a plurality of sensing data of a target position in an electric power line, wherein the plurality of sensing data comprises temperature data, gas concentration data and electric field intensity data; a determination module configured to determine a wildfire risk level of the target position based on the plurality of sensing data, wherein the wildfire risk level comprises low risk, medium risk, high risk or extremely high risk; and a warning module configured to perform a warning operation on the target position based on the wildfire risk level.

[0013] According to still another aspect of the embodiments of the present application, there is also provided a non-volatile storage medium comprising a stored program, wherein the program, when executed, controls a device in which the non-volatile storage medium is located to perform any of the above electric power wildfire early warning methods based on quantum measurement technology.

[0014] According to still another aspect of the embodiments of the present application, there is also provided a computer device comprising a processor configured to execute a program, wherein the program, when executed, performs any of the above electric power wildfire early warning methods based on quantum measurement technology.

[0015] According to still another aspect of the embodiments of the present application, there is also provided a computer program product comprising a computer program configured to, when executed by a processor, implement any of the above electric power wildfire early warning methods based on quantum measurement technology.

[0016] In the embodiments of the present application, the electric power wildfire early warning method based on quantum measurement technology is adopted, the quantum sensor is used to acquire a plurality of sensing data of a target position in an electric power line, wherein the plurality of sensing data comprises temperature data, gas concentration data and electric field intensity data; a wildfire risk level of the target position is determined based on the plurality of sensing data, wherein the wildfire risk level comprises low risk, medium risk, high risk or extremely high risk; and a warning operation is performed on the target position based on the wildfire risk level, thereby achieving the purpose of early and accurate wildfire early warning for the target position in the electric power line, and realizing the technical effects of improving the electric power wildfire early warning speed, accuracy and data transmission security, and further solving the technical problems of low monitoring accuracy, slow response, easy interference and unsafe data transmission in the traditional electric power wildfire early warning method. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0018] Figure 1A hardware structure block diagram of a computer terminal for implementing a power wildfire early warning method based on quantum measurement technology is shown.

[0019] Figure 2 A flowchart of a power wildfire early warning method based on quantum measurement technology is provided according to an embodiment of the present application.

[0020] Figure 3 A processing flowchart of a wildfire early warning based on quantum measurement technology is provided according to an optional embodiment of the present application.

[0021] Figure 4 A structure block diagram of a power wildfire early warning device based on quantum measurement technology is provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to make the personnel in the technical field better understand the present application scheme, the technical scheme in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all. Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without creative labor should belong to the scope of protection of the present application.

[0023] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0024] According to an embodiment of the present application, a power wildfire early warning method embodiment based on quantum measurement technology is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that described herein.

[0025] The method embodiment provided by the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1A hardware block diagram of a computer terminal for implementing a power field wildfire early warning method based on quantum measurement technology is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0026] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0027] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the power fire early warning method based on quantum measurement technology in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned application program of the power fire early warning method based on quantum measurement technology. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0028] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0029] Figure 2 This is a flowchart illustrating a power field wildfire early warning method based on quantum measurement technology according to an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0030] Step S201: Using a quantum sensor, acquire multiple sensing data at the target location in the power line, including temperature data, gas concentration data, and electric field strength data.

[0031] In this step, quantum sensors utilize principles of quantum mechanics, such as quantum dot fluorescence lifetime thermometry, quantum electric field sensing for detecting gas concentration and electric field strength via quantum quantum interference, to achieve precise measurement of environmental parameters. Specifically, temperature data is captured by a quantum temperature sensor, which monitors minute temperature fluctuations near power line joints and insulators based on changes in the fluorescence lifetime of quantum dots, with an accuracy of ±0.01℃, enabling early identification of localized overheating. Gas concentration data is provided by a quantum gas sensor, which uses quantum quantum interference to detect characteristic gases such as carbon monoxide and methane, with a detection limit as low as 1 ppb, allowing for the detection of abnormal gas concentrations in the early stages of a fire. Electric field strength data is collected by a quantum electric field sensor, monitoring the distortion of the electric field around the power line. When wildfires cause air ionization, the electric field signal exhibits characteristic changes, indirectly reflecting the fire situation. These various types of data collected by quantum sensors are preprocessed and feature extracted through edge computing nodes, and then transmitted in encrypted form to a cloud-based early warning platform for further risk assessment and early warning decision-making.

[0032] Step S202: Based on multiple sensor data, determine the wildfire risk level of the target location, whereby the wildfire risk level includes low risk, medium risk, high risk, or extremely high risk.

[0033] In this step, multiple sensor data, such as temperature, gas concentration, and electric field strength, acquired by quantum sensors, are preprocessed and feature extracted via edge computing nodes before being uploaded to a cloud-based early warning platform. On this platform, advanced data analysis algorithms are used to comprehensively evaluate this sensor data to determine the wildfire risk level at the target location. The risk level is divided into four levels: low, medium, high, and extremely high, to quantify the likelihood and urgency of wildfires, providing a scientific basis for subsequent early warning and response measures.

[0034] Step S203: Based on the wildfire risk level, issue an early warning for the target location.

[0035] In this step, differentiated early warning operations are implemented for target locations along power lines based on the assessment results of wildfire risk levels. For low-risk levels, the system only records data without issuing alarms; for medium-risk levels, it pushes alerts to maintenance personnel, suggesting increased inspection frequency; for high-risk levels, it triggers audible and visual warnings and sends detailed information to relevant emergency departments for rapid response; and for extremely high-risk levels, in addition to the above warnings, the system automatically sends an emergency notification to the power dispatch center, suggesting immediate adjustments to power grid operation strategies, such as load shifting, to prevent potential wildfire hazards. Through this series of operations, timely and effective early warning management is achieved for different risk levels.

[0036] Through the above steps, the goal of early and accurate warning of wildfires at target locations along power lines is achieved, thereby improving the speed, accuracy, and data transmission security of power-related wildfire warnings. This also solves the technical problems of low monitoring accuracy, slow response, susceptibility to interference, and insecure data transmission in traditional power-related wildfire warning methods.

[0037] As an optional embodiment, multiple sensing data of a target location in a power line are acquired using a quantum sensor, including: acquiring multiple raw data using a quantum sensor according to a preset acquisition frequency; filtering noise from the multiple raw data at an edge computing node based on adaptive wavelet transform to obtain multiple initial data; acquiring encrypted data from the edge computing node, wherein the encrypted data is obtained by encrypting the multiple initial data using a quantum key, and the quantum key is generated based on a quantum key distribution protocol; and decrypting the encrypted data to obtain multiple sensing data.

[0038] Optionally, data is first acquired using a quantum sensor. Specifically, the quantum sensing terminal acquires temperature T, gas concentration C, and electric field strength E data at a frequency of 1 time per second, generating a data set including a timestamp t and location information. raw data frame Raw data frame It can be represented as:

[0039]

[0040] Among them, quantum sensing technology achieves high-precision measurement based on the Heisenberg uncertainty principle, with temperature measurement accuracy reaching ±0.1℃, gas concentration resolution reaching the ppb level, and electric field strength measurement error less than 0.5%.

[0041] Secondly, after receiving data, the edge computing nodes use adaptive wavelet transform for noise filtering. Let the original signal be f(t), which can be expressed as follows after discrete wavelet transform:

[0042]

[0043] Where a is the scale parameter and b is the translation parameter. This is the mother wavelet function. By adaptively adjusting parameters a and b, high-frequency noise is effectively filtered out.

[0044] Secondly, multiple initial data sets are encrypted using quantum keys at edge computing nodes. Quantum encryption transmission integrates QKD units and establishes a quantum key channel with the cloud-based early warning platform. During data transmission, "one-time pad" encryption is used to ensure that the original monitoring data and early warning instructions are not tampered with or stolen, meeting the data security level protection requirements of the power system. Specifically, in quantum encryption, a quantum key K is generated based on a quantum key distribution (QKD) protocol, such as the BB84 protocol. This is used for initial early warning information. and raw data Encryption is performed, and the encryption process can be represented as follows:

[0045]

[0046] Where E is the encryption function, ⊕ is the XOR operation, and C is the ciphertext. Transmission to the cloud platform is achieved via dedicated power fiber optic cable, leveraging the low-loss characteristics of fiber optics to ensure low transmission latency. .

[0047] Finally, the encrypted data is transmitted to the cloud platform via optical fiber. The cloud platform stores the asymmetric key used for decryption, which can be used to decrypt the data based on the asymmetric encryption algorithm and obtain multiple sensor data.

[0048] As an optional embodiment, the wildfire risk level of a target location is determined based on multiple sensor data, including: acquiring multiple environmental data and route topology data corresponding to the target location, wherein the multiple environmental data include wind speed data, vegetation type, humidity data, temperature data and slope data; inputting the multiple environmental data, route topology data and multiple sensor data into a preset level assessment model to obtain the wildfire risk level, wherein the level assessment model is a random forest model.

[0049] Optionally, the system first acquires real-time environmental parameter data such as wind speed (v), vegetation type (V), air humidity (H), temperature (T), and terrain slope (θ), using these as input variables for the model. Wind speed (v) directly affects the fire's spread rate; vegetation type (V) is categorized into seven levels based on flammability, including coniferous forest, shrubs, and grassland; air humidity (H) and temperature (T) reflect the degree of environmental dryness; and terrain slope (θ) affects the fire's climbing speed. Simultaneously, the system acquires route topology data to understand the specific layout of the route and its surrounding physical environment, enabling a more accurate assessment of the potential impact of wildfires on the route.

[0050] Subsequently, the above-mentioned multiple data are jointly input into a preset random forest level evaluation model. The random forest model is a machine learning method based on decision trees, which can process a large amount of complex data and output high-precision prediction results. In this optional embodiment, the model has been trained and optimized with historical wildfire cases, and can dynamically adjust the weights of environmental factors to more accurately reflect the wildfire risk level under current conditions. Specifically, each parameter is assigned a dynamic weight based on historical disaster relevance , and the weighted sum formula is used to calculate the final risk level R:

[0051]

[0052] Specifically, it can be set by the following method:

[0053]

[0054] Among them, represents the importance ratio of the i-th factor relative to the j-th factor, and its specific value follows the 1-9 scale method.

[0055] Finally, according to the calculated risk level R, the risks can be divided into four warning levels: low risk (R < 30 points), medium risk (30 ≤ R ≤ 60 points), high risk (60 < R ≤ 85 points), and extremely high risk (R > 85 points).

[0056] As an optional embodiment, it further includes: recording the wildfire risk level and warning operations; updating the parameters of the level evaluation model based on the wildfire risk level and warning operations.

[0057] Optionally, based on the actual effects of the recorded wildfire risk levels and warning operations, the system regularly updates the parameters of the random forest level evaluation model. Specifically, after each warning, the data , disposal records are stored in the historical database :

[0058]

[0059] Every month, machine learning algorithms (such as gradient descent method) are used with new data to iteratively optimize the risk assessment model parameters and update the weights and each scoring function to continuously improve the warning accuracy. By introducing new data samples, the model can learn the influence pattern of the latest environmental conditions on wildfire risks, as well as the feedback effect of warning operations on the actual prevention effect. This dynamic update mechanism enables the model to continuously adapt to environmental changes, improve the evaluation accuracy, and ensure the long-term effectiveness of the warning system.

[0060] As an optional embodiment, based on the wildfire risk level, early warning operations are performed on the target location, including: recording the wildfire risk level and not issuing an early warning when the wildfire risk level is low; or sending an early warning message to the user terminal when the wildfire risk level is medium; or triggering an early warning device and sending an early warning message to the user terminal when the wildfire risk level is high; or diverting line load and sending an early warning message to the user terminal when the wildfire risk level is extremely high.

[0061] Optionally, after determining the wildfire risk level, corresponding early warning and response strategies will be implemented based on the different levels. Specifically:

[0062] When the assessed risk level is low, the system believes that the possibility of wildfires occurring under the current conditions is small. At this time, the system will record and archive the risk level information for subsequent data analysis and model optimization, but will not send early warning information to users or relevant departments to avoid unnecessary alarms and reduce the system's false alarm rate.

[0063] At the medium-risk level, the system considers a wildfire to be possible but still manageable. In this case, the system will send warning messages to maintenance personnel via SMS, mobile application push notifications, etc., prompting them to strengthen monitoring and inspections to ensure timely response to any possible changes in the fire situation.

[0064] When the risk level reaches high risk, the system determines that a wildfire may be about to occur or is developing, and will immediately trigger the audible and visual warning device. At the same time, it will send warning information to the power operation and maintenance department and local emergency agencies through multiple channels, including detailed fire location, risk level and suggested preliminary response measures.

[0065] When faced with an extremely high risk level, the system considers the wildfire threat to be extremely urgent. In addition to the above-mentioned early warning operations, it will automatically send instructions to the power dispatch center, suggesting that the power grid operation strategy be adjusted immediately, such as transferring line loads to other safe lines to prevent the fire from affecting the power supply. At the same time, it will urgently notify the fire department to ensure that professional rescue is obtained as soon as possible.

[0066] As an optional embodiment, the method further includes: determining the temperature change amplitude based on temperature data; determining the gas concentration growth rate based on gas concentration data; determining whether the temperature change amplitude exceeds a first preset threshold, whether the gas concentration growth rate exceeds a second preset threshold, and whether the electric field strength data exceeds a third preset threshold, respectively, to obtain preliminary warning information, wherein the preliminary warning information includes single parameter anomaly or multiple parameter anomalies; and issuing an alarm instruction to the alarm device corresponding to the target location when the preliminary warning information indicates multiple parameter anomalies.

[0067] Optionally, firstly, the temperature abrupt change amplitude is , representing the first derivative of temperature with respect to time, reflects the rate of temperature change; the gas concentration growth rate is This represents the trend of gas concentration change. Next, the characteristic parameters (temperature abrupt change amplitude, gas concentration growth rate) are compared with preset thresholds. Specifically, the aforementioned preset thresholds can be obtained through training on historical wildfire cases; let the first preset threshold be... This is the temperature change threshold, and the second preset threshold is... That is, the gas concentration growth rate threshold, the third preset threshold is This refers to the electric field strength threshold. The judgment is made based on the calculated data and the aforementioned preset threshold.

[0068]

[0069]

[0070]

[0071] Based on the comparison results above, preliminary early warning information can be obtained: if a single characteristic parameter exceeds its preset threshold, the system will generate a "low-risk preliminary warning"; if two or more parameters are abnormal, a "high-risk preliminary warning" will be triggered. When the preliminary warning information indicates multiple parameter anomalies, the system immediately issues instructions to alarm devices near the target location to activate audible and visual alarms, and simultaneously sends warning information to relevant departments to facilitate rapid response and prevent potential wildfire threats to power facilities. This early warning mechanism based on multi-parameter cross-validation, combined with the high sensitivity of quantum sensors and the high efficiency of data processing, achieves accurate and timely early warning of power plant wildfires, significantly enhancing the safety protection capabilities of the power system.

[0072] As an optional embodiment, Figure 3 This is a flowchart of a wildfire early warning process based on quantum measurement technology, provided by an optional embodiment of the present invention. Figure 3 As shown, firstly, data is collected using quantum sensors distributed on both sides of the line, including micro-nano level fluctuations in temperature, ppm level changes in gas concentration, and anomalies in electric field strength. The deployment density of these sensors ensures comprehensive monitoring.

[0073] Secondly, the edge computing nodes preprocess the received data, using adaptive wavelet transform to eliminate environmental noise and ensure data purity. Subsequently, the CNN model embedded within the nodes analyzes the data, extracting key parameters such as the magnitude of temperature fluctuations and the rate of increase in gas concentration to preliminarily determine if there are signs of wildfires and generate an initial warning. In cases of multiple parameter anomalies, the system further determines a high risk of wildfires, thus triggering the warning process.

[0074] Furthermore, quantum encryption technology is employed for the transmission of early warning information, ensuring secure and reliable data transfer between edge computing nodes and the cloud-based early warning platform, preventing data leakage or malicious tampering during transmission. The cloud-based early warning platform utilizes powerful computing resources, integrating multiple data sources, including historical wildfire cases, real-time meteorological information, and geographic data. It assesses the risk level of wildfires using a random forest algorithm and, based on the risk level, determines whether an early warning is necessary and its severity, thereby implementing different response measures. Finally, the data generated after each early warning and the corresponding measures are compiled and stored in a historical database for model optimization. This process, leveraging quantum technology, significantly improves monitoring accuracy, response speed, and data security, providing strong support for wildfire prevention and control along power lines.

[0075] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0076] Through the above description of the embodiments, those skilled in the art can clearly understand that the power wildfire early warning method based on quantum measurement technology according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0077] According to embodiments of the present invention, an apparatus for implementing the above-described power field wildfire early warning method based on quantum measurement technology is also provided. Figure 4 This is a structural block diagram of a power fire early warning device based on quantum measurement technology according to an embodiment of the present invention, such as... Figure 4 As shown, the power fire early warning device based on quantum measurement technology includes: an acquisition module 41, a determination module 42, and an early warning module 43. The power fire early warning device based on quantum measurement technology will be described below.

[0078] The acquisition module 41 is used to acquire multiple sensing data of the target location in the power line through a quantum sensor, including temperature data, gas concentration data and electric field strength data.

[0079] The determination module 42, connected to the acquisition module 41, is used to determine the wildfire risk level of the target location based on multiple sensor data, wherein the wildfire risk level includes low risk, medium risk, high risk or extremely high risk.

[0080] The early warning module 43, connected to the determination module 42, is used to perform early warning operations on the target location based on the wildfire risk level.

[0081] It should be noted that the acquisition module 41, determination module 42, and early warning module 43 mentioned above correspond to steps S201 to S203 in the embodiments. Multiple modules and their corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiments. It should also be noted that the above modules, as part of the device, can run on the computer terminal 10 provided in the embodiments.

[0082] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0083] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the power fire early warning method and device based on quantum measurement technology in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned power fire early warning method based on quantum measurement technology. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0084] The processor can access information and applications stored in the memory via a transmission device to perform the following steps: acquiring multiple sensor data points at a target location in a power line using a quantum sensor, including temperature data, gas concentration data, and electric field strength data; determining the wildfire risk level of the target location based on the multiple sensor data, including low risk, medium risk, high risk, or extremely high risk; and issuing an early warning for the target location based on the wildfire risk level.

[0085] Optionally, the processor may also execute program code for the following steps: acquiring multiple sensing data at a target location in a power line using a quantum sensor, including: acquiring multiple raw data using a quantum sensor according to a preset acquisition frequency; filtering noise from the multiple raw data at an edge computing node based on adaptive wavelet transform to obtain multiple initial data; acquiring encrypted data from the edge computing node, wherein the encrypted data is obtained by encrypting the multiple initial data using a quantum key, and the quantum key is generated based on a quantum key distribution protocol; and decrypting the encrypted data to obtain multiple sensing data.

[0086] Optionally, the processor may also execute program code for the following steps: determining the wildfire risk level of a target location based on multiple sensor data, including: acquiring multiple environmental data and route topology data corresponding to the target location, wherein the multiple environmental data include wind speed data, vegetation type, humidity data, temperature data, and slope data; inputting the multiple environmental data, route topology data, and multiple sensor data into a preset level assessment model to obtain the wildfire risk level, wherein the level assessment model is a random forest model.

[0087] Optionally, the processor may also execute program code that includes the following steps: recording wildfire risk levels and early warning operations; and updating the parameters of the risk assessment model based on the wildfire risk levels and early warning operations.

[0088] Optionally, the processor may also execute program code for the following steps: performing early warning operations on the target location based on the wildfire risk level, including: recording the wildfire risk level and not issuing an early warning when the wildfire risk level is low; or sending an early warning message to the user terminal when the wildfire risk level is medium; or triggering an early warning device and sending an early warning message to the user terminal when the wildfire risk level is high; or diverting line load and sending an early warning message to the user terminal when the wildfire risk level is extremely high.

[0089] Optionally, the processor may also execute program code that includes the following steps: determining the temperature change amplitude based on temperature data; determining the gas concentration growth rate based on gas concentration data; determining whether the temperature change amplitude exceeds a first preset threshold, whether the gas concentration growth rate exceeds a second preset threshold, and whether the electric field strength data exceeds a third preset threshold, to obtain preliminary warning information, wherein the preliminary warning information includes single parameter anomaly or multiple parameter anomalies; and issuing an alarm indication to the alarm device corresponding to the target location when the preliminary warning information is multiple parameter anomalies.

[0090] This invention provides a power line wildfire early warning scheme based on quantum measurement technology. By using quantum sensors, multiple sensor data points at a target location along a power line are acquired, including temperature data, gas concentration data, and electric field strength data. Based on this data, the wildfire risk level at the target location is determined, categorized as low, medium, high, or extremely high risk. Based on this risk level, an early warning operation is performed at the target location, achieving accurate early warning of wildfires at target locations along power lines. This solves the technical problems of low monitoring accuracy, slow response, susceptibility to interference, and insecure data transmission in traditional power line wildfire early warning methods.

[0091] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0092] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the aforementioned non-volatile storage medium can be used to store the program code executed by the power wildfire early warning method based on quantum measurement technology provided in the above embodiments.

[0093] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0094] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: acquiring multiple sensing data of a target location in a power line using a quantum sensor, wherein the multiple sensing data includes temperature data, gas concentration data, and electric field strength data; determining the wildfire risk level of the target location based on the multiple sensing data, wherein the wildfire risk level includes low risk, medium risk, high risk, or extremely high risk; and performing an early warning operation on the target location based on the wildfire risk level.

[0095] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: acquiring multiple sensing data of a target location in a power line using a quantum sensor, including: acquiring multiple raw data using a quantum sensor according to a preset acquisition frequency; filtering noise from the multiple raw data on an edge computing node based on adaptive wavelet transform to obtain multiple initial data; acquiring encrypted data from the edge computing node, wherein the encrypted data is obtained by encrypting the multiple initial data using a quantum key, and the quantum key is generated based on a quantum key distribution protocol; and decrypting the encrypted data to obtain multiple sensing data.

[0096] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining the wildfire risk level of a target location based on multiple sensor data, including: acquiring multiple environmental data and route topology data corresponding to the target location, wherein the multiple environmental data include wind speed data, vegetation type, humidity data, temperature data, and slope data; inputting the multiple environmental data, route topology data, and multiple sensor data into a preset level assessment model to obtain the wildfire risk level, wherein the level assessment model is a random forest model.

[0097] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: further including: recording wildfire risk levels and early warning operations; updating the parameters of the level assessment model based on the wildfire risk levels and early warning operations.

[0098] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: based on the wildfire risk level, perform an early warning operation on the target location, including: if the wildfire risk level is low, record the wildfire risk level and do not issue an early warning; or if the wildfire risk level is medium, send an early warning message to the user terminal; or if the wildfire risk level is high, trigger the early warning device and send an early warning message to the user terminal; or if the wildfire risk level is extremely high, transfer the line load and send an early warning message to the user terminal.

[0099] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: further including: determining the temperature change amplitude based on temperature data; determining the gas concentration growth rate based on gas concentration data; determining whether the temperature change amplitude exceeds a first preset threshold, whether the gas concentration growth rate exceeds a second preset threshold, and whether the electric field strength data exceeds a third preset threshold, respectively, to obtain preliminary warning information, wherein the preliminary warning information includes single parameter anomaly or multiple parameter anomalies; and issuing an alarm indication to the alarm device corresponding to the target location when the preliminary warning information is multiple parameter anomalies.

[0100] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can: acquire multiple sensing data of a target location in a power line using a quantum sensor, wherein the multiple sensing data includes temperature data, gas concentration data, and electric field strength data; determine the wildfire risk level of the target location based on the multiple sensing data, wherein the wildfire risk level includes low risk, medium risk, high risk, or extremely high risk; and perform early warning operations on the target location based on the wildfire risk level.

[0101] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0102] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0104] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0105] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0106] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0107] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A power-based wildfire early warning method based on quantum measurement technology, characterized in that, include: A quantum sensor is used to acquire multiple sensing data at a target location in a power line, including temperature data, gas concentration data, and electric field strength data. Based on the multiple sensor data, the wildfire risk level of the target location is determined, wherein the wildfire risk level includes low risk, medium risk, high risk or extremely high risk; Based on the aforementioned wildfire risk level, an early warning operation is performed on the target location.

2. The method according to claim 1, characterized in that, The process of acquiring multiple sensing data points at a target location in a power line using quantum sensors includes: According to the preset acquisition frequency, the quantum sensor is used to acquire multiple raw data respectively; At the edge computing node, noise filtering is performed on the multiple original data based on adaptive wavelet transform to obtain multiple initial data. Encrypted data is obtained from the edge computing node, wherein the encrypted data is obtained by encrypting the plurality of initial data based on a quantum key, and the quantum key is generated based on a quantum key distribution protocol; The encrypted data is decrypted to obtain the multiple sensor data.

3. The method according to claim 1, characterized in that, The process of determining the wildfire risk level of the target location based on the multiple sensor data includes: Acquire multiple environmental data and route topology data corresponding to the target location, wherein the multiple environmental data include wind speed data, vegetation type, humidity data, temperature data and slope data; The multiple environmental data, the line topology data, and the multiple sensor data are input into a preset level assessment model to obtain the wildfire risk level, wherein the level assessment model is a random forest model.

4. The method according to claim 3, characterized in that, Also includes: Record the aforementioned wildfire risk level and the aforementioned early warning operation; Based on the stated wildfire risk level and the stated early warning operation, the parameters of the level assessment model are updated.

5. The method according to claim 1, characterized in that, The operation of issuing an early warning for the target location based on the wildfire risk level includes: If the wildfire risk level is low, the wildfire risk level is recorded and no warning is issued. Or, if the wildfire risk level is medium risk, a warning message may be sent to the user's device. Or, if the wildfire risk level is high, the early warning device will be triggered and the early warning information will be sent to the user terminal. Alternatively, if the wildfire risk level is described as extremely high, the line load may be diverted and the warning information may be sent to the user terminal.

6. The method according to any one of claims 1 to 5, characterized in that, Also includes: Based on the temperature data, determine the magnitude of the temperature change. Based on the gas concentration data, determine the gas concentration growth rate; The system determines whether the temperature change amplitude exceeds a first preset threshold, whether the gas concentration growth rate exceeds a second preset threshold, and whether the electric field strength data exceeds a third preset threshold, and obtains preliminary warning information, wherein the preliminary warning information includes single parameter anomaly or multiple parameter anomalies. If the initial warning information indicates that the multiple parameters are abnormal, an alarm instruction is sent to the alarm device corresponding to the target location.

7. A power-based wildfire early warning device based on quantum measurement technology, characterized in that, include: The acquisition module is used to acquire multiple sensing data of a target location in a power line through a quantum sensor, wherein the multiple sensing data include temperature data, gas concentration data and electric field strength data; The determination module is used to determine the wildfire risk level of the target location based on the multiple sensor data, wherein the wildfire risk level includes low risk, medium risk, high risk or extremely high risk; The early warning module is used to issue early warnings for the target location based on the wildfire risk level.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the non-volatile storage medium to execute the power wildfire early warning method based on quantum measurement technology as described in any one of claims 1 to 6.

9. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the power-based wildfire early warning method based on quantum measurement technology as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the power-based wildfire early warning method based on quantum measurement technology as described in any one of claims 1 to 6.

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