Environmental information processing method and device of distribution line, electronic equipment and storage medium

Abstract

The invention relates to the technical field of power distribution Internet of Things, and particularly relates to an environment information processing method and device of a power distribution line, electronic equipment and a storage medium, and the method comprises the steps: obtaining a plurality of pieces of environment information time sequence data, collected by a plurality of sensors, of a local region where an Internet of Things feeder terminal is located; selecting target data from each piece of environment information time sequence data; inputting the multiple pieces of target data into the OS-ELM model to obtain data fusion features output by the OS-ELM model; and inputting the data fusion features into a pre-stored D-S evidence theory model to obtain a forest fire risk prediction result output by the D-S evidence theory model. The technical scheme can accurately predict the forest fire risk, and is mainly used for forest fire prevention of the power distribution network.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power distribution Internet of Things, and in particular to a method, device, electronic device and storage medium for processing environmental information of a power distribution line. Background Art

[0002] In recent years, the trend of global warming has become increasingly obvious, and wildfire accidents have occurred frequently. Power failure is one of the important causes of wildfires, and its early stage of causing wildfires is highly concealed, difficult to detect in time, and easily leads to serious wildfire accidents. In the power network, the medium-voltage distribution network is large in scale, widely distributed, and has low insulation and maintenance standards. The probability of failure is 2-3 orders of magnitude higher than that of the high-voltage transmission network. At the same time, in order to pursue high power supply reliability, the medium-voltage distribution network widely adopts a small current grounding method. In this way, a single-phase grounding fault is not handled immediately, allowing the fault arc and arc sparks to have sufficient time to ignite the surrounding combustibles. According to incomplete statistics, more than 80% of wildfires ignited by power failures at home and abroad occurred in medium-voltage distribution networks. Therefore, it can be said that the key to preventing wildfires caused by power failures lies in the scientific handling of distribution network faults, especially single-phase grounding faults. In order to solve the wildfire accidents that may be caused by medium-voltage distribution line faults, single-phase grounding fault tripping, compression protection delay, cancellation of reclosing and other fault handling measures are currently widely adopted, which effectively curbs the momentum of wildfires caused by power failures.

[0003] However, the inventors found that due to the complexity of factors causing wildfires, sometimes a distribution network fault will not cause a wildfire but will still cause a trip. For example, sometimes when it is raining, even if a single-phase grounding fault generates an arc spark, it will not cause a wildfire. This will lead to mismatch and mismatch of fault handling measures, resulting in many unnecessary trips and seriously deteriorating the reliability of distribution network power supply. Therefore, how to resolve the contradiction between wildfire prevention and reliable power supply in the distribution network, make the distribution network fault handling measures more accurate, and significantly reduce unnecessary tripping events has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] In order to solve the problems in the related art, the embodiments of the present disclosure provide a method, a device, an electronic device and a storage medium for processing environmental information of a power distribution line.

[0005] In a first aspect, an embodiment of the present disclosure provides a method for processing environmental information of a distribution line, which is applied to an IoT feeder terminal, including:

[0006] Acquire multiple environmental information time series data of the local area where the IoT feeder terminal is located collected by multiple sensors;

[0007] Select target data from each piece of environmental information time series data;

[0008] Inputting a plurality of selected target data into an online sequential extreme learning machine (OS-ELM) model to obtain data fusion features output by the OS-ELM model;

[0009] The data fusion features are input into a pre-stored DS evidence theory model to obtain a wildfire risk prediction result output by the DS evidence theory model.

[0010] In a possible implementation manner, the step of selecting target data from each piece of environmental information time series data includes:

[0011] Preprocessing the multiple environmental information time series data to obtain multiple environmental feature time series data that meet prediction requirements;

[0012] The particle swarm PSO algorithm is used to select the target data from each environmental feature time series data.

[0013] In a possible implementation, the method of selecting the target data from each environmental feature time series data using a particle swarm optimization (PSO) algorithm includes:

[0014] For each piece of environmental characteristic time series data, the environmental characteristic time series data is simulated as a particle swarm, and the speed and position of each particle in the particle swarm are determined according to the following formula:

[0015]

[0016] in, represents the velocity of the i-th particle at the k+1th update, represents the velocity of the i-th particle at the k-th update, represents the position of the i-th particle at the k+1th update, represents the position of the i-th particle at the k-th update, represents the individual extreme value of the i-th particle at the k-th update, represents the group extreme value at the kth update, c1 is the individual learning factor in the PSO algorithm, c2 is the social learning factor in the PSO algorithm, and w k is the inertia weight factor of the PSO algorithm at the kth update, and rand(0,1) represents a random value between 0 and 1;

[0017] When the termination condition of the PSO algorithm is reached, the updating is stopped and the group extreme value at the last update is obtained as the target data selected from the environmental feature time series data.

[0018] In a possible implementation, the method further includes:

[0019] The inertia weight factor w in the PSO algorithm is calculated according to the following formula k :

[0020]

[0021] Among them, w min and w max is the preset minimum and maximum value of the inertia weight factor, n is the sliding window of the inertia weight factor, and w j is the inertia weight factor at the jth update, β is the preset descent control parameter, T max is the maximum evolutionary generation;

[0022] The individual learning factor c1 and the social learning factor c2 in the PSO algorithm are calculated according to the following formula:

[0023]

[0024] Among them, c min and c max is the preset minimum and maximum value of the learning factor.

[0025] In a possible implementation, the method further includes:

[0026] Receive a disposal strategy table issued by a master station device, wherein the disposal strategy table records disposal strategies corresponding to wildfire risk prediction results;

[0027] Based on the handling strategy table, the handling strategy corresponding to the wildfire risk prediction result is executed.

[0028] In a possible implementation, the method further includes:

[0029] Sending the data fusion feature and its corresponding wildfire risk prediction result to the master station device so that the master station device can re-predict a new wildfire risk prediction result, and send the new wildfire risk prediction result to the IoT feeder terminal;

[0030] Receive new wildfire risk prediction results issued by the master station equipment;

[0031] Based on the handling strategy table, the handling strategy corresponding to the new wildfire risk prediction result is executed.

[0032] In a possible implementation, the method further includes:

[0033] An updated DS evidence theory model sent by a master station device is received, wherein the updated DS evidence theory model is trained by the master station device based on the data fusion features uploaded by the IoT feeder terminal and actual wildfire results.

[0034] In a second aspect, an embodiment of the present disclosure provides a method for processing environmental information of a power distribution line, which is applied to a master station device, including:

[0035] Receive data fusion features uploaded by each IoT feeder terminal and its corresponding wildfire risk prediction result, wherein the data fusion features are obtained by the IoT feeder terminal performing data selection and feature extraction fusion of an online sequential extreme learning machine (OS-ELM) model on multiple environmental information time series data of the local area where the IoT feeder terminal is located collected by multiple sensors; the wildfire risk prediction result is obtained by the IoT feeder terminal inputting the data fusion features into a pre-stored DS evidence theory model and executing the DS evidence theory model;

[0036] Based on the data fusion characteristics uploaded by each IoT feeder terminal and its corresponding wildfire risk prediction result, determine whether the wildfire risk prediction result of the IoT feeder terminal is accurate;

[0037] When the wildfire risk prediction result of the Internet of Things feeder terminal is inaccurate, a new wildfire risk prediction result is obtained and the new wildfire risk prediction result is sent to the Internet of Things feeder terminal.

[0038] In a possible implementation, the determining whether the wildfire risk prediction result of the IoT feeder terminal is accurate based on the data fusion characteristics uploaded by each IoT feeder terminal and its corresponding wildfire risk prediction result includes:

[0039] For each IoT feeder terminal, obtaining the wildfire risk prediction results of other IoT feeder terminals within a predetermined distance range from the IoT feeder terminal;

[0040] Determining whether the wildfire risk prediction result of the IoT feeder terminal is correct based on the wildfire risk prediction results of the other IoT feeder terminals;

[0041] When the wildfire risk prediction result of the IoT feeder terminal is inaccurate, obtaining a new wildfire risk prediction result includes:

[0042] When the wildfire risk prediction result of the Internet of Things feeder terminal is inaccurate, a new wildfire risk prediction result of the Internet of Things feeder terminal is determined based on the wildfire risk prediction results of the other Internet of Things feeder terminals.

[0043] In a possible implementation, the determining whether the wildfire risk prediction result of the IoT feeder terminal is accurate based on the data fusion characteristics uploaded by each IoT feeder terminal and its corresponding wildfire risk prediction result includes:

[0044] For each IoT feeder terminal, obtaining data fusion features of other IoT feeder terminals within a predetermined distance range from the IoT feeder terminal;

[0045] Inputting the data fusion features of the other IoT feeder terminals and the data fusion features of the IoT feeder terminal into the DS evidence theory model to obtain a new wildfire risk prediction result output by the DS evidence theory model;

[0046] Compare the wildfire risk prediction result of the IoT feeder terminal with the new wildfire risk prediction result to determine whether the wildfire risk prediction result of the IoT feeder terminal is accurate.

[0047] In a possible implementation, the method further includes:

[0048] Based on the data fusion features uploaded by each IoT feeder terminal and its corresponding real wildfire results, the DS evidence theory model is trained to obtain an updated DS evidence theory model;

[0049] The updated DS evidence theory model is sent to each IoT feeder terminal.

[0050] In a third aspect, an embodiment of the present disclosure provides an environmental information processing device for a distribution line, which is applied to an IoT feeder terminal, including:

[0051] An acquisition module is configured to acquire multiple environmental information time series data of a local area where the IoT feeder terminal is located collected by multiple sensors;

[0052] A selection module is configured to select target data from each piece of environmental information time series data;

[0053] A fusion module is configured to input a plurality of selected target data into an online sequential extreme learning machine (OS-ELM) model to obtain data fusion features output by the OS-ELM model;

[0054] The prediction module is configured to input the data fusion features into a pre-stored DS evidence theory model to obtain a wildfire risk prediction result output by the DS evidence theory model.

[0055] In a possible implementation, the selection module is configured to:

[0056] Preprocessing the multiple environmental information time series data to obtain multiple environmental feature time series data that meet prediction requirements;

[0057] The particle swarm PSO algorithm is used to select the target data from each environmental feature time series data.

[0058] In a possible implementation, the selection module uses a particle swarm optimization (PSO) algorithm to select the target data from each environmental feature time series data and is configured as follows:

[0059] For each piece of environmental characteristic time series data, the environmental characteristic time series data is simulated as a particle swarm, and the speed and position of each particle in the particle swarm are determined according to the following formula:

[0060]

[0061] in, represents the velocity of the i-th particle at the k+1th update, represents the velocity of the i-th particle at the k-th update, represents the position of the i-th particle at the k+1th update, represents the position of the i-th particle at the k-th update, represents the individual extreme value of the i-th particle at the k-th update, represents the group extreme value at the kth update, c1 is the individual learning factor in the PSO algorithm, c2 is the social learning factor in the PSO algorithm, and w k is the inertia weight factor of the PSO algorithm at the kth update, and rand(0,1) represents a random value between 0 and 1;

[0062] When the termination condition of the PSO algorithm is reached, the updating is stopped and the group extreme value at the last update is obtained as the target data selected from the environmental feature time series data.

[0063] In a possible implementation, the device further includes:

[0064] The parameter determination module is configured to calculate the inertia weight factor w in the PSO algorithm according to the following formula k :

[0065]

[0066] Among them, w min and w max is the preset minimum and maximum value of the inertia weight factor, n is the sliding window of the inertia weight factor, and w j is the inertia weight factor at the jth update, β is the preset descent control parameter, T max is the maximum evolutionary generation;

[0067] The individual learning factor c1 and the social learning factor c2 in the PSO algorithm are calculated according to the following formula:

[0068]

[0069] Among them, c min and c max is the preset minimum and maximum value of the learning factor.

[0070] In a possible implementation, the device further includes:

[0071] A receiving module is configured to receive a disposal strategy table issued by a master station device, wherein the disposal strategy table records disposal strategies corresponding to wildfire risk prediction results;

[0072] The strategy execution module is configured to execute the handling strategy corresponding to the wildfire risk prediction result based on the handling strategy table.

[0073] In a possible implementation, the device further includes:

[0074] A data reporting module is configured to send the data fusion feature and its corresponding wildfire risk prediction result to a master station device so that the master station device re-predicts a new wildfire risk prediction result and sends the new wildfire risk prediction result to the IoT feeder terminal;

[0075] A result receiving module is configured to receive new wildfire risk prediction results sent by the master station device;

[0076] The new strategy execution module is configured to execute the treatment strategy corresponding to the new wildfire risk prediction result based on the treatment strategy table.

[0077] In a possible implementation, the device further includes:

[0078] The model structure module is configured to receive an updated DS evidence theory model sent by a master station device, wherein the updated DS evidence theory model is trained by the master station device based on the data fusion features uploaded by the IoT feeder terminal and actual wildfire results.

[0079] In a fourth aspect, an embodiment of the present disclosure provides an environment information processing device for a power distribution line, which is applied to a master station device, including:

[0080] The data receiving module is configured to receive data fusion features uploaded by each IoT feeder terminal and its corresponding wildfire risk prediction result, wherein the data fusion features are obtained by the IoT feeder terminal performing data selection and feature extraction fusion of an online sequential extreme learning machine OS-ELM model on multiple environmental information time series data of the local area where the IoT feeder terminal is located collected by multiple sensors; the wildfire risk prediction result is obtained by the IoT feeder terminal inputting the data fusion features into a pre-stored DS evidence theory model and executing the DS evidence theory model;

[0081] A result determination module is configured to determine whether the wildfire risk prediction result of the IoT feeder terminal is accurate based on the data fusion characteristics uploaded by each IoT feeder terminal and its corresponding wildfire risk prediction result;

[0082] The result sending module is configured to obtain a new wildfire risk prediction result and send the new wildfire risk prediction result to the IoT feeder terminal when the wildfire risk prediction result of the IoT feeder terminal is inaccurate.

[0083] In a possible implementation, the result determination module is configured as follows:

[0084] For each IoT feeder terminal, obtaining the wildfire risk prediction results of other IoT feeder terminals within a predetermined distance range from the IoT feeder terminal;

[0085] Determining whether the wildfire risk prediction result of the IoT feeder terminal is correct based on the wildfire risk prediction results of the other IoT feeder terminals;

[0086] The part of the result delivery module that obtains a new wildfire risk prediction result when the wildfire risk prediction result of the IoT feeder terminal is inaccurate is configured as follows:

[0087] When the wildfire risk prediction result of the Internet of Things feeder terminal is inaccurate, a new wildfire risk prediction result of the Internet of Things feeder terminal is determined based on the wildfire risk prediction results of the other Internet of Things feeder terminals.

[0088] In a possible implementation, the result determination module is configured as follows:

[0089] For each IoT feeder terminal, obtaining data fusion features of other IoT feeder terminals within a predetermined distance range from the IoT feeder terminal;

[0090] Inputting the data fusion features of the other IoT feeder terminals and the data fusion features of the IoT feeder terminal into the DS evidence theory model to obtain a new wildfire risk prediction result output by the DS evidence theory model;

[0091] Compare the wildfire risk prediction result of the IoT feeder terminal with the new wildfire risk prediction result to determine whether the wildfire risk prediction result of the IoT feeder terminal is accurate.

[0092] In a possible implementation, the device further includes:

[0093] A model training module is configured to train the DS evidence theory model based on the data fusion features uploaded by each IoT feeder terminal and its corresponding real wildfire results to obtain an updated DS evidence theory model;

[0094] The model delivery module is configured to deliver the updated DS evidence theory model to each IoT feeder terminal.

[0095] In a fifth aspect, an embodiment of the present disclosure provides an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer instructions, and wherein the one or more computer instructions are executed by the processor to implement a method as described in any one of the first aspect or the second aspect.

[0096] In a sixth aspect, a computer-readable storage medium is provided in an embodiment of the present disclosure, on which computer instructions are stored. When the computer instructions are executed by a processor, a method as described in any one of the first aspect or the second aspect is implemented.

[0097] According to the technical solution provided by the embodiment of the present disclosure, multiple environmental information time series data of the local area where the IoT feeder terminal is located, which are collected by multiple sensors, can be obtained through the IoT feeder terminal, and target data can be selected from each environmental information time series data; the multiple target data are input into the OS-ELM model to obtain the data fusion characteristics output by the OS-ELM model; the data fusion characteristics are input into the preset DS evidence theory model to obtain the wildfire risk prediction result output by the DS evidence theory model; in this way, by processing the environmental information of the local area where the IoT feeder terminal is located in the distribution line, accurate prediction of wildfires can be achieved, and then accurate disposal can be carried out based on the prediction, reducing unnecessary tripping caused by mismatch and mismatch of disposal measures.

[0098] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] Other features, objectives and advantages of the present disclosure will become more apparent through the following detailed description of non-limiting embodiments in conjunction with the accompanying drawings. In the accompanying drawings:

[0100] Figure 1 A flow chart of a method for processing environmental information of a power distribution line provided by an embodiment of the present disclosure is shown.

[0101] Figure 2 A flow chart of an environmental information processing method for a power distribution line applied to a master station device provided by an embodiment of the present disclosure is shown.

[0102] Figure 3A structural block diagram of an environment information processing device for a power distribution line provided by an embodiment of the present disclosure is shown.

[0103] Figure 4 A structural block diagram of an environment information processing device for a power distribution line applied to a master station device provided by an embodiment of the present disclosure is shown.

[0104] Figure 5 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown.

[0105] Figure 6 A schematic diagram showing the structure of a computer system suitable for implementing the method of the embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0106] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. In addition, for the sake of clarity, parts not related to the description of the exemplary embodiments are omitted in the accompanying drawings.

[0107] In the present disclosure, it should be understood that terms such as "include" or "have" are intended to indicate the presence of features, numbers, steps, behaviors, components, parts, or a combination thereof disclosed in the present specification, and are not intended to exclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or a combination thereof exist or are added.

[0108] It should also be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0109] Figure 1 FIG. 1 is a flow chart showing a method for processing environmental information of a power distribution line provided by an embodiment of the present disclosure. Figure 1 As shown, the method includes the following steps S101-S104:

[0110] In step S101, multiple environmental information time series data of the local area where the IoT feeder terminal is located collected by multiple sensors are obtained;

[0111] In step S102, target data is selected from each piece of environmental information time series data;

[0112] In step S103, a plurality of selected target data are input into an online sequential extreme learning machine (OS-ELM) model to obtain data fusion features output by the OS-ELM model;

[0113] In step S104, the data fusion features are input into a pre-stored DS evidence theory model to obtain a wildfire risk prediction result output by the DS evidence theory model.

[0114] In one possible implementation, the environmental information processing method of the distribution line is applicable to an IoT feeder terminal that can perform environmental information processing of the distribution line. The IoT feeder terminal can be an intelligent terminal device installed in a distribution room or feeder, and can communicate with a remote distribution master station device.

[0115] In one possible implementation, forest and grassland areas often have complex environments, changeable weather, and prominent micro-meteorological characteristics. Traditional meteorological monitoring stations are expensive, difficult to communicate and obtain electricity, and it is difficult to achieve extensive monitoring of the micro-environment of distribution lines, and it is also difficult to quickly interact with the distribution automation system. Therefore, this implementation provides an Internet of Things feeder terminal, which can obtain environmental information collected by these sensors from multiple sensors. The environmental information is various environmental information of the local area where the Internet of Things feeder terminal is located. The environmental information includes various information that has a certain impact on the initiation of wildfires, and may include temperature information, humidity information, smoke information, rainfall information, wind speed information, and other environmental information. Each sensor can periodically collect corresponding environmental information and form an environmental information time series data in time order; in this way, multiple environmental information time series data can be obtained from multiple sensors.

[0116] In a possible implementation, the multiple sensors may include multiple sensors for collecting multiple environmental information, such as a temperature sensor for collecting temperature information, a humidity sensor for collecting humidity information, a smoke sensor for collecting smoke information, a rainfall sensor for collecting rainfall information, a wind speed sensor for collecting wind speed information, etc. In order to collect environmental information of the local area, multiple sensors of each sensor may be arranged and distributed in the local area.

[0117] In a possible implementation, target data can be selected from each piece of environmental information time series data and input into an OS-ELM (Online Sequential Extreme Learning Machine) model. The target data can be a piece of data randomly selected from each piece of environmental information time series data, or a piece of data selected using a preset algorithm.

[0118] In a possible implementation, the OS-ELM model is used to extract features from multiple input target data, mainly by classifying the multiple input target data with similar attributes into one category according to the similarity of the attributes, extracting features from the fused data, and obtaining data fusion features.

[0119] The OS-ELM model is divided into two parts. The first part is to use the ELM algorithm to calculate and initialize the output weight β through a small number of training samples. (0), the second part starts online learning. Every time a new target data arrives, a new output weight β is obtained through a recursive formula (k+1) In this way, when the selected multiple target data are input into the OS-ELM model, a new weight β can be calculated according to the recursive formula, and the data fusion features of the multiple target data can be obtained based on the weight.

[0120] In a possible implementation, OS-ELM belongs to a supervised learning method, that is, a selected set of data is used for network training and learning. Based on the principle of neural network, some excitation functions are often selected as the transfer function g(x) of neurons. Such functions are selected because they are infinitely integrable functions, and the outputs of these functions are between positive and negative 1. Therefore, it can be considered that the data fusion characteristics of the output multiple target data are some probability t i , and this probability just meets the input of the DS evidence theory model. The data fusion characteristics of the multiple target data have certain fuzziness. The DS evidence theory model can further fuse these data to remove the fuzziness of the data to achieve the final decision output.

[0121] In a possible implementation, the DS (Dempster-Shafer's) evidence theory model is in a hypothesis space, by synthesizing certain evidence to achieve information fusion, and inferring useful facts; in this implementation, the DS evidence theory model is mainly used to synthesize evidence, that is, the data fusion features of multiple target data to determine whether a wildfire will be caused. The data fusion features can be input into the preset DS evidence theory model to obtain the wildfire risk prediction result output by the DS evidence theory model.

[0122] For example, in this implementation, the recognition framework of the DS evidence theory model can be a plurality of events composed of a free combination of open flame, no open flame, interference fire, no interference fire, smoldering fire, no ignition fire, etc. Each data fusion feature can be used as a piece of evidence to calculate the probability of occurrence of each event in the recognition framework under different evidence, and then according to the evidence synthesis rules, the different evidences (data fusion features) are fused, that is, evidence synthesis, to obtain the evidence synthesis result, that is, the probability of occurrence of each event in the recognition framework after the evidence is fused.

[0123] In this implementation mode, a plurality of environmental information time series data of the local area where the IoT feeder terminal is located, which are collected by a plurality of sensors, can be obtained through the IoT feeder terminal, and target data input into the OS-ELM model can be selected from each environmental information time series data; a plurality of target data are input into the OS-ELM model to obtain data fusion features output by the OS-ELM model; the data fusion features are input into a preset DS evidence theory model to obtain a wildfire risk prediction result output by the DS evidence theory model; in this way, by processing the environmental information of the local area where the IoT feeder terminal is located in the distribution line, accurate prediction of wildfires can be achieved, and then accurate disposal can be carried out based on the prediction, thereby reducing unnecessary tripping caused by mismatch and mismatch of disposal measures.

[0124] In a possible implementation manner, the step of selecting target data from each piece of environmental information time series data includes:

[0125] Preprocessing the multiple environmental information time series data to obtain multiple environmental feature time series data that meet prediction requirements;

[0126] The particle swarm PSO algorithm is used to select the target data from each environmental feature time series data.

[0127] In this embodiment, multiple environmental information time series data can be preprocessed to obtain multiple environmental feature time series data that meet the prediction requirements. The preprocessing includes at least one of denoising, smoothing and normalization. Denoising refers to removing data that is obviously not within a reasonable range. For example, for temperature data, data with very high temperature values ​​such as more than 50 degrees or very low temperature values ​​such as less than minus 20 degrees can be removed; the data values ​​in each environmental information time series data will normally change gradually over time, so the mutation data in the data can be removed by smoothing. Normalization refers to changing the data to a fixed interval (range). For example, the environmental information time series data can be mapped to between 0 and 1. Multiple algorithms such as maximum and minimum normalization, mean variance standardization, decimal calibration, and quantitative feature binarization can be used for normalization. Taking the maximum and minimum normalization algorithm as an example, the following formula can be used for normalization:

[0128]

[0129] Among them, x is the data value before normalization, x′ is the data value after normalization, and x min is the minimum value of the data, x max is the maximum value among the data values.

[0130] In this implementation, the PSO algorithm simulates birds in a flock by designing a massless particle, which has only two properties: speed and position. The speed represents the speed of movement, and the position represents the direction of movement. Each particle searches for the optimal solution in the search space individually, and records it as the current individual extreme value, and shares the individual extreme value with other particles in the entire particle swarm. The best individual extreme value is found as the current global optimal solution of the entire particle swarm. All particles in the particle swarm adjust their speed and position according to the current individual extreme value they find and the current global optimal solution shared by the entire particle swarm.

[0131] In this embodiment, the PSO algorithm can be used to select the optimal data, i.e., the target data, from a piece of environmental feature time series data corresponding to each sensor to be input into the OS-ELM model. A particle swarm can be used to simulate each piece of environmental feature time series data. The global optimal solution of the particle swarm searched by the PSO algorithm is the target data selected from each piece of environmental feature time series data.

[0132] In a possible implementation manner, the adopting of the PSO algorithm to select the target data from each environmental feature time series data includes:

[0133] For each piece of environmental characteristic time series data, the environmental characteristic time series data is simulated as a particle swarm, and the speed and position of each particle in the particle swarm are determined according to the following formula:

[0134]

[0135] in, represents the velocity of the i-th particle at the k+1th update, represents the velocity of the i-th particle at the k-th update, represents the position of the i-th particle at the k+1th update, represents the position of the i-th particle at the k-th update, represents the individual extreme value of the i-th particle at the k-th update, represents the group extreme value at the kth update, c1 is the individual learning factor in the PSO algorithm, c2 is the social learning factor in the PSO algorithm, and w k is the inertia weight factor of the PSO algorithm at the kth update, and rand(0,1) represents a random value between 0 and 1;

[0136] When the termination condition of the PSO algorithm is reached, the updating is stopped and the group extreme value at the last update is obtained as the target data selected from the environmental feature time series data.

[0137] In this embodiment, a particle swarm can be used to simulate an environmental feature time series data corresponding to a sensor, and the swarm extreme value (i.e., the global optimal solution) searched by the PSO algorithm is the target data searched from the environmental feature time series data.

[0138] In this embodiment, the above-mentioned and The calculation formula updates the position and velocity of the particle, while considering the individual extreme value and group extreme value of the particle; the objective function can be preset to evaluate the fitness of each particle, that is, the prediction accuracy or error of the OS-ELM model; according to the fitness of the particle, the individual extreme value and group extreme value are updated to determine whether the termination condition such as the maximum number of iterations or a certain preset accuracy requirement is reached. When the termination condition of the PSO algorithm is reached, the update is stopped, and the group extreme value at the last update is the optimal parameter input to the OS-ELM in the particle swarm, that is, the target data selected by the environmental feature time series data.

[0139] In a possible implementation, the method further includes:

[0140] The inertia weight factor w in the PSO algorithm is calculated according to the following formula k :

[0141]

[0142] Among them, w min and w max is the preset minimum and maximum value of the inertia weight factor, n is the sliding window of the inertia weight factor, and w j is the inertia weight factor at the jth update, β is the preset descent control parameter, T max is the maximum evolutionary generation;

[0143] The individual learning factor c1 and the social learning factor c2 in the PSO algorithm are calculated according to the following formula:

[0144]

[0145] Among them, c min and c max is the preset minimum and maximum value of the learning factor.

[0146] In this implementation, when the inertia weight factor in the PSO algorithm approaches 1, the global search capability is strong, which is suitable for the global search in the early stage of the PSO algorithm; when the inertia weight factor approaches 0, the local search capability of the PSO algorithm is improved, the global search capability is reduced, and it is suitable for the local search in the later stage of the PSO algorithm. At the same time, the individual learning factor c1 and the social learning factor c2 in the PSO algorithm will also affect the search capability of the particle swarm. In order to maintain a high inertia weight factor in the early stage of the algorithm and take the smallest inertia weight factor in the later stage, an inertia weight factor reduction strategy based on a sliding average can be adopted to effectively eliminate the impact of data jitter and make the inertia weight factor adaptively decrease. The above-mentioned w can be used. aver The calculation formula is used to calculate the sliding mean w of the inertia weight factor aver , and calculate the inertia weight factor w at the kth update based on this k .

[0147] In this implementation, the values ​​of the individual learning factor c1 and the social learning factor c2 in the PSO algorithm can be changed synchronously with the inertia weight, which can further optimize the PSO algorithm and better search for the global optimal solution.

[0148] In a possible implementation, the method further includes:

[0149] Receive a disposal strategy table issued by a master station device, wherein the disposal strategy table records disposal strategies corresponding to wildfire risk prediction results;

[0150] Based on the handling strategy table, the handling strategy corresponding to the wildfire risk prediction result is executed.

[0151] In this implementation, the IoT feeder terminal can communicate with the remote power distribution master station device and receive the handling strategy table issued by the master station device. The handling strategy table records the handling strategy corresponding to the wildfire risk prediction result. In this way, after determining the wildfire risk prediction result, the IoT feeder terminal can execute the handling strategy corresponding to the wildfire risk prediction result according to the record in the handling strategy table. For example, the handling strategy may include tripping when the determined probability of an open fire exceeds a first predetermined probability value, and not tripping when the determined probability of an interference fire exceeds a second predetermined probability value and the probability of an open fire is less than a third predetermined probability value; etc.

[0152] In a possible implementation, the method further includes:

[0153] Sending the data fusion feature and its corresponding wildfire risk prediction result to the master station device so that the master station device can re-predict a new wildfire risk prediction result, and distribute the new wildfire risk prediction result to the IoT feeder terminal;

[0154] Receive new wildfire risk prediction results issued by the master station equipment;

[0155] Based on the handling strategy table, the handling strategy corresponding to the new wildfire risk prediction result is executed.

[0156] In this implementation, the IoT feeder terminal can also send the data fusion characteristics and their corresponding wildfire risk prediction results to the master station device. The master station device can receive the data fusion characteristics and their corresponding wildfire risk prediction results reported by multiple IoT feeder terminals, the positions of each IoT feeder terminal pre-stored in the master station device, and for each IoT feeder terminal, the master station device can obtain the wildfire risk prediction results of other IoT feeder terminals within a predetermined distance range from the IoT feeder terminal, and determine whether the wildfire risk prediction result of the IoT feeder terminal is correct based on the wildfire risk prediction results of other IoT feeder terminals. If the similarity between the wildfire risk prediction result of the Internet of Things feeder terminal and the wildfire risk prediction results of other Internet of Things feeder terminals exceeds the predetermined threshold, then the wildfire risk prediction result of the Internet of Things feeder terminal is judged to be correct. If the similarity between the wildfire risk prediction result of the Internet of Things feeder terminal and the wildfire risk prediction results of other Internet of Things feeder terminals does not exceed the predetermined threshold, then the wildfire risk prediction result of the Internet of Things feeder terminal is judged to be incorrect. At this time, the master station device can calculate the average value of the wildfire risk prediction results of other Internet of Things feeder terminals as the new wildfire risk prediction result of the Internet of Things feeder terminal. Alternatively, the master station device may input the data fusion features of the IoT feeder terminal and other IoT feeder terminals whose distances to the IoT feeder terminal are within a predetermined distance range into the DS evidence theory model, execute the DS evidence theory model, obtain a new wildfire risk prediction result output by the DS evidence theory model, and directly calculate the similarity between the new wildfire risk prediction result and the wildfire risk prediction result of the IoT feeder terminal. If the similarity is high and exceeds a predetermined threshold, the wildfire risk prediction result of the IoT feeder terminal is judged to be accurate; if the similarity is low, the wildfire risk prediction result of the IoT feeder terminal is judged to be inaccurate.

[0157] In this implementation, the master station device can comprehensively consider the data of multiple IoT feeder terminals in an area to determine the wildfire risk prediction results of the corresponding IoT feeder terminals. When a measurement error occurs in the sensor corresponding to a certain IoT feeder terminal, resulting in an erroneous wildfire risk prediction result, the master station device can promptly discover the error and send a new wildfire risk prediction result to the IoT feeder terminal. The IoT feeder terminal can execute the disposal strategy corresponding to the new wildfire risk prediction result based on the disposal strategy table; for example, when an erroneous prediction occurs in the IoT feeder terminal and a trip is performed, the master station device can promptly discover the error, send a new wildfire risk prediction result, and cause the IoT feeder terminal to reclose; or, when an erroneous prediction occurs in the IoT feeder terminal and it is judged that the error did not trip when there is a wildfire risk, the master station device can promptly discover the error, send a new wildfire risk prediction result, and cause the IoT feeder terminal to perform a trip operation.

[0158] In a possible implementation, the method further includes:

[0159] An updated DS evidence theory model sent by a master station device is received, wherein the updated DS evidence theory model is trained by the master station device based on the data fusion features uploaded by the IoT feeder terminal and actual wildfire results.

[0160] In this implementation, as time goes by, the master station device can continuously obtain the data fusion features uploaded by each IoT feeder terminal and obtain the actual wildfire results. Therefore, the master station device can train and update the DS evidence theory model based on the data fusion features uploaded by the IoT feeder terminal and the actual wildfire results. In this way, as time goes by, the more training data there is, the more accurate the prediction of the DS evidence theory model will be.

[0161] Figure 2 A flow chart of a method for processing environmental information of a power distribution line applied to a master station device provided by an embodiment of the present disclosure is shown. Figure 2 As shown, the method includes the following steps S201-S203:

[0162] In step S201, the data fusion features uploaded by each IoT feeder terminal and their corresponding wildfire risk prediction results are received;

[0163] In step S202, based on the data fusion characteristics uploaded by each IoT feeder terminal and its corresponding wildfire risk prediction result, it is determined whether the wildfire risk prediction result of the IoT feeder terminal is accurate;

[0164] In step S203, when the wildfire risk prediction result of the IoT feeder terminal is inaccurate, a new wildfire risk prediction result is obtained and sent to the IoT feeder terminal.

[0165] In a possible implementation, the method for processing environmental information of a distribution line is applicable to a master station device that can perform environmental information processing of a distribution line, and the master station device can communicate with an Internet of Things feeder terminal.

[0166] In one possible implementation, forest and grassland areas often have complex environments, changeable weather, and prominent micro-meteorological characteristics. Traditional meteorological monitoring stations are expensive, difficult to communicate and obtain electricity, and it is difficult to achieve extensive monitoring of the micro-environment of distribution lines, and it is also difficult to quickly interact with the distribution automation system. Therefore, this implementation provides an Internet of Things feeder terminal, which can obtain environmental information collected by these sensors from multiple sensors. The environmental information is various environmental information of the local area where the Internet of Things feeder terminal is located. The environmental information includes various information that has a certain impact on the initiation of wildfires, and may include temperature information, humidity information, smoke information, rainfall information, wind speed information, and other environmental information. Each sensor can periodically collect corresponding environmental information and form an environmental information time series data in time order; in this way, multiple environmental information time series data can be obtained from multiple sensors.

[0167] In a possible implementation, the IoT feeder terminal can select target data from each environmental information time series data and input it into the online sequential extreme learning machine OS-ELM model; input the multiple target data into the OS-ELM model to obtain data fusion features output by the OS-ELM model; input the data fusion features into a pre-stored DS evidence theory model to obtain a wildfire risk prediction result output by the DS evidence theory model; and then send the data fusion features and their corresponding wildfire risk prediction results to the master station device.

[0168] In a possible implementation manner, the master station device can receive data fusion features reported by multiple IoT feeder terminals and their corresponding wildfire risk prediction results. After receiving these data, the master station device can comprehensively consider the data of multiple IoT feeder terminals in an area to determine the wildfire risk prediction results of the corresponding IoT feeder terminals. When a measurement error occurs in a sensor corresponding to a certain IoT feeder terminal, resulting in an error in the wildfire risk prediction result, the master station device can promptly discover the error and send a new wildfire risk prediction result to the IoT feeder terminal. The IoT feeder terminal can execute the disposal strategy corresponding to the new wildfire risk prediction result based on the disposal strategy table; for example, when an error prediction occurs in the IoT feeder terminal and a trip is performed, the master station device can promptly discover the error and send a new wildfire risk prediction result to make the IoT feeder terminal reclose; or, when an error prediction occurs in the IoT feeder terminal and it is judged that the error does not trip when there is a wildfire risk, the master station device can promptly discover the error and send a new wildfire risk prediction result to make the IoT feeder terminal perform a trip operation.

[0169] In a possible implementation, the determining whether the wildfire risk prediction result of the IoT feeder terminal is accurate based on the data fusion characteristics uploaded by each IoT feeder terminal and its corresponding wildfire risk prediction result includes:

[0170] For each IoT feeder terminal, obtaining the wildfire risk prediction results of other IoT feeder terminals within a predetermined distance range from the IoT feeder terminal;

[0171] Determining whether the wildfire risk prediction result of the IoT feeder terminal is correct based on the wildfire risk prediction results of the other IoT feeder terminals;

[0172] When the wildfire risk prediction result of the IoT feeder terminal is inaccurate, obtaining a new wildfire risk prediction result includes:

[0173] When the wildfire risk prediction result of the Internet of Things feeder terminal is inaccurate, a new wildfire risk prediction result of the Internet of Things feeder terminal is determined based on the wildfire risk prediction results of the other Internet of Things feeder terminals.

[0174] In this embodiment, the positions of each IoT feeder terminal are pre-stored in the master station device. For each IoT feeder terminal, the master station device can obtain the wildfire risk prediction results of other IoT feeder terminals whose distance from the IoT feeder terminal is within a predetermined distance range, and determine whether the wildfire risk prediction result of the IoT feeder terminal is correct based on the wildfire risk prediction results of other IoT feeder terminals. If the similarity between the wildfire risk prediction result of the IoT feeder terminal and the wildfire risk prediction results of other IoT feeder terminals exceeds the predetermined threshold, then the wildfire risk prediction result of the IoT feeder terminal is determined to be correct. If the similarity between the wildfire risk prediction result of the IoT feeder terminal and the wildfire risk prediction results of other IoT feeder terminals does not exceed the predetermined threshold, then the wildfire risk prediction result of the IoT feeder terminal is determined to be incorrect. At this time, the master station device can calculate the average value of the wildfire risk prediction results of other IoT feeder terminals as the new wildfire risk prediction result of the IoT feeder terminal.

[0175] In a possible implementation, the determining whether the wildfire risk prediction result of the IoT feeder terminal is accurate based on the data fusion characteristics uploaded by each IoT feeder terminal and its corresponding wildfire risk prediction result includes:

[0176] For each IoT feeder terminal, obtaining data fusion features of other IoT feeder terminals within a predetermined distance range from the IoT feeder terminal;

[0177] Inputting the data fusion features of the other IoT feeder terminals and the data fusion features of the IoT feeder terminal into the DS evidence theory model to obtain a new wildfire risk prediction result output by the DS evidence theory model;

[0178] Compare the wildfire risk prediction result of the IoT feeder terminal with the new wildfire risk prediction result to determine whether the wildfire risk prediction result of the IoT feeder terminal is accurate.

[0179] In this embodiment, the master station device can input the data fusion characteristics of the IoT feeder terminal and other IoT feeder terminals whose distance from the IoT feeder terminal is within a predetermined distance range into the DS evidence theory model, execute the DS evidence theory model, obtain a new wildfire risk prediction result output by the DS evidence theory model, and directly calculate the similarity between the new wildfire risk prediction result and the wildfire risk prediction result of the IoT feeder terminal. If the similarity is high and exceeds a predetermined threshold, the wildfire risk prediction result of the IoT feeder terminal is judged to be accurate; if the similarity is low, the wildfire risk prediction result of the IoT feeder terminal is judged to be inaccurate.

[0180] In a possible implementation, the method further includes:

[0181] Based on the data fusion features uploaded by each IoT feeder terminal and its corresponding real wildfire results, the DS evidence theory model is trained to obtain an updated DS evidence theory model;

[0182] The updated DS evidence theory model is sent to each IoT feeder terminal.

[0183] In this implementation, as time goes by, the master station device can continuously obtain the data fusion features uploaded by each IoT feeder terminal and obtain the actual wildfire results. Therefore, the master station device can train and update the DS evidence theory model based on the data fusion features uploaded by the IoT feeder terminal and the actual wildfire results. In this way, as time goes by, the more training data there is, the more accurate the prediction of the DS evidence theory model will be.

[0184] The present disclosure also provides an environmental information processing device for a power distribution line, Figure 3 The structure block diagram of the environment information processing device for the power distribution line provided by the embodiment of the present disclosure is shown. The device can be implemented as part or all of the electronic device through software, hardware or a combination of both. Figure 3 As shown, the environmental information processing device of the power distribution line includes:

[0185] An acquisition module 301 is configured to acquire multiple environmental information time series data of a local area where the IoT feeder terminal is located, collected by multiple sensors;

[0186] A selection module 302 is configured to select target data from each piece of environmental information time series data;

[0187] A fusion module 303 is configured to input a plurality of selected target data into an online sequential extreme learning machine OS-ELM model to obtain data fusion features output by the OS-ELM model;

[0188] The prediction module 304 is configured to input the data fusion features into a pre-stored DS evidence theory model to obtain a wildfire risk prediction result output by the DS evidence theory model.

[0189] In a possible implementation, the selection module is configured to:

[0190] The particle swarm PSO algorithm is used to select the target data input into the OS-ELM model from each environmental feature time series data.

[0191] In a possible implementation manner, the selection module uses the PSO algorithm to select the target data from each environmental feature time series data and is configured as follows:

[0192] For each piece of environmental characteristic time series data, the environmental characteristic time series data is simulated as a particle swarm, and the speed and position of each particle in the particle swarm are determined according to the following formula:

[0193]

[0194] in, represents the velocity of the i-th particle at the k+1th update, represents the velocity of the i-th particle at the k-th update, represents the position of the i-th particle at the k+1th update, represents the position of the i-th particle at the k-th update, represents the individual extreme value of the i-th particle at the k-th update, represents the group extreme value at the kth update, c1 is the individual learning factor in the PSO algorithm, c2 is the social learning factor in the PSO algorithm, and w k is the inertia weight factor of the PSO algorithm at the kth update, and rand(0,1) represents a random value between 0 and 1;

[0195] When the termination condition of the PSO algorithm is reached, the updating is stopped and the group extreme value at the last update is obtained as the target data selected from the environmental feature time series data.

[0196] In a possible implementation, the device further includes:

[0197] The parameter determination module is configured to calculate the inertia weight factor w in the PSO algorithm according to the following formula k :

[0198]

[0199] Among them, w min and w max is the preset minimum and maximum value of the inertia weight factor, n is the sliding window of the inertia weight factor, and w j is the inertia weight factor at the jth update, β is the preset descent control parameter, T max is the maximum evolutionary generation;

[0200] The individual learning factor c1 and the social learning factor c2 in the PSO algorithm are calculated according to the following formula:

[0201]

[0202] Among them, cmin and c max is the preset minimum and maximum value of the learning factor.

[0203] In a possible implementation, the device further includes:

[0204] A receiving module is configured to receive a disposal strategy table issued by a master station device, wherein the disposal strategy table records disposal strategies corresponding to wildfire risk prediction results;

[0205] The strategy execution module is configured to execute the handling strategy corresponding to the wildfire risk prediction result based on the handling strategy table.

[0206] In a possible implementation, the device further includes:

[0207] A data reporting module is configured to send the data fusion feature and its corresponding wildfire risk prediction result to a master station device so that the master station device re-predicts a new wildfire risk prediction result and sends the new wildfire risk prediction result to the IoT feeder terminal;

[0208] A result receiving module is configured to receive new wildfire risk prediction results sent by the master station device;

[0209] The new strategy execution module is configured to execute the treatment strategy corresponding to the new wildfire risk prediction result based on the treatment strategy table.

[0210] In a possible implementation, the device further includes:

[0211] The model structure module is configured to receive an updated DS evidence theory model sent by a master station device, wherein the updated DS evidence theory model is trained by the master station device based on the data fusion features uploaded by the IoT feeder terminal and actual wildfire results.

[0212] The present disclosure also provides an environmental information processing device for a power distribution line, Figure 4 The structure block diagram of the environment information processing device for the power distribution line of the master station device provided by the embodiment of the present disclosure is shown. The device can be implemented as part or all of the electronic device through software, hardware or a combination of both. Figure 4 The environmental information processing device of the power distribution line includes:

[0213] The data receiving module 401 is configured to receive data fusion features uploaded by each IoT feeder terminal and its corresponding wildfire risk prediction result, wherein the data fusion features are obtained by the IoT feeder terminal performing data selection and feature extraction fusion of an online sequential extreme learning machine OS-ELM model on multiple environmental information time series data of the local area where the IoT feeder terminal is located collected by multiple sensors; the wildfire risk prediction result is obtained by the IoT feeder terminal inputting the data fusion features into a pre-stored DS evidence theory model and executing the DS evidence theory model;

[0214] The result determination module 402 is configured to determine whether the wildfire risk prediction result of the IoT feeder terminal is accurate based on the data fusion characteristics uploaded by each IoT feeder terminal and its corresponding wildfire risk prediction result;

[0215] The result sending module 403 is configured to obtain a new wildfire risk prediction result and send the new wildfire risk prediction result to the IoT feeder terminal when the wildfire risk prediction result of the IoT feeder terminal is inaccurate.

[0216] In a possible implementation, the result determination module is configured as follows:

[0217] For each IoT feeder terminal, obtaining the wildfire risk prediction results of other IoT feeder terminals within a predetermined distance range from the IoT feeder terminal;

[0218] Determining whether the wildfire risk prediction result of the IoT feeder terminal is correct based on the wildfire risk prediction results of the other IoT feeder terminals;

[0219] The part of the result delivery module that obtains a new wildfire risk prediction result when the wildfire risk prediction result of the IoT feeder terminal is inaccurate is configured as follows:

[0220] When the wildfire risk prediction result of the Internet of Things feeder terminal is inaccurate, a new wildfire risk prediction result of the Internet of Things feeder terminal is determined based on the wildfire risk prediction results of the other Internet of Things feeder terminals.

[0221] In a possible implementation, the result determination module is configured as follows:

[0222] For each IoT feeder terminal, obtaining data fusion features of other IoT feeder terminals within a predetermined distance range from the IoT feeder terminal;

[0223] Inputting the data fusion features of the other IoT feeder terminals and the data fusion features of the IoT feeder terminal into the DS evidence theory model to obtain a new wildfire risk prediction result output by the DS evidence theory model;

[0224] Compare the wildfire risk prediction result of the IoT feeder terminal with the new wildfire risk prediction result to determine whether the wildfire risk prediction result of the IoT feeder terminal is accurate.

[0225] In a possible implementation, the device further includes:

[0226] A model training module is configured to train the DS evidence theory model based on the data fusion features uploaded by each IoT feeder terminal and its corresponding real wildfire results to obtain an updated DS evidence theory model;

[0227] The model delivery module is configured to deliver the updated DS evidence theory model to each IoT feeder terminal.

[0228] The technical terms and technical features mentioned in the implementation manner of the present device are the same as or similar to those mentioned in the implementation manner of the above method. For the explanation and description of the technical terms and technical features involved in the present device, reference may be made to the explanation and description of the implementation manner of the above method, and they will not be repeated here.

[0229] The present disclosure also discloses an electronic device, Figure 5 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown.

[0230] like Figure 5 As shown, the electronic device 500 includes a memory 501 and a processor 502, wherein the memory 501 is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor 502 to implement the method according to an embodiment of the present disclosure.

[0231] Figure 6 A schematic diagram showing the structure of a computer system suitable for implementing the method of the embodiment of the present disclosure is shown.

[0232] like Figure 6 As shown, the computer system 600 includes a processing unit 601, which can perform various processes in the above-mentioned embodiments according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the computer system 600 are also stored. The processing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0233] The following components are connected to the I / O interface 605: an input part 606 including a keyboard, a mouse, etc.; an output part 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage part 608 including a hard disk, etc.; and a communication part 609 including a network interface card such as a LAN card, a modem, etc. The communication part 609 performs communication processing via a network such as the Internet. The drive 610 is also connected to the I / O interface 605 as needed. Removable media 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., are installed on the drive 610 as needed, so that the computer program read therefrom is installed into the storage part 608 as needed. Among them, the processing unit 601 can be implemented as a processing unit such as a CPU, a GPU, a TPU, an FPGA, an NPU, etc.

[0234] In particular, according to an embodiment of the present disclosure, the method described above can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes computer instructions, and the computer instructions are executed by a processor to implement the method steps described above. In such an embodiment, the computer program product can be downloaded and installed from a network through the communication part 609, and / or installed from a removable medium 611.

[0235] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0236] The units or modules involved in the embodiments described in the present disclosure may be implemented by software or programmable hardware. The units or modules described may also be set in a processor, and the names of these units or modules do not constitute limitations on the units or modules themselves in some cases.

[0237] As another aspect, the present disclosure further provides a computer-readable storage medium, which may be a computer-readable storage medium included in the electronic device or computer system in the above embodiment; or a computer-readable storage medium that exists independently and is not assembled into a device. The computer-readable storage medium stores one or more programs, and the programs are used by one or more processors to execute the method described in the present disclosure.

[0238] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present disclosure is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other.

Claims

1. A method for processing environmental information of a power distribution line, characterized in that: Applied to IoT feeder terminals, including: Acquire multiple environmental information time series data of the local area where the IoT feeder terminal is located collected by multiple sensors; Select target data from each piece of environmental information time series data; Inputting a plurality of selected target data into an online sequential extreme learning machine (OS-ELM) model to obtain data fusion features output by the OS-ELM model; The data fusion features are input into a pre-stored DS evidence theory model to obtain a wildfire risk prediction result output by the DS evidence theory model.

2. The method according to claim 1, characterized in that The step of selecting target data from each piece of environmental information time series data includes: Preprocessing the multiple environmental information time series data to obtain multiple environmental feature time series data that meet prediction requirements; The particle swarm PSO algorithm is used to select the target data from each environmental feature time series data.

3. The method according to claim 2, characterized in that The adopting of the particle swarm PSO algorithm to select the target data from each environmental feature time series data includes: For each piece of environmental characteristic time series data, the environmental characteristic time series data is simulated as a particle swarm, and the speed and position of each particle in the particle swarm are determined according to the following formula: in, represents the velocity of the i-th particle at the k+1th update, represents the velocity of the i-th particle at the k-th update, represents the position of the i-th particle at the k+1th update, represents the position of the i-th particle at the k-th update, represents the individual extreme value of the i-th particle at the k-th update, represents the group extreme value at the kth update, c1 is the individual learning factor in the PSO algorithm, c2 is the social learning factor in the PSO algorithm, and w k is the inertia weight factor of the PSO algorithm at the kth update, and rand(0,1) represents a random value between 0 and 1; When the termination condition of the PSO algorithm is reached, the updating is stopped and the group extreme value at the last update is obtained as the target data selected from the environmental feature time series data.

4. The method according to claim 3, characterized in that The method further comprises: The inertia weight factor w in the PSO algorithm is calculated according to the following formula k : Among them, w min and w max is the preset minimum and maximum value of the inertia weight factor, n is the sliding window of the inertia weight factor, and w j is the inertia weight factor at the jth update, β is the preset descent control parameter, T max is the maximum evolutionary generation; The individual learning factor c1 and the social learning factor c2 in the PSO algorithm are calculated according to the following formula: Among them, c min and c max is the preset minimum and maximum value of the learning factor.

5. The method according to claim 1, characterized in that The method further comprises: Receive a disposal strategy table issued by a master station device, wherein the disposal strategy table records disposal strategies corresponding to wildfire risk prediction results; Based on the handling strategy table, the handling strategy corresponding to the wildfire risk prediction result is executed.

6. The method according to claim 5, characterized in that The method further comprises: Sending the data fusion feature and its corresponding wildfire risk prediction result to the master station device so that the master station device can re-predict a new wildfire risk prediction result, and send the new wildfire risk prediction result to the IoT feeder terminal; Receive new wildfire risk prediction results issued by the master station equipment; Based on the handling strategy table, the handling strategy corresponding to the new wildfire risk prediction result is executed.

7. The method according to claim 5, characterized in that The method further comprises: An updated DS evidence theory model sent by a master station device is received, wherein the updated DS evidence theory model is trained by the master station device based on the data fusion features uploaded by the IoT feeder terminal and actual wildfire results.

8. A method for processing environmental information of a power distribution line, characterized in that: Applicable to master station equipment, including: Receive data fusion features uploaded by each IoT feeder terminal and its corresponding wildfire risk prediction result, wherein the data fusion features are obtained by the IoT feeder terminal performing data selection and feature extraction fusion of an online sequential extreme learning machine (OS-ELM) model on multiple environmental information time series data of the local area where the IoT feeder terminal is located collected by multiple sensors; the wildfire risk prediction result is obtained by the IoT feeder terminal inputting the data fusion features into a pre-stored DS evidence theory model and executing the DS evidence theory model; Based on the data fusion characteristics uploaded by each IoT feeder terminal and its corresponding wildfire risk prediction result, determine whether the wildfire risk prediction result of the IoT feeder terminal is accurate; When the wildfire risk prediction result of the Internet of Things feeder terminal is inaccurate, a new wildfire risk prediction result is obtained and the new wildfire risk prediction result is sent to the Internet of Things feeder terminal.

9. The method according to claim 8, characterized in that The determining whether the wildfire risk prediction result of the IoT feeder terminal is accurate based on the data fusion characteristics uploaded by each IoT feeder terminal and its corresponding wildfire risk prediction result includes: For each IoT feeder terminal, obtaining the wildfire risk prediction results of other IoT feeder terminals within a predetermined distance range from the IoT feeder terminal; Determining whether the wildfire risk prediction result of the IoT feeder terminal is correct based on the wildfire risk prediction results of the other IoT feeder terminals; When the wildfire risk prediction result of the IoT feeder terminal is inaccurate, obtaining a new wildfire risk prediction result includes: When the wildfire risk prediction result of the Internet of Things feeder terminal is inaccurate, a new wildfire risk prediction result of the Internet of Things feeder terminal is determined based on the wildfire risk prediction results of the other Internet of Things feeder terminals.

10. The method according to claim 8, characterized in that The determining whether the wildfire risk prediction result of the IoT feeder terminal is accurate based on the data fusion characteristics uploaded by each IoT feeder terminal and its corresponding wildfire risk prediction result includes: For each IoT feeder terminal, obtaining data fusion features of other IoT feeder terminals within a predetermined distance range from the IoT feeder terminal; Inputting the data fusion features of the other IoT feeder terminals and the data fusion features of the IoT feeder terminal into the DS evidence theory model to obtain a new wildfire risk prediction result output by the DS evidence theory model; Compare the wildfire risk prediction result of the IoT feeder terminal with the new wildfire risk prediction result to determine whether the wildfire risk prediction result of the IoT feeder terminal is accurate.

11. The method according to claim 8, characterized in that The method further comprises: Based on the data fusion features uploaded by each IoT feeder terminal and its corresponding real wildfire results, the DS evidence theory model is trained to obtain an updated DS evidence theory model; The updated DS evidence theory model is sent to each IoT feeder terminal.

12. An environmental information processing device for a power distribution line, characterized in that: Applied to IoT feeder terminals, including: An acquisition module is configured to acquire multiple environmental information time series data of a local area where the IoT feeder terminal is located collected by multiple sensors; A selection module is configured to select target data from each piece of environmental information time series data; A fusion module is configured to input a plurality of selected target data into an online sequential extreme learning machine (OS-ELM) model to obtain data fusion features output by the OS-ELM model; The prediction module is configured to input the data fusion features into a pre-stored DS evidence theory model to obtain a wildfire risk prediction result output by the DS evidence theory model.

13. The device according to claim 12, characterized in that The selection module is configured as follows: Preprocessing the multiple environmental information time series data to obtain multiple environmental feature time series data that meet prediction requirements; The particle swarm PSO algorithm is used to select the target data from each environmental feature time series data.

14. The device according to claim 13, characterized in that The selection module adopts the particle swarm PSO algorithm to select the target data from each environmental feature time series data and is configured as follows: For each piece of environmental characteristic time series data, the environmental characteristic time series data is simulated as a particle swarm, and the speed and position of each particle in the particle swarm are determined according to the following formula: in, represents the velocity of the i-th particle at the k+1th update, represents the velocity of the i-th particle at the k-th update, represents the position of the i-th particle at the k+1th update, represents the position of the i-th particle at the k-th update, represents the individual extreme value of the i-th particle at the k-th update, represents the group extreme value at the kth update, c1 is the individual learning factor in the PSO algorithm, c2 is the social learning factor in the PSO algorithm, and w k is the inertia weight factor of the PSO algorithm at the kth update, and rand(0,1) represents a random value between 0 and 1; When the termination condition of the PSO algorithm is reached, the updating is stopped and the group extreme value at the last update is obtained as the target data selected from the environmental feature time series data.

15. The device according to claim 14, characterized in that The device also includes: The parameter determination module is configured to calculate the inertia weight factor w in the PSO algorithm according to the following formula k : Among them, w min and w max is the preset minimum and maximum value of the inertia weight factor, n is the sliding window of the inertia weight factor, and w j is the inertia weight factor at the jth update, β is the preset descent control parameter, T max is the maximum evolutionary generation; The individual learning factor c1 and the social learning factor c2 in the PSO algorithm are calculated according to the following formula: Among them, c min and c max is the preset minimum and maximum value of the learning factor.

16. The device according to claim 12, characterized in that The device also includes: A receiving module is configured to receive a disposal strategy table issued by a master station device, wherein the disposal strategy table records disposal strategies corresponding to wildfire risk prediction results; The strategy execution module is configured to execute the handling strategy corresponding to the wildfire risk prediction result based on the handling strategy table.

17. The device according to claim 16, characterized in that The device also includes: A data reporting module is configured to send the data fusion feature and its corresponding wildfire risk prediction result to a master station device so that the master station device re-predicts a new wildfire risk prediction result and sends the new wildfire risk prediction result to the IoT feeder terminal; A result receiving module is configured to receive new wildfire risk prediction results sent by the master station device; The new strategy execution module is configured to execute the treatment strategy corresponding to the new wildfire risk prediction result based on the treatment strategy table.

18. The device according to claim 16, characterized in that The device also includes: The model structure module is configured to receive an updated DS evidence theory model sent by a master station device, wherein the updated DS evidence theory model is trained by the master station device based on the data fusion features uploaded by the IoT feeder terminal and actual wildfire results.

19. An environmental information processing device for a power distribution line, characterized in that: Applicable to master station equipment, including: The data receiving module is configured to receive data fusion features uploaded by each IoT feeder terminal and its corresponding wildfire risk prediction result, wherein the data fusion features are obtained by the IoT feeder terminal performing data selection and feature extraction fusion of an online sequential extreme learning machine OS-ELM model on multiple environmental information time series data of the local area where the IoT feeder terminal is located collected by multiple sensors; the wildfire risk prediction result is obtained by the IoT feeder terminal inputting the data fusion features into a pre-stored DS evidence theory model and executing the DS evidence theory model; A result determination module is configured to determine whether the wildfire risk prediction result of the IoT feeder terminal is accurate based on the data fusion characteristics uploaded by each IoT feeder terminal and its corresponding wildfire risk prediction result; The result sending module is configured to obtain a new wildfire risk prediction result and send the new wildfire risk prediction result to the IoT feeder terminal when the wildfire risk prediction result of the IoT feeder terminal is inaccurate.

20. The device according to claim 19, characterized in that The result determination module is configured as follows: For each IoT feeder terminal, obtaining the wildfire risk prediction results of other IoT feeder terminals within a predetermined distance range from the IoT feeder terminal; Determining whether the wildfire risk prediction result of the IoT feeder terminal is correct based on the wildfire risk prediction results of the other IoT feeder terminals; The part of the result delivery module that obtains a new wildfire risk prediction result when the wildfire risk prediction result of the IoT feeder terminal is inaccurate is configured as follows: When the wildfire risk prediction result of the Internet of Things feeder terminal is inaccurate, a new wildfire risk prediction result of the Internet of Things feeder terminal is determined based on the wildfire risk prediction results of other Internet of Things feeder terminals.

21. The device according to claim 19, characterized in that The result determination module is configured as follows: For each IoT feeder terminal, obtaining data fusion features of other IoT feeder terminals within a predetermined distance range from the IoT feeder terminal; Inputting the data fusion features of the other IoT feeder terminals and the data fusion features of the IoT feeder terminal into the DS evidence theory model to obtain a new wildfire risk prediction result output by the DS evidence theory model; Compare the wildfire risk prediction result of the IoT feeder terminal with the new wildfire risk prediction result to determine whether the wildfire risk prediction result of the IoT feeder terminal is accurate.

22. The device according to claim 19, characterized in that The device also includes: A model training module is configured to train the DS evidence theory model based on the data fusion features uploaded by each IoT feeder terminal and its corresponding real wildfire results to obtain an updated DS evidence theory model; The model delivery module is configured to deliver the updated DS evidence theory model to each IoT feeder terminal.

23. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method according to any one of claims 1 to 11.

24. A readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed by a processor, the method described in any one of claims 1 to 11 is implemented.

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