Power failure area alarm method and device, electronic equipment and computer readable medium

Abstract

The embodiment of the invention discloses a power failure area alarm method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises the following steps: for each power device, executing a generation step: determining a target power data sequence corresponding to the power device; determining a sequence of a first data interception time period, a second data interception time period, a third data interception time period and a fourth data interception time period; generating a first power data sub-sequence set, a second power data sub-sequence set, a third power data sub-sequence set and a fourth power data sub-sequence set; generating equipment anomaly detection information; determining equipment abnormity reason information and equipment influence range information; generating equipment operation summary information; and executing power operation abnormity display, and performing alarm processing on the corresponding power failure area. According to the embodiment, the power operation abnormal condition corresponding to the target power area can be accurately and efficiently displayed, and equipment maintenance and power failure warning can be carried out on the power equipment in abnormal operation in time.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to a power outage area alarm method, device, electronic device, and computer-readable medium. Background Art

[0002] At present, with the continuous development of electricity, abnormal detection of electricity is also one of the directions that the power field needs to focus on. For abnormal detection of power equipment, the usual method is to receive abnormal detection information corresponding to each power equipment in the power area, which is obtained through manual detection, to obtain the abnormal operation status of each power equipment.

[0003] However, when the above method is used to determine the abnormal operation of power equipment in the power area, the following technical problems often occur: There are a large number of power equipment in the power area. The efficiency of abnormal detection through manual detection is low, and the accuracy cannot be effectively guaranteed. In addition, the results of manual detection need to be summarized and sent, which wastes a lot of time. As a result, when faced with abnormal power equipment, the maintenance of power equipment cannot be carried out in time, which will waste power resources.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0005] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0006] Some embodiments of the present disclosure propose a power outage area alarm method, device, electronic device, and computer-readable medium to solve one or more of the technical problems mentioned in the above background technology section.

[0007] In a first aspect, some embodiments of the present disclosure provide a method for warning a power outage area, comprising: obtaining a set of power data sequences corresponding to each power device in a target power area, wherein each power device has a corresponding power data sequence; for each of the above-mentioned power devices, performing the following generation steps: determining the power data sequence corresponding to the above-mentioned power device as a target power data sequence; determining a first data interception time period, a second data interception time period, a third data interception time period and a fourth data interception time period sequence corresponding to the above-mentioned power device for the current time, wherein the above-mentioned first data interception time period includes the above-mentioned second data interception time period, the corresponding duration of the above-mentioned first data interception time period is greater than the corresponding duration of the above-mentioned second data interception time period, the above-mentioned fourth data interception time period sequence includes the above-mentioned third data interception time period, and the central time corresponding to the above-mentioned third data interception time period has the same date relationship with the above-mentioned current time; based on the above-mentioned target power data sequence, generating a sequence of the above-mentioned first data interception time period for the target power data sequence. a first power data subsequence corresponding to the above-mentioned second data interception time period, a second power data subsequence corresponding to the above-mentioned second data interception time period, a third power data subsequence corresponding to the above-mentioned third data interception time period, and a fourth power data subsequence set corresponding to the above-mentioned fourth data interception time period; based on the above-mentioned first power data subsequence, the above-mentioned second power data subsequence, the above-mentioned third power data subsequence, and the above-mentioned fourth power data subsequence set, using a pre-trained device abnormality detection model, generate equipment abnormality detection information for the above-mentioned power equipment; in response to determining that the above-mentioned equipment abnormality detection information represents the operation abnormality of the above-mentioned power equipment, determine the equipment abnormality cause information and equipment impact range information corresponding to the above-mentioned power equipment; based on the above-mentioned equipment abnormality cause information, the above-mentioned target power data sequence, and the above-mentioned equipment impact range information, generate equipment operation summary information for the above-mentioned power equipment; based on the obtained equipment operation summary information, perform power operation abnormality display for each of the above-mentioned power equipment, and perform alarm processing on the power outage area corresponding to each of the above-mentioned power equipment.

[0008] In a second aspect, some embodiments of the present disclosure provide a power outage area alarm device, including: an acquisition unit, configured to acquire a set of power data sequences corresponding to each power device in a target power area, wherein each power device has a corresponding power data sequence; a generation unit, configured to perform the following generation steps for each of the above-mentioned power devices: determine the power data sequence corresponding to the above-mentioned power device as a target power data sequence; determine a first data interception time period, a second data interception time period, a third data interception time period and a fourth data interception time period sequence corresponding to the above-mentioned power device for the current time, wherein the above-mentioned first data interception time period includes the above-mentioned second data interception time period, the corresponding duration of the above-mentioned first data interception time period is greater than the corresponding duration of the above-mentioned second data interception time period, the above-mentioned fourth data interception time period sequence includes the above-mentioned third data interception time period, and the central time corresponding to the above-mentioned third data interception time period has the same date relationship with the above-mentioned current time; generate the above-mentioned first data interception time period according to the above-mentioned target power data sequence. a first power data subsequence corresponding to a time period, a second power data subsequence corresponding to the second data interception time period, a third power data subsequence corresponding to the third data interception time period, and a fourth power data subsequence set corresponding to the fourth data interception time period; based on the first power data subsequence, the second power data subsequence, the third power data subsequence, and the fourth power data subsequence set, using a pre-trained device abnormality detection model, generating device abnormality detection information for the above-mentioned power equipment; in response to determining that the above-mentioned device abnormality detection information represents the operation abnormality of the above-mentioned power equipment, determining the device abnormality cause information and the equipment impact range information corresponding to the above-mentioned power equipment; based on the above-mentioned device abnormality cause information, the above-mentioned target power data sequence, and the above-mentioned equipment impact range information, generating device operation summary information for the above-mentioned power equipment; an execution unit, configured to perform power operation abnormality display for each of the above-mentioned power equipment according to the obtained equipment operation summary information, and perform alarm processing on the power outage area corresponding to each of the above-mentioned power equipment.

[0009] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

[0010] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner in the first aspect is implemented.

[0011] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the power outage area alarm method of some embodiments of the present disclosure, the abnormal power operation situation corresponding to the target power area can be accurately and efficiently displayed, and the abnormal power equipment can be timely maintained. Specifically, the reason why the relevant power operation abnormality is not accurate enough is that there are a large number of power equipment in the power area, and the efficiency of abnormal detection through manual detection is low, and the accuracy cannot be effectively guaranteed. In addition, it is necessary to summarize and send the results of manual detection, which wastes a lot of time, resulting in the inability to timely maintain the power equipment in the face of abnormal operation, which will result in a waste of power resources.

[0012] Based on this, the power outage area alarm method of some embodiments of the present disclosure first obtains the power data sequence set corresponding to each power device in the target power area. Among them, each power device has a corresponding power data sequence. Here, the power data sequence set is used as the basic data set of the device, so as to accurately generate the device abnormality detection information corresponding to each device in the subsequent. Then, for each of the above-mentioned power devices, the following generation steps are performed: the first step is to determine the power data sequence corresponding to the above-mentioned power device as the target power data sequence, so as to serve as the operation data set corresponding to the power device, so as to facilitate the subsequent generation of device abnormality detection information according to the operation status reflected by the operation data set. The second step is to determine the first data interception time period, the second data interception time period, the third data interception time period and the fourth data interception time period sequence corresponding to the above-mentioned power device for the current time. Among them, the above-mentioned first data interception time period includes the above-mentioned second data interception time period. The corresponding duration of the above-mentioned first data interception time period is greater than the corresponding duration of the above-mentioned second data interception time period. The above-mentioned fourth data interception time period sequence includes the above-mentioned third data interception time period. The central time corresponding to the above-mentioned third data interception time period has the same date relationship with the above-mentioned current time. Here, for the current time, different data interception time periods are set to facilitate the subsequent power data subsequence sets under each data interception time period. In the third step, according to the above target power data sequence, the first power data subsequence corresponding to the above first data interception time period, the second power data subsequence corresponding to the above second data interception time period, the third power data subsequence corresponding to the above third data interception time period, and the fourth power data subsequence set corresponding to the above fourth data interception time period are generated to obtain power data for each period of the current time, adding more power data features to make subsequent equipment abnormality detection more accurate. In the fourth step, according to the above first power data subsequence, the above second power data subsequence, the above third power data subsequence, and the above fourth power data subsequence set, the pre-trained equipment abnormality detection model can be used to accurately generate equipment abnormality detection information for the above power equipment. In the fifth step, in response to determining that the above equipment abnormality detection information represents the abnormal operation of the above power equipment, the equipment abnormality cause information and equipment impact range information corresponding to the above power equipment can be accurately determined, so as to facilitate the subsequent determination of the comprehensive state of the equipment operation process corresponding to the power equipment. In the sixth step, based on the above-mentioned equipment abnormality cause information, the above-mentioned target power data sequence and the above-mentioned equipment impact range information, the equipment operation summary information for the above-mentioned power equipment can be accurately generated. Finally, based on the obtained equipment operation summary information, the power operation abnormality display for each of the above-mentioned power equipment is performed, and the alarm processing is performed on the power outage area corresponding to each of the above-mentioned power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0014] Figure 1 is a flow chart of some embodiments of the power outage area alarm method according to the present disclosure; Figure 2 It is a structural schematic diagram of some embodiments of the power outage area alarm device according to the present disclosure; Figure 3 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0015] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0016] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0017] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0018] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0019] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0020] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0021] refer to Figure 1 , shows a process 100 of some embodiments of the power outage area alarm method according to the present disclosure. The power outage area alarm method comprises the following steps: Step 101: Acquire a power data sequence set corresponding to each power device in a target power area.

[0022] In some embodiments, the execution subject (for example, an electronic device) of the above-mentioned power outage area alarm method can obtain the power data sequence set corresponding to each power device in the target power area through a wired connection method or a wireless connection method. Among them, each power device has a corresponding power data sequence. Among them, the target power area may be a power area where power equipment abnormality detection is to be performed. For example, the target power area may be Haidian District, Beijing. Each power device may be various types of power devices in the target power area. In practice, each power device may include: a transformer, a generator, and a motor. There is a one-to-one correspondence between the power devices in each power device and the power data sequence in the power data sequence set. The power data sequence can characterize the power data of the power device at the current time and within a predetermined historical time period before the current time. That is, the time period corresponding to the power data sequence includes: the current time period and the predetermined historical time period. The power data may be the equipment operation data of the power device. For example, for the power device as a transformer, the corresponding power data may include: voltage change, rated voltage, number of phases, and number of windings.

[0023] Step 102: for each of the above-mentioned electrical devices, perform the following generation steps: Step 1021, determining the power data sequence corresponding to the above power equipment as the target power data sequence.

[0024] In some embodiments, the execution subject may determine the power data sequence corresponding to the power device as the target power data sequence, wherein the target power data sequence is a power data sequence in the power data sequence set that characterizes the operation status of the device corresponding to the power device.

[0025] Step 1022, determining a sequence of a first data capture time period, a second data capture time period, a third data capture time period and a fourth data capture time period corresponding to the above-mentioned power equipment for the current time.

[0026] In some embodiments, the execution subject may determine the first data interception time period, the second data interception time period, the third data interception time period and the fourth data interception time period sequence corresponding to the power equipment for the current time. Among them, the first data interception time period includes the second data interception time period. The corresponding duration of the first data interception time period is greater than the corresponding duration of the second data interception time period. The fourth data interception time period sequence includes the third data interception time period. The central time corresponding to the third data interception time period has the same date relationship with the current time. The first data interception time period may be the sum of the time periods of the historical time periods under the first duration before the current time and the current time. The second data interception time period may be the sum of the time periods of the historical time periods under the second duration before the current time. The duration corresponding to the first duration is a multiple of the duration corresponding to the second duration. That is, the first data interception time period may be a historical time period representing a long term for the current time. The second data interception time period may be a historical time period representing a short term for the current time. For example, the current time is June 1, 2024. The corresponding second data interception time period may be the previous month before June 1, 2024. That is, the second data interception time period may be "May 1, 2024-June 1, 2024". The corresponding first data interception time period may be the year before June 1, 2024. That is, the first data interception time period may be "June 1, 2023-June 1, 2024". The time center point corresponding to the third data interception time period has a same-date association relationship with the current time. The same-date association relationship may be a same-day relationship. For example, the current time is June 1, 2024. The month and day corresponding to the time center point corresponding to the third data interception time period may be June 1. The third data interception time period may be a historical time period set for the current time and a predetermined time period range. The year corresponding to the third data interception time period is the year before the year corresponding to the current time. For example, the current time is June 1, 2024. The third data interception time period may be "May 1, 2023-July 1, 2023". The time center point corresponding to the third data interception time period may be June 1, 2023. Each fourth data interception time period in the fourth data interception time period sequence also has a same date association relationship with the current time. The year sequence corresponding to the fourth data interception time period sequence is each connected year. Moreover, the year corresponding to the third data interception time period is the largest year among the years corresponding to the fourth data interception time period sequence. For example, the current time is June 1, 2024. The year sequence corresponding to the fourth data interception time period sequence can be {2019, 2020, 2021, 2022, 2023}.The fourth data capture time period sequence can be {“May 1, 2019-July 1, 2019”, May 1, 2020-July 1, 2020, May 1, 2021-July 1, 2021, May 1, 2022-July 1, 2022, May 1, 2023-July 1, 2023}.

[0027] In some optional implementations of some embodiments, the determining of the sequence of the first data interception time period, the second data interception time period, the third data interception time period and the fourth data interception time period corresponding to the electric power device for the current time may include the following steps: The first step is to determine the equipment type and equipment location information corresponding to the above-mentioned power equipment. Among them, the equipment category can characterize the equipment category corresponding to the power equipment. In practice, the equipment type can be set according to the equipment function and related parameters of the power equipment. For example, the equipment type can be but not limited to at least one of the following: a transformer of the first specification, a transformer of the second specification, a generator of the first specification, and a generator of the second specification. The equipment location information can be the regional location of the power equipment in the target power area.

[0028] In the second step, according to the above-mentioned equipment type and the area information of the above-mentioned equipment, the first data interception time, the second data interception time, the third data interception time and the fourth data interception time sequence corresponding to the above-mentioned power equipment are obtained from the target association table. Among them, the target association table can characterize the correspondence between the equipment type, the area information of the equipment, the first data interception time, the second data interception time, the third data interception time and the fourth data interception time sequence. It can be obtained that the target association table can be a table pre-set for the target power area.

[0029] As an example, the execution entity may determine the first data capture duration, the second data capture duration, the third data capture duration and the fourth data capture duration sequence corresponding to the device type and the device location information by querying the target association table.

[0030] The third step is to obtain the power fluctuation cycle corresponding to the above power equipment, wherein the power fluctuation cycle can represent the operation state change cycle of the power equipment during operation.

[0031] The fourth step is to adjust the first data capture duration, the second data capture duration, the third data capture duration and the fourth data capture duration according to the power fluctuation cycle to generate a first adjustment duration, a second adjustment duration, a third adjustment duration and a fourth adjustment duration sequence.

[0032] As an example, the execution subject may determine the maximum value of the first data interception duration and the power fluctuation cycle as the first adjustment duration. The maximum value of the second data interception duration and the power fluctuation cycle is determined as the second adjustment duration. The maximum value of the third data interception duration and the power fluctuation cycle is determined as the third adjustment duration. The maximum value of each fourth data interception duration and the power fluctuation cycle is determined as the fourth adjustment duration, and a fourth adjustment duration sequence is obtained.

[0033] The fifth step is to generate a sequence of the first data capture time period, the second data capture time period, the third data capture time period and the fourth data capture time period based on the first adjustment time period, the second adjustment time period, the third adjustment time period, the fourth adjustment time period and the current time.

[0034] As an example, the execution subject may perform time segment capture for the current time according to the correspondence between the current time and the first adjustment duration to generate the first data capture time segment. Similarly, the generation of the second data capture time segment, the third data capture time segment, and the fourth data capture time segment sequence may refer to the generation of the first data capture time segment.

[0035] Step 1023, based on the above-mentioned target power data sequence, generate a first power data subsequence corresponding to the above-mentioned first data interception time period, a second power data subsequence corresponding to the above-mentioned second data interception time period, a third power data subsequence corresponding to the above-mentioned third data interception time period and a fourth power data subsequence set corresponding to the above-mentioned fourth data interception time period.

[0036] In some embodiments, the execution subject may generate, according to the target power data sequence, a first power data subsequence corresponding to the first data interception time period, a second power data subsequence corresponding to the second data interception time period, a third power data subsequence corresponding to the third data interception time period, and a fourth power data subsequence set corresponding to the fourth data interception time period. Among them, there is a one-to-one correspondence between the time in the first data interception time period and the first power data in the first power data subsequence. The first power data subsequence may characterize the equipment operation of the power equipment in the first data interception time period. There is a one-to-one correspondence between the time in the second data interception time period and the second power data in the second power data subsequence. The second power data subsequence may characterize the equipment operation of the power equipment in the second data interception time period. There is a one-to-one correspondence between the time in the third data interception time period and the third power data in the third power data subsequence. The third power data subsequence may characterize the equipment operation of the power equipment in the third data interception time period. There is a one-to-one correspondence between the time in the fourth data interception time period sequence and the fourth power data in the fourth power data subsequence set. The fourth power data subsequence set can represent the equipment operation status of the power equipment within the above-mentioned fourth data capture time period sequence.

[0037] As an example, the above-mentioned execution entity can extract a data set under the first data interception time period, a data set under the second data interception time period, a data set under the third data interception time period, and a data set under the fourth data interception time period sequence from the above-mentioned target power data sequence, as the first power data subsequence, the second power data subsequence, the third power data subsequence, and the fourth power data subsequence set, respectively.

[0038] Step 1024: Generate device anomaly detection information for the power equipment using a pre-trained device anomaly detection model based on the first power data subsequence, the second power data subsequence, the third power data subsequence and the fourth power data subsequence set.

[0039] In some embodiments, the execution subject may generate device anomaly detection information for the power equipment according to the first power data subsequence, the second power data subsequence, the third power data subsequence, and the fourth power data subsequence set, using a pre-trained device anomaly detection model. The device anomaly detection model may be a neural network model for generating device anomaly detection information. In practice, the device anomaly detection model may be trained by conventional back propagation. The device anomaly detection information may characterize whether the power equipment has an abnormal operating condition. In practice, the device anomaly detection information may be in the form of a score. The higher the value, the higher the probability of characterizing the existence of an abnormal problem in the power equipment. For example, the device anomaly detection model includes: multiple encoding layers, feature splicing layers, convolutional layers, and output layers based on a temporal neural network model.

[0040] As an example, first, the execution subject may perform data vectorization processing on the first power data subsequence, the second power data subsequence, the third power data subsequence, and the fourth power data subsequence set, respectively, to generate the first power vector subsequence, the second power vector subsequence, the third power vector subsequence, and the fourth power vector subsequence set. Then, the first power vector subsequence, the second power vector subsequence, the third power vector subsequence, and the fourth power vector subsequence set are spliced ​​to generate a power splicing vector. Finally, the power splicing vector is input into the device abnormality detection model to generate device abnormality detection information.

[0041] In some optional implementations of some embodiments, the generating of the device abnormality detection information for the power device using a pre-trained device abnormality detection model according to the first power data subsequence, the second power data subsequence, the third power data subsequence, and the fourth power data subsequence set may include the following steps: The first step is to remove the second power data subsequence from the first power data subsequence to obtain a power data subsequence after removal.

[0042] In the second step, the above-mentioned removed power data subsequence is input into the first time series feature extraction model included in the above-mentioned device abnormality detection model to generate the time series feature information corresponding to the above-mentioned removed power data subsequence as the first time series feature information. The first time series feature extraction model can be a neural network model for extracting time series feature information. For example, the first time series feature extraction model can be a first number of layers of recurrent neural network model.

[0043] In the third step, the first time series feature information is input into the second time series feature extraction model included in the device abnormality detection model to generate the time series feature information corresponding to the first power data subsequence as the second time series feature information. The second time series feature extraction model may be a neural network model for extracting time series feature information. For example, the first time series feature extraction model may be a recurrent neural network model with a second number of layers.

[0044] In a fourth step, the second power data subsequence is input into the second time series feature extraction model to generate time series feature information corresponding to the second power data subsequence as third time series feature information.

[0045] The fifth step is to concatenate the first time series characteristic information and the second time series characteristic information to generate first concatenated information.

[0046] In the sixth step, the first time series characteristic information and the third time series characteristic information are concatenated to generate second concatenated information.

[0047] In the seventh step, the first splicing information and the second splicing information are spliced ​​in the depth direction to generate third splicing information, wherein the depth direction may be the depth direction of a matrix or an image.

[0048] In the eighth step, the third splicing information is input into the attention mechanism model included in the device abnormality detection model to generate a future power data subsequence corresponding to the target future time period. For example, the attention mechanism model can be a multi-head attention mechanism model. The future power data subsequence can be the future power data corresponding to each future time point in the target future time period.

[0049] In the ninth step, the future power data subsequence and the second power data subsequence are concatenated to generate a first future power data sequence.

[0050] The tenth step is to generate at least one data sequence indicator information corresponding to the third power data subsequence as at least one first data sequence indicator information. The data sequence indicator information can characterize the indicator content of the third power data subsequence under the corresponding sequence indicator. The sequence indicator can characterize the characteristic performance of the third power data subsequence in a certain aspect. For example, the data sequence indicator information may include: increase rate, growth amount, decrease rate, decrease amount, fluctuation amount, and average amount.

[0051] Step 11: Generate at least one data sequence indicator information corresponding to the first future power data sequence as at least one second data sequence indicator information. For specific implementation methods, please refer to the generation of the first data sequence indicator information. In step 12, in response to determining that the indicator information difference between the at least one first data sequence indicator information and the at least one second data sequence indicator information satisfies the first preset indicator condition, determine the data subsequence indicator information corresponding to the fourth power data subsequence set as the first data subsequence indicator information. The first preset indicator condition may be that the indicator difference corresponding to each sequence indicator is greater than the corresponding preset difference.

[0052] In the thirteenth step, the first future power data sequence is added to the fourth power data subsequence set to generate a first added power data subsequence set.

[0053] The fourteenth step is to determine the inter-data subsequence indicator information corresponding to the first added power data subsequence set as the second inter-data subsequence indicator information.

[0054] In step 15, in response to determining that the index information difference between the index information between the first data subsequence and the index information between the second data subsequence meets the second preset index condition, the first future power data sequence is input into the first device abnormality detection information generation model included in the device abnormality detection model to generate the first device abnormality detection information. Among them, the second preset index condition may be that the index difference corresponding to each sequence index is less than or equal to the corresponding preset difference. The first device abnormality detection information generation model may be a neural network model for generating device abnormality detection information. For example, the first device abnormality detection information generation model may be an LSTM model. And the first device abnormality detection information generation model may be trained together with the second device abnormality detection information generation model. The second device abnormality detection information generation model may be a neural network model for generating device abnormality detection information. The network structure between the first device abnormality detection information generation model and the second device abnormality detection information generation model may be the same or different, and the specific customized setting is based on the scenario.

[0055] In the sixteenth step, the future power data subsequence and the first power data subsequence are concatenated to generate a second future power data sequence.

[0056] In the seventeenth step, the second future power data sequence is input into the second device abnormality detection information generation model included in the device abnormality detection model to generate second device abnormality detection information.

[0057] In the eighteenth step, the model output weight corresponding to the first device abnormality detection information generation model and the model output weight corresponding to the second device abnormality detection information generation model are obtained as the first model output weight and the second model output weight respectively.

[0058] In the nineteenth step, the device abnormality detection information is generated according to the output weight of the first model, the output weight of the second model, the first device abnormality detection information and the second device abnormality detection information.

[0059] As an example, the execution entity may perform weighted sum processing on the first model output weight, the second model output weight, the first device anomaly detection information, and the second device anomaly detection information to generate the device anomaly detection information.

[0060] Optionally, the steps further include: In the first step, in response to determining that the indicator information difference between the at least one first data sequence indicator information and the at least one second data sequence indicator information does not meet the first preset indicator condition, a candidate power data subsequence corresponding to the first historical time period is determined from the third power data subsequence as the first candidate power data subsequence. The first historical time period and the target future time period have a same date correspondence relationship.

[0061] In the second step, for each fourth power data subsequence in the fourth power data subsequence set, a candidate power data subsequence corresponding to the second historical time period is determined from the fourth power data subsequence as the second candidate power data subsequence, wherein the second historical time period has a same date correspondence with the target future time period.

[0062] The third step is to generate at least one data sequence indicator information corresponding to the first candidate power data subsequence as at least one third data sequence indicator information.

[0063] The fourth step is to generate at least one data sequence indicator information corresponding to the above-mentioned future power data subsequence as at least one fourth data sequence indicator information.

[0064] The fifth step, in response to determining that the indicator information difference between the above-mentioned at least one third data sequence indicator information and the above-mentioned at least one fourth data sequence indicator information meets the above-mentioned first preset indicator condition, determines the inter-data subsequence indicator information corresponding to the above-mentioned fourth power data subsequence set as the first inter-data subsequence indicator information.

[0065] The sixth step is to add the first future power data sequence to the fourth power data subsequence set to generate an added power data subsequence set.

[0066] The seventh step is to determine the inter-data subsequence indicator information corresponding to the added power data subsequence set as the second inter-data subsequence indicator information.

[0067] In the eighth step, in response to determining that the indicator information difference between the indicator information between the above-mentioned first data subsequences and the indicator information between the above-mentioned second data subsequences does not meet the second preset indicator condition, the above-mentioned future power data subsequence is added to the obtained second candidate power data subsequence set to generate a third added power data subsequence set.

[0068] The ninth step is to determine the data subsequence index information corresponding to the second candidate power data subsequence set as the third data subsequence index information.

[0069] The tenth step is to determine the inter-data subsequence indicator information corresponding to the third added power data subsequence set as the fourth inter-data subsequence indicator information.

[0070] In the eleventh step, in response to determining that the indicator information difference between the indicator information between the third data subsequences and the indicator information between the fourth data subsequences satisfies the second preset indicator condition, the first future power data sequence is input into the first device abnormality detection information generation model included in the device abnormality detection model to generate the first device abnormality detection information.

[0071] In the twelfth step, the future power data subsequence and the first power data subsequence are concatenated to generate a second future power data sequence.

[0072] In the thirteenth step, the second future power data sequence is input into the second device abnormality detection information generation model included in the device abnormality detection model to generate second device abnormality detection information.

[0073] In the fourteenth step, the model output weight corresponding to the first device abnormality detection information generation model and the model output weight corresponding to the second device abnormality detection information generation model are obtained as the first model output weight and the second model output weight respectively.

[0074] In the fifteenth step, the device abnormality detection information is generated according to the output weight of the first model, the output weight of the second model, the first device abnormality detection information and the second device abnormality detection information.

[0075] As an example, the execution entity may perform weighted sum processing on the first model output weight, the second model output weight, the first device anomaly detection information, and the second device anomaly detection information to generate device anomaly detection information.

[0076] Step 1025, in response to determining that the device abnormality detection information indicates that the power device is operating abnormally, determine device abnormality cause information and device impact range information corresponding to the power device.

[0077] In some embodiments, in response to determining that the above-mentioned device abnormality detection information indicates that the above-mentioned power equipment is operating abnormally, the above-mentioned execution subject may determine the device abnormality cause information and device impact range information corresponding to the above-mentioned power equipment. In practice, when the score corresponding to the device abnormality detection information is greater than 70 points, it indicates that the power equipment is operating abnormally. The device abnormality cause information may be the cause of the abnormality of the power equipment. The device impact range information may be the area range where the power transmission is affected by the abnormality of the equipment.

[0078] As an example, the above-mentioned execution subject can generate the above-mentioned equipment abnormality cause information based on the target power data sequence using the power equipment operation abnormality cause generation model. In practice, the power equipment operation abnormality cause generation model can be a classification model for generating equipment abnormality cause information. For example, the power equipment operation abnormality cause generation model can be a support vector machine. The above-mentioned execution subject can obtain the query power equipment association graph. Then, based on the equipment abnormality cause information, determine the associated electronic devices that affect the operation. Then, by traversing the associated electronic devices on the power equipment association graph, determine the affected power equipment set. Finally, based on the affected power equipment set, generate the equipment impact range information.

[0079] Step 1026: Generate equipment operation summary information for the power equipment based on the equipment abnormality cause information, the target power data sequence and the equipment impact range information.

[0080] In some embodiments, the execution subject may generate equipment operation summary information for the power equipment according to the equipment abnormality cause information, the target power data sequence and the equipment impact range information. The equipment operation summary information may be information that summarizes various data of the power equipment during operation.

[0081] As an example, the execution subject may generate equipment operation summary information using the equipment identifier corresponding to the power equipment as a key and the equipment abnormality cause information, the target power data sequence and the corresponding content of the equipment impact range information as values.

[0082] In some optional implementations of some embodiments, the generating of the equipment operation summary information for the power equipment according to the equipment abnormality cause information, the target power data sequence and the equipment impact range information may include the following steps: The first step is to determine the power data trend graph corresponding to the target power data sequence, wherein the power data trend graph may represent a change trend graph of the target power data sequence in at least one power feature.

[0083] The second step is to obtain the abnormal maintenance status, abnormal maintenance time and corresponding real-time maintenance video corresponding to the above-mentioned power equipment. The abnormal maintenance status can be information indicating whether the current power equipment is in a maintenance state. That is, the abnormal maintenance status can be one of the following: in a maintenance state, not in a maintenance state. The abnormal maintenance time can be the time required for the current power equipment to be successfully repaired. The real-time maintenance video can be a video of the real-time maintenance processing of the power equipment.

[0084] The third step is to generate a video link corresponding to the above real-time maintenance video, wherein the video link can be a video opening link corresponding to the real-time maintenance video.

[0085] The fourth step is to splice the above-mentioned equipment abnormality cause information, the above-mentioned target power data sequence, the above-mentioned equipment impact range information, the above-mentioned abnormal maintenance status and the above-mentioned video link according to a predetermined format to obtain the above-mentioned equipment operation summary information.

[0086] Step 103, based on the obtained summary information of the operation of each device, execute the display of power operation abnormality for the above-mentioned each power device, and perform alarm processing on the power outage area corresponding to the above-mentioned each power device.

[0087] In some embodiments, the execution subject may display power operation anomalies for each of the power devices and perform alarm processing for the power outage areas corresponding to each of the power devices based on the obtained summary information of the operation of each device.

[0088] As an example, the execution subject may use the summary information of each device operation as the device operation data of each power device, and display the operation status of each power device on the target interface.

[0089] In some optional implementations of some embodiments, the above-mentioned display of power operation abnormality for each of the above-mentioned power devices according to the obtained summary information of operation of each device may include the following steps: The first step is to determine the power operation impact information corresponding to each sub-area in the target power area according to the above-mentioned summary information of the operation of each device, and obtain each power operation impact information. Among them, the power operation impact information can represent the information of the power operation status in the sub-area. The power operation impact information can be numerical information. The higher the corresponding numerical value of the power operation impact information, the better the operation status in the corresponding sub-area.

[0090] As an example, for each sub-area, first, based on the summary information of the operation of each device, determine the power equipment in the sub-area that has abnormalities, and obtain the abnormal power equipment set. Then, determine the influence coefficient corresponding to each power equipment in the abnormal power equipment set, and obtain the influence coefficient set. Among them, the influence coefficient can represent the importance of the normal operation of the power equipment. Finally, add the influence coefficients in the influence coefficient set to generate an added value as the power operation influence information.

[0091] In the second step, according to the above-mentioned power operation impact information and the above-mentioned equipment operation summary information, determine the rendering mode corresponding to each power equipment and the rendering mode corresponding to each sub-area. Among them, the rendering mode includes: transparent rendering mode and color rendering mode. Among them, the transparent rendering mode can be a mode of performing semi-transparent rendering processing of power equipment or sub-areas on the digital twin model corresponding to the target power area. The color rendering mode can be a mode of performing color rendering processing of power equipment or sub-areas on the digital twin model corresponding to the target power area.

[0092] As an example, the execution subject may obtain a first association table representing the correspondence between the device abnormality cause information and the rendering mode, and determine the rendering mode corresponding to each power device according to the summary information of each device operation. The execution subject may obtain a second association table representing the correspondence between the power operation impact information and the rendering mode, and determine the rendering mode corresponding to each sub-area according to each power operation impact information.

[0093] In the third step, according to the rendering method corresponding to each power device and the rendering method corresponding to each sub-area, corresponding rendering processing is performed on each power device and each sub-area in the digital twin model corresponding to the above-mentioned target power area.

[0094] In some optional implementations of some embodiments, after step 103, the steps further include: In the first step, in response to receiving the equipment display information for the target power equipment in the above-mentioned digital twin model, a device information category selection interface for the above-mentioned target power equipment is popped up. Among them, the target power equipment may be a device for which relevant equipment information is to be displayed. The equipment display information may be equipment information related to the target power equipment. The equipment information category selection interface may be an interface for selecting equipment information categories. The equipment information category selection interface displays multiple equipment information categories. The multiple equipment information categories may include: equipment cause category, equipment impact range category, abnormal maintenance status category, abnormal maintenance time category, and real-time maintenance video category.

[0095] The second step is to determine the device information category corresponding to the device information category selection information as the target device information category in response to receiving the device information category selection information for the device information category selection interface, wherein the device information category selection information may be information for selecting the target device information category.

[0096] The third step is to obtain the category information corresponding to the target equipment information category from the equipment operation summary information corresponding to the target power equipment.

[0097] The fourth step is to display the above category information in the target position of the digital twin model on the interface, where the target position can be a pre-set position.

[0098] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the power outage area alarm method of some embodiments of the present disclosure, the corresponding power operation abnormality of the target power area can be accurately and efficiently displayed, and the equipment maintenance of the abnormally operating power equipment can be carried out in time. Specifically, the reason for the inaccuracy of the relevant power operation abnormality is that there are a large number of power equipment in the power area, and the efficiency of abnormal detection by manual detection is low, and the accuracy cannot be effectively guaranteed. In addition, it is necessary to summarize and send the results of the manual detection, which wastes a lot of time, resulting in the inability to timely maintain the power equipment in the face of abnormal operation of the power equipment, which will result in a waste of power resources. Based on this, the power outage area alarm method of some embodiments of the present disclosure, first, obtains the power data sequence set corresponding to each power equipment in the target power area. Among them, each power equipment has a corresponding power data sequence. Here, the power data sequence set is used as the basic data set of the equipment, so as to accurately generate the equipment abnormality detection information corresponding to each device in the subsequent. Then, for each of the above-mentioned power devices, the following generation steps are performed: the first step is to determine the power data sequence corresponding to the above-mentioned power device as the target power data sequence, as the operating data set corresponding to the power device, so as to facilitate the subsequent generation of equipment abnormality detection information according to the operating conditions reflected in the operating data set. The second step is to determine the first data interception time period, the second data interception time period, the third data interception time period and the fourth data interception time period sequence corresponding to the above-mentioned power device for the current time. Among them, the above-mentioned first data interception time period includes the above-mentioned second data interception time period. The corresponding duration of the above-mentioned first data interception time period is greater than the corresponding duration of the above-mentioned second data interception time period. The above-mentioned fourth data interception time period sequence includes the above-mentioned third data interception time period. The central time corresponding to the above-mentioned third data interception time period has the same date relationship with the above-mentioned current time. Here, for the current time, different data interception time periods are set to facilitate the subsequent power data subsequence sets under each data interception time period. In the third step, based on the above target power data sequence, the first power data subsequence corresponding to the above first data interception time period, the second power data subsequence corresponding to the above second data interception time period, the third power data subsequence corresponding to the above third data interception time period, and the fourth power data subsequence set corresponding to the above fourth data interception time period are generated to obtain power data for each period of the current time, adding more power data features to make subsequent equipment anomaly detection more accurate. In the fourth step, based on the above first power data subsequence, the above second power data subsequence, the above third power data subsequence, and the above fourth power data subsequence set, the pre-trained equipment anomaly detection model can be used to accurately generate equipment anomaly detection information for the above power equipment.In the fifth step, in response to determining that the above-mentioned equipment abnormality detection information represents the above-mentioned power equipment operation abnormality, the equipment abnormality cause information and equipment impact range information corresponding to the above-mentioned power equipment can be accurately determined, so as to facilitate the subsequent determination of the comprehensive state of the equipment operation process corresponding to the power equipment. In the sixth step, based on the above-mentioned equipment abnormality cause information, the above-mentioned target power data sequence and the above-mentioned equipment impact range information, the equipment operation summary information for the above-mentioned power equipment can be accurately generated. Finally, based on the obtained summary information of each equipment operation, the power operation abnormality display for each of the above-mentioned power equipment is executed, and the power outage area corresponding to each of the above-mentioned power equipment is alarmed.

[0099] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a power outage area alarm device. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the power outage area alarm device can be specifically applied to various electronic devices.

[0100] like Figure 2As shown, a power outage area alarm device 200 includes: an acquisition unit 201, a generation unit 202 and an execution unit 203. The acquisition unit 201 is configured to acquire a set of power data sequences corresponding to each power device in the target power area, wherein each power device has a corresponding power data sequence; the generation unit 202 is configured to perform the following generation steps for each of the above-mentioned power devices: determine the power data sequence corresponding to the above-mentioned power device as the target power data sequence; determine the first data interception time period, the second data interception time period, the third data interception time period and the fourth data interception time period sequence corresponding to the above-mentioned power device for the current time, wherein the above-mentioned first data interception time period includes the above-mentioned second data interception time period, the corresponding duration of the above-mentioned first data interception time period is greater than the corresponding duration of the above-mentioned second data interception time period, the above-mentioned fourth data interception time period sequence includes the above-mentioned third data interception time period, and the central time corresponding to the above-mentioned third data interception time period has the same date relationship with the above-mentioned current time; according to the above-mentioned target power data sequence, generate the first power data sequence corresponding to the above-mentioned first data interception time period subsequence, a second power data subsequence corresponding to the above-mentioned second data interception time period, a third power data subsequence corresponding to the above-mentioned third data interception time period and a fourth power data subsequence set corresponding to the above-mentioned fourth data interception time period; based on the above-mentioned first power data subsequence, the above-mentioned second power data subsequence, the above-mentioned third power data subsequence and the above-mentioned fourth power data subsequence set, using a pre-trained equipment abnormality detection model, generate equipment abnormality detection information for the above-mentioned power equipment; in response to determining that the above-mentioned equipment abnormality detection information represents the abnormal operation of the above-mentioned power equipment, determine the equipment abnormality cause information and equipment impact range information corresponding to the above-mentioned power equipment; based on the above-mentioned equipment abnormality cause information, the above-mentioned target power data sequence and the above-mentioned equipment impact range information, generate equipment operation summary information for the above-mentioned power equipment; the execution unit 203 is configured to perform power operation abnormality display for each of the above-mentioned power equipment according to the obtained equipment operation summary information, and perform alarm processing on the power outage area corresponding to each of the above-mentioned power equipment.

[0101] It is understandable that the units recorded in the power outage area alarm device 200 are similar to those in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the power outage area alarm device 200 and the units contained therein, and will not be repeated here.

[0102] Reference below Figure 3 , which shows a structural schematic diagram of an electronic device (eg, an electronic device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0103] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 to a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0104] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0105] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0106] It should be noted that the computer-readable medium in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0107] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0108] The computer-readable medium may be included in the electronic device; or it may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains a set of power data sequences corresponding to each power device in the target power area, wherein each power device has a corresponding power data sequence; for each of the power devices, the following generation steps are performed: determine the power data sequence corresponding to the power device as the target power data sequence; determine the first data interception time period, the second data interception time period, the third data interception time period and the fourth data interception time period sequence corresponding to the power device for the current time, wherein the first data interception time period includes the second data interception time period, the corresponding duration of the first data interception time period is greater than the corresponding duration of the second data interception time period, the fourth data interception time period sequence includes the third data interception time period, and the central time corresponding to the third data interception time period has the same date relationship with the current time; based on the target power data sequence, generate the first data interception time period, the second data interception time period, the third data interception time period and the fourth data interception time period sequence. A first power data subsequence corresponding to a data interception time period, a second power data subsequence corresponding to the second data interception time period, a third power data subsequence corresponding to the third data interception time period and a fourth power data subsequence set corresponding to the fourth data interception time period; based on the first power data subsequence, the second power data subsequence, the third power data subsequence and the fourth power data subsequence set, using a pre-trained device abnormality detection model, generating equipment abnormality detection information for the above-mentioned power equipment; in response to determining that the above-mentioned equipment abnormality detection information represents the abnormal operation of the above-mentioned power equipment, determining the equipment abnormality cause information and equipment impact range information corresponding to the above-mentioned power equipment; based on the above-mentioned equipment abnormality cause information, the above-mentioned target power data sequence and the above-mentioned equipment impact range information, generating equipment operation summary information for the above-mentioned power equipment; based on the obtained equipment operation summary information, performing power operation abnormality display for each of the above-mentioned power equipment, and performing alarm processing on the power outage area corresponding to each of the above-mentioned power equipment.

[0109] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0110] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the 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 implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square 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 square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart 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.

[0111] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be provided in a processor, for example, may be described as: a processor includes an acquisition unit, a generation unit, and an execution unit. The names of these units do not constitute a limitation on the units themselves in certain circumstances, for example, the acquisition unit may also be described as a "unit for acquiring a set of power data sequences corresponding to each power device in the target power area".

[0112] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0113] The above descriptions are only some preferred embodiments 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 embodiments of the present disclosure is not limited to the technical solutions 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 above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A power outage area alarm method, comprising: Acquire a power data sequence set corresponding to each power device in the target power area, wherein each power device has a corresponding power data sequence; For each of the various electrical devices, the following generation steps are performed: determining a power data sequence corresponding to the power equipment as a target power data sequence; Determine a first data interception time period, a second data interception time period, a third data interception time period and a fourth data interception time period sequence corresponding to the electric power equipment for the current time, wherein the first data interception time period includes the second data interception time period, the corresponding duration of the first data interception time period is greater than the corresponding duration of the second data interception time period, the fourth data interception time period sequence includes the third data interception time period, and the central time corresponding to the third data interception time period has the same date relationship with the current time; According to the target power data sequence, a first power data subsequence corresponding to the first data interception time period, a second power data subsequence corresponding to the second data interception time period, a third power data subsequence corresponding to the third data interception time period, and a fourth power data subsequence set corresponding to the fourth data interception time period are generated; Generate device abnormality detection information for the power device using a pre-trained device abnormality detection model according to the first power data subsequence, the second power data subsequence, the third power data subsequence, and the fourth power data subsequence set; In response to determining that the device abnormality detection information indicates that the power device is operating abnormally, determining device abnormality cause information and device impact range information corresponding to the power device; Generate equipment operation summary information for the power equipment according to the equipment abnormality cause information, the target power data sequence and the equipment impact range information; Based on the obtained summary information of the operation of each device, the abnormal power operation of each power device is displayed, and the alarm processing is performed on the power outage area corresponding to each power device.

2. The method according to claim 1, wherein: The performing of displaying power operation abnormality for each power device according to the obtained summary information of operation of each device includes: Determine the power operation impact information corresponding to each sub-area in the target power area according to the summary information of each device operation, and obtain each power operation impact information; Determine, according to the individual power operation impact information and the individual equipment operation summary information, a rendering mode corresponding to each power equipment and a rendering mode corresponding to each sub-area, wherein the rendering modes include: a transparent rendering mode and a color rendering mode; According to the rendering mode corresponding to each power device and the rendering mode corresponding to each sub-area, corresponding rendering processing is performed on each power device and each sub-area in the digital twin model corresponding to the target power area.

3. The method according to claim 1, wherein: The determining of a sequence of a first data interception time period, a second data interception time period, a third data interception time period and a fourth data interception time period corresponding to the electric power equipment for the current time includes: Determine the device type corresponding to the electric power device and the device location information; According to the device type and the area information of the device, obtaining a first data interception time, a second data interception time, a third data interception time and a fourth data interception time sequence corresponding to the power device from the target association table; Obtaining a power fluctuation period corresponding to the power equipment; According to the power fluctuation cycle, adjusting the first data interception duration, the second data interception duration, the third data interception duration, and the fourth data interception duration to generate a first adjustment duration, a second adjustment duration, a third adjustment duration, and a fourth adjustment duration sequence; A sequence of the first data interception time period, the second data interception time period, the third data interception time period and the fourth data interception time period is generated according to the first adjustment time period, the second adjustment time period, the third adjustment time period, the fourth adjustment time period and the current time.

4. The method according to claim 2, wherein: The generating of equipment operation summary information for the power equipment according to the equipment abnormality cause information, the target power data sequence and the equipment impact range information includes: Determine a power data trend graph corresponding to the target power data sequence; Obtaining the abnormal maintenance status, abnormal maintenance time and corresponding real-time maintenance video corresponding to the electric power equipment; Generate a video link corresponding to the real-time maintenance video; The equipment abnormality cause information, the target power data sequence, the equipment impact range information, the abnormal maintenance status and the video link are spliced ​​according to a predetermined format to obtain the equipment operation summary information.

5. The method according to claim 4, wherein: The method further comprises: In response to receiving device display information for a target power device in the digital twin model, popping up a device information category selection interface for the target power device; In response to receiving device information category selection information for the device information category selection interface, determining a device information category corresponding to the device information category selection information as a target device information category; Acquire category information corresponding to the target equipment information category from the equipment operation summary information corresponding to the target power equipment; The category information is displayed on an interface at a target location in the digital twin model.

6. The method according to claim 1, wherein: The generating of device abnormality detection information for the power device using a pre-trained device abnormality detection model according to the first power data subsequence, the second power data subsequence, the third power data subsequence, and the fourth power data subsequence set includes: Removing the second power data subsequence from the first power data subsequence to obtain a post-removal power data subsequence; Inputting the removed power data subsequence into a first time series feature extraction model included in the device abnormality detection model to generate time series feature information corresponding to the removed power data subsequence as first time series feature information; Inputting the first time series feature information into a second time series feature extraction model included in the device abnormality detection model to generate time series feature information corresponding to the first power data subsequence as second time series feature information; Inputting the second power data subsequence into the second time series feature extraction model to generate time series feature information corresponding to the second power data subsequence as third time series feature information; splicing the first time series characteristic information and the second time series characteristic information to generate first splicing information; splicing the first time series characteristic information and the third time series characteristic information to generate second splicing information; Splicing the first splicing information and the second splicing information in a depth direction to generate third splicing information; Inputting the third splicing information into the attention mechanism model included in the device abnormality detection model to generate a future power data subsequence corresponding to the target future time period; splicing the future power data subsequence and the second power data subsequence to generate a first future power data sequence; generating at least one data sequence indicator information corresponding to the third power data subsequence as at least one first data sequence indicator information; generating at least one data sequence indicator information corresponding to the first future power data sequence as at least one second data sequence indicator information; In response to determining that the indicator information difference between the at least one first data sequence indicator information and the at least one second data sequence indicator information satisfies the first preset indicator condition, determining the inter-data subsequence indicator information corresponding to the fourth power data subsequence set as the first inter-data subsequence indicator information; adding the first future power data sequence to the fourth power data subsequence set to generate a first added power data subsequence set; Determine the inter-data subsequence indicator information corresponding to the first added power data subsequence set as the second inter-data subsequence indicator information; In response to determining that the indicator information difference between the indicator information between the first data subsequences and the indicator information between the second data subsequences satisfies a second preset indicator condition, inputting the first future power data sequence into a first device abnormality detection information generation model included in the device abnormality detection model to generate first device abnormality detection information; splicing the future power data subsequence and the first power data subsequence to generate a second future power data sequence; inputting the second future power data sequence into a second device abnormality detection information generation model included in the device abnormality detection model to generate second device abnormality detection information; Obtaining a model output weight corresponding to the first device abnormality detection information generation model and a model output weight corresponding to the second device abnormality detection information generation model, as the first model output weight and the second model output weight, respectively; The device abnormality detection information is generated according to the first model output weight, the second model output weight, the first device abnormality detection information and the second device abnormality detection information.

7. The method according to claim 6, wherein: The method further comprises: In response to determining that the indicator information difference between the at least one first data sequence indicator information and the at least one second data sequence indicator information does not meet the first preset indicator condition, determining a candidate power data subsequence corresponding to a first historical time period from the third power data subsequence as a first candidate power data subsequence, wherein the first historical time period has a same date correspondence relationship with the target future time period; For each fourth power data subsequence in the fourth power data subsequence set, determining a candidate power data subsequence corresponding to a second historical time period from the fourth power data subsequence as a second candidate power data subsequence, wherein the second historical time period has a same date correspondence relationship with the target future time period; generating at least one data sequence indicator information corresponding to the first candidate power data subsequence as at least one third data sequence indicator information; generating at least one data sequence indicator information corresponding to the future power data subsequence as at least one fourth data sequence indicator information; In response to determining that the indicator information difference between the at least one third data sequence indicator information and the at least one fourth data sequence indicator information satisfies the first preset indicator condition, determining the inter-data subsequence indicator information corresponding to the fourth power data subsequence set as the first inter-data subsequence indicator information; adding the first future power data sequence to the fourth power data subsequence set to generate an added power data subsequence set; Determine the data subsequence index information corresponding to the added power data subsequence set as the second data subsequence index information; In response to determining that the indicator information difference between the indicator information between the first data subsequences and the indicator information between the second data subsequences does not meet a second preset indicator condition, adding the future power data subsequence to the obtained second candidate power data subsequence set to generate a third added power data subsequence set; Determine the inter-data subsequence indicator information corresponding to the second candidate power data subsequence set as the third inter-data subsequence indicator information; Determine the inter-data subsequence indicator information corresponding to the third added power data subsequence set as the fourth inter-data subsequence indicator information; In response to determining that the indicator information difference between the indicator information between the third data subsequences and the indicator information between the fourth data subsequences satisfies a second preset indicator condition, inputting the first future power data sequence into a first device abnormality detection information generation model included in the device abnormality detection model to generate first device abnormality detection information; splicing the future power data subsequence and the first power data subsequence to generate a second future power data sequence; inputting the second future power data sequence into a second device abnormality detection information generation model included in the device abnormality detection model to generate second device abnormality detection information; Obtaining a model output weight corresponding to the first device abnormality detection information generation model and a model output weight corresponding to the second device abnormality detection information generation model, as the first model output weight and the second model output weight, respectively; The device abnormality detection information is generated according to the first model output weight, the second model output weight, the first device abnormality detection information and the second device abnormality detection information.

8. A power outage area alarm device, comprising: An acquisition unit is configured to acquire a power data sequence set corresponding to each power device in a target power area, wherein each power device has a corresponding power data sequence; A generating unit is configured to perform the following generating steps for each of the power devices in the various power devices: determining a power data sequence corresponding to the power device as a target power data sequence; determining a first data interception time period, a second data interception time period, a third data interception time period and a fourth data interception time period sequence corresponding to the power device for the current time, wherein the first data interception time period includes the second data interception time period, the first data interception time period corresponding to the first data interception time period is longer than the second data interception time period corresponding to the second data interception time period, the fourth data interception time period sequence includes the third data interception time period, and the central time corresponding to the third data interception time period has the same date relationship with the current time; generating a first data interception time period corresponding to the first data interception time period according to the target power data sequence; a power data subsequence, a second power data subsequence corresponding to the second data interception time period, a third power data subsequence corresponding to the third data interception time period, and a fourth power data subsequence set corresponding to the fourth data interception time period; based on the first power data subsequence, the second power data subsequence, the third power data subsequence, and the fourth power data subsequence set, using a pre-trained device abnormality detection model, generating device abnormality detection information for the power equipment; in response to determining that the device abnormality detection information represents abnormal operation of the power equipment, determining device abnormality cause information and device impact range information corresponding to the power equipment; generating device operation summary information for the power equipment based on the device abnormality cause information, the target power data sequence, and the device impact range information; The execution unit is configured to perform power operation abnormality display for each power device according to the obtained summary information of the operation of each device, and to perform alarm processing on the power outage area corresponding to each power device.

9. An electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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