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

Abstract

Embodiments of the present disclosure disclose a power grid information display method, apparatus, electronic device, and computer-readable medium. A specific implementation of the method includes: obtaining a real-time power transmission, transformation, and distribution equipment data set; generating an information set of abnormally operating equipment; in response to determining that it is not an empty set, determining abnormal area information; for the abnormal area information, performing a first generation step: determining an operating equipment information set; performing an abnormal degree area division on the abnormal area information; for each abnormal division area information, determining an equipment problem information set and a corresponding problem probability; screening out equipment problem information that meets the corresponding problem conditions to obtain at least one target equipment problem information; and displaying a sequence of abnormal division area information and at least one target equipment problem information in a power digital twin model. This implementation can accurately and effectively generate abnormal information at the regional granularity and equipment granularity in the case of abnormalities in the target power grid area.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technologies, and more particularly, to methods, devices, electronic devices, and computer-readable media for power grid information display. Background Art

[0002] Currently, with the continuous development of electric power, the effective detection of power transmission, transformation, and distribution equipment largely determines the power transmission efficiency. For the display of problems of each device in a region, the commonly used method is to summarize the device problems in the region by manually reporting the device problems for each device problem display.

[0003] However, when using the above method, the following technical problems often exist:

[0004] There is an upstream and downstream relationship of power transportation among the devices in power transmission, transformation, and distribution equipment. When a device has a problem, it is impossible to quickly and accurately determine the main device problem that actually occurs, resulting in the need to check each reported device problem one by one, leading to low efficiency in solving the fault problems of power equipment.

[0005] The above information disclosed in this background art section is only used to enhance the understanding of the background of the inventive concept, and thus, it may include information that does not form the prior art known to those of ordinary skill in the art in this country. Summary of the Invention

[0006] This section of the present disclosure is used to briefly introduce concepts that will be described in detail in the following detailed implementation section. This section of the present disclosure is not intended to identify the key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] Some embodiments of the present disclosure propose methods, devices, electronic devices, and computer-readable media for power grid information display to solve one or more of the technical problems mentioned in the above background art section.

[0008] In a first aspect, some embodiments of the present disclosure provide a method for displaying power grid information, including: using each device sensor pre-deployed in a target power grid area to obtain a real-time power transmission, transformation, and distribution device data set corresponding to the target power grid area during a real-time time period, where the real-time power transmission, transformation, and distribution device data set includes: device data sequences corresponding to each power transmission device, device data sequences corresponding to each power transformation device, and device data sequences corresponding to each power distribution device; generating device information indicating abnormal operation according to the real-time power transmission, transformation, and distribution device data set to obtain an abnormal operation device information set; in response to determining that the abnormal operation device information set is not an empty set, for the abnormal area information corresponding to the abnormal operation device information set, perform the following first generation step: determining the operation device information set corresponding to the abnormal area information; according to the device data sequence corresponding to each operation device information in the operation device information set, performing abnormal degree area division on the abnormal area information to generate an abnormal division area information sequence, where the abnormal division area information in the abnormal division area information sequence is sorted in descending order of abnormal degree; for each abnormal division area information in the abnormal division area information sequence, determining the device problem information set corresponding to the abnormal division area information and the problem probability corresponding to each device problem information; according to the obtained problem probability set sequence and the abnormal division area information sequence, screening out the device problem information that meets the corresponding problem conditions from the obtained device problem information set sequence as the target device problem information to obtain at least one target device problem information; displaying 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, where the power digital twin model also supports displaying the device data sequence and operation state corresponding to each operation device information in the abnormal area information.

[0009] Second aspect, some embodiments of the present disclosure provide a power grid information display device, including: an acquisition unit configured to use each device sensor pre-deployed in a target power grid area to acquire a real-time power transmission, transformation, and distribution equipment data set corresponding to the target power grid area during a real-time time period, where the real-time power transmission, transformation, and distribution equipment data set includes: device data sequences corresponding to each power transmission equipment, device data sequences corresponding to each power transformation equipment, and device data sequences corresponding to each power distribution equipment; a generation unit configured to generate device information indicating an abnormal operation according to the real-time power transmission, transformation, and distribution equipment data set to obtain an abnormal operation device information set; an execution unit configured to, in response to determining that the abnormal operation device information set is not an empty set, perform the following first generation step for the abnormal area information corresponding to the abnormal operation device information set: determine an operation device information set corresponding to the abnormal area information; perform an abnormal degree area division on the abnormal area information according to the device data sequence corresponding to each operation device information in the operation device information set to generate an abnormal division area information sequence, where the abnormal division area information in the abnormal division area information sequence is sorted in descending order of abnormal degree; for each abnormal division area information in the abnormal division area information sequence, determine a device problem information set corresponding to the abnormal division area information and a problem probability corresponding to each device problem information; screen out device problem information that meets the corresponding problem conditions from the obtained device problem information set sequence according to the obtained problem probability set sequence and the abnormal division area information sequence as target device problem information to obtain at least one target device problem information; display the abnormal division area information sequence and the at least one target device problem information in a power digital twin model corresponding to the target power grid area, where the power digital twin model also supports displaying the device data sequences and operation states corresponding to each operation device information in the abnormal area information.

[0010] Third aspect, some embodiments of the present disclosure provide an electronic device, including: one or more processors; a storage device storing one or more programs thereon, when the one or more programs are executed by the one or more processors, enabling the one or more processors to implement the method described in any implementation manner of the first aspect.

[0011] Fourth aspect, some embodiments of the present disclosure provide a computer-readable medium storing a computer program thereon, where the program, when executed by a processor, implements the method described in any implementation manner of the first aspect.

[0012] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: Through the power grid information display method of some embodiments of the present disclosure, in the case of an abnormality in the target power grid area, abnormal information at the regional granularity and device granularity can be accurately and effectively generated. Specifically, the reason for the low efficiency in solving related equipment problems is as follows: Each device in the power transmission, transformation, and distribution equipment has an upstream and downstream relationship in power transportation. When a problem occurs in a device, it is impossible to timely and accurately determine the main device problem that actually occurs, resulting in the need to check each reported device problem one by one, leading to a low efficiency in solving the fault problems of power equipment. Based on this, in the power grid information display method of some embodiments of the present disclosure, first, using each device sensor pre-deployed in the target power grid area, a real-time power transmission, transformation, and distribution equipment data set corresponding to the target power grid area in a real-time time period is obtained, where the real-time power transmission, transformation, and distribution equipment data set includes: device data sequences corresponding to each power transmission device, device data sequences corresponding to each power transformation device, and device data sequences corresponding to each power distribution device. Here, by obtaining the real-time power transmission, transformation, and distribution equipment data set, the operation data corresponding to each device is obtained to facilitate the subsequent preliminary determination of the device information set with abnormal operation. Then, according to the real-time power transmission, transformation, and distribution equipment data set, device information indicating abnormal operation can be accurately generated to obtain an abnormal operation device information set. Next, in response to determining that the abnormal operation device information set is not an empty set, for the abnormal area information corresponding to the abnormal operation device information set, the following first generation step is executed: The first step is to determine the operation device information set corresponding to the abnormal area information to analyze whether each operation device in the abnormal area information is a main device fault problem. The second step is to accurately divide the abnormal degree of the abnormal area information according to the device data sequence corresponding to each operation device information in the operation device information set to generate an abnormal division area information sequence, where the abnormal division area information in the abnormal division area information sequence is sorted in descending order of abnormal degree. Here, by dividing the abnormal area information, the device abnormal conditions in each sub-area information can be clarified to perform comprehensive device abnormal analysis and effective display of device abnormalities for the device abnormal conditions in each sub-area information, enabling viewers to effectively see the device abnormal conditions in each sub-area. The third step is to determine the device problem information set corresponding to each abnormal division area information in the abnormal division area information sequence and the problem probability corresponding to each device problem information. Here, by determining the problem probability and the device problem information set, each device problem in the abnormal division area and the problem probability of each device problem being a key device problem can be determined to facilitate the subsequent screening of important device problems.Fourthly, according to the obtained sequence of problem probability sets and the sequence of abnormal division region information, accurately screen out the device problem information that meets the corresponding problem conditions from the obtained sequence of device problem information sets as the target device problem information, and obtain at least one target device problem information. Fifthly, display the sequence of abnormal division region information and the at least one target device problem information in the power digital twin model corresponding to the target power grid region, where the power digital twin model also supports displaying the sequence of device data and the operating status corresponding to each operating device information in the abnormal region information. In summary, by determining the main device problems in each sub-region of the abnormal region information and displaying the relevant abnormal information of the sub-region, the abnormal information at the regional granularity and the device granularity can be accurately and effectively generated. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In combination with the accompanying drawings and with reference to the following specific embodiments, the above and other features, advantages and aspects of the various embodiments of the present disclosure will become more apparent. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic and the elements and elements are not necessarily drawn to scale.

[0014] Figure 1 is a flowchart of some embodiments of the power grid information display method according to the present disclosure;

[0015] Figure 2 is a schematic structural diagram of some embodiments of the power grid information display device according to the present disclosure;

[0016] Figure 3 is a schematic structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0018] In addition, it should be noted that for the sake of convenience of description, only the parts related to the relevant invention are shown in the drawings. Without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

[0019] It should be noted that concepts such as "first" and "second" mentioned in this 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.

[0020] It should be noted that the modifications of "one" and "multiple" mentioned in this disclosure are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly specified in the context, it should be understood as "one or more".

[0021] The names of the messages or information exchanged between multiple devices in the embodiments of this disclosure are only for illustrative purposes, and are not used to limit the scope of these messages or information.

[0022] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0023] Reference Figure 1 , which shows the flow 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:

[0024] Step 101, using each device sensor pre-deployed in the target power grid area, obtain the real-time transmission, transformation and distribution equipment data set corresponding to the target power grid area within the real-time time period.

[0025] In some embodiments, the execution subject of the above power grid information display method (for example, an electronic device) can use each device sensor 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 within the real-time time period through a wired connection method or a wireless connection method. Among them, the target power grid area can be the power grid area currently to be detected for power supply anomalies. For example, the target power grid area can be Beijing. The device sensor can be various forms of sensors for collecting device operation data. The real-time time period can be a predetermined time period before the current time. For example, if the current time is exactly 17 o'clock, the corresponding real-time time period can be from 8 o'clock to 17 o'clock. Among them, the real-time transmission, transformation and distribution equipment data set includes: the device data sequence corresponding to each transmission equipment, the device data sequence corresponding to each transformation equipment, and the device data sequence corresponding to each distribution equipment. The transmission equipment can be a power equipment for power transmission. The transformation equipment can be a power equipment for power change (for example, voltage change). The distribution equipment can be a power equipment for distribution processing. Each transmission equipment has a corresponding device data sequence. Each transformation equipment has a corresponding device data sequence. Each distribution equipment has a corresponding device data sequence. The device data can be the operation data of the corresponding power equipment at the corresponding time point. The device data in the device data sequence has a one-to-one correspondence with the time points in each time point of the real-time time period.

[0026] Step 102: Generate device information indicating abnormal operation based on the real-time power transmission, transformation, and distribution device dataset to obtain an abnormal operation device information set.

[0027] In some embodiments, the above-mentioned execution entity may generate device information indicating abnormal operation based on the real-time power transmission, transformation, and distribution device dataset to obtain an abnormal operation device information set. Among them, the abnormal operation device information may be the device information of a power device that may indicate abnormal operation. The device information may be the device identifier or model of the power device.

[0028] As an example, for each real-time power transmission, transformation, and distribution device data in the real-time power transmission, transformation, and distribution device dataset, first, determine the power device corresponding to the real-time power transmission, transformation, and distribution device data as the target power device. Then, determine at least one historical device data sequence corresponding to the target power device under at least one historical time period to obtain at least one historical device data sequence. Next, determine the index information set corresponding to the device data sequence of the target power device as the real-time index information set. Then, determine the index information set corresponding to each historical device data sequence in at least one historical device data sequence as the historical index information set to obtain at least one historical index information set. Further, for each index in the index set, perform a weighted summation process on at least one index information corresponding to the index in the at least one historical index information set to generate weighted summation index information. Finally, determine the index difference information between the weighted summation index information set and the real-time index information set. In response to determining that the index difference information is greater than the target value, generate abnormal operation device information indicating abnormal operation for the target power device.

[0029] Step 103: In response to determining that the abnormal operation device information set is not an empty set, perform the following first generation step for the abnormal area information corresponding to the abnormal operation device information set:

[0030] Step 1031: Determine the operation device information set corresponding to the abnormal area information.

[0031] In some embodiments, the above-mentioned execution entity may determine the operation device information set corresponding to the abnormal area information. Among them, the operation device information in the operation device information set may be the device information of the devices operating within the area corresponding to the abnormal area information during the real-time time period. Among them, the abnormal area information may be an area indicating a possible large-scale device operation abnormal situation.

[0032] As an example, first, the above-mentioned execution entity can determine the location information corresponding to each piece of abnormal operation device information in the abnormal operation device information set to obtain a location information set. Then, perform regional division on the location information set to obtain a region information corresponding region that includes the location information set. Finally, determine the region information corresponding to the divided region obtained as the abnormal region information.

[0033] As an example, the above-mentioned execution entity can determine the operation device information set corresponding to the abnormal region information by means of device information query.

[0034] Step 1032, perform abnormal degree regional division on the abnormal region information according to the device data sequence corresponding to each piece of operation device information in the operation device information set to generate an abnormal division region information sequence.

[0035] In some embodiments, the above-mentioned execution entity can perform abnormal degree regional division on the abnormal region information according to the device data sequence corresponding to each piece of operation device information in the operation device information set to generate an abnormal division region information sequence. Among them, the abnormal division region information in the abnormal division region information sequence is sorted in descending order of abnormal degree. For example, the abnormal division region information sequence can be [first abnormal division region information, second abnormal division region information, third abnormal division region information]. The abnormal degree of the devices in the region corresponding to the first abnormal division region information is higher than that of the devices in the region corresponding to the second abnormal division region information. The abnormal degree of the devices in the region corresponding to the second abnormal division region information is higher than that of the devices in the region corresponding to the third abnormal division region information.

[0036] In some optional implementation manners 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. Among them, the first abnormal division region information, the second abnormal division region information, and the third abnormal division region information can be obtained by dividing the abnormal region information. And the number of divided region information is 3 which is determined in advance.

[0037] Optionally, the above-mentioned execution entity performs abnormal degree regional division on the abnormal region information according to the device data sequence corresponding to each piece of operation device information in the operation device information set to generate an abnormal division region information sequence, including the following steps:

[0038] The first step, for each piece of operation device information in the operation device information set, perform the following second generation step:

[0039] Sub-step 1: Determine the device data sequence corresponding to the operating device information as the target device data sequence. The target device data sequence may be the device operation data of the operating device information at each time point within the real-time time period.

[0040] Sub-step 2: Obtain the device data sequence for the operating device information within the same-period time period as the first historical device data sequence. The same-period time period may be a time period that has a same-period relationship with the real-time time period. For example, the real-time time period is from 8:00 am to 5:00 pm on the 11th. The target device data sequence may be the operation data of the operating device between 8:00 am and 5:00 pm on the 11th. The corresponding same-period time period may be from 8:00 am to 5:00 pm on the 10th. The first historical device data sequence may be the operation data of the operating device between 8:00 am and 5:00 pm on the 10th.

[0041] Sub-step 3: Obtain the device data sequence for the operating device information within 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 a time period adjacent relationship. For example, the real-time time period is from 8:00 am to 5:00 pm on the 11th. The target device data sequence may be the operation data of the operating device between 8:00 am and 5:00 pm on the 11th. The corresponding adjacent historical time period may be from 12:00 am to 8:00 am on the 11th. The second historical device data sequence may be the operation data of the operating device between 12:00 am and 8:00 am on the 11th.

[0042] Sub-step 4: Generate the device anomaly coefficient of the device corresponding to the operating device information at the real-time time period according to the first historical device data sequence, the target device data sequence, and the second historical device data sequence. The device anomaly coefficient may characterize the anomaly degree of the device corresponding to the operating device information at the real-time time period. The higher the corresponding device anomaly coefficient, the higher the anomaly degree of the corresponding device at the real-time time period.

[0043] Optionally, first, the above-mentioned execution entity can input the first historical device data sequence into a pre-trained first time-series device feature information extraction model to generate first historical device feature information. Then, the above-mentioned execution entity can input the second historical device data sequence into a pre-trained second time-series device feature information extraction model to generate second historical device feature information. Next, the above-mentioned execution entity can 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 subjected to feature fusion to generate fused device feature information. Finally, the fused device feature information is input into the anomaly degree information generation layer to generate a device anomaly coefficient and an anomaly 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 can be different from each other, and they are all time-series neural network models. Moreover, 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 for extracting feature information from corresponding 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 anomaly degree information generation layer can be a network layer for generating anomaly degree information. The anomaly degree information can include: a device anomaly coefficient and an anomaly category. Among them, the anomaly category can be a category label indicating whether there is an anomaly in the corresponding device. In practice, the anomaly degree information generation layer can be a multi-layer cascaded convolutional layer + attention layer + output layer. 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 anomaly degree information generation layer can be trained together through a conventional model training method.

[0044] Step 2: According to the distribution corresponding to the obtained set of equipment anomaly coefficients, use the target area division method to generate the first abnormal division area information, the second abnormal division area information, and the third abnormal division area information. Among them, the average value of the equipment anomaly coefficients corresponding to the first abnormal division area information is higher than that of the second abnormal division area information, and the average value of the equipment anomaly coefficients corresponding to the second abnormal division area information is higher than that of the third abnormal division area information. The variance values of the equipment anomaly coefficients corresponding to the first abnormal division area information, the variance values of the equipment anomaly coefficients corresponding to the second abnormal division area information, and the variance values of the equipment anomaly coefficients corresponding to the third abnormal division area information are all less than the preset variance value. Among them, the preset variance value can be a variance value set in advance. The distribution corresponding to the set of equipment anomaly coefficients can be the numerical distribution of each equipment anomaly coefficient in the target power grid area. The target area division method can indicate that the average value of the equipment anomaly coefficients corresponding to the first abnormal division area information is higher than that of the second abnormal division area information, the average value of the equipment anomaly coefficients corresponding to the second abnormal division area information is higher than that of the third abnormal division area information, and the variance values of the equipment anomaly coefficients corresponding to the first abnormal division area information, the variance values of the equipment anomaly coefficients corresponding to the second abnormal division area information, and the variance values of the equipment anomaly coefficients corresponding to the third abnormal division area information are all less than the preset variance value.

[0045] As an example, first, the above-mentioned execution entity can determine the equipment location corresponding to each equipment anomaly coefficient in the set of equipment anomaly coefficients to obtain a set of equipment locations. Then, the set of equipment locations and the set of equipment anomaly coefficients are combined correspondingly to generate a set of combined information, where the combined information includes: equipment anomaly coefficients and equipment locations. Finally, use a data optimizer to determine the first abnormal division area information, the second abnormal division area information, and the third abnormal division area information for the set of combined information.

[0046] The above technical solution and its related content, as an inventive point of an embodiment of the present disclosure, solve the technical problem that the abnormal degree analysis of abnormal area information cannot be effectively carried out, resulting in the inability to quickly understand the information of the equipment actually mainly having abnormal operation. Based on this, the present disclosure discloses that by generating the anomaly coefficient corresponding to each operating equipment information, the abnormal degree of each area is determined, so as to determine the abnormal situation within the area corresponding to the abnormal area information, which is convenient for accurately determining the information of the equipment actually mainly having abnormal operation.

[0047] Step 1033: For each abnormal division area information in the sequence of abnormal division area information, determine the set of equipment problem information corresponding to the abnormal division area information and the problem probability corresponding to each equipment problem information.

[0048] In some embodiments, the above-mentioned execution entity may, for each piece of abnormal division area information in the abnormal division area information sequence, determine the set of device problem information corresponding to the abnormal division area information and the problem probability corresponding to each piece of device problem information. The set of device problem information may be various device fault problems that may exist in the devices within the area corresponding to the abnormal division area information. The problem probability may be the probability that the corresponding device problem information is the main device problem. The greater the problem probability, the greater the probability that the corresponding device problem information is the main device problem.

[0049] In some optional implementation manners of some embodiments, the above-mentioned execution entity may determine the set of device problem information corresponding to the abnormal division area information and the problem probability corresponding to each piece of device problem information, including the following steps:

[0050] First step, obtain at least one piece of operating device information corresponding to the abnormal division area information.

[0051] Second step, determine the overall operating abnormality information of the area corresponding to the abnormal division area information. The overall operating abnormality information of the area may be the overall operating state of each device in the abnormal division area information. In practice, the overall operating abnormality information of the area may be information in numerical form or in label form. The overall operating abnormality information of the area is determined based on the sequence position of the abnormal division area information in the abnormal division area information sequence.

[0052] As an example, first, determine at least one reported abnormal situation corresponding to at least one piece of operating device information. Then, determine at least one reported abnormal situation. Finally, determine the weighted sum value between the proportion value of the reported abnormality and the sequence position as the overall operating abnormality information of the area.

[0053] Third step, for each piece of operating device information in the at least one piece of operating device information, perform the following third generation step:

[0054] Sub-step 1, determine the set of core candidate device problem matching information corresponding to the operating device information. The core candidate device problem matching information includes: at least one core candidate device problem. The set of core candidate device problem matching information may be various device sub-problems under the first-level device problems that occur 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 the core device problem that 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.

[0055] Sub-step 2: Determine the overall regional operation impact information corresponding to each core candidate device problem matching information in the core candidate device problem matching information set, to obtain the overall regional operation impact information set. Among them, the overall regional operation impact information may be the degree of impact on the overall operation of each device in the region when at least one core candidate device problem corresponding to the core candidate device problem matching information occurs. In practice, the overall regional operation impact information may be information of numerical type or information of label type.

[0056] As an example, the above-mentioned execution entity may query the overall regional operation impact information corresponding to each core candidate device problem matching information through an association table pre-registered to represent the corresponding relationship between the core candidate device problem matching information and the overall operation impact information, to obtain the overall regional operation impact information set.

[0057] Sub-step 3: Determine the information similarity between each overall regional operation impact information in the overall regional operation impact information set and the overall regional operation anomaly information, to obtain the information similarity set.

[0058] As an example, the above-mentioned execution entity may determine the subtraction value between each overall regional operation impact information and the overall regional operation anomaly information as the information similarity.

[0059] Sub-step 4: According to the information similarity set, screen out the target core candidate device problems from the core candidate device problem set corresponding to the operating device information, to obtain the target core candidate device problem set. Among them, the core candidate device problem set corresponding to the operating device information may be a complete set of core device problems that often occur in the device corresponding to the operating device information. Among them, the target core candidate device problem set may be a set of core device problems that may exist in the operating device information and cause anomalies.

[0060] Sub-step 5: Determine the first anomaly probability corresponding to the target core candidate device problem set. Among them, the first anomaly probability may be the probability value that the device corresponding to the operating device information has the target core candidate device problem set. In practice, by using a large language model, a prompt word may be generated through the first anomaly probability to generate the probability size that the device corresponding to the operating device information has the target core candidate device problem set. Among them, the large language model is a model plugin installed on the background terminal corresponding to the digital twin model.

[0061] Fourth step: Remove the operating device information corresponding to the first anomaly probability less than the first predetermined probability from the at least one operating device information, to obtain the operating device information group. Among them, the first predetermined probability may be a preset probability value.

[0062] Step 5: Generate a set of estimated candidate device problem sets for the operating device information group based on the obtained at least one target core candidate device problem set. Among them, there is a one-to-one correspondence between the operating device information in the operating device information group and the estimated candidate device problem sets in the set of estimated candidate device problem sets. 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.

[0063] Step 6: Determine the set of estimated candidate device problem sets as the device problem information set.

[0064] Step 7: Determine the problem sub-probability corresponding to each estimated candidate device problem in the set of estimated candidate device problem sets, and obtain the problem sub-probability as the problem probability. Among them, the generation method corresponding to the problem sub-probability can refer to the generation method corresponding to the first abnormal probability.

[0065] In some optional implementation manners of some embodiments, generating a set of estimated candidate device problem sets for the operating device information group based on the obtained at least one target core candidate device problem set includes the following steps:

[0066] Step 1: For each operating device information in the operating device information group, determine the set of common candidate device problem matching information corresponding to the operating device information. Among them, the common candidate device problem matching information includes: at least one common candidate device problem. The common candidate device problem matching information can be the matching information of common candidate device problems that often appear in the operating device information. The common candidate device problem can be a device problem with a frequency of occurrence higher than the target frequency in the daily operation of the operating device information. The matching manners of the common candidate device problems corresponding to the various common candidate device problem matching information in the set of common candidate device problem matching information are different.

[0067] Step 2: Combine the target core candidate device problem set group corresponding to the operating device information group and at least one set of common candidate device problem matching information in the obtained set of common candidate device problem matching information sets multiple times in different ways to generate multiple combined problem set groups.

[0068] Step 3: For each combined problem set group in the multiple combined problem set groups, perform the following fourth generation step:

[0069] Sub-step 1: Determine the second abnormal probability corresponding to the combined problem set group. Among them, the second abnormal probability can represent the occurrence probability of the combined problem set group. Among them, the determination of the second abnormal probability can refer to the generation of the first abnormal probability.

[0070] Sub-step 2: Determine the median probability of each first abnormal probability corresponding to the target core candidate device problem set group.

[0071] Sub-step 3: Subtract the median probability from the second abnormal probability to generate a subtraction probability.

[0072] Fourth step: Remove the combination problem set groups corresponding to the subtraction probabilities less than the second predetermined probability from the multiple combination problem set groups to generate the multiple combination problem set groups after removal.

[0073] Fifth step: In response to determining that the multiple combination problem set groups after removal are empty, determine the target core candidate device problem set group as the estimated candidate device problem set group.

[0074] Sixth step: In response to determining that the multiple combination problem set groups after removal are not empty, determine the multiple combination problem set groups after removal as the estimated candidate device problem set group.

[0075] In some alternative implementation manners of some embodiments, the above execution subject may screen out the target core candidate device problems from the core candidate device problems corresponding to the operating device information according to the information similarity set to obtain the target core candidate device problem set, including the following:

[0076] First step: Remove the information similarities less than the third predetermined probability from the information similarity set to obtain the information similarity set after removal. Wherein, the third predetermined probability may be a pre-determined probability value.

[0077] Third step: Determine the core candidate device problem matching information corresponding to each information similarity in the information similarity set after removal to obtain the core candidate device problem matching information set.

[0078] Fourth step: For each core candidate problem information in the core candidate device problem set, perform the following fifth generation steps:

[0079] Sub-step 1: Determine the core candidate device problem matching information in the core candidate device problem matching information set where the core candidate problem information exists to obtain the core candidate device problem matching information group.

[0080] Sub-step 2: Determine the number of information corresponding to the core candidate device problem matching information group.

[0081] Sub-step 3: Determine the information similarity group corresponding to the core candidate device problem matching information group.

[0082] Sub-step 4: Determine the core problem probability corresponding to the core candidate problem information according to the information similarity group and the number of information.

[0083] Optionally, first, determine the probability interval corresponding to the number of pieces of information. Among them, the magnitude corresponding to the number of pieces of information corresponds to the corresponding probability interval. Then, remove the information similarities that are not within the probability interval from the information similarity group to obtain the information similarity group after removal. Finally, take the average value of each information similarity in the information similarity group after removal as the core problem probability.

[0084] In the fifth step, screen out the core candidate device problems corresponding to the core problem probability that meet the preset probability condition from the core candidate device problem set as the target core candidate device problems, and obtain the target core candidate device problem set. Among them, the preset probability condition can be the core candidate device problems with the core problem probability greater than the target core problem probability value.

[0085] Step 1044: According to the obtained problem probability set sequence and the abnormal division region information sequence, screen 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.

[0086] In some embodiments, the above-mentioned execution entity can screen out the 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 region information sequence as the target device problem information, and obtain at least one target device problem information. Among them, the problem condition can be the device problem information with the corresponding problem occurrence probability greater than the preset probability value. The problem occurrence probability can be the probability magnitude of the corresponding device problem information appearing. The problem occurrence probability is more accurate than the problem probability. The preset probability value can be a pre-determined probability value. For example, the preset probability value can be 80%.

[0087] As an example, for each device problem information in the device problem information set sequence, first, the above-mentioned execution entity can determine the problem probability and the abnormal division region information corresponding to the device problem information as the target abnormal division region information. Then, determine the abnormal weight corresponding to the abnormal division region information. Next, multiply the problem probability by the abnormal weight to generate the problem occurrence probability. Finally, screen out the device problem information that meets 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.

[0088] Step 1045: Display the abnormal division region information sequence and the at least one target device problem information in the power digital twin model corresponding to the target power grid region.

[0089] In some embodiments, the above-mentioned execution entity may 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. The power digital twin model may 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 displaying the device data sequence and operating status corresponding to each operating device information in the abnormal area information. The operating status may be information indicating whether the operating device information is in an abnormal state.

[0090] In some optional implementation manners of some embodiments, the steps further include:

[0091] First step, in response to receiving the abnormal division area display information for the power digital twin model, pop up a corresponding abnormal division area display window in the power digital twin model to display the abnormal division area and the target device problem information set corresponding to the target device information set where the corresponding device location is within the abnormal division area. The abnormal division area display information may be a display request for displaying the abnormal division area information. The abnormal division area display window may be a display window for displaying the abnormal division area. The target device problem information set corresponds to the device set 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.

[0092] Second step, in response to receiving the click information for the target device information, pop up the device basic information corresponding to the clicked target device information and the corresponding target device problem information.

[0093] In some optional implementation manners of some embodiments, the steps further include:

[0094] In response to receiving the device display information for the power digital twin model, the above-mentioned execution entity may display the operating status and the device data sequence of the operating 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.

[0095] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: Through the power grid information display method of some embodiments of the present disclosure, in the case of an abnormality in the target power grid area, abnormal information at the regional granularity and device granularity can be accurately and effectively generated. Specifically, the reason for the low efficiency in solving related equipment problems is that there is an upstream and downstream relationship in power transmission among the various devices in the power transmission, transformation, and distribution equipment. When a problem occurs in a device, it is impossible to timely and accurately determine the main device problem that actually occurs, resulting in the need to check each reported device problem one by one, leading to a low efficiency in solving the fault problems of power equipment. Based on this, in the power grid information display method of some embodiments of the present disclosure, first, using each device sensor pre-deployed in the target power grid area, a real-time power transmission, transformation, and distribution equipment data set corresponding to the target power grid area in a real-time time period is obtained, where the real-time power transmission, transformation, and distribution equipment data set includes: device data sequences corresponding to each power transmission device, device data sequences corresponding to each transformation device, and device data sequences corresponding to each distribution device. Here, by obtaining the real-time power transmission, transformation, and distribution equipment data set, the operating data corresponding to each device is obtained to facilitate the subsequent preliminary determination of the device information set with abnormal operation. Then, based on the real-time power transmission, transformation, and distribution equipment data set, device information indicating abnormal operation can be accurately generated to obtain an abnormal operation device information set. Then, in response to determining that the abnormal operation device information set is not an empty set, for the abnormal area information corresponding to the abnormal operation device information set, the following first generation step is performed: The first step is to determine the operating device information set corresponding to the abnormal area information to analyze whether each operating device in the abnormal area information is a main device fault problem. The second step is that according to the device data sequence corresponding to each operating device information in the operating device information set, the abnormal area information can be accurately divided into abnormal degree areas to generate an abnormal division area information sequence, where the abnormal division area information in the abnormal division area information sequence is sorted in descending order of abnormal degree. Here, by dividing the abnormal area information, the device abnormal conditions in each sub-area information can be clarified, so as to conduct comprehensive device abnormal analysis and effective display of device abnormalities for the device abnormal conditions in each sub-area information, enabling viewers to effectively see the device abnormal conditions in each sub-area. The third step is that 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. Here, by determining the problem probability and the device problem information set, each device problem in the abnormal division area and the problem probability of each device problem being a key device problem can be determined, so as to facilitate the subsequent screening of important device problems.Fourth, according to the obtained sequence of problem probability sets and the sequence of abnormal division region information, accurately screen out the device problem information that meets the corresponding problem conditions from the obtained sequence of device problem information sets as the target device problem information, so as to obtain at least one target device problem information. Fifth, display the sequence of abnormal division region information and the at least one target device problem information in the power digital twin model corresponding to the target power grid region, where the power digital twin model also supports displaying the sequence of device data and the operating status corresponding to each operating device information in the abnormal region information. In summary, by determining the main device problems in each sub-region of the abnormal region information and displaying the relevant abnormal information of the sub-region, the abnormal information at the regional granularity and the device granularity can be accurately and effectively generated.

[0096] Further reference 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, and these device embodiments correspond to Figure 1 the method embodiments shown, and the power grid information display device can be specifically applied to various electronic devices.

[0097] As Figure 2As shown in the figure, a power grid information display device 200 includes: an acquisition unit 201, a generation unit 202, and an execution unit 203. Among them, the acquisition unit 201 is configured to use each device sensor pre-deployed in the target power grid area to acquire a real-time power transmission, transformation, and distribution equipment data set corresponding to the target power grid area during a real-time time period, where the real-time power transmission, transformation, and distribution equipment data set includes: an equipment data sequence corresponding to each power transmission equipment, an equipment data sequence corresponding to each power transformation equipment, and an equipment data sequence corresponding to each power distribution equipment; the generation unit 202 is configured to generate device information indicating an abnormal operation according to the real-time power transmission, transformation, and distribution equipment data set to obtain an abnormal operation device information set; the execution unit 203 is configured to, in response to determining that the abnormal operation device information set is not an empty set, perform the following first generation step for the abnormal area information corresponding to the abnormal operation device information set: determine an operation device information set corresponding to the abnormal area information; according to the equipment data sequence corresponding to each operation device information in the operation device information set, perform an abnormal degree area division on the abnormal area information to generate an abnormal division area information sequence, where the abnormal division area information in the abnormal division area information sequence is sorted in descending order of abnormal degree; for each abnormal division area information in the abnormal division area information sequence, determine 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, screen out device problem information that meets the corresponding problem conditions from the obtained device problem information set sequence as target device problem information to obtain at least one target device problem information; display the abnormal division area information sequence and the at least one target device problem information in a power digital twin model corresponding to the target power grid area, where the power digital twin model also supports displaying the equipment data sequence and operation status corresponding to each operation device information in the abnormal area information.

[0098] It can be understood that the various units described in the power grid information display device 200 correspond to the respective steps in the method described in the reference Figure 1 Therefore, the operations, features, and beneficial effects described above for the method also apply to the power grid information display device 200 and the units included therein, and will not be elaborated here.

[0099] Next, refer to Figure 3 , which shows a schematic structural diagram of an electronic device (for example, 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 impose any limitations on the functions and usage scopes of the embodiments of the present disclosure.

[0100] AsFigure 3 As shown, the electronic device 300 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 301, which may perform various appropriate actions and processes according to a program stored in the read-only memory (ROM) 302 or a program loaded from the storage device 308 into the 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 through a bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0101] Generally, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, 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 wiredly to exchange data. Although Figure 3 the electronic device 300 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. Instead, more or fewer devices may be implemented or had. Figure 3 Each block shown in may represent a device or, as needed, multiple devices.

[0102] In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. 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 contains program codes for performing the methods shown in the flowcharts. In such some embodiments, the computer program may 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 functions defined in the methods of some embodiments of the present disclosure are performed.

[0103] It should be noted that in some embodiments of the present disclosure, the above-mentioned computer-readable medium may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium 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 that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, 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, which carries computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium, which can 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.

[0104] In some embodiments, the client and the server can communicate using any currently known or future-developed network protocol such as HTTP (HyperText Transfer Protocol), and can be interconnected with digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include local area networks ("LAN"), wide area networks ("WAN"), the Internet (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0105] The above computer-readable medium may be included in the above electronic device; or it may exist separately and not be assembled into the electronic device. The above computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device is caused to: utilize each device sensor pre-deployed in the target power grid area to obtain a real-time power transmission, transformation, and distribution device data set corresponding to the target power grid area during a real-time time period, wherein the real-time power transmission, transformation, and distribution device data set includes: a device data sequence corresponding to each power transmission device, a device data sequence corresponding to each power transformation device, and a device data sequence corresponding to each power distribution device; generate device information indicating an abnormal operation according to the real-time power transmission, transformation, and distribution device data set to obtain an abnormal operation device information set; in response to determining that the abnormal operation device information set is not an empty set, for the abnormal area information corresponding to the abnormal operation device information set, perform the following first generation step: determine the operation device information set corresponding to the abnormal area information; perform an abnormal degree area division on the abnormal area information according to the device data sequence corresponding to each operation device information in the operation device 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 of abnormal degree; 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; screen out the device problem information that meets the corresponding problem conditions from the obtained device problem information set sequence according to the obtained problem probability set sequence and the abnormal division area information sequence as the target device problem information to obtain at least one target device problem information; 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, wherein the power digital twin model also supports displaying the device data sequence and the operation state corresponding to each operation device information in the abnormal area information.

[0106] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone 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 kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0107] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0108] The units described in some embodiments of the present disclosure may be implemented in software or in hardware. The described units may also be provided in a processor. For example, a processor may be described as including 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 "the unit that uses each device sensor pre-deployed in the target power grid area to acquire the real-time power transmission, transformation, and distribution device data set corresponding to the target power grid area during a real-time time period".

[0109] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), Systems on Chip (SOCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0110] The above description is only some preferred embodiments of the present disclosure and an explanation of the technical principles applied. 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 the 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, technical solutions formed by mutually replacing the above features with technical features having similar functions (but not limited to) 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; The problem sub-probability corresponding to each estimated candidate device problem in the estimated candidate device problem set is determined, and the problem sub-probability is obtained 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 acquire a real-time transmission, transformation and distribution equipment data set corresponding to the target power grid area and within a real-time time period by using various equipment sensors pre-deployed in the target power grid area, 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 areas according to the abnormal degree 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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