Power grid information display method and device, electronic equipment and computer readable medium

Abstract

The embodiment of the invention discloses a power grid information display method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises the following steps: acquiring a real-time transmission, transformation and distribution equipment data set; generating an abnormal operation equipment information set; in response to determining that the set is not the null set, determining abnormal region information; for the abnormal region information, executing a first generation step: determining an operation equipment information set; performing abnormal degree region division on the abnormal region information; determining an equipment problem information set and a corresponding problem probability for each piece of abnormal division region information; screening out the equipment problem information meeting the corresponding problem conditions, and obtaining at least one piece of target equipment problem information; and displaying the abnormal division region information sequence and the problem information of the at least one piece of target equipment in the power digital twin model. According to the embodiment, under the condition that the target power grid region is abnormal, the abnormal information under the region granularity and the equipment granularity can be accurately and effectively generated.

Description

Technical Field

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

[0002] At present, with the continuous development of electricity, the effective detection of transmission and distribution equipment largely determines the efficiency of power transmission. For the display of various equipment problems in the area, the usual method is to manually report the equipment problems, summarize the equipment problems in the area, and display the various equipment problems.

[0003] However, when the above method is adopted, the following technical problems often occur: The various devices in the transmission and distribution equipment have upstream and downstream relationships in power transportation. When problems occur in the equipment, the main equipment problems that actually occurred cannot be identified in a timely and accurate manner, resulting in the need to check each reported equipment problem one by one, resulting in inefficient resolution of power equipment failure problems.

[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 grid information display 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 displaying power grid information, comprising: utilizing various equipment sensors pre-deployed in a target power grid area to obtain a real-time transmission, transformation and distribution equipment data set corresponding to the target power grid area within a real-time time period, wherein the real-time transmission, transformation and distribution equipment data set comprises: an equipment data sequence corresponding to each transmission equipment, an equipment data sequence corresponding to each substation equipment and an equipment data sequence corresponding to each distribution equipment; based on the real-time transmission, transformation and distribution equipment data set, generating equipment information characterizing the existence of operating abnormalities to obtain an abnormal operating equipment information set; in response to determining that the abnormal operating equipment information set is not an empty set, executing the following first generation step for abnormal area information corresponding to the abnormal operating equipment information set: determining an operating equipment information set corresponding to the abnormal area information; performing abnormal process on the abnormal area information based on the equipment data sequence corresponding to each operating equipment information in the operating equipment information set; Degree area division is performed to generate an abnormal division area information sequence, wherein the abnormal division area information in the abnormal division area information sequence is sorted in descending order according to the abnormality degree; for each abnormal division area information in the abnormal division area information sequence, the equipment problem information set corresponding to the abnormal division area information and the problem probability corresponding to each equipment problem information are determined; according to the obtained problem probability set sequence and the abnormal division area information sequence, the equipment problem information that meets the corresponding problem condition is screened out from the obtained equipment problem information set sequence as the target equipment problem information, and at least one target equipment problem information is obtained; the abnormal division area information sequence and the at least one target equipment problem information are displayed in the electric power digital twin model corresponding to the target power grid area, wherein the electric power digital twin model also supports the display of the equipment data sequence and the operating status corresponding to each operating equipment information in the abnormal area information.

[0008] In a second aspect, some embodiments of the present disclosure provide a power grid information display device, including: an acquisition unit, configured to use various equipment sensors pre-deployed in the target power grid area to acquire a real-time transmission, transformation and distribution equipment data set corresponding to the target power grid area within a real-time time period, wherein the real-time transmission, transformation and distribution equipment data set includes: an equipment data sequence corresponding to each transmission equipment, an equipment data sequence corresponding to each substation equipment and an equipment data sequence corresponding to each distribution equipment; a generation unit, configured to generate equipment information characterizing the existence of operation abnormalities based on the real-time transmission, transformation and distribution equipment data set to obtain an abnormal operation equipment information set; an execution unit, configured to, in response to determining that the abnormal operation equipment information set is not an empty set, perform the following first generation step for the abnormal area information corresponding to the abnormal operation equipment information set: determine the operation equipment information set corresponding to the abnormal area information; according to the equipment data sequence corresponding to each operation equipment information in the operation equipment information set, The abnormal area information is divided into areas according to the degree of abnormality to generate an abnormal area information sequence, wherein the abnormal area information in the abnormal area information sequence is sorted in descending order of the degree of abnormality; for each abnormal area information in the abnormal area information sequence, determine the equipment problem information set corresponding to the abnormal area information and the problem probability corresponding to each equipment problem information; according to the obtained problem probability set sequence and the abnormal area information sequence, filter out equipment problem information that meets the corresponding problem conditions from the obtained equipment problem information set sequence as target equipment problem information, and obtain at least one target equipment problem information; display the abnormal area information sequence and the at least one target equipment problem information in the electric power digital twin model corresponding to the target power grid area, wherein the electric power digital twin model also supports displaying the equipment data sequence and operating status corresponding to each operating equipment information in the abnormal area information.

[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 embodiments of the present disclosure have the following beneficial effects: through the power grid information display method of some embodiments of the present disclosure, when an abnormality occurs in the target power grid area, abnormal information at the regional granularity and the equipment granularity can be accurately and effectively generated. Specifically, the reason for the low efficiency of solving related equipment problems is that the various devices in the transmission, transformation and distribution equipment have an upstream and downstream relationship of power transportation. When a problem occurs in the equipment, the main equipment problem that actually occurs cannot be determined in time and accurately, resulting in the need to check each reported equipment problem one by one, resulting in low efficiency in solving the fault problem of the power equipment. Based on this, the power grid information display method of some embodiments of the present disclosure, first, using the various equipment sensors pre-deployed in the target power grid area, obtains the real-time transmission, transformation and distribution equipment data set corresponding to the target power grid area within the real-time time period, wherein the real-time transmission, transformation and distribution equipment data set includes: the equipment data sequence corresponding to each transmission equipment, the equipment data sequence corresponding to each substation equipment and the equipment data sequence corresponding to each distribution equipment. Here, by obtaining the real-time transmission, transformation and distribution equipment data set, the operation data corresponding to each equipment is obtained, so as to facilitate the subsequent preliminary determination of the equipment information set with operation abnormalities. Then, according to the real-time transmission and distribution equipment data set, the equipment information representing the existence of abnormal operation can be accurately generated to obtain the abnormal operation equipment information set. Then, in response to determining that the abnormal operation equipment information set is not an empty set, the following first generation step is performed for the abnormal area information corresponding to the abnormal operation equipment information set: the first step is to determine the operation equipment information set corresponding to the abnormal area information, so as to analyze whether it is a major equipment failure problem for each operation equipment in the abnormal area information. The second step is to accurately divide the abnormal area information into regions according to the abnormal degree according to the equipment data sequence corresponding to each operation equipment information in the operation equipment information set, so as to generate an abnormal division area information sequence, wherein the abnormal division area information in the abnormal division area information sequence is sorted in order from high to low according to the abnormal degree. Here, by dividing the abnormal area information, the abnormal situation of the equipment in each sub-area information can be clarified, so as to conduct a comprehensive equipment abnormality analysis and effective display of the equipment abnormality for the equipment abnormality of each sub-area information, so that the viewer can effectively see the abnormal situation of the equipment in each sub-area. The third step is to determine the device problem information set corresponding to the abnormal division area information and the problem probability corresponding to each device problem information for each abnormal division area information in the abnormal division area information sequence. Here, by determining the problem probability and the device problem information set, each device problem in the abnormal division area and the problem probability that each device problem is a key device problem can be determined, so as to facilitate the subsequent screening of important device problems.The fourth step is to accurately screen out the equipment problem information that meets the corresponding problem conditions from the obtained equipment problem information set sequence according to the obtained problem probability set sequence and the abnormal division area information sequence, and obtain at least one target equipment problem information as the target equipment problem information. The fifth step is to display the abnormal division area information sequence and the at least one target equipment problem information in the power digital twin model corresponding to the target power grid area, wherein the power digital twin model also supports the display of the equipment data sequence and operating status corresponding to each operating equipment information in the abnormal area information. In summary, by determining the main equipment problems of each sub-area of ​​the abnormal area information and displaying the relevant abnormal information of the sub-area, the abnormal information at the regional granularity and the equipment granularity can be accurately and effectively generated. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] 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.

[0013] Figure 1 is a flow chart of some embodiments of the power grid information display method according to the present disclosure; Figure 2 is a schematic diagram of the structure of some embodiments of the power grid information display 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

[0014] 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.

[0015] 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.

[0016] 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.

[0017] 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".

[0018] 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.

[0019] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0020] refer to Figure 1 , shows a process 100 of some embodiments of the power grid information display method according to the present disclosure. The power grid information display method includes the following steps: Step 101, using various equipment sensors pre-deployed in a target power grid area, obtain a real-time transmission, transformation and distribution equipment data set corresponding to the target power grid area within a real-time time period.

[0021] In some embodiments, the execution subject (for example, an electronic device) of the above-mentioned power grid information display method can use various equipment sensors pre-deployed in the target power grid area to obtain the real-time transmission, transformation and distribution equipment data set corresponding to the target power grid area in the real-time time period through a wired connection method or a wireless connection method. Among them, the target power grid area may be the power grid area currently to be detected for power supply anomaly. For example, the target power grid area may be Beijing. The equipment sensor may be a sensor of various forms for collecting equipment operation data. The real-time time period may be a pre-determined time period under the current time. For example, the current time is 17:00, and the corresponding real-time time period may be 8:00-17:00. Among them, the real-time transmission, transformation and distribution equipment data set includes: equipment data sequences corresponding to each transmission equipment, equipment data sequences corresponding to each substation equipment, and equipment data sequences corresponding to each distribution equipment. The transmission equipment may be an electric power equipment for power transmission. The substation equipment may be an electric power equipment for power change (for example, voltage change). The distribution equipment may be an electric power equipment for power distribution processing. Each transmission equipment has a corresponding equipment data sequence. Each substation equipment has a corresponding equipment data sequence. Each distribution equipment has a corresponding equipment data sequence. The equipment data may be the operation data of the corresponding power equipment at the corresponding time point. There is a one-to-one correspondence between the equipment data in the equipment data sequence and the time points in the corresponding time points of the real-time time period.

[0022] Step 102: Generate device information indicating abnormal operation based on the real-time transmission, transformation and distribution equipment data set to obtain an abnormal operation equipment information set.

[0023] In some embodiments, the execution subject may generate device information indicating abnormal operation based on the real-time transmission and distribution equipment data set to obtain an abnormal operation device information set. The abnormal operation device information may be device information of power equipment indicating that the device operation may be abnormal. The device information may be a device identification or model of the power equipment.

[0024] As an example, for each real-time transmission and distribution equipment data in the real-time transmission and distribution equipment data set, first, determine the power equipment corresponding to the real-time transmission and distribution equipment data as the target power equipment. Then, determine the historical equipment data sequence under at least one historical time period corresponding to the target power equipment, and obtain at least one historical equipment data sequence. Next, determine the indicator information set under the equipment data sequence corresponding to the target power equipment as the real-time indicator information set. Then, determine the indicator information set corresponding to each historical equipment data sequence in at least one historical equipment data sequence as the historical indicator information set, and obtain at least one historical indicator information set. Further, for each indicator in the indicator set, perform weighted summation processing on at least one indicator information corresponding to the indicator in the at least one historical indicator information set to generate weighted sum indicator information. Finally, determine the indicator difference information between the weighted sum indicator information set and the real-time indicator information set. In response to determining that the indicator difference information is greater than the target value, generate abnormal operation equipment information for the target power equipment that indicates the existence of abnormal operation.

[0025] Step 103: in response to determining that the abnormal operation device information set is not an empty set, the following first generation step is performed for the abnormal area information corresponding to the abnormal operation device information set: Step 1031, determining the operating device information set corresponding to the abnormal area information.

[0026] In some embodiments, the execution subject may determine a set of operating device information corresponding to the abnormal area information. The operating device information in the operating device information set may be device information operating in a real-time time period in an area corresponding to the abnormal area information. The abnormal area information may be an area representing a possible large-scale abnormal operation of equipment.

[0027] As an example, first, the execution subject may determine the location information corresponding to each abnormal operation device information in the abnormal operation device information set to obtain the location information set. Then, the region division for the location information set is performed to obtain the region corresponding to the region information including the location information set. Finally, the region information corresponding to the obtained divided region is determined as the abnormal region information.

[0028] As an example, the execution subject may determine the operating device information set corresponding to the abnormal area information by querying device information.

[0029] Step 1032: According to the device data sequence corresponding to each piece of operating device information in the operating device information set, the abnormal area information is divided into regions according to the abnormality degree, so as to generate an abnormality division region information sequence.

[0030] In some embodiments, the execution subject may divide the abnormal area information into regions of abnormal degree according to the device data sequence corresponding to each operating device information in the operating device information set to generate an abnormal division area information sequence. The abnormal division area information in the abnormal division area information sequence is sorted in descending order of abnormal degree. For example, the abnormal division area information sequence may be [first abnormal division area information, second abnormal division area information, third abnormal division area information]. The abnormal degree of the equipment in the region corresponding to the first abnormal division area information is higher than the abnormal degree of the equipment in the region corresponding to the second abnormal division area information. The abnormal degree of the equipment in the region corresponding to the second abnormal division area information is higher than the abnormal degree of the equipment in the region corresponding to the third abnormal division area information.

[0031] In some optional implementations of some embodiments, the abnormal division region information sequence includes: first abnormal division region information, second abnormal division region information and third abnormal division region information. The first abnormal division region information, the second abnormal division region information and the third abnormal division region information may be obtained by dividing the abnormal region information. And the number of divisions of the region information is 3 which is predetermined.

[0032] Optionally, the execution subject divides the abnormal area information into abnormal degree areas according to the device data sequence corresponding to each piece of operating device information in the operating device information set to generate an abnormal division area information sequence, including the following steps: In the first step, for each piece of running device information in the running device information set, the following second generation step is performed: Sub-step 1: determining a device data sequence corresponding to the running device information as a target device data sequence, wherein the target device data sequence may be device running data at each time point of the running device information within a real-time time period.

[0033] Sub-step 2, obtaining the device data sequence for the operating device information in the same period of time as the first historical device data sequence. The same period of time may be a period of time that is in a same period of time with the real-time period of time. For example, the real-time period of time is from 8:00 to 17:00 on the 11th. The target device data sequence may be the operating data of the operating device between 8:00 and 17:00 on the 11th. The corresponding same period of time may be from 8:00 to 17:00 on the 10th. The first historical device data sequence may be the operating data of the operating device between 8:00 and 17:00 on the 10th.

[0034] Sub-step 3, obtaining the device data sequence for the operating device information in the adjacent historical time period as the second historical device data sequence. The adjacent historical time period may be a historical time period before the real-time time period and having an adjacent time period relationship. For example, the real-time time period is from 8:00 to 17:00 on the 11th. The target device data sequence may be the operating data of the operating device between 8:00 and 17:00 on the 11th. The corresponding adjacent historical time period may be from 0:00 to 8:00 on the 11th. The second historical device data sequence may be the operating data of the operating device between 0:00 and 8:00 on the 11th.

[0035] Sub-step 4: Generate a device abnormality coefficient of the device corresponding to the running device information in the real-time time period based on the first historical device data sequence, the target device data sequence and the second historical device data sequence. The device abnormality coefficient can represent the degree of abnormality of the device corresponding to the running device information in the real-time time period. The higher the corresponding device abnormality coefficient, the higher the degree of abnormality of the corresponding device in the real-time time period.

[0036] Optionally, first, the execution subject may input the first historical device data sequence into a pre-trained first time series device feature information extraction model to generate the first historical device feature information. Then, the execution subject may input the second historical device data sequence into a pre-trained second time series device feature information extraction model to generate the second historical device feature information. Then, the execution subject may input the target device data sequence into a pre-trained third time series device feature information extraction model to generate device feature information. Then, the first historical device feature information, the second historical device feature information and the device feature information are feature fused to generate fused device feature information. Finally, the fused device feature information is input into the abnormality degree information generation layer to generate the device abnormality coefficient and abnormality category. Among them, the model structures corresponding to the first time series device feature information extraction model, the second time series device feature information extraction model and the third time series device feature information extraction model may be different, and all of them are time series neural network models. And the first time series device feature information extraction model, the second time series device feature information extraction model and the third time series device feature information extraction model are all neural network models that extract feature information corresponding to time series data. For example, the first time series device feature information extraction model, the second time series device feature information extraction model, and the third time series device feature information extraction model can be recurrent neural network models with different numbers of network layers. The abnormality degree information generation layer can be a network layer that generates abnormality degree information. The abnormality degree information may include: device abnormality coefficient and abnormality category. Among them, the abnormality category can be a category label that characterizes whether the corresponding device has an abnormality. In practice, the abnormality degree information generation layer can be a multi-layer series of convolutional layers + attention layers + output layers. The first time series device feature information extraction model, the second time series device feature information extraction model, the third time series device feature information extraction model and the abnormality degree information generation layer can be trained together through conventional model training methods.

[0037] In the second step, according to the distribution corresponding to the obtained equipment abnormality coefficient set, the first abnormality division area information, the second abnormality division area information and the third abnormality division area information are generated by using the target area division method. Among them, the average value of the equipment abnormality coefficient corresponding to the first abnormality division area information is higher than that of the second abnormality division area information, and the average value of the equipment abnormality coefficient corresponding to the second abnormality division area information is higher than that of the third abnormality division area information. The variance value of the equipment abnormality coefficient corresponding to the first abnormality division area information, the variance value of the equipment abnormality coefficient corresponding to the second abnormality division area information and the variance value of the equipment abnormality coefficient corresponding to the third abnormality division area information are all less than the preset variance value. Among them, the preset variance value can be a pre-set variance value. The distribution corresponding to the equipment abnormality coefficient set can be the numerical distribution of each equipment abnormality coefficient in the target power grid area. The target area division method can characterize that the average value of the equipment abnormality coefficient corresponding to the first abnormality division area information is higher than that of the second abnormality division area information, the average value of the equipment abnormality coefficient corresponding to the second abnormality division area information is higher than that of the third abnormality division area information, and the variance value of the equipment abnormality coefficient corresponding to the first abnormality division area information, the variance value of the equipment abnormality coefficient corresponding to the second abnormality division area information and the variance value of the equipment abnormality coefficient corresponding to the third abnormality division area information are all less than the preset variance value.

[0038] As an example, first, the execution subject can determine the device position corresponding to each device abnormality coefficient in the device abnormality coefficient set to obtain a device position set. Then, the device position set and the device abnormality coefficient set are correspondingly combined to generate a combined information set, wherein the combined information includes: device abnormality coefficient and device position. Finally, the data optimizer is used to determine the first abnormal division area information, the second abnormal division area information, and the third abnormal division area information for the combined information set.

[0039] The above technical solution and its related contents, as an inventive point of the embodiment of the present disclosure, solve the technical problem that the abnormal degree analysis of the abnormal area information is not effectively performed, resulting in the inability to quickly understand the information of the equipment that actually mainly experiences abnormal operation. Based on this, the present disclosure discloses determining the abnormal degree of each area by generating an abnormal coefficient corresponding to each operating equipment information, thereby determining the abnormal situation in the area corresponding to the abnormal area information, so as to accurately determine the equipment information that actually mainly experiences abnormal operation.

[0040] Step 1033: for each abnormal division area information in the abnormal division area information sequence, determine the device problem information set corresponding to the abnormal division area information and the problem probability corresponding to each device problem information.

[0041] In some embodiments, the execution subject may determine, for each abnormal division area information in the abnormal division area information sequence, a set of equipment problem information corresponding to the abnormal division area information and a problem probability corresponding to each equipment problem information. The equipment problem information set may be various equipment failure problems that may exist in the equipment in the area corresponding to the abnormal division area information. The problem probability may be the probability that the corresponding equipment problem information is a major equipment problem. The greater the problem probability, the greater the probability that the corresponding equipment problem information is a major equipment problem.

[0042] In some optional implementations of some embodiments, the execution subject may determine the device problem information set corresponding to the abnormal division area information and the problem probability corresponding to each device problem information, including the following steps: The first step is to obtain at least one piece of operating equipment information corresponding to the abnormal division area information.

[0043] The second step is to determine the regional overall operation abnormality information corresponding to the abnormal division area information. The regional overall operation abnormality information may be the overall operation status corresponding to each device in the abnormal division area information. In practice, the regional overall operation abnormality information may be information in the form of numerical values ​​or information in the form of labels. The regional overall operation abnormality information is determined based on the sequence position of the abnormal division area information in the abnormal division area information sequence.

[0044] As an example, first, at least one reported abnormality corresponding to at least one operating device information is determined. Then, at least one reported abnormality is determined. Finally, a weighted sum value between the reported abnormality ratio and the sequence position is determined as the overall operating abnormality information of the region.

[0045] In the third step, for each piece of operating device information in the at least one operating device information, the following third generating step is performed: Sub-step 1, determining a core candidate device problem matching information set corresponding to the operating device information. The core candidate device problem matching information includes: at least one core candidate device problem. The core candidate device problem matching information set may be each device sub-problem under the first-level device problem that occurs in the device corresponding to the operating device information. For the operating device information, the corresponding core candidate device problem matching information may be one of the following: first-level device problem, second-level device problem. The core candidate device problem may be a core device problem that occurs in the operating device information and affects the normal operation of the operating device. The first-level device problem is the core candidate device problem. The number of core candidate device problems in the core candidate device problem matching information is at least one.

[0046] Sub-step 2, determining the regional overall operation impact information corresponding to each core candidate device problem collocation information in the core candidate device problem collocation information set, and obtaining the regional overall operation impact information set. The regional overall operation impact information may be the degree of overall operation impact on each device in the region when the core candidate device problem collocation information corresponds to at least one core candidate device problem. In practice, the regional overall operation impact information may be information of a numerical type or information of a label type.

[0047] As an example, the above-mentioned execution entity can query the regional overall operation impact information corresponding to each core candidate device problem collocation information through a pre-registered association table representing the corresponding relationship between the core candidate device problem collocation information and the overall operation impact information to obtain a set of regional overall operation impact information.

[0048] Sub-step 3: determining the information similarity between each regional overall operation impact information in the regional overall operation impact information set and the regional overall operation abnormality information to obtain an information similarity set.

[0049] As an example, the execution entity may determine the subtraction value between the overall operation impact information of each region and the overall operation abnormality information of the region as the information similarity.

[0050] Sub-step 4, based on the information similarity set, select the target core candidate device problems from the core candidate device problem set corresponding to the running device information to obtain the target core candidate device problem set. The core candidate device problem set corresponding to the running device information can be a complete set of core device problems that often occur in the device corresponding to the running device information. The target core candidate device problem set can be a set of core device problems that may exist in the running device information and cause anomalies.

[0051] Sub-step 5, determining the first abnormal probability corresponding to the target core candidate device problem set. The first abnormal probability may be the probability value of the target core candidate device problem set appearing in the device corresponding to the running device information. In practice, the probability of the target core candidate device problem set appearing in the device corresponding to the running device information can be generated by using a large language model and generating prompt words through the first abnormal probability. The large language model is a model plug-in carried by the background terminal corresponding to the digital twin model.

[0052] In the fourth step, the operating device information corresponding to the first abnormal probability less than the first predetermined probability is removed from the at least one operating device information to obtain an operating device information group, wherein the first predetermined probability may be a preset probability value.

[0053] The fifth step is to generate an estimated candidate device problem set group for the operating device information group based on the obtained at least one target core candidate device problem set. There is a one-to-one correspondence between the operating device information in the operating device information group and the estimated candidate device problem set in the estimated candidate device problem set group. The estimated candidate device problem set can be a set of device problems that may exist in the device corresponding to the estimated operating device information.

[0054] In the sixth step, the estimated candidate device problem set group is determined as a device problem information set.

[0055] Step 7: determine the problem sub-probability corresponding to each estimated candidate device problem in the estimated candidate device problem set, and obtain the problem sub-probability as the problem probability. The generation method corresponding to the problem sub-probability can refer to the generation method corresponding to the first abnormal probability.

[0056] In some optional implementations of some embodiments, generating an estimated candidate device problem set group for the operating device information group according to the obtained at least one target core candidate device problem set includes the following steps: The first step is to determine, for each piece of operating device information in the operating device information group, a set of common candidate device problem matching information corresponding to the operating device information. The common candidate device problem matching information includes: at least one common candidate device problem. The common candidate device problem matching information may be matching information of common candidate device problems that frequently appear in the operating device information. Common candidate device problems may be device problems that appear in the operating device information at a frequency higher than a target frequency in daily life. The matching methods of the common candidate device problems corresponding to the common candidate device problem matching information in the common candidate device problem matching information set are different.

[0057] In the second step, the target core candidate device problem set group corresponding to the operating device information group and at least one common candidate device problem collocation information set in the obtained common candidate device problem collocation information set group are combined multiple times in different ways to generate multiple combined problem set groups.

[0058] In the third step, for each of the plurality of combined question set groups, the following fourth generation step is performed: Sub-step 1, determining a second abnormal probability corresponding to the combined problem set group. The second abnormal probability can represent the probability of occurrence of the combined problem set group. The determination of the second abnormal probability can refer to the generation of the first abnormal probability.

[0059] Sub-step 2: determining the median probability of each first abnormality probability corresponding to the target core candidate device problem set group.

[0060] Sub-step 3: subtract the median probability from the second abnormal probability to generate a subtraction probability.

[0061] In the fourth step, the combined problem set groups whose corresponding subtraction probabilities are less than the second predetermined probability are removed from the plurality of combined problem set groups to generate a plurality of combined problem set groups after removal.

[0062] In a fifth step, in response to determining that the plurality of combined problem sets after removal are empty, determining the target core candidate device problem set group as the estimated candidate device problem set group.

[0063] In a sixth step, in response to determining that the plurality of combined problem set groups after the removal are not empty, the plurality of combined problem set groups after the removal are determined as estimated candidate device problem set groups.

[0064] In some optional implementations of some embodiments, the execution subject may filter out target core candidate device problems from the core candidate device problem set corresponding to the running device information according to the information similarity set, and obtain the target core candidate device problem set, including the following: In the first step, information similarities less than a third predetermined probability are removed from the information similarity set to obtain a removed information similarity set, wherein the third predetermined probability may be a predetermined probability value.

[0065] The third step is to determine the core candidate device problem collocation information corresponding to each information similarity in the removed information similarity set to obtain a core candidate device problem collocation information set.

[0066] Step 4: for each core candidate question information in the core candidate device question set, execute the following fifth generation step: Sub-step 1: determining the core candidate device problem collocation information that contains the core candidate problem information in the core candidate device problem collocation information set, and obtaining a core candidate device problem collocation information group.

[0067] Sub-step 2: determining the number of information corresponding to the core candidate device problem collocation information group.

[0068] Sub-step 3: determining the information similarity group corresponding to the core candidate device problem collocation information group.

[0069] Sub-step 4: determining the core question probability corresponding to the core candidate question information according to the information similarity group and the number of information.

[0070] Optionally, first, determine the probability interval corresponding to the number of information. The size corresponding to the number of information corresponds to the corresponding probability interval. Then, remove the information similarities that are not in the probability interval from the information similarity group to obtain the removed information similarity group. Finally, the average value of each information similarity in the removed information similarity group is used as the core problem probability.

[0071] Step 5: Select the core candidate device problems whose corresponding core problem probabilities meet the preset probability conditions from the core candidate device problem set as the target core candidate device problems, and obtain the target core candidate device problem set. The preset probability condition may be the core candidate device problems whose core problem probabilities are greater than the target core problem probability value.

[0072] Step 1044, based on the obtained problem probability set sequence and the abnormal division area information sequence, filter out the device problem information that meets the corresponding problem condition from the obtained device problem information set sequence as the target device problem information, and obtain at least one target device problem information.

[0073] In some embodiments, the execution subject may filter out device problem information that meets the corresponding problem condition from the obtained device problem information set sequence according to the obtained problem probability set sequence and the abnormal division area information sequence, and obtain at least one target device problem information as the target device problem information. The problem condition may be device problem information whose probability of occurrence of the corresponding problem is greater than a preset probability value. The probability of occurrence of the problem may be the probability of occurrence of the corresponding device problem information. The probability of occurrence of the problem is more accurate than the probability of the problem. The preset probability value may be a predetermined probability value. For example, the preset probability value may be 80%.

[0074] As an example, for each device problem information in the device problem information set sequence, first, the above-mentioned execution subject can determine the problem probability and abnormal division area information corresponding to the device problem information as the target abnormal division area information. Then, determine the abnormal weight corresponding to the abnormal division area information. Next, multiply the problem probability and the abnormal weight to generate the problem occurrence probability. Finally, filter out the device problem information that satisfies the corresponding problem occurrence probability greater than the preset probability value from the device problem information set sequence as the target device problem information, and obtain at least one target device problem information.

[0075] Step 1045, displaying the abnormal division area information sequence and the at least one target equipment problem information in the power digital twin model corresponding to the target power grid area.

[0076] In some embodiments, the above-mentioned execution subject can display the abnormal division area information sequence and the at least one target device problem information in the power digital twin model corresponding to the target power grid area. Among them, the power digital twin model can be a digital twin model established for the target power grid area to display various power data. Among them, the power digital twin model also supports the display of the device data sequence and operating status corresponding to each operating device information in the abnormal area information. The operating status can be information that characterizes whether the operating device information is in an abnormal state.

[0077] In some optional implementations of some embodiments, the steps further include: In the first step, in response to receiving the abnormal division area display information for the electric power digital twin model, a corresponding abnormal division area display pop-up window is popped up in the electric power digital twin model to display the abnormal division area and the target device problem information set corresponding to the target device information set whose corresponding device position is within the abnormal division area. Among them, the abnormal division area display information can be a display request for displaying the abnormal division area information. The abnormal division area display pop-up window can be a display pop-up window for displaying the abnormal division area. The target device problem information set corresponding device set is within the abnormal division area. There is a one-to-one correspondence between the target device problem information in the target device problem information set and the target device information in the target device information set.

[0078] In the second step, in response to receiving click information on the target device information, basic device information and corresponding target device problem information corresponding to the clicked target device information are popped up.

[0079] In some optional implementations of some embodiments, the steps further include: In response to receiving the device display information for the power digital twin model, the execution subject may display the operating status and device data sequence of the running device corresponding to the device display information. The device display information may be information for displaying device-related data. The device display information includes: the device information to be displayed and the device display requirement information.

[0080] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the power grid information display method of some embodiments of the present disclosure, when an abnormality occurs in the target power grid area, abnormal information at the regional granularity and the equipment granularity can be accurately and effectively generated. Specifically, the reason for the low efficiency of solving related equipment problems is that the various devices in the transmission, transformation and distribution equipment have an upstream and downstream relationship of power transportation. When a problem occurs in the equipment, the main equipment problem that actually occurs cannot be determined in time and accurately, resulting in the need to check each reported equipment problem one by one, resulting in low efficiency in solving the fault problem of the power equipment. Based on this, the power grid information display method of some embodiments of the present disclosure, first, using the various equipment sensors pre-deployed in the target power grid area, obtains the real-time transmission, transformation and distribution equipment data set corresponding to the target power grid area within the real-time time period, wherein the real-time transmission, transformation and distribution equipment data set includes: the equipment data sequence corresponding to each transmission equipment, the equipment data sequence corresponding to each substation equipment and the equipment data sequence corresponding to each distribution equipment. Here, by obtaining the real-time transmission, transformation and distribution equipment data set, the operation data corresponding to each equipment is obtained, so as to facilitate the subsequent preliminary determination of the equipment information set with operation abnormalities. Then, according to the real-time transmission and distribution equipment data set, the equipment information representing the existence of abnormal operation can be accurately generated to obtain the abnormal operation equipment information set. Then, in response to determining that the abnormal operation equipment information set is not an empty set, the following first generation step is performed for the abnormal area information corresponding to the abnormal operation equipment information set: the first step is to determine the operation equipment information set corresponding to the abnormal area information, so as to analyze whether it is a major equipment failure problem for each operation equipment in the abnormal area information. The second step is to accurately divide the abnormal area information into regions according to the abnormal degree according to the equipment data sequence corresponding to each operation equipment information in the operation equipment information set, so as to generate an abnormal division area information sequence, wherein the abnormal division area information in the abnormal division area information sequence is sorted in order from high to low according to the abnormal degree. Here, by dividing the abnormal area information, the abnormal situation of the equipment in each sub-area information can be clarified, so as to conduct a comprehensive equipment abnormality analysis and effective display of the equipment abnormality for the equipment abnormality of each sub-area information, so that the viewer can effectively see the abnormal situation of the equipment in each sub-area. The third step is to determine the device problem information set corresponding to the abnormal division area information and the problem probability corresponding to each device problem information for each abnormal division area information in the abnormal division area information sequence. Here, by determining the problem probability and the device problem information set, each device problem in the abnormal division area and the problem probability that each device problem is a key device problem can be determined, so as to facilitate the subsequent screening of important device problems.The fourth step is to accurately screen out the equipment problem information that meets the corresponding problem conditions from the obtained equipment problem information set sequence according to the obtained problem probability set sequence and the abnormal division area information sequence, and obtain at least one target equipment problem information as the target equipment problem information. The fifth step is to display the abnormal division area information sequence and the at least one target equipment problem information in the power digital twin model corresponding to the target power grid area, wherein the power digital twin model also supports the display of the equipment data sequence and operating status corresponding to each operating equipment information in the abnormal area information. In summary, by determining the main equipment problems of each sub-area of ​​the abnormal area information and displaying the relevant abnormal information of the sub-area, the abnormal information at the regional granularity and the equipment granularity can be accurately and effectively generated.

[0081] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a power grid information display device. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the power grid information display device can be specifically applied to various electronic devices.

[0082] like Figure 2As shown, a power grid information display device 200 includes: an acquisition unit 201, a generation unit 202 and an execution unit 203. The acquisition unit 201 is configured to use various equipment sensors pre-deployed in the target power grid area to acquire a real-time transmission, transformation and distribution equipment data set corresponding to the target power grid area within a real-time time period, wherein the real-time transmission, transformation and distribution equipment data set includes: an equipment data sequence corresponding to each transmission equipment, an equipment data sequence corresponding to each substation equipment and an equipment data sequence corresponding to each distribution equipment; the generation unit 202 is configured to generate equipment information representing the existence of operation abnormalities based on the real-time transmission, transformation and distribution equipment data set to obtain an abnormal operation equipment information set; the execution unit 203 is configured to, in response to determining that the abnormal operation equipment information set is not an empty set, perform the following first generation step for the abnormal area information corresponding to the abnormal operation equipment information set: determine the operation equipment information set corresponding to the abnormal area information; perform the operation equipment information set on the abnormal area information according to the equipment data sequence corresponding to each operation equipment information in the operation equipment information set; Perform abnormality degree area division to generate an abnormality division area information sequence, wherein the abnormality division area information in the abnormality division area information sequence is sorted in descending order of abnormality degree; for each abnormality division area information in the abnormality division area information sequence, determine the equipment problem information set corresponding to the abnormality division area information and the problem probability corresponding to each equipment problem information; according to the obtained problem probability set sequence and the abnormality division area information sequence, filter out the equipment problem information that meets the corresponding problem condition from the obtained equipment problem information set sequence as the target equipment problem information, and obtain at least one target equipment problem information; display the abnormality division area information sequence and the at least one target equipment problem information in the power digital twin model corresponding to the target power grid area, wherein the power digital twin model also supports displaying the equipment data sequence and operation status corresponding to each operating equipment information in the abnormal area information.

[0083] It is understandable that the units recorded in the power grid information display device 200 are similar to the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the power grid information display device 200 and the units contained therein, and will not be repeated here.

[0084] 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 3 The 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist independently without being assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: utilizes various equipment sensors pre-deployed in the target power grid area to obtain a real-time transmission, transformation and distribution equipment data set corresponding to the target power grid area within a real-time time period, wherein the real-time transmission, transformation and distribution equipment data set includes: equipment data sequences corresponding to each transmission equipment, equipment data sequences corresponding to each substation equipment and equipment data sequences corresponding to each distribution equipment; based on the real-time transmission, transformation and distribution equipment data set, generates equipment information representing the existence of operation abnormalities to obtain an abnormal operation equipment information set; in response to determining that the abnormal operation equipment information set is not an empty set, for the abnormal area information corresponding to the abnormal operation equipment information set, performs the following first generation step: determines the operation equipment information set corresponding to the abnormal area information; based on the equipment data sequence corresponding to each operation equipment information in the operation equipment information set, generates the abnormal operation equipment information set corresponding to the abnormal area information; ... The normal area information is divided into areas with abnormality degrees to generate an abnormal division area information sequence, wherein the abnormal division area information in the abnormal division area information sequence is sorted in descending order of abnormality degree; for each abnormal division area information in the abnormal division area information sequence, the equipment problem information set corresponding to the abnormal division area information and the problem probability corresponding to each equipment problem information are determined; according to the obtained problem probability set sequence and the abnormal division area information sequence, the equipment problem information that meets the corresponding problem conditions is screened out from the obtained equipment problem information set sequence as the target equipment problem information, and at least one target equipment problem information is obtained; the abnormal division area information sequence and the at least one target equipment problem information are displayed in the power digital twin model corresponding to the target power grid area, wherein the power digital twin model also supports the display of the equipment data sequence and the operating status corresponding to each operating equipment information in the abnormal area information.

[0091] 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).

[0092] 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.

[0093] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be arranged in a processor, for example, may be described as: a processor includes an acquisition unit, a generation unit, and an execution unit. Among them, the names of these units do not constitute a limitation on the unit itself in some cases. For example, the acquisition unit may also be described as "a unit that utilizes various device sensors pre-deployed in the target power grid area to obtain a real-time transmission and distribution equipment data set corresponding to the target power grid area within a real-time time period."

[0094] 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.

[0095] 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 method for displaying power grid information, comprising: Using various equipment sensors pre-deployed in the target power grid area, a real-time transmission, transformation and distribution equipment data set corresponding to the target power grid area within a real-time time period is obtained, wherein the real-time transmission, transformation and distribution equipment data set includes: an equipment data sequence corresponding to each transmission equipment, an equipment data sequence corresponding to each substation equipment and an equipment data sequence corresponding to each distribution equipment; According to the real-time transmission and distribution equipment data set, generating equipment information indicating abnormal operation, and obtaining an abnormal operation equipment information set; In response to determining that the abnormal operation device information set is not an empty set, the following first generating step is performed for the abnormal area information corresponding to the abnormal operation device information set: Determine the operating equipment information set corresponding to the abnormal area information; According to the device data sequence corresponding to each piece of operating device information in the operating device information set, the abnormal region information is divided into regions according to the abnormality degree to generate an abnormality division region information sequence, wherein the abnormality division region information in the abnormality division region information sequence is sorted in descending order according to the abnormality degree; For each abnormal division area information in the abnormal division area information sequence, determining a device problem information set corresponding to the abnormal division area information and a problem probability corresponding to each device problem information; According to the obtained problem probability set sequence and the abnormal division area information sequence, device problem information that meets the corresponding problem condition is screened out from the obtained device problem information set sequence as target device problem information, and at least one target device problem information is obtained; The abnormal division area information sequence and the at least one target equipment problem information are displayed in the power digital twin model corresponding to the target power grid area, wherein the power digital twin model also supports displaying the equipment data sequence and operating status corresponding to each operating equipment information in the abnormal area information.

2. The method according to claim 1, wherein: The method further comprises: In response to receiving the abnormal division area display information for the electric power digital twin model, a corresponding abnormal division area display pop-up window is popped up in the electric power digital twin model to display the abnormal division area and the target device problem information set corresponding to the target device information set whose corresponding device location is within the abnormal division area; In response to receiving click information for the target device information, basic device information and corresponding target device problem information corresponding to the clicked target device information are popped up.

3. The method according to claim 1, wherein: The method further comprises: In response to receiving the device display information for the power digital twin model, the operating status and device data sequence of the operating device corresponding to the device display information are displayed.

4. The method according to claim 1, wherein: The determining of the device problem information set corresponding to the abnormal division area information and the problem probability corresponding to each device problem information includes: Acquire at least one piece of operating equipment information corresponding to the abnormal division area information; Determine the regional overall operation abnormality information corresponding to the abnormal division region information, wherein the regional overall operation abnormality information is determined based on the sequence position of the abnormal division region information in the abnormal division region information sequence; For each piece of operating device information in the at least one operating device information, the following third generating step is performed: Determine a core candidate device problem collocation information set corresponding to the running device information, wherein the core candidate device problem collocation information includes: at least one core candidate device problem; Determine the regional overall operation impact information corresponding to each core candidate device problem collocation information in the core candidate device problem collocation information set to obtain the regional overall operation impact information set; Determine the information similarity between each regional overall operation impact information in the regional overall operation impact information set and the regional overall operation abnormality information to obtain an information similarity set; According to the information similarity set, target core candidate device problems are screened out from the core candidate device problem set corresponding to the running device information to obtain a target core candidate device problem set, wherein the number of problems corresponding to the target core candidate device problem set is less than a preset number of problems; Determine a first abnormality probability corresponding to the target core candidate device problem set; Removing the operating equipment information corresponding to the first abnormal probability less than the first predetermined probability from the at least one operating equipment information to obtain an operating equipment information group; Generating an estimated candidate device problem set group for the operating device information group according to the obtained at least one target core candidate device problem set; Determining the estimated candidate device problem set group as a device problem information set; Determine the problem sub-probability corresponding to each estimated candidate device problem in the estimated candidate device problem set group, and obtain the problem sub-probability as the problem probability.

5. The method according to claim 4, wherein: The step of generating an estimated candidate device problem set group for the operating device information group based on the obtained at least one target core candidate device problem set comprises: For each piece of operating device information in the operating device information group, determining a common candidate device problem collocation information set corresponding to the operating device information, wherein the common candidate device problem collocation information includes: at least one common candidate device problem; Performing multiple different combinations of the target core candidate device problem set group corresponding to the operating device information group and at least one common candidate device problem collocation information set in the obtained common candidate device problem collocation information set group to generate multiple combined problem set groups; For each of the plurality of combined question set groups, the following fourth generating step is performed: Determine a second abnormal probability corresponding to the combined problem set group; Determine the median probability of each first abnormality probability corresponding to the target core candidate device problem set group; subtracting the median probability from the second abnormal probability to generate a subtracted probability; Remove the combination problem set groups whose corresponding subtraction probabilities are less than a second predetermined probability from the plurality of combination problem set groups, so as to generate a plurality of combination problem set groups after removal; In response to determining that the removed multiple combined problem set groups are empty, determining the target core candidate device problem set group as an estimated candidate device problem set group; In response to determining that the plurality of combined problem set groups after the removal are not empty, the plurality of combined problem set groups after the removal are determined as estimated candidate device problem set groups.

6. The method according to claim 5, wherein: The step of screening out target core candidate device problems from the core candidate device problem set corresponding to the running device information according to the information similarity set to obtain the target core candidate device problem set includes: removing information similarities less than a third predetermined probability from the information similarity set to obtain a removed information similarity set; Determine the core candidate device problem collocation information corresponding to each information similarity in the removed information similarity set to obtain a core candidate device problem collocation information set; For each core candidate question information in the core candidate device question set, the following fifth generation step is performed: Determine the core candidate device problem collocation information that contains the core candidate problem information in the core candidate device problem collocation information set, and obtain a core candidate device problem collocation information group; Determine the number of information corresponding to the core candidate device problem collocation information group; Determine an information similarity group corresponding to the core candidate device problem collocation information group; Determining the core question probability corresponding to the core candidate question information according to the information similarity group and the number of information; The core candidate device problems whose corresponding core problem probabilities meet the preset probability conditions are selected from the core candidate device problem set as the target core candidate device problems, thereby obtaining the target core candidate device problem set.

7. A power grid information display device, comprising: The acquisition unit is configured to use various equipment sensors pre-deployed in the target power grid area to acquire a real-time transmission, transformation and distribution equipment data set corresponding to the target power grid area within a real-time time period, wherein the real-time transmission, transformation and distribution equipment data set includes: an equipment data sequence corresponding to each transmission equipment, an equipment data sequence corresponding to each substation equipment and an equipment data sequence corresponding to each distribution equipment; A generating unit is configured to generate device information indicating abnormal operation according to the real-time transmission, transformation and distribution equipment data set, and obtain an abnormal operation equipment information set; The execution unit is configured to, in response to determining that the abnormal operation equipment information set is not an empty set, perform the following first generation step for the abnormal area information corresponding to the abnormal operation equipment information set: determine the operation equipment information set corresponding to the abnormal area information; divide the abnormal area information into abnormal degree areas according to the equipment data sequence corresponding to each operation equipment information in the operation equipment information set to generate an abnormal division area information sequence, wherein the abnormal division area information in the abnormal division area information sequence is sorted in descending order according to the abnormal degree; for each abnormal division area information in the abnormal division area information sequence, determine the equipment problem information set corresponding to the abnormal division area information and the problem probability corresponding to each equipment problem information; according to the obtained problem probability set sequence and the abnormal division area information sequence, filter out the equipment problem information that meets the corresponding problem condition from the obtained equipment problem information set sequence as the target equipment problem information, and obtain at least one target equipment problem information; display the abnormal division area information sequence and the at least one target equipment problem information in the power digital twin model corresponding to the target power grid area, wherein the power digital twin model also supports displaying the equipment data sequence and operation status corresponding to each operation equipment information in the abnormal area information.

8. 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 6.

9. 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 6 is implemented.

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