Starlight map display method and device, electronic equipment and computer readable medium

Abstract

The embodiment of the invention discloses a starlight map 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 power grid data set; generating a real-time electric power starlight map according to the real-time power grid data set; performing image optimization processing on the real-time electric power starlight map to generate a processed starlight map; in response to the received display information for the processed starlight map, displaying the processed starlight map on a target page in the target display terminal; in response to the received region delineation information and type option control screening information, performing local region amplification and type screening display processing on the processed starlight map displayed by the target page to generate a processed starlight map; and displaying the processed starlight map on the target page. According to the embodiment, on the basis of the real-time power grid data set, generation of the starlight map is accurately processed, the display interaction effect of the starlight map is greatly improved, and the power display effect of the target area is improved.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to star map display methods, devices, electronic devices, and computer-readable media. Background Art

[0002] At present, starlight charts (i.e. starlight maps) can effectively display the power usage of various regions, and also effectively display the prosperity of the region and effectively monitor power. For the display of starlight charts, the usual method is: first, according to the power consumption of the region, artificially use relevant image drawing software to draw a starlight chart of power consumption. Then, the starlight chart of power consumption is directly displayed.

[0003] However, when using the above method to display starlight maps, the following technical problems often occur: The image information in the star map generated by image drawing software is not accurate enough, and due to the large area, it often generates a large workload and low efficiency, resulting in poor display effect.

[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 star map display methods, devices, electronic devices and computer-readable media 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 starlight map display method, comprising: obtaining a real-time power grid data set for a target area, wherein the real-time power grid data includes: power consumption coordinates, power consumption objects, power voltage, power current and power consumption; based on the above-mentioned real-time power grid data set, using generative and adversarial neural network models to generate a real-time power starlight map for the above-mentioned target area; performing image optimization processing on the above-mentioned real-time power starlight map to generate a processed starlight map; in response to receiving display information for the above-mentioned processed starlight map, displaying the above-mentioned processed starlight map on a target page in a target display terminal, wherein the above-mentioned target page has at least one type option control, wherein the above-mentioned at least one type option control includes: Type option controls corresponding to electrical coordinates, type option controls corresponding to electrical objects, type option controls corresponding to electrical voltage, type option controls corresponding to electrical current, and type option controls corresponding to electrical power consumption; in response to receiving the area delineation information and type option control screening information for the target page, locally amplifying and type screening and displaying the processed star map displayed on the target page to generate a processed star map, wherein the area range corresponding to the processed star map is the same as the area range corresponding to the area delineation information, and the type option control corresponding to the processed star map is the same as the type option control corresponding to the type option control screening information; and displaying the processed star map on the target page.

[0008] In a second aspect, some embodiments of the present disclosure provide a star map display device, comprising: an acquisition unit, configured to acquire a real-time power grid data set for a target area, wherein the real-time power grid data includes: power consumption coordinates, power consumption objects, power voltage, power current and power consumption; a generation unit, configured to generate a real-time power star map for the target area based on the real-time power grid data set using a generative and adversarial neural network model; an image optimization processing unit, configured to perform image optimization processing on the real-time power star map to generate a processed star map; a first display unit, configured to display the processed star map on a target page in a target display terminal in response to receiving display information for the processed star map, wherein the target page has at least one type option control, wherein the at least one The type option controls include: type option controls corresponding to electricity coordinates, type option controls corresponding to electricity objects, type option controls corresponding to electricity voltage, type option controls corresponding to electricity current and type option controls corresponding to electricity consumption; a processing unit is configured to, in response to receiving area delineation information and type option control filtering information for the target page, perform local area magnification and type filtering and display processing on the processed star map displayed on the target page to generate a processed star map, wherein the area range corresponding to the processed star map is the same as the range corresponding to the area delineation information, and the type option control corresponding to the processed star map is the same as the type option control corresponding to the type option control filtering information; a second display unit is configured to display the processed star map on the target page.

[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 starlight map display method of some embodiments of the present disclosure, on the basis of the real-time power grid data set, not only the generation of the processing starlight map is accurately realized, but also the display interaction effect of the starlight map is greatly improved, and the power display effect of the target area is improved. Specifically, the reason for the inaccurate generation of the relevant starlight map and the poor display effect of the starlight map is that the image information in the generated starlight map using the image drawing software is not accurate enough, and because the area is large, it often generates a large workload and low efficiency, resulting in poor display effect. Based on this, the starlight map display method of some embodiments of the present disclosure first obtains a real-time power grid data set for the target area. Among them, the real-time power grid data includes: power consumption coordinates, power consumption objects, power consumption voltage, power consumption current and power consumption. Here, the acquired real-time power grid data set is used as a basic data set to subsequently ensure the data accuracy of the generated starlight map. Then, based on the above-mentioned real-time power grid data set, using the generative and adversarial neural network models, a real-time power starlight map for the above-mentioned target area can be accurately and efficiently generated. Next, the real-time power starlight map is subjected to image optimization processing to generate a processed starlight map, so as to optimize and enhance the starlight map and improve the display effect. Next, in response to receiving the display information for the processed starlight map, the processed starlight map is displayed on the target page in the target display terminal. Among them, there is at least one type option control on the target page. Among them, the at least one type option control includes: a type option control corresponding to the power coordinates, a type option control corresponding to the power object, a type option control corresponding to the power voltage, a type option control corresponding to the power current, and a type option control corresponding to the power consumption. Here, by setting at least one type option control on the target page, a diversified display of the starlight map can be achieved, which greatly improves the display effect of the starlight map. Furthermore, in response to receiving the area delineation information and type option control screening information for the above-mentioned target page, the processed starlight map displayed on the above-mentioned target page is locally enlarged and type screened and displayed to generate a processed starlight map, wherein the area range corresponding to the above-mentioned processed starlight map is the same as the area range corresponding to the above-mentioned area delineation information, and the type option control corresponding to the above-mentioned processed starlight map is the same as the type option control corresponding to the above-mentioned type option control screening information. Here, relevant users are supported to manually delineate areas and select type option controls to achieve targeted display of starlight maps, which greatly improves the use efficiency of starlight maps and improves the display effect. Finally, the processed starlight map is displayed on the above-mentioned target page. In summary, by using generative and adversarial neural network models, the data feature information in the real-time power grid data set can be fully utilized to accurately generate real-time power starlight maps. On this basis, the full display of the starlight map can be achieved through the delineation of relevant controls and areas of the target page. 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 starlight map display method according to the present disclosure; Figure 2 is a schematic structural diagram of some embodiments of the starlight map 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 starlight map display method according to the present disclosure. The starlight map display method includes the following steps: Step 101: Acquire a real-time power grid data set for a target area.

[0021] In some embodiments, the execution subject of the above-mentioned starlight map display method can obtain a real-time power grid data set for the target area through a wired connection method or a wireless connection method. Among them, the target area can be an area to be displayed with a starlight effect. In practice, the target area can be a target city. For example, the target area can be Beijing. The starlight effect can be an electricity usage effect. The real-time power grid data set can be a power grid data set obtained in real time. Each real-time power grid data exists in a sub-area in the corresponding target area. The real-time power grid data is the power usage data of each power-consuming object in the sub-area. The real-time power grid data includes: power coordinates, power objects, power voltage, power current and power consumption. The power coordinates can be the regional coordinate range of the sub-area corresponding to the real-time power grid data. The power object can be each power-consuming object in the sub-area corresponding to the real-time power grid data. In practice, the power object can include but is not limited to at least one of the following: a household, a factory, and a school. The power voltage can be the regional power voltage size of the sub-area corresponding to the real-time power grid data. The power current can be the regional power current size of the sub-area corresponding to the real-time power grid data. The power consumption can be the regional total power consumption of the sub-area corresponding to the real-time power grid data.

[0022] Step 102: Based on the above real-time power grid data set, a real-time power star map for the above target area is generated using a generative and adversarial neural network model.

[0023] In some embodiments, the execution subject may generate a real-time power star map for the target area using a generative and adversarial neural network model based on the real-time power grid data set. The generative and adversarial neural network model may generate a network model of a power star map based on a real-time power grid data set. The real-time power star map may be a star map that displays the current brightness of the power consumption in the target area.

[0024] As an example, the above-mentioned execution entity can input the real-time power grid data set into the above-mentioned generative and adversarial neural network model to generate a real-time power star map.

[0025] Regarding the technical problem: the generation of the real-time power star map of the target area is not accurate enough, resulting in the subsequent display of the power situation in the target area not being detailed enough. Combining the technical advantages / market understanding of the inventor and the team, we decided to adopt the following solution: In some optional implementations of some embodiments, the generative and adversarial neural network models include: a generative model set and an adversarial model. The generative model set includes: a plurality of generative models with different network structures. For example, the generative model set may include: convolutional layers with different numbers of layers connected in series, and residual layers with different numbers of layers connected in series.

[0026] Optionally, based on the above real-time power grid data set, using generative and adversarial neural network models, generating a real-time power starlight map for the above target area includes the following steps: In the first step, data preprocessing is performed on each real-time power grid data set in the real-time power grid data set to generate a preprocessed power grid data set. The data preprocessing may include but is not limited to at least one of the following: data supplementation, data replacement, and data format conversion.

[0027] In the second step, the preprocessed power grid data set is input into each generative model in the generative model set in the generative and adversarial neural network model to generate an initial power grid starlight map and obtain an initial power grid starlight map set.

[0028] In the third step, each initial power grid star map in the above initial power grid star map set is input into the adversarial model to generate star map authenticity information and star map probability information, and obtain a star map authenticity information set and a star map probability information set. The star map authenticity information can be information that characterizes whether the generated power grid star map is authentic. The star map probability information can characterize the authenticity of the star map authenticity information.

[0029] The fourth step is to select the starlight map authenticity information representing that the generated power grid starlight map is authentic information from the starlight map authenticity information set, and obtain at least one starlight map authenticity information.

[0030] In the fifth step, the starlight map probability information with the largest corresponding probability value is selected from the at least one starlight map probability information as the target starlight map probability information. There is a one-to-one correspondence between the starlight map probability information in the at least one starlight map probability information and the starlight map authenticity information in the at least one starlight map authenticity information.

[0031] In the sixth step, the initial power grid starlight map corresponding to the target starlight map probability information is determined as the real-time power starlight map.

[0032] The above-mentioned optional implementation methods and optional contents in some embodiments, as an inventive point of the present disclosure, solve the technical problem mentioned that "the generation of the real-time power star map of the target area is not accurate enough, resulting in the subsequent display of the power situation in the target area not being detailed enough". Based on this, the present disclosure can accurately generate a real-time power star map based on the data of the real-time power grid data set by designing the generative model set and the adversarial model form included in the above-mentioned device state prediction information generation model generative and adversarial neural network model.

[0033] Step 103: Perform image optimization processing on the real-time power starlight map to generate a processed starlight map.

[0034] In some embodiments, the execution entity may perform image optimization processing on the real-time power starlight map to generate a processed starlight map.

[0035] As an example, first, the execution subject may perform image enhancement on the real-time power starlight map to generate an enhanced starlight map. Then, the enhanced starlight map may be subjected to image denoising to generate a denoised starlight map. Next, the denoised starlight map may be subjected to chromatic aberration correction to generate a corrected starlight map as the processed starlight map.

[0036] Step 104: in response to receiving the display information for the processed starlight map, display the processed starlight map on a target page in a target display terminal.

[0037] In some embodiments, in response to receiving display information for the above-mentioned processing starlight map, the above-mentioned execution subject may display the above-mentioned processing starlight map on the target page in the target display terminal. The display information may be information for displaying the processing starlight map. In practice, the display information may be a display request. The target display terminal may be a terminal for displaying the starlight map. The target page may be a page for displaying the starlight map. The above-mentioned target page has at least one type option control, wherein the at least one type option control includes: a type option control corresponding to the power coordinates, a type option control corresponding to the power object, a type option control corresponding to the power voltage, a type option control corresponding to the power current, and a type option control corresponding to the power consumption. The type option control corresponding to the power coordinates may be a control for displaying the power coordinates in the processing starlight map. The type option control corresponding to the power object may be a control for displaying the power object in the processing starlight map. The type option control corresponding to the power voltage may be a control for displaying the power voltage size in the processing starlight map. The type option control corresponding to the power current may be a control for displaying the power current size in the processing starlight map. The type option control corresponding to the power consumption may be a control for displaying the power consumption value in the processing starlight map.

[0038] Step 105, in response to receiving the area delineation information and type option control screening information for the target page, the processed star map displayed on the target page is locally enlarged and type screened for display, so as to generate a processed star map.

[0039] In some embodiments, in response to receiving the area delineation information and type option control screening information for the target page, the execution subject may perform local area magnification and type screening display processing on the processed starlight map displayed on the target page to generate a processed starlight map. The area range corresponding to the processed starlight map is the same as the area range corresponding to the area delineation information, and the type option control corresponding to the processed starlight map is the same as the type option control corresponding to the type option control screening information. The area delineation information may be the area information delineated in the processed starlight map. In practice, the delineation method may be delineated by a stylus on the target page. The area delineation information may characterize the area range information of the delineated area. The type option control screening information may be control screening information for at least one type option control. The local area magnification may be to magnify the target page in a targeted manner according to the delineated area to display the starlight display of the delineated area. The type screening display processing may be to display the type data under the screened type option control. For example, the type option control screening information corresponding to the type option control set includes: the type option control corresponding to the power consumption coordinates and the type option control corresponding to the power consumption object. After processing, the electricity consumption coordinates and electricity consumption objects will be displayed in the star map.

[0040] As an example, first, the execution subject may zoom in on a local area of ​​the processed starlight map displayed on the target page to generate a local area starlight map. Then, the local area starlight map is subjected to type screening and display processing to add a control data set corresponding to the corresponding screened type option control to generate a processed starlight map.

[0041] Step 106, displaying the processed starlight map on the target page.

[0042] In some embodiments, the execution entity may display the processed star map on the target page.

[0043] In some optional implementations of some embodiments, after step 106, the steps further include: In the first step, the processed starlight map is added to a pre-stored historical starlight atlas to generate an added starlight atlas. The historical starlight map in the historical starlight atlas may be a starlight map generated at a historical time.

[0044] The second step is to obtain the historical overall starlight change information corresponding to the above historical starlight atlas. The historical overall starlight change information can characterize the overall starlight changes of each historical starlight map in the historical starlight atlas. The historical overall starlight change information can include: historical starlight brightness change information and historical starlight position change information. The historical starlight brightness change information can characterize the changes in starlight brightness in historical time periods. The historical starlight position change information can characterize the changes in starlight positions in historical time periods.

[0045] The third step is to obtain at least one historical starlight image from the historical starlight image collection, the time interval between which and the corresponding time of the processed starlight image is less than the target time length. The target time length may be a preset time length. For example, the target time length may be 1 week. The historical starlight images in the historical starlight image collection have corresponding historical time.

[0046] The fourth step is to input the at least one historical starlight map and the processed starlight map into a starlight change information generation model to generate starlight change information. The starlight change information generation model may be a neural network model for generating starlight change information. In practice, the starlight change information generation model may be a convolutional neural network model + a temporal neural network model. The starlight change information may include: starlight brightness change information and starlight position change information. The starlight brightness change information may characterize the change in starlight brightness. The starlight position change information may characterize the change in starlight position.

[0047] The fifth step is to determine the difference information between the above historical overall starlight change information and the above starlight change information. The difference information may include: first difference sub-information between the historical starlight brightness change information and the starlight brightness change information, and second difference sub-information between the historical starlight position change information and the starlight position change information.

[0048] Step 6: Generate real-time starlight change information based on the above difference information and the above added starlight atlas.

[0049] Optionally, the generating of real-time starlight change information according to the difference information and the added starlight atlas may include the following steps: The first step is to determine the change area information group corresponding to the above difference information. The change area information group includes: at least one change area information for the first difference sub-information and at least one change area information for the second difference sub-information. The first difference sub-information is the difference information between the historical starlight brightness change information and the starlight brightness change information. The second difference sub-information is the difference information between the historical starlight position change information and the starlight position change information. The change area information in the change area information group can be the area information where the starlight brightness change or starlight position change occurs. In practice, the change area information can be the area range information of the change area.

[0050] In the second step, according to the above-mentioned change region information group, each added starlight image in the above-mentioned added starlight image set is segmented to generate a change region image group, and a change region image group set is obtained. Each change region image group in the change region image group set has a corresponding added starlight image.

[0051] As an example, the execution entity may perform image region cropping on each added starlight map in the added starlight map set according to the region range information corresponding to the changed region information group to generate a changed region map group and obtain a changed region map group set.

[0052] The third step is to perform regional combination on each of the change region maps in the change region map set to generate a change region map set group, wherein each of the change region maps in each change region map set has the same region range information.

[0053] Step 4: For each change region atlas in the above change region atlas group, perform the following generation steps: In the first sub-step, each change area map in the above-mentioned change area map set is input into the key information generation model to generate key information and obtain a key information set. The key information generation model can be a neural network model for generating key information. In practice, the key information can be at least one key starlight element information. For example, the key information generation model can be a multi-head classification model. The output of each head model of the key information generation model corresponds to the key starlight element information.

[0054] The second sub-step is to generate the change information between each key information in the above key information set as the real-time starlight change sub-information.

[0055] The fifth step is to update the above historical overall starlight change information according to the obtained real-time starlight change sub-information set to generate update information as the real-time starlight change information.

[0056] As an example, the above-mentioned execution subject may replace the corresponding information in the historical overall starlight change information with the real-time starlight change sub-information set to generate update information as the real-time starlight change information.

[0057] Optionally, the steps further include: The first step is to determine whether there is sub-region information indicating abnormal power usage based on the above real-time starlight change information.

[0058] As an example, first, the execution subject can obtain the real-time power planning information corresponding to each sub-area in the target area to obtain a real-time power planning information set. Then, the real-time power planning information set is compared with the real-time starlight change information to generate comparison information. Finally, in response to determining that the information gap represented by the comparison information is less than a preset value, it is determined that there is no sub-area information representing abnormal power usage. In response to determining that the information gap represented by the comparison information is greater than or equal to a preset value, it is determined that there is sub-area information representing abnormal power usage.

[0059] The second step is to obtain a power data subset corresponding to the above sub-region information in response to determining the existence.

[0060] As an example, in response to determining that there is sub-region information indicating abnormal power usage, a power data subset corresponding to the sub-region information is obtained from the power data set.

[0061] The third step is to perform data verification on the above power data subset to generate verification information.

[0062] As an example, the above-mentioned execution entity may perform a rationality check and a data authenticity check on the power data subset to generate verification information.

[0063] In a fourth step, in response to determining that the verification information indicates that there is abnormality in the power data, abnormal power usage information is generated.

[0064] The abnormal power usage information may be an abnormal reason corresponding to at least one power data in a power data subset that is unreasonable power data or false power data.

[0065] The fifth step is to generate power dispatch information based on the abnormal power usage information, wherein the power dispatch information may be an adjustment process for adjusting power.

[0066] As an example, the execution subject may generate power dispatch information according to the power usage abnormality information by using the association table between the power usage abnormality information and the power dispatch information. The association table may represent the association relationship between the abnormality cause and the power dispatch information.

[0067] The sixth step is to execute power dispatch according to the above power dispatch information.

[0068] Optionally, the steps further include: The first step is, in response to determining that it does not exist, generating a first future starlight map sequence for a target future time period according to the above-mentioned real-time starlight change information and historical starlight change information sequence.

[0069] As an example, the execution entity may generate a first future starlight map sequence for a target future time period using a temporal neural network model (eg, an LSTM model) based on the real-time starlight change information and the historical starlight change information sequence.

[0070] The second step is to obtain the first key event information set within the above-mentioned target future time period. The target future time period may be a predetermined time period for which star map prediction is to be performed. The first key event information in the first key event information set may be event information of key events to be performed in the target area within the target future time period. It should be noted that the power required for the event corresponding to the first key event information needs to be pre-configured. The first key event information may be event information that represents the official launch of the target construction project within the target future time period.

[0071] The third step is to generate the first power demand information and the first power time information corresponding to each first key event information in the first key event information set, and obtain the first power demand information set and the first power time information set. The first power demand information may be a power demand amount predetermined for the first key event information. The first power time information may be a power time predetermined for the first key event information.

[0072] The fourth step is to adjust the starlight of the first future starlight map in the first future starlight map sequence according to the first power demand information set and the first power consumption time information set to generate an adjusted first future starlight map sequence.

[0073] As an example, first, the electricity demand in the above-mentioned first electricity demand information is quantified to generate electricity demand data. Then, the above-mentioned first electricity time information and the above-mentioned electricity demand data are data-fused to generate fused data. Next, in response to determining that the demand corresponding to the electricity demand data is greater than the target demand, based on the above-mentioned real-time starlight change information, the historical starlight change information sequence and the above-mentioned fused data, a time series neural network model is used to generate an adjusted first future starlight map sequence. In response to determining that the demand corresponding to the electricity demand data is less than or equal to the target demand, the first future starlight map sequence is determined to be the above-mentioned adjusted first future starlight map sequence.

[0074] Step 5: According to the adjusted first future starlight map sequence, corresponding power equipment scheduling and power scheduling are performed. Power equipment scheduling can be equipment scheduling of each power grid equipment. Power scheduling can be scheduling of power generation, power output and power transportation.

[0075] In a sixth step, in response to determining existence, the processed starlight map is subjected to starlight adjustment according to the power dispatching information to generate an adjusted starlight map.

[0076] As an example, the execution subject may perform starlight adjustments on each starlight element in the processed starlight map according to the dispatching process corresponding to the power dispatching information to generate an adjusted starlight map.

[0077] The seventh step is to generate real-time starlight change information for the adjusted starlight map as target real-time starlight change information, wherein the target real-time starlight change information can represent the transformation of each starlight element in the adjusted starlight map.

[0078] Step 8: Generate a second future starlight map sequence for the target future time period based on the target real-time starlight change information and the historical starlight change information sequence, wherein there is a one-to-one correspondence between the future time in each future time in the target future time period and the second future starlight map in the second future starlight map sequence.

[0079] As an example, the above-mentioned execution entity can use a temporal neural network model to generate a second future starlight map sequence for a target future time period based on the above-mentioned target real-time starlight change information and historical starlight change information sequence.

[0080] The ninth step is to obtain a second key event information set within the above-mentioned target future time period. The second key event information in the second key event information set may be event information of a key event to be carried out in the target area within the target future time period. It should be noted that the power required for the event corresponding to the second key event information needs to be pre-configured. The second key event information may be event information representing the official launch of the target construction project within the target future time period.

[0081] Step 10: Generate the second power demand information and the second power time information corresponding to each second key event information in the second key event information set to obtain the second power demand information set and the second power time information set. The specific implementation method and related terms are not repeated here, please refer to the explanation of the first power demand information and the first power time information.

[0082] In the eleventh step, according to the second power demand information set and the second power time information set, the second future starlight map in the second future starlight map sequence is adjusted to generate an adjusted second future starlight map sequence. The specific implementation method will not be described in detail, and please refer to the generation of the first future starlight map sequence.

[0083] Step 12: According to the adjusted second future starlight image sequence, corresponding power equipment scheduling and power scheduling are performed. The specific implementation method will not be described in detail.

[0084] Optionally, the step of inputting the at least one historical starlight map and the processed starlight map into a starlight variation information generation model to generate starlight variation information includes: The first step is to replace each pixel in the above-mentioned at least one historical starlight map with the corresponding power intensity information to obtain a first power intensity matrix. The power intensity information is a numerical value representing the power intensity of the area corresponding to the pixel, and each pixel has corresponding power intensity information. In practice, the power intensity information can be but is not limited to at least one of the following: value 1, value 2, value 3, value 4. The power intensity represented by value 1 is greater than the power intensity represented by value 2. The power intensity represented by value 2 is greater than the power intensity represented by value 3. The power intensity represented by value 3 is greater than the power intensity represented by value 4. There is a corresponding pixel interval for value 1. There is also a corresponding pixel interval for value 2. There is also a corresponding pixel interval for value 3. There is also a corresponding pixel interval for value 4.

[0085] In the first step, each pixel in the processed starlight image is replaced with corresponding power intensity information to obtain a second power intensity matrix.

[0086] The second step is to combine the obtained at least one first power intensity matrix and the second power intensity matrix in a time sequence to generate a power intensity matrix sequence, wherein the time sequence may be the time when the starlight map is generated.

[0087] The third step is to combine the at least one historical starlight map and the processed starlight map accordingly according to the chronological order to generate a starlight map sequence.

[0088] The fourth step is to perform matrix enhancement on each power intensity matrix in the power intensity matrix sequence to generate an enhanced matrix and obtain an enhanced matrix sequence, wherein the matrix dimension corresponding to the enhanced matrix is ​​a preset multiple of the matrix dimension corresponding to the power intensity matrix.

[0089] As an example, the execution entity may perform matrix gradient expansion on each power intensity matrix in the power intensity matrix sequence to generate an enhanced matrix with a predetermined expansion multiple, thereby obtaining an enhanced matrix sequence.

[0090] In the fifth step, the enhanced matrix sequence is input into the matrix change information generation model included in the starlight change information generation model to generate matrix change information. The matrix change information generation model may be a neural network model for generating matrix change information. In practice, the matrix change information generation model may be a Transformer model. The matrix change information may characterize the matrix element change and matrix element change range of the enhanced matrix in the enhanced matrix sequence.

[0091] In the sixth step, according to the above-mentioned matrix change information, each starlight map in the above-mentioned starlight map sequence is input into the first feature extraction model based on the attention mechanism to generate starlight map feature information and obtain a starlight map feature information sequence. Among them, the first feature extraction model based on the attention mechanism can be a convolutional neural network model with multiple layers connected in series (that is, a network model with attention feature extraction constituted by multiple layers of convolutional neural network models connected in series). During the convolution process, the first feature extraction model based on the attention mechanism will perform high-weight convolution on the matrix element change range corresponding to the matrix change information, so as to especially convolve the pixel information in the starlight map with the matrix element change range corresponding to the matrix change information to generate the starlight map feature information.

[0092] The seventh step is to perform matrix pruning on the power intensity matrix in the power intensity matrix sequence according to the matrix change information to generate a pruning matrix and obtain a pruning matrix sequence.

[0093] As an example, the execution subject may perform matrix pruning on the power intensity matrix in the power intensity matrix sequence according to the matrix element variation range corresponding to the matrix variation information to generate a pruning matrix and obtain a pruning matrix sequence.

[0094] In the eighth step, the cropping matrix in the above cropping matrix sequence is input into the second feature extraction model included in the above starlight change information generation model to generate matrix feature information and obtain a matrix feature information sequence. In practice, the second feature extraction model can be a multi-layer convolutional neural network model. The matrix feature information can characterize the matrix semantic features of the cropping matrix.

[0095] In the ninth step, the matrix feature information in the matrix feature information sequence and the star map feature information in the star map feature information sequence are correspondingly concatenated to generate concatenated feature information and obtain a concatenated feature information sequence.

[0096] As an example, the above-mentioned execution entity may perform feature information splicing on the matrix feature information in the matrix feature information sequence and the star map feature information of the same star map in the star map feature information sequence to generate spliced ​​feature information and obtain a spliced ​​feature information sequence.

[0097] In the tenth step, the spliced ​​feature information sequence is input into the time series neural network model included in the starlight variation information generation model to generate the starlight variation information. In practice, the time series neural network model can be a multi-layer cascaded recurrent neural network model.

[0098] Optionally, the above-mentioned key information includes: at least one key power consumption object information, at least one key power consumption area information, and at least one key power consumption brightness information. Among them, the key power consumption object information can be pre-registered object information whose power consumption exceeds a first predetermined value. The key power consumption area information can be pre-registered power consumption area information whose power consumption exceeds a second predetermined value. At least one key power consumption brightness information can be power consumption brightness change information of the area corresponding to the key power consumption area information.

[0099] Optionally, the step of inputting each change region map in the change region map set into the key information generation model to generate key information may include the following steps: In the first step, the above-mentioned change area map is input into the key power-consuming object information extraction model based on the attention mechanism model included in the above-mentioned key information generation model to generate at least one key power-consuming object information. In practice, the key power-consuming object information extraction model may include: a convolutional neural network model + a multi-head attention mechanism model.

[0100] In the second step, the area information centered on at least one key electricity-consuming object information is screened out from the above-mentioned change area map to obtain a candidate key electricity-consuming area information set.

[0101] The third step is to merge the regional information of each candidate key power consumption area information in the above candidate key power consumption area information set according to the regional distance between each key power consumption area information in the above candidate key power consumption area information set to generate the above at least one key power consumption area information.

[0102] As an example, the execution entity may merge the information area information of at least one candidate key power consumption area information whose concentrated area distance among the candidate key power consumption area information is less than a third predetermined value to generate key power consumption area information, and obtain the at least one key power consumption area information.

[0103] The fourth step is to determine, for each key power consumption area information in the at least one key power consumption area information, an average power consumption brightness value of the area corresponding to the key power consumption area information as the key power consumption brightness information.

[0104] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the starlight map display method of some embodiments of the present disclosure, on the basis of the real-time power grid data set, not only the generation of the processing starlight map is accurately realized, but also the display interaction effect of the starlight map is greatly improved, and the power display effect of the target area is improved. Specifically, the reason for the inaccurate generation of the relevant starlight map and the poor display effect of the starlight map is that the image information in the generated starlight map using the image drawing software is not accurate enough, and because the area is large, it often generates a large workload and low efficiency, resulting in poor display effect. Based on this, the starlight map display method of some embodiments of the present disclosure first obtains a real-time power grid data set for the target area. Among them, the real-time power grid data includes: power consumption coordinates, power consumption objects, power consumption voltage, power consumption current and power consumption. Here, the acquired real-time power grid data set is used as a basic data set to subsequently ensure the data accuracy of the generated starlight map. Then, based on the above-mentioned real-time power grid data set, using the generative and adversarial neural network models, a real-time power starlight map for the above-mentioned target area can be accurately and efficiently generated. Next, the real-time power starlight map is subjected to image optimization processing to generate a processed starlight map, so as to optimize and enhance the starlight map and improve the display effect. Next, in response to receiving the display information for the processed starlight map, the processed starlight map is displayed on the target page in the target display terminal. Among them, there is at least one type option control on the target page. Among them, the at least one type option control includes: a type option control corresponding to the power coordinates, a type option control corresponding to the power object, a type option control corresponding to the power voltage, a type option control corresponding to the power current, and a type option control corresponding to the power consumption. Here, by setting at least one type option control on the target page, a diversified display of the starlight map can be achieved, which greatly improves the display effect of the starlight map. Furthermore, in response to receiving the area delineation information and type option control screening information for the above-mentioned target page, the processed starlight map displayed on the above-mentioned target page is locally enlarged and type screened and displayed to generate a processed starlight map, wherein the area range corresponding to the above-mentioned processed starlight map is the same as the area range corresponding to the above-mentioned area delineation information, and the type option control corresponding to the above-mentioned processed starlight map is the same as the type option control corresponding to the above-mentioned type option control screening information. Here, relevant users are supported to manually delineate areas and select type option controls to achieve targeted display of starlight maps, which greatly improves the use efficiency of starlight maps and improves the display effect. Finally, the processed starlight map is displayed on the above-mentioned target page. In summary, by using generative and adversarial neural network models, the data feature information in the real-time power grid data set can be fully utilized to accurately generate real-time power starlight maps. On this basis, the full display of the starlight map can be achieved through the delineation of relevant controls and areas of the target page.

[0105] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a starlight map display device. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the star map display device can be specifically applied to various electronic devices.

[0106] like Figure 2 As shown, a starlight map display device 200 includes: an acquisition unit 201, a generation unit 202, an image optimization processing unit 203, a first display unit 204, a processing unit 205 and a second display unit 206. The acquisition unit 201 is configured to acquire a real-time power grid data set for a target area, wherein the real-time power grid data includes: power consumption coordinates, power consumption objects, power consumption voltage, power consumption current and power consumption; the generation unit 202 is configured to generate a real-time power starlight map for the target area according to the real-time power grid data set using a generative and adversarial neural network model; the image optimization processing unit 203 is configured to perform image optimization processing on the real-time power starlight map to generate a processed starlight map; the first display unit 204 is configured to display the processed starlight map on a target page in a target display terminal in response to receiving display information for the processed starlight map, wherein the target page has at least one type option control, wherein the at least one type option control includes: Type option controls corresponding to electricity coordinates, type option controls corresponding to electricity objects, type option controls corresponding to electricity voltage, type option controls corresponding to electricity current and type option controls corresponding to electricity consumption; a processing unit 205 is configured to, in response to receiving area delineation information and type option control filtering information for the target page, perform local area magnification and type filtering and display processing on the processed star map displayed on the target page to generate a processed star map, wherein the area range corresponding to the processed star map is the same as the area range corresponding to the area delineation information, and the type option control corresponding to the processed star map is the same as the type option control corresponding to the type option control filtering information; a second display unit 206 is configured to display the processed star map on the target page.

[0107] It is understandable that the units described in the star map display device 200 are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the star map display device 200 and the units included therein, and will not be described in detail here.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] It should be noted that the computer-readable medium mentioned above 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 above 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, which carries a computer-readable program code. This propagated data signal 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.

[0113] 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.

[0114] 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: obtains a real-time power grid data set for the target area, wherein the real-time power grid data includes: power consumption coordinates, power consumption objects, power voltage, power current and power consumption; generates a real-time power star map for the above-mentioned target area based on the above-mentioned real-time power grid data set using generative and adversarial neural network models; performs image optimization processing on the above-mentioned real-time power star map to generate a processed star map; in response to receiving display information for the above-mentioned processed star map, displays the above-mentioned processed star map on a target page in a target display terminal, wherein the above-mentioned target page has at least one type option control, wherein the above-mentioned at least one The type option control includes: a type option control corresponding to the electricity coordinates, a type option control corresponding to the electricity object, a type option control corresponding to the electricity voltage, a type option control corresponding to the electricity current and a type option control corresponding to the electricity consumption; in response to receiving the area delineation information and the type option control filtering information for the above-mentioned target page, the processed star map displayed on the above-mentioned target page is locally enlarged and type filtered and displayed to generate a processed star map, wherein the area range corresponding to the above-mentioned processed star map is the same as the range corresponding to the above-mentioned area delineation information, and the type option control corresponding to the above-mentioned processed star map is the same as the type option control corresponding to the above-mentioned type option control filtering information; the above-mentioned processed star map is displayed on the above-mentioned target page.

[0115] 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).

[0116] 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.

[0117] The units described in some embodiments of the present disclosure may be implemented by software or hardware. The units described may also be set in a processor, for example, it may be described as: a processor includes an acquisition unit, a generation unit, an image optimization processing unit, a first display unit, a processing unit, and a second display unit. The names of these units do not constitute a limitation on the units themselves in some cases. For example, the acquisition unit may also be described as a "unit for acquiring a real-time power grid data set for a target area."

[0118] 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.

[0119] 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 star map display method, comprising: Acquire a real-time power grid data set for a target area, wherein the real-time power grid data includes: power consumption coordinates, power consumption objects, power consumption voltage, power consumption current, and power consumption; According to the real-time power grid data set, using generative and adversarial neural network models, a real-time power starlight map for the target area is generated; Performing image optimization processing on the real-time power starlight map to generate a processed starlight map; In response to receiving display information for the processed starlight map, displaying the processed starlight map on a target page in a target display terminal, wherein the target page has at least one type option control, wherein the at least one type option control includes: a type option control corresponding to power consumption coordinates, a type option control corresponding to power consumption objects, a type option control corresponding to power consumption voltage, a type option control corresponding to power consumption current, and a type option control corresponding to power consumption; In response to receiving the area delineation information and the type option control filtering information for the target page, a local area magnification and type filtering display processing are performed on the processed star map displayed on the target page to generate a processed star map, wherein the area range corresponding to the processed star map is the same as the range corresponding to the area delineation information, and the type option control corresponding to the processed star map is the same as the type option control corresponding to the type option control filtering information; The processed starlight map is displayed on the target page.

2. The method according to claim 1, wherein: The method further comprises: Adding the processed starlight map to a pre-stored historical starlight map set to generate an added starlight map set; Obtaining historical overall starlight change information corresponding to the historical starlight atlas; Acquire from the historical starlight atlas at least one historical starlight image whose time interval with the time corresponding to the processed starlight image is less than the target time length; Inputting the at least one historical starlight map and the processed starlight map into a starlight variation information generation model to generate starlight variation information; Determining difference information between the historical overall starlight change information and the starlight change information; Real-time starlight change information is generated according to the difference information and the added starlight atlas.

3. The method according to claim 2, wherein: Generating real-time starlight change information according to the difference information and the added starlight atlas includes: Determine a change region information group corresponding to the difference information; According to the change region information group, performing image region segmentation on each post-addition starlight image in the post-addition starlight image set to generate a change region image group, thereby obtaining a change region image group set; Performing region combination on each change region map in the change region map set to generate a change region map set group; For each change region atlas in the change region atlas group, the following generation steps are performed: Inputting each change region map in the change region map set into a key information generation model to generate key information, thereby obtaining a key information set; Generate change information between each key information in the key information set as real-time starlight change sub-information; According to the obtained real-time starlight change sub-information set, the change information of the historical overall starlight change information is updated to generate update information as the real-time starlight change information.

4. The method according to claim 3, wherein: The method further comprises: Determining whether there is sub-region information indicating abnormal power usage based on the real-time starlight change information; In response to determining that the sub-region information exists, obtaining a power data subset corresponding to the sub-region information; Performing data verification on the power data subset to generate verification information; In response to determining that the verification information indicates that there is an abnormality in the power data, generating abnormal power usage information; generating power dispatch information according to the power usage abnormality information; Power dispatching is performed according to the power dispatching information.

5. The method according to claim 4, wherein: The method further comprises: In response to determining that there is no such information, generating a first future starlight map sequence for a target future time period according to the real-time starlight change information and the historical starlight change information sequence; Acquire a first key event information set within the target future time period; Generate first power demand information and first power usage time information corresponding to each first key event information in the first key event information set to obtain a first power demand information set and a first power usage time information set; According to the first electricity demand information set and the first electricity consumption time information set, performing starlight adjustment on the first future starlight map in the first future starlight map sequence to generate an adjusted first future starlight map sequence; According to the adjusted first future starlight map sequence, executing corresponding electrical equipment scheduling and electricity scheduling; In response to determining that it exists, performing starlight adjustment on the processed starlight map according to the power dispatch information to generate an adjusted starlight map; Generating real-time starlight change information for the adjusted starlight map as target real-time starlight change information; Generating a second future starlight map sequence for a future time period of the target according to the target real-time starlight change information and the historical starlight change information sequence; Acquire a second key event information set within the target future time period; Generate second power demand information and second power usage time information corresponding to each second key event information in the second key event information set to obtain a second power demand information set and a second power usage time information set; According to the second electricity demand information set and the second electricity consumption time information set, performing starlight adjustment on the second future starlight map in the second future starlight map sequence to generate an adjusted second future starlight map sequence; According to the adjusted second future starlight map sequence, corresponding electrical equipment scheduling and electricity scheduling are performed.

6. The method according to claim 5, wherein: The step of inputting the at least one historical starlight map and the processed starlight map into a starlight variation information generation model to generate starlight variation information comprises: For each historical starlight map in the at least one historical starlight map, each pixel in the historical starlight map is replaced with corresponding power intensity information to obtain a first power intensity matrix, wherein the power intensity information is a numerical value representing the power intensity of an area corresponding to the pixel, and each pixel has corresponding power intensity information; Replacing each pixel in the processed starlight image with corresponding power intensity information to obtain a second power intensity matrix; According to the time sequence, correspondingly combining the obtained at least one first power intensity matrix and the second power intensity matrix to generate a power intensity matrix sequence; According to the chronological order, the at least one historical starlight map and the processed starlight map are correspondingly combined to generate a starlight map sequence; Performing matrix enhancement on each power intensity matrix in the power intensity matrix sequence to generate an enhanced matrix, thereby obtaining an enhanced matrix sequence, wherein the matrix dimension corresponding to the enhanced matrix is ​​a preset multiple of the matrix dimension corresponding to the power intensity matrix; Inputting the enhanced matrix sequence into a matrix change information generation model included in the starlight change information generation model to generate matrix change information; According to the matrix change information, each starlight image in the starlight image sequence is input into a first feature extraction model based on an attention mechanism to generate starlight image feature information, thereby obtaining a starlight image feature information sequence; Performing matrix pruning on the power intensity matrix in the power intensity matrix sequence according to the matrix change information to generate a pruning matrix, thereby obtaining a pruning matrix sequence; Inputting the cropping matrix in the cropping matrix sequence into the second feature extraction model included in the starlight variation information generation model to generate matrix feature information, thereby obtaining a matrix feature information sequence; Correspondingly splicing the matrix feature information in the matrix feature information sequence and the star map feature information in the star map feature information sequence to generate spliced ​​feature information, thereby obtaining a spliced ​​feature information sequence; The spliced ​​feature information sequence is input into the temporal neural network model included in the starlight variation information generation model to generate the starlight variation information.

7. The method according to claim 6, wherein: The key information includes: at least one key power-consuming object information, at least one key power-consuming area information, and at least one key power-consuming brightness information; and The step of inputting each change region map in the change region map set into a key information generation model to generate key information includes: Inputting the change area map into a key power-consuming object information extraction model based on an attention mechanism model included in the key information generation model to generate at least one key power-consuming object information; Filtering out area information centered on at least one key power-consuming object information from the change area map to obtain a candidate key power-consuming area information set; Merging the regional information of each candidate key power consumption area information in the candidate key power consumption area information set according to the regional distance between each candidate key power consumption area information in the candidate key power consumption area information set to generate the at least one key power consumption area information; For each key power consumption area information in the at least one key power consumption area information, an average power consumption brightness value of the area corresponding to the key power consumption area information is determined as the key power consumption brightness information.

8. A star map display device, comprising: An acquisition unit is configured to acquire a real-time power grid data set for a target area, wherein the real-time power grid data includes: power consumption coordinates, power consumption objects, power consumption voltage, power consumption current and power consumption; A generating unit is configured to generate a real-time power star map for the target area based on the real-time power grid data set by using a generative and adversarial neural network model; An image optimization processing unit is configured to perform image optimization processing on the real-time power starlight map to generate a processed starlight map; A first display unit is configured to display the processed starlight map on a target page in a target display terminal in response to receiving display information for the processed starlight map, wherein the target page has at least one type option control, wherein the at least one type option control includes: a type option control corresponding to power consumption coordinates, a type option control corresponding to power consumption objects, a type option control corresponding to power consumption voltage, a type option control corresponding to power consumption current, and a type option control corresponding to power consumption; a processing unit configured to, in response to receiving the area delineation information and the type option control screening information for the target page, perform local area magnification and type screening display processing on the processed star map displayed on the target page to generate a processed star map, wherein the area range corresponding to the processed star map is the same as the range corresponding to the area delineation information, and the type option control corresponding to the processed star map is the same as the type option control corresponding to the type option control screening information; The second display unit is configured to display the processed starlight map on the target page.

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

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

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