Early warning method and system for power supply load of charging station by power distribution network

Abstract

The invention provides an early warning method and system for a power distribution network to a power supply load of a charging station, and the method comprises the steps: carrying out the convolution processing of the power supply load data of the charging station, and obtaining a convolution result of the power supply load data of the charging station; integrating the convolution results of the charging station power supply load data to obtain at least one power supply load factor directory; and obtaining key label data corresponding to key labels in the power supply load data of the charging station by combining convolution results included in the at least one power supply load factor catalog, determining an abnormal load according to the key label data, and performing early warning on the abnormal load. The key label data corresponding to the key label in the power supply load data of the charging station is determined by analyzing each convolution result in each power supply load factor catalog step by step, the abnormal load is determined according to the key label data, early warning is performed on the abnormal load, and the early warning accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of early warning technology, and in particular to an early warning method and system for a distribution network to supply power to a charging station load. Background Art

[0002] With the continuous development and progress of electric vehicles, charging station power supply is now a common and commonly used thing. The load of the charging station is a very important indicator, because the charging pile load test is essential to ensure equipment safety, evaluate performance, improve user experience and comply with industry standards. Through testing, safety hazards can be discovered, performance can be evaluated, stability and efficiency can be improved, and industry specifications can be met.

[0003] Therefore, how to provide early warning for the power supply load of the charging station is currently a relatively difficult technology to overcome. Summary of the invention

[0004] In order to improve the technical problems existing in the related technologies, the present application provides an early warning method and system for a distribution network to supply power to a charging station load.

[0005] In a first aspect, a method for early warning of a charging station power supply load by a distribution network is provided, the method comprising at least: performing convolution processing on charging station power supply load data to obtain a convolution result of the charging station power supply load data; integrating the convolution results of the charging station power supply load data to obtain at least one power supply load factor directory; combining the convolution results contained in the at least one power supply load factor directory to obtain key label data corresponding to key labels in the charging station power supply load data, determining abnormal loads according to the key label data, and issuing an early warning for the abnormal loads.

[0006] In an independently implemented embodiment, the convolution results contained in each of the at least one power supply load factor directory are combined to obtain key label data corresponding to the key label in the power supply load data of the charging station, including: analyzing the convolution results contained in the first power supply load factor directory among the at least one power supply load factor directory to obtain a matching relationship between at least one key label contained in the first power supply load factor directory and at least one key label data; combining the matching relationship between at least one key label contained in the first power supply load factor directory and at least one key label data, analyzing the convolution results contained in the subsequent power supply load factor directory of the first power supply load factor directory.

[0007] In an independently implemented embodiment, the convolution result of the first power supply load factor directory includes the convolution result of at least one important attribute set in the first power supply load factor directory; the convolution result contained in the first power supply load factor directory in the at least one power supply load factor directory is analyzed to obtain a matching relationship between at least one key label and at least one key label data contained in the first power supply load factor directory, including: obtaining a first key label included in the convolution result of the first important attribute set in the at least one important attribute set, and the at least one key label includes the first key label; determining whether the key label data of the first key label is included in the convolution result of at least one second important attribute set similar to the first important attribute set in the at least one important attribute set; on the premise that the key label data of the first key label does not exist in the convolution result of the at least one second important attribute set, identifying the key label data of the first key label in the convolution result of at least one third important attribute set, wherein the third important attribute set is a potential key description set excluding the first important attribute set and the at least one second important attribute set in the at least one important attribute set.

[0008] In an independently implemented embodiment, the convolution result contained in the subsequent power supply load factor directory of the first power supply load factor directory is analyzed based on the matching relationship between at least one key tag and at least one key tag data contained in the first power supply load factor directory, including: if the key tag data corresponding to the first key tag does not exist in the convolution result of the first power supply load factor directory, identifying the key tag data corresponding to the first key tag in the convolution result of the subsequent power supply load factor directory; and / or if the key tag corresponding to the first key tag data does not exist in the convolution result of the first power supply load factor directory, identifying the key tag corresponding to the first key tag data in the convolution result of the subsequent power supply load factor directory.

[0009] In an independently implemented embodiment, the convolution result contained in the subsequent power supply load factor directory of the first power supply load factor directory is analyzed in combination with the matching relationship between at least one key tag and at least one key tag data contained in the first power supply load factor directory, and also includes: analyzing the convolution result of the subsequent power supply load factor directory in combination with the identification result to obtain the matching relationship between the key tag and the key tag data contained in the subsequent power supply load factor directory.

[0010] In an independently implemented embodiment, key tag data corresponding to key tags in the charging station power supply load data is obtained, abnormal load is determined according to the key tag data, and after the abnormal load is warned, it also includes: combining the projection relationship between several key tags of the charging station power supply load data, verifying the key tag data corresponding to the several key tags.

[0011] In an independently implemented embodiment, the key tag data corresponding to the several key tags are verified, including: on the premise that the several key tags include the first key tag, the second key tag and the third key tag belonging to the same power supply load factor directory, verifying whether the first data corresponding to the first key tag and the second data corresponding to the second key tag belonging to the same power supply load factor directory are weighted and processed to see whether they conform to the important data corresponding to the third key tag; and / or on the premise that the several key tags include at least one important key tag and all key tags belonging to the same queue, verifying whether the splicing result of the important data corresponding to all important key tags belonging to the same queue conforms to all quantization results corresponding to all key tags.

[0012] In an independently implemented embodiment, after verifying the key tag data corresponding to the several key tags, it also includes: on the premise that the result of the verification indicates that the projection relationship does not meet the preset requirements, debugging the key tag data corresponding to the several key tags until the debugging result enables the projection relationship to be realized.

[0013] In an independently implemented embodiment, before performing convolution processing on the charging station power supply load data, the method also includes: determining whether the charging station power supply load data meets the standard load detection indication; on the premise that it is determined that the charging station power supply load data does not meet the standard load detection indication, updating the charging station power supply load data to obtain the updated charging station power supply load data; the convolution processing on the charging station power supply load data includes: performing convolution processing on the updated charging station power supply load data.

[0014] In a second aspect, an early warning system for a distribution network supplying power to a charging station load is provided, comprising a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute it to implement the above method.

[0015] The embodiment of the present application provides a method and system for early warning of the power supply load of a charging station by a distribution network. The method performs convolution processing on the power supply load data of the charging station to obtain the convolution result of the power supply load data of the charging station; the convolution result of the power supply load data of the charging station is integrated to obtain at least one power supply load factor directory; the convolution results contained in each of the at least one power supply load factor directory are combined to obtain key label data corresponding to the key label in the power supply load data of the charging station, and the abnormal load is determined according to the key label data, and an early warning is issued for the abnormal load. In this way, the key label data corresponding to the key label in the power supply load data of the charging station is determined by gradually analyzing each convolution result in each power supply load factor directory, and the abnormal load is determined according to the key label data, and an early warning is issued for the abnormal load, thereby improving the accuracy of the early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flow chart of a method for early warning of a charging station power supply load by a distribution network provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to better understand the above technical scheme, the technical scheme of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0019] See also Figure 1 , shows an early warning method for a distribution network to supply power to a charging station load, the method may include the technical solutions described in steps 100 and 200.

[0020] Step 100, performing convolution processing on the charging station power supply load data to obtain the convolution result of the charging station power supply load data; integrating the convolution result of the charging station power supply load data to obtain at least one power supply load factor catalog.

[0021] Step 200, combining the convolution results contained in the at least one power supply load factor directory, obtain key label data corresponding to the key label in the power supply load data of the charging station, determine the abnormal load according to the key label data, and issue an early warning for the abnormal load.

[0022] It should be understood that when executing the contents described in the above steps 100 and 200, the charging station power supply load data is convolved to obtain the convolution result of the charging station power supply load data; the convolution result of the charging station power supply load data is integrated to obtain at least one power supply load factor directory; the convolution results contained in the at least one power supply load factor directory are combined to obtain key label data corresponding to the key label in the charging station power supply load data, and the abnormal load is determined according to the key label data, and an early warning is issued for the abnormal load. In this way, the key label data corresponding to the key label in the charging station power supply load data is determined by gradually analyzing each convolution result in each power supply load factor directory, and the abnormal load is determined according to the key label data, and an early warning is issued for the abnormal load, thereby improving the accuracy of the early warning.

[0023] In a possible embodiment, the inventors have discovered after long-term research that when combining the convolution results contained in each of the at least one power supply load factor catalog, there may be a problem of convolution anomaly, which makes it difficult to accurately obtain the key label data corresponding to the key label in the power supply load data of the charging station, determine the abnormal load based on the key label data, and issue an early warning for the abnormal load. In order to improve the above technical problems, the step 200 described in combining the convolution results contained in each of the at least one power supply load factor catalog, obtaining the key label data corresponding to the key label in the power supply load data of the charging station, determining the abnormal load based on the key label data, and issuing an early warning for the abnormal load, can specifically include the contents described in the following steps 210 and 220.

[0024] Step 210: Analyze the convolution result contained in the first power supply load factor directory in the at least one power supply load factor directory to obtain a matching relationship between at least one key tag contained in the first power supply load factor directory and at least one key tag data.

[0025] Step 220: analyzing the convolution result contained in the next power supply load factor directory of the first power supply load factor directory in combination with the matching relationship between the at least one key tag contained in the first power supply load factor directory and the at least one key tag data.

[0026] It should be understood that when executing the contents described in the above steps 210 and 220, combined with the convolution results contained in the at least one power supply load factor directory, the problem of convolution anomalies is improved, so that the key label data corresponding to the key labels in the power supply load data of the charging station can be accurately obtained, and the abnormal load can be determined based on the key label data, and an early warning can be issued for the abnormal load.

[0027] In a possible embodiment, the inventors have discovered after long-term research that the convolution result of the first power supply load factor directory includes the convolution result of at least one important attribute set in the first power supply load factor directory; when analyzing the convolution result contained in the first power supply load factor directory in the at least one power supply load factor directory, there may be an inaccurate analysis problem, which makes it difficult to accurately obtain the matching relationship between at least one key tag and at least one key tag data contained in the first power supply load factor directory. In order to improve the above technical problems, the step described in step 210 of analyzing the convolution result contained in the first power supply load factor directory in the at least one power supply load factor directory to obtain the matching relationship between at least one key tag and at least one key tag data contained in the first power supply load factor directory can specifically include the contents described in the following steps 211-213.

[0028] Step 211: obtaining a convolution result of a first important attribute set in the at least one important attribute set, wherein the convolution result includes a first key label, and the at least one key label includes the first key label.

[0029] Step 212: Determine whether the convolution result of at least one second important attribute set similar to the first important attribute set in the at least one important attribute set contains the key label data of the first key label.

[0030] Step 213, on the premise that the key label data of the first key label does not exist in the convolution result of the at least one second important attribute set, identify the key label data of the first key label in the convolution result of at least one third important attribute set, wherein the third important attribute set is a potential key description set excluding the first important attribute set and the at least one second important attribute set from the at least one important attribute set.

[0031] It should be understood that when executing the contents described in the above steps 211 to 213, when analyzing the convolution results contained in the first power supply load factor directory in the at least one power supply load factor directory, the problem of inaccurate analysis is improved, so that the matching relationship between at least one key label contained in the first power supply load factor directory and at least one key label data can be accurately obtained.

[0032] In a possible embodiment, the inventors have discovered after long-term research that when analyzing the convolution results contained in the subsequent power supply load factor directory of the first power supply load factor directory in combination with the matching relationship between at least one key tag and at least one key tag data contained in the first power supply load factor directory, there may be a problem of inaccurate analysis. In order to improve the above technical problem, the step 220 of analyzing the convolution results contained in the subsequent power supply load factor directory of the first power supply load factor directory in combination with the matching relationship between at least one key tag and at least one key tag data contained in the first power supply load factor directory may specifically include the contents described in the following steps 221 and 222.

[0033] Step 221: If the key tag data corresponding to the first key tag does not exist in the convolution result of the first power supply load factor directory, identify the key tag data corresponding to the first key tag in the convolution result of the subsequent power supply load factor directory.

[0034] Step 222, and / or if the key tag corresponding to the first key tag data does not exist in the convolution result of the first power supply load factor directory, identify the key tag corresponding to the first key tag data in the convolution result of the subsequent power supply load factor directory.

[0035] It should be understood that when executing the contents described in the above steps 221 and 222, combined with the matching relationship between at least one key tag contained in the first power supply load factor directory and at least one key tag data, when analyzing the convolution results contained in the subsequent power supply load factor directory of the first power supply load factor directory, the problem of inaccurate analysis is improved, thereby ensuring the accuracy of the analysis.

[0036] Based on the above foundation, the convolution result contained in the subsequent power supply load factor directory of the first power supply load factor directory is analyzed in combination with the matching relationship between at least one key tag and at least one key tag data contained in the first power supply load factor directory, and it can also include the following content: in combination with the identification result, the convolution result of the subsequent power supply load factor directory is analyzed to obtain the matching relationship between the key tag and the key tag data contained in the subsequent power supply load factor directory.

[0037] It should be understood that the accuracy of the analysis can be effectively improved through the above specific implementation steps.

[0038] Based on the above foundation, the key tag data corresponding to the key tag in the charging station power supply load data is obtained, the abnormal load is determined according to the key tag data, and after the abnormal load is warned, the following contents may also be included: combined with the projection relationship between several key tags of the charging station power supply load data, the key tag data corresponding to the several key tags are verified.

[0039] It should be understood that, through the above specific implementation steps, the accuracy of verification of key tag data can be effectively improved.

[0040] In a possible embodiment, the inventors have discovered through long-term research that when verifying the key tag data corresponding to the several key tags, there may be problems with inaccurate verification, resulting in the inability to accurately obtain all quantitative results. In order to improve the above technical problems, the step of verifying the key tag data corresponding to the several key tags may specifically include the contents described in the following steps a1 and a2.

[0041] Step a1, on the premise that the several key tags include the first key tag, the second key tag and the third key tag belonging to the same power supply load factor directory, verify whether the first data corresponding to the first key tag and the second data corresponding to the second key tag belonging to the same power supply load factor directory are weighted and processed to see whether they meet the important data corresponding to the third key tag.

[0042] Step a2, and / or on the premise that the several key tags include at least one important key tag and all key tags belonging to the same queue, verify whether the splicing results of the important data corresponding to all important key tags belonging to the same queue are consistent with all quantization results corresponding to all key tags.

[0043] It should be understood that when executing the contents described in the above steps a1 and a2, when verifying the key tag data corresponding to the several key tags, the problem of inaccurate verification is improved, so that all quantification results can be accurately obtained.

[0044] Based on the above foundation, after verifying the key tag data corresponding to the several key tags, the following contents may also be included: on the premise that the result of the verification indicates that the projection relationship does not meet the preset requirements, debugging the key tag data corresponding to the several key tags until the debugging result enables the projection relationship to be realized.

[0045] It should be understood that by debugging the key tag data corresponding to several key tags, the debugging result can be effectively improved so that the accurate projection relationship can be achieved.

[0046] Based on the above foundation, before the convolution processing is performed on the charging station power supply load data, the following contents may also be included: determining whether the charging station power supply load data meets the standard load detection indication; on the premise of determining that the charging station power supply load data does not meet the standard load detection indication, updating the charging station power supply load data to obtain the updated charging station power supply load data.

[0047] It should be understood that by continuously updating the charging station power supply load data, the charging station power supply load data can be accurately obtained.

[0048] In a possible embodiment, the inventors have discovered after long-term research that when performing convolution processing on the charging station power supply load data, there may be a problem of inaccurate convolution, and therefore further processing is required. In order to improve the above technical problems, a further implementation method is as follows, which performs convolution processing on the updated charging station power supply load data.

[0049] Under the above premise, a computer-readable storage medium is also provided, on which a computer program stored implements the above method when running.

[0050] In summary, based on the above scheme, the power supply load data of the charging station is convolved to obtain the convolution result of the power supply load data of the charging station; the convolution results of the power supply load data of the charging station are integrated to obtain at least one power supply load factor directory; the convolution results contained in the at least one power supply load factor directory are combined to obtain key label data corresponding to the key labels in the power supply load data of the charging station, and the abnormal load is determined according to the key label data, and an early warning is issued for the abnormal load. In this way, the key label data corresponding to the key label in the power supply load data of the charging station is determined by gradually analyzing each convolution result in each power supply load factor directory, and the abnormal load is determined according to the key label data, and an early warning is issued for the abnormal load, thereby improving the accuracy of the early warning.

[0051] It should be understood that the system and its modules shown above can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or a dedicated design hardware. It should be understood by those skilled in the art that the above methods and systems can be implemented using computer executable instructions and / or included in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software such as executed by various types of processors, and can also be implemented by a combination of the above hardware circuits and software (e.g., firmware).

[0052] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or may be any other beneficial effects that may be obtained.

[0053] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and commissioning to the present application. Such modifications, improvements and commissioning are suggested in the present application, so such modifications, improvements and commissioning still belong to the spirit and scope of the exemplary embodiments of the present application.

[0054] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0055] In addition, it should be understood by those skilled in the art that various aspects of the present application can be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present application can be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present application may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0056] A computer storage medium may include a propagated data signal containing computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination. A computer storage medium may be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit the program for use. The program code on the computer storage medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0057] The computer program coding required for the operation of each part of the application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages, etc. The program coding can be run completely on the user's computer, or run on the user's computer as an independent software package, or run partly on the user's computer and partly on the remote computer, or run completely on the remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0058] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all debugging and equivalent combinations that are consistent with the essence and scope of the embodiments of this application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0059] Similarly, it should be noted that in order to simplify the description of the disclosure of this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the object of this application are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0060] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used for the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers allow adaptive changes. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which can be changed according to the required characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of the present application are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0061] Each patent, patent application, patent application disclosure, and other materials, such as articles, books, instructions, publications, documents, etc., cited in this application are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the content of this application, documents that limit the broadest scope of the claims of this application (currently or later attached to this application) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the attached materials of this application are inconsistent or conflicting with the content described in this application, the descriptions, definitions, and / or use of terms in this application shall prevail.

[0062] Finally, it should be understood that the embodiments described in this application are only used to illustrate the principles of the embodiments of the present application. Other variations may also fall within the scope of the present application. Therefore, as an example and not a limitation, the alternative configurations of the embodiments of the present application may be considered to be consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly introduced and described in the present application.

[0063] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for early warning of a power supply load of a charging station by a distribution network, characterized in that: The method at least comprises: Performing convolution processing on the power supply load data of the charging station to obtain a convolution result of the power supply load data of the charging station; integrating the convolution result of the power supply load data of the charging station to obtain at least one power supply load factor catalog; In combination with the convolution results contained in the at least one power supply load factor directory, key label data corresponding to the key label in the power supply load data of the charging station is obtained, and the abnormal load is determined according to the key label data, and an early warning is issued for the abnormal load.

2. The method according to claim 1, characterized in that: The step of combining the convolution results contained in each of the at least one power supply load factor directory to obtain key label data corresponding to the key label in the power supply load data of the charging station includes: Analyze the convolution result contained in the first power supply load factor directory in the at least one power supply load factor directory to obtain a matching relationship between at least one key tag contained in the first power supply load factor directory and at least one key tag data; In combination with the matching relationship between at least one key tag and at least one key tag data included in the first power supply load factor directory, the convolution result included in the subsequent power supply load factor directory of the first power supply load factor directory is analyzed.

3. The method according to claim 2, characterized in that The convolution result of the first power supply load factor directory includes the convolution result of at least one important attribute set in the first power supply load factor directory; the convolution result contained in the first power supply load factor directory in the at least one power supply load factor directory is analyzed to obtain a matching relationship between at least one key tag contained in the first power supply load factor directory and at least one key tag data, including: The convolution result of the first important attribute set in the at least one important attribute set includes a first key label, and the at least one key label includes the first key label; Determining whether the key label data of the first key label is included in the convolution result of at least one second important attribute set similar to the first important attribute set in the at least one important attribute set; On the premise that the key label data of the first key label does not exist in the convolution result of the at least one second important attribute set, the key label data of the first key label is identified in the convolution result of at least one third important attribute set, wherein the third important attribute set is a potential key description set of the at least one important attribute set purged of the first important attribute set and the at least one second important attribute set.

4. The method according to claim 2, characterized in that: The step of analyzing the convolution result contained in a power supply load factor directory subsequent to the first power supply load factor directory in combination with the matching relationship between the at least one key tag contained in the first power supply load factor directory and the at least one key tag data comprises: If the key tag data corresponding to the first key tag does not exist in the convolution result of the first power supply load factor catalog, identifying the key tag data corresponding to the first key tag in the convolution result of the latter power supply load factor catalog; And / or if the key tag corresponding to the first key tag data does not exist in the convolution result of the first power supply load factor directory, the key tag corresponding to the first key tag data is identified in the convolution result of the subsequent power supply load factor directory.

5. The method according to claim 4, characterized in that The method of analyzing the convolution result contained in the subsequent power supply load factor directory of the first power supply load factor directory in combination with the matching relationship between at least one key tag and at least one key tag data contained in the first power supply load factor directory also includes: analyzing the convolution result of the subsequent power supply load factor directory in combination with the identification result to obtain the matching relationship between the key tag and the key tag data contained in the subsequent power supply load factor directory.

6. The method according to claim 5, characterized in that The method further includes obtaining key tag data corresponding to key tags in the charging station power supply load data, determining abnormal loads according to the key tag data, and issuing an early warning for the abnormal load, and further comprising: verifying the key tag data corresponding to the several key tags in combination with the projection relationship between the several key tags of the charging station power supply load data.

7. The method according to claim 6, characterized in that Verifying the key tag data corresponding to the plurality of key tags includes: On the premise that the plurality of key tags include a first key tag, a second key tag, and a third key tag belonging to the same power supply load factor catalog, verifying whether weighted processing of first data corresponding to the first key tag and second data corresponding to the second key tag belonging to the same power supply load factor catalog meets the important data corresponding to the third key tag; And / or on the premise that the several key tags include at least one important key tag and all key tags belonging to the same queue, verify whether the splicing results of the important data corresponding to all important key tags belonging to the same queue are consistent with all quantization results corresponding to all key tags.

8. The method according to claim 6, characterized in that After verifying the key tag data corresponding to the plurality of key tags, the method further includes: debugging the key tag data corresponding to the plurality of key tags, on the premise that the verification result indicates that the projection relationship does not meet the preset requirements, until the debugging result enables the projection relationship to be realized.

9. The method according to claim 5, characterized in that Before performing convolution processing on the charging station power supply load data, the method also includes: determining whether the charging station power supply load data meets the standard load detection indication; on the premise of determining that the charging station power supply load data does not meet the standard load detection indication, updating the charging station power supply load data to obtain the updated charging station power supply load data; the convolution processing on the charging station power supply load data includes: performing convolution processing on the updated charging station power supply load data.

10. An early warning system for power supply loads of charging stations by a distribution network, characterized in that: The invention comprises a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute the computer program to implement the method according to any one of claims 1 to 9.

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