Underlying data reporting optimization method and device based on cloud platform

Abstract

The invention relates to the technical field of data transmission, and provides an underlying data reporting optimization method and device based on a cloud platform. The method comprises the following steps: a relay device sends an access request message to a cloud platform; the access request message carries an equipment identifier of relay equipment; the cloud platform determines a first jurisdiction area of the relay device according to the device identifier of the relay device, searches a first historical mapping function set of the first jurisdiction area, and issues the first historical mapping function set to the relay device; when the relay equipment subsequently receives the electric energy data from the data acquisition equipment, a corresponding first function main body is matched from a first historical mapping function set according to the acquisition time of the electric energy data; and sending the function identifier of the first function main body and the mapping parameter generated by matching to the cloud platform as substitution of each electric energy data in the matched time period. According to the method, the effect of compressing transmission data is achieved, the amount of data needing to be transmitted is greatly reduced, the network burden is reduced, and bandwidth consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the field of data transmission technology, and in particular to a method and device for optimizing underlying data reporting based on a cloud platform. Background Art

[0002] With the in-depth evolution of the Energy Internet, the power system is accelerating its transformation toward a coordinated "source-grid-load-storage" model. This process is accompanied by the continuous integration of numerous data acquisition devices, including power station-side data acquisition equipment, smart meters, and feeder terminal units (FTUs). This is gradually building a comprehensive power grid cloud platform that integrates energy data from the consumer, generation, distribution, and substation sides, thereby forming a fully integrated power grid communication and transmission system. With the establishment of this grid communication and transmission system, the massive amount of data that needs to be reported from data acquisition devices to the comprehensive power grid cloud platform has increased bandwidth consumption and placed significant pressure on the data transmission network.

[0003] In view of this, overcoming the defects of the prior art is an urgent problem to be solved in this technical field. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for optimizing underlying data reporting based on a cloud platform, so as to solve the problems of large consumption of data transmission network bandwidth resources and high network pressure in the integrated power grid cloud platform in the prior art.

[0005] The present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for optimizing underlying data reporting based on a cloud platform, which is applied to a power grid communication transmission system. The power grid communication transmission system includes a cloud platform, a relay device, and a data acquisition device. The method includes: When the relay device accesses the cloud platform, it sends an access request message to the cloud platform; wherein the access request message carries the device identifier of the relay device; The cloud platform determines, based on the device identifier of the relay device, a first jurisdiction corresponding to the relay device, and searches for a corresponding historical mapping function set in the first jurisdiction; If a first historical mapping function set corresponding to the first jurisdiction is found, the first historical mapping function set is sent to the relay device; If the search shows that there is no historical mapping function set corresponding to the first jurisdiction area, generating a first historical mapping function set according to the historical electric energy data within the first jurisdiction area, and sending the first historical mapping function set to the relay device; When the relay device subsequently receives the electric energy data from the data acquisition device, it matches the corresponding first function body from the first historical mapping function set according to the acquisition time of the electric energy data, and sends the function identifier of the first function body and the mapping parameters generated by the matching to the cloud platform; The cloud platform uses the function identifier of the first function body and the mapping parameters to restore the change trend of the electric energy data and the key electric energy indicators; A historical mapping function set includes each historical time period and a function body corresponding to each historical time period, and the key electric energy indicators include actual inflection point data and total power.

[0006] Preferably, the matching of the corresponding first function body from the first historical mapping function set according to the collection time of the electric energy data, and sending the function identifier of the first function body and the mapping parameters generated by the matching to the cloud platform specifically includes: The cloud platform also regularly sends the time period offset coefficient, time period compensation coefficient and value floating range to the relay device; Compensating the first historical time period using the time period offset coefficient and the time period compensation coefficient to obtain a first time period to be matched; Adding the electric energy data in the first time period to be matched to obtain the actual total electric energy in the first time period to be matched; and finding the actual inflection point data from the electric energy data in the first time period to be matched; Generate mapping parameters using the actual total electric energy, the actual inflection point data, and a first function body corresponding to the first historical time period, and substitute the mapping parameters into the first function body to obtain a mapping function to be matched; Calculating the trend difference and the value difference between the electric energy data in the first to-be-matched time period and the mapping function to be matched; If the trend difference is less than or equal to the preset threshold, and the numerical difference is within the numerical floating range, the function identifier and each mapping parameter of the first function body are sent to the cloud platform.

[0007] Preferably, the using the actual total electric energy, the actual inflection point data, and the first function body corresponding to the first historical time period to generate each mapping parameter, and substituting each mapping parameter into the first function body to obtain the mapping function to be matched specifically includes: Solve the first preset equation to obtain the first time period mapping parameter and the second time period mapping parameters ; Solve the second preset equation to get the time offset parameter and other mapping parameters; Among them, the first preset equation is ; is the start time of the first function body, is the end time of the first function body, is the mapping parameter for the first time period, Mapping parameters for the first time period; The second preset equation is ; is the initial time period mapping relationship between the first time period to be matched and the first function body, , is the mapping function to be matched, For electric energy data, is the starting time of the first time period to be matched, is the end time of the first time period to be matched, is the time point of the actual inflection point data, is the value of the actual inflection point data, is the value of the preset inflection point data, The time point for the preset inflection point data.

[0008] Preferably, the trend difference is ; The numerical difference is ; in, is the mapping function to be matched, For electric energy data, is the derivative of the electric energy data, is the derivative of the first function to be matched, is the starting time of the first time period to be matched, is the end time of the first time period to be matched, is the initial time period mapping relationship between the first time period to be matched and the first function body, is the time offset parameter.

[0009] Preferably, the step of searching whether the first jurisdiction area has a corresponding historical mapping function set specifically includes: Pre-setting a plurality of jurisdiction types, and pre-analyzing each jurisdiction type to obtain a corresponding historical mapping function set; wherein the plurality of jurisdiction types include one or more of a residential electricity usage jurisdiction, a commercial street electricity usage jurisdiction, a factory electricity usage jurisdiction, a charging pile electricity usage jurisdiction, a substation data jurisdiction, a hydropower generation jurisdiction, a photovoltaic power generation jurisdiction, a wind power generation jurisdiction, and a transformer data jurisdiction; According to the first jurisdiction type corresponding to the first jurisdiction area, selecting a historical mapping function set corresponding to the first jurisdiction type as a default mapping function set; Check whether the cloud platform stores a custom function body for the first jurisdiction. If so, use the custom function body to replace the corresponding function body in the default mapping function set to obtain a historical mapping function set corresponding to the first jurisdiction.

[0010] Preferably, the access request message includes a capability identifier, a device identifier of the relay device, the total number of data acquisition devices managed by the relay device, and a device identifier of each data acquisition device managed by the relay device; The first historical mapping function set is carried in an access response message and sent to the relay device, and the access response message includes a capability identifier, a device identifier of the relay device, the total number of function bodies in the first historical mapping function set, each historical time period in the first historical mapping function set, and a function identifier of each function body in the first historical mapping function set.

[0011] Preferably, the function identifier of the first function body and the mapping parameters generated by matching are carried in a data reporting message and sent to the cloud platform, and the data reporting message includes a capability identifier, the type and quantity of electric energy data, and compressed data of each type of electric energy data; Among them, the compressed data includes data identifiers of various types of electric energy data, time period serial numbers, start time of the matching time period, end time of the matching time period, function identifier of the first function body corresponding to the matching time period, the total number of mapping parameters and each mapping parameter.

[0012] Preferably, the function body in each historical mapping function set is a combination of one or more of a sine function, a linear function, an exponential function and a logarithmic function.

[0013] In a second aspect, the present invention further provides a cloud platform-based underlying data reporting optimization device, which is used to implement the cloud platform-based underlying data reporting optimization method described in the first aspect, and the device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to execute the underlying data reporting optimization method based on the cloud platform described in the first aspect.

[0014] In a third aspect, the present invention further provides a non-volatile computer storage medium, wherein the computer storage medium stores computer-executable instructions, and the computer-executable instructions are executed by one or more processors to complete the method described in the first aspect.

[0015] In a fourth aspect, a chip is provided, comprising: a processor and an interface, for calling and running a computer program stored in a memory, and executing any method of the first aspect.

[0016] In a fifth aspect, a computer program product comprising instructions is provided, which, when executed on a computer or a processor, causes the computer or the processor to execute any of the methods of the first aspect.

[0017] The present invention matches the function body in the historical mapping function set with the electric energy data, and uses the mapping parameters generated by the matching as a substitute for the electric energy data in the matching time period to send to the cloud platform, thereby compressing the transmitted data, greatly reducing the amount of data required to be transmitted, reducing the network burden, and reducing bandwidth consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0019] Figure 1 This is a schematic diagram of a power grid communication transmission architecture provided by an embodiment of the present invention; Figure 2 This is a flow chart of a method for optimizing underlying data reporting based on a cloud platform provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an access request message in a cloud platform-based underlying data reporting optimization method provided by an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an access response message in a cloud platform-based underlying data reporting optimization method provided by an embodiment of the present invention; Figure 5 This is a structural diagram of a data reporting message in a cloud platform-based underlying data reporting optimization method provided by an embodiment of the present invention; Figure 6 This is a flow chart of a method for optimizing underlying data reporting based on a cloud platform provided by an embodiment of the present invention; Figure 7 This is a flow chart of a method for optimizing underlying data reporting based on a cloud platform provided by an embodiment of the present invention; Figure 8 This is a schematic diagram of the architecture of a cloud platform-based underlying data reporting optimization device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] Unless the context requires otherwise, throughout the specification and claims, the term "including" is to be interpreted as meaning open inclusion, that is, "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "example", "specific example" or "some examples" and the like are intended to indicate that the specific features, structures, materials or characteristics associated with the embodiment or example are included in at least one embodiment or example of the present disclosure. The schematic representation of the above terms does not necessarily refer to the same embodiment or example. In addition, the specific features, structures, materials or characteristics may be included in any one or more embodiments or examples in any appropriate manner, that is, although they may be carried in the embodiments or examples of the above terms due to reasons such as the order and position of appearance, it is not limited to that they can be carried in combination by one embodiment or example.

[0022] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present disclosure, unless otherwise specified, "multiple" means two or more. In addition, for example, the description may also use the method of adding "A" and "B" at the end to describe the same type of nouns as two independent individuals. In this case, the corresponding features defined as "A" and "B" are only used to distinguish the description purposes of the same type of individuals, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated.

[0023] In the description of the present invention, the expression "A and / or B" (where A and B are used to formally represent specific characteristic contents) is involved, and the corresponding expressions include the following three combinations: only A, only B, and a combination of A and B.

[0024] As used herein, "about," "substantially," or "approximately" includes the stated value and an average value that is within an acceptable range of deviation from the particular value as determined by one of ordinary skill in the art taking into account the measurements in question and errors associated with measurement of the particular quantity (i.e., limitations of the measurement system).

[0025] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0026] Embodiment 1: In order to make the purpose, technical solutions and advantages of the present invention more clear, this embodiment also uses Figure 1 As an example, the power grid communication transmission system is briefly described. Figure 1 As shown, a comprehensive power grid cloud platform (also called a cloud platform) is established with integrated access to the power generation system, the substation system, the distribution system, and the power consumption system, thereby forming a fully integrated power grid communication and transmission system. In this system, the data acquisition equipment at each bottom layer collects power data. For example, in the power generation system and the substation system, the data acquisition equipment is an intelligent electronic device that collects power generation data or substation data. In the distribution system, the data acquisition equipment is the feeder terminal unit (FTU), the distribution transformer supervisory terminal unit (TTU), the remote terminal unit (RTU), and the phasor measurement unit (Phasor Measurement Unit). In the power consumption system, the data acquisition device is a smart meter or a control center of a charging pile. After the data acquisition device collects the corresponding power data, the power data is reported to the relay device, and the relay device reports it to the cloud platform step by step. The relay device can be a front-end machine, a server of a power station, a server of a substation, a concentrator (Central Coordinator, abbreviated as: CCO), a collector (Station, abbreviated as: STA) and one or more of the control centers of charging piles. In actual use, the specific selection of which relay device to execute the method described in this embodiment is determined by those skilled in the art based on the density division and transmission demand analysis of the jurisdiction.

[0027] With the establishment of the integrated power grid cloud platform, a large amount of data needs to be reported from data acquisition equipment to the integrated power grid cloud platform, which intensifies the consumption of bandwidth resources and also brings great pressure to the data transmission network. In order to solve this problem, embodiment 1 of the present invention provides a bottom layer data reporting optimization method based on the cloud platform, which is applied to the power grid communication transmission system. The power grid communication transmission system includes a cloud platform, a relay device and a data acquisition device; Figure 2 As shown, the method includes: In step 201, when accessing the cloud platform, the relay device sends an access request message to the cloud platform; wherein the access request message carries the device identifier of the relay device.

[0028] In step 202, the cloud platform determines the first jurisdiction corresponding to the relay device according to the device identification of the relay device, and searches for a corresponding historical mapping function set in the first jurisdiction.

[0029] In step 203, if a first historical mapping function set corresponding to the first jurisdiction is found, the first historical mapping function set is sent to the relay device.

[0030] In step 204, if a historical mapping function set corresponding to the first jurisdictional area is found to not exist, a first historical mapping function set is generated based on the historical power data within the first jurisdictional area, and the first historical mapping function set is sent to the relay device. Each jurisdictional area is pre-divided by a skilled person in the art based on the characteristics of each area. Furthermore, when a worker installs the corresponding device, the worker's commissioning terminal sends the device's location information to the cloud platform. Alternatively, if the device itself has a positioning function, the device includes the location information in the access request message. Similarly, each data acquisition device downstream of the relay device also reports its own device identification and location information to the relay device, so that the relay device includes the location information in the access request message and reports it to the cloud platform. Alternatively, when the worker installs the data acquisition device, the worker sends the location information to the cloud platform, so that the cloud platform can use the device identification and location information to manage the device and jurisdictional area.

[0031] In actual use, before sending the access request message, the relay device also checks whether the first historical mapping function set exists locally. If so, the access request message carries an unnecessary function set identifier, and the cloud platform does not send the first historical mapping function set to the relay device.

[0032] In step 205, when the relay device subsequently receives the electric energy data from the data acquisition device, it matches the corresponding first function body from the first historical mapping function set according to the acquisition time of the electric energy data, and sends the function identifier of the first function body and the mapping parameters generated by the match to the cloud platform as a substitute for each electric energy data within the matching time period; the matching time period is the time period of each electric energy data successfully matched by the first function body, and the sending of the function identifier of the first function body and the mapping parameters generated by the match to the cloud platform as a substitute for each electric energy data within the matching time period can be understood as: not sending multiple electric energy data within the matching time period to the cloud platform, but sending the function identifier of the first function body and the mapping parameters generated by the match to the cloud platform as one piece of data, thereby achieving compression of the amount of transmitted data.

[0033] In step 206, the cloud platform uses the function identifier of the first function body and the mapping parameters to restore the change trend of the electric energy data and the key electric energy indicators.

[0034] Among them, a historical mapping function set includes each historical time period and a function body corresponding to each historical time period, the key electric energy indicator includes actual inflection point data and total power, and the matching generation of the mapping function is also achieved using the key electric energy indicator. The function body can be understood as a mapping function with variable mapping parameters. The function body in each historical mapping function set is a combination of one or more of a sine function, a linear function, an exponential function, and a logarithmic function. In actual use, for each function body, a corresponding start time, end time, and preset inflection point data are also set to characterize which section of the function body is used for matching electric energy data, wherein the start time, end time, and preset inflection point data are all selected by those skilled in the art with reference to the default mapping function. The default mapping function is the mapping function generated when the mapping parameters of the function body are all default parameters.

[0035] The sine function is expressed as , and its corresponding default mapping function is ; The linear function is expressed as: , and its corresponding default mapping function is ; The exponential function is expressed as: , and its corresponding default mapping function is ; The logarithmic function is expressed as: , and its corresponding default mapping function is ,in, and are mapping parameters, is a fixed value obtained by those skilled in the art based on analysis of historical electric energy data. It should be noted that the mapping parameters of the function body also include a time offset value, a first time period mapping parameter, and a second time period mapping parameter. These parameters are used to map actual time to the time of the default mapping function. In the function body, since actual time does not yet exist, the time offset value, the first time period mapping parameter, and the second time period mapping parameter are not reflected in the function body or the default mapping function. However, this does not mean that they do not exist.

[0036] This embodiment is designed in this way considering that the cloud platform does not need to control each subsystem (i.e., power generation system, substation system, distribution system, and power consumption system) in real time, but mainly plays the role of monitoring the status of each subsystem, and the real-time monitoring and processing of abnormal status in the subsystem is achieved through the reporting and analysis of alarm information. Therefore, in summary, the cloud platform does not have high requirements for the real-time transmission of electric energy data, so it can be transmitted according to the method described in this embodiment. Moreover, the cloud platform's monitoring of data in the subsystem is mainly used for overall monitoring, that is, monitoring the trend and total data of the data (such as total power consumption or total power generation, etc.), and does not pay much attention to local details. Therefore, this embodiment uses actual inflection point data and total power as key point energy indicators to match and generate mapping parameters, thereby first ensuring the trend and total data of the data.

[0037] This embodiment uses the function body in the historical mapping function set to match the electric energy data, and uses the mapping parameters generated by the matching as a substitute for the electric energy data in the matching time period to send to the cloud platform, thereby compressing the transmitted data, greatly reducing the amount of data required to be transmitted, reducing the network burden, and reducing bandwidth consumption.

[0038] In a specific application scenario, the access request message includes a capability identifier, a device identifier of the relay device, the total number of data acquisition devices managed by the relay device, and the device identifier of each data acquisition device managed by the relay device. It may also include the software version number of the relay device, the location information of the relay device, and the location information of each data acquisition device. In an optional implementation, the access request message may be a SYN message under the TCP protocol, such as Figure 3 As shown, the message type flag is 0x002 (i.e., 0b000010). When the relay device has the ability to execute the above-mentioned step 205, the ability flag is 1, otherwise it is 0; when the relay device can obtain the positioning information by itself, the positioning information flag is 1, and the positioning information of the relay device is 16 bytes. Otherwise, the positioning information flag is 0, and the positioning information of the relay device is 0 bytes (i.e., the positioning information of the relay device is not included). In the relevant information of each data acquisition device, the positioning information flag of the data acquisition device is also provided. When the data acquisition device can obtain the positioning information by itself, the positioning information flag of the data acquisition device is 1, and the positioning information of the data acquisition device is 16 bytes. Otherwise, the positioning information flag of the data acquisition device is 0, and the positioning information of the data acquisition device is 0 bytes (i.e., the positioning information of the data acquisition device is not included).

[0039] The capability identifier is used to identify whether the relay device has the capability to execute the above step 205. For relay devices that do not support the capability of the above step 205, the capability identifier is not carried in the access request message, so that the cloud platform can process it according to the conventional relay device and achieve compatibility between new and old devices.

[0040] The software version number is used to identify whether the capabilities of the relay device match those of the cloud platform, mainly to check whether the function identifiers of each function body match on the relay device and the cloud platform. If the cloud platform identifies a match, it will send the following access response message carrying the capability identifier.

[0041] The first historical mapping function set is carried in an access response message and sent to the relay device, and the access response message includes a capability identifier, a device identifier of the relay device, the total number of function bodies in the first historical mapping function set, each historical time period in the first historical mapping function set, and a function identifier of each function body in the first historical mapping function set.

[0042] The access response message may be a SYN-ACK message under the TCP protocol, such as Figure 4 As shown, the message header of the message is consistent with the message header format of the access request message, but the message type flag in the access response message is 0x012 (i.e., 0b010010), the capability flag is 1, and, in an optional implementation, the mapping function is matched with a day as a cycle, that is, each historical time period in the historical mapping function set is within one day and accurate to the minute, so the start time and end time of each historical time period are represented by 4 bytes, which is expressed as a cycle count of 1440 minutes, that is, the start time and end time of each time period are within the range of 1~1440.

[0043] In actual use, after receiving the access response message, the relay device also sends an access confirmation message (such as an ACK message of the TCP protocol) to the cloud platform to implement a three-way handshake with the cloud platform.

[0044] In an optional embodiment, the function identifier of the first function body and the mapping parameters generated by matching are carried in a data reporting message and sent to the cloud platform. The data reporting message includes a capability identifier, the number of types of electric energy data, and compressed data of each type of electric energy data.

[0045] The compressed data includes the data identifier of each type of electric energy data, the time period serial number, the start time of the matching time period, the end time of the matching time period, the function identifier of the first function body corresponding to the matching time period, the total number of mapping parameters and each mapping parameter, such as Figure 5As shown, the data identifier of the electric energy data can be the object attribute descriptor (OAD) of each electric energy data in the DLT698.45 protocol or the data identifier (DI) in the DLT645-2007 protocol. The electric energy data with the same data identifier is classified into Class 1, and the electric energy data represented by different data are classified into different classes. The data identifier of the electric energy data is reported by the data acquisition device. The message header of the data reporting message is consistent with the message header format of the access request message, but the message type flag in the data reporting message is 0x10 (i.e., 0b010000).

[0046] It should be noted here that Figure 3 、 Figure 4 and Figure 5 This is only a sample message and does not necessarily mean that the format of each message in actual use must be the same. Figure 3 、 Figure 4 or Figure 5 As shown, in actual use, each message may also include verification information, data frame length, packet end identifier and other information, and when transmitting the message, byte alignment may also be involved, causing the format of the message to change.

[0047] In some practical application scenarios, the process of searching whether the first jurisdiction has a corresponding historical mapping function set is as follows: Figure 6 As shown, specifically including: In step 301, multiple types of jurisdictions are pre-set, and a corresponding historical mapping function set is pre-analyzed for each type of jurisdiction; wherein the multiple types of jurisdictions include household electricity use jurisdiction, commercial street electricity use jurisdiction, factory electricity use jurisdiction, charging pile electricity use jurisdiction, substation data jurisdiction, hydropower generation jurisdiction, photovoltaic power generation jurisdiction, wind power generation jurisdiction and transformer data jurisdiction; the user electricity use jurisdiction is the jurisdiction where ordinary household users account for the majority of electricity use, and its electric energy data is usually the electricity use data of ordinary households; the commercial street electricity use jurisdiction is the jurisdiction where commercial electricity accounts for the majority of electricity use. For most jurisdictions, electricity data is typically commercial electricity data. For factory electricity jurisdictions, where factory electricity consumption accounts for the majority, electricity data is typically factory electricity data. For hydropower jurisdictions, where hydropower stations are located, electricity data is hydropower data. For photovoltaic power jurisdictions, where photovoltaic stations are located, electricity data is photovoltaic data. For wind power jurisdictions, where wind stations are located, electricity data is typically wind power data. For transformer data jurisdictions, where substations are located, electricity data is typically substation-related data. While electricity data from different types of jurisdictions has different characteristics, electricity data within the same type of jurisdiction has universal adaptability, meaning that the changing trends are relatively similar (for example, photovoltaic power generation data varies with sunlight, typically showing a trend of increasing and then decreasing power generation (e.g., a sine function can be used to represent this). Therefore, jurisdictions are divided according to their characteristics.

[0048] In step 302, based on the first jurisdiction type corresponding to the first jurisdiction area, a historical mapping function set corresponding to the first jurisdiction type is selected as a default mapping function set.

[0049] In step 303, a search is performed to determine whether a custom function body of the first jurisdiction is stored in the cloud platform. If a custom function body exists, the custom function body is used to replace the corresponding function body in the default mapping function set to obtain a historical mapping function set corresponding to the first jurisdiction.

[0050] When generating a custom function body, a time period is also generated accordingly, and the custom function body of the time period is used to replace the corresponding function body of the time period in the default mapping function set, such as The body of the custom function for the time period is , the default mapping function is concentrated The function body of the time period is , then use replace .

[0051] The custom function body can be generated by the cloud platform when it finds that there is no historical mapping function set corresponding to the first jurisdiction. That is, when it finds that there is no historical mapping function set corresponding to the first jurisdiction, each function body in the historical mapping function set of the first jurisdiction type is first used to match the electric energy data within the first jurisdiction. If the match is successful, the function body is considered to be applicable to the first jurisdiction. If the match is unsuccessful, the historical data within the time period of the first jurisdiction during which the match was unsuccessful is analyzed to obtain the custom function body. In subsequent use, the successfully matched function body and the custom function body together constitute the historical mapping function set for the first jurisdiction, that is, steps 301 to 303 above. The process of step 204 above can be understood as including the process of generating the custom function body and the process of steps 301 to 303 above.

[0052] Among them, the analysis of historical data in the time period where the matching was unsuccessful within the first jurisdiction is obtained by matching the electric energy data in the time period with all mapping functions (not only the function bodies in the historical mapping function set of the first jurisdiction type, but also the function bodies that are not in the historical mapping function set), and the function body that is finally successfully matched is used as the custom function body.

[0053] In a preferred embodiment, according to the collection time of the electric energy data, the corresponding first function body is matched from the first historical mapping function set, and the function identifier of the first function body and the mapping parameters generated by the matching are sent to the cloud platform as a replacement for each electric energy data in the matching time period, such as Figure 7 As shown, specifically including: In step 401, the cloud platform also regularly sends the time period offset coefficient, time period compensation coefficient and numerical floating interval to the relay device; wherein, for a historical mapping function set of a relay device, each function body can correspond to a time period offset coefficient, time period compensation coefficient and numerical floating interval, or multiple function bodies in the historical mapping function set can share a time period offset coefficient, time period compensation coefficient and numerical floating interval, and the sending cycle is obtained by technical personnel in this field based on empirical analysis.

[0054] In step 402, the first historical time period is compensated using the time period offset coefficient and the time period compensation coefficient to obtain a first time period to be matched; if the first historical time period is , the time period offset coefficient is , the time period compensation coefficient is , then the first time period to be matched is ,in, , .

[0055] In step 403, the electric energy data in the first time period to be matched are added together to obtain the actual total electric energy in the first time period to be matched; and the actual inflection point data is found from the electric energy data in the first time period to be matched.

[0056] In step 404, the actual total electric energy, the actual inflection point data and the first function body corresponding to the first historical time period are used to generate various mapping parameters, and the various mapping parameters are substituted into the first function body to obtain the mapping function to be matched; in actual use, the actual inflection point data can be peak data, that is, the maximum electric energy data in the first time period to be matched.

[0057] In step 405 , the trend difference and the value difference between the electric energy data in the first time period to be matched and the mapping function to be matched are calculated.

[0058] In step 406, if the trend difference is less than or equal to the preset threshold, and the numerical difference is within the numerical floating range, then the first time period to be matched is the matching time period, and the function identifier and mapping parameters of the first function body are sent to the cloud platform as a substitute for the electric energy data in the first time period to be matched.

[0059] The preset threshold is obtained by those skilled in the art based on empirical analysis. When the trend difference is less than or equal to the preset threshold, it can be considered that the change trend of the electric energy data in the first time period to be matched is consistent with the change trend of the mapping function to be matched. The time period offset coefficient, time period compensation coefficient and numerical floating interval are all obtained by the cloud platform based on factors such as date, weather, holidays and the change pattern of historical data. For example, in summer, when the sunlight time becomes longer, the time period compensation coefficient corresponding to the photovoltaic power generation data can be appropriately increased.

[0060] The method of using the actual total electric energy, the actual inflection point data, and the first function body corresponding to the first historical time period to generate mapping parameters, and substituting the mapping parameters into the first function body to obtain the mapping function to be matched specifically includes: Solve the first preset equation to obtain the first time period mapping parameter and the second time period mapping parameters ; Solve the second preset equation to obtain the time offset parameter and other mapping parameters (such as the mapping parameters above and ).

[0061] Among them, the first preset equation is ; is the start time of the first function body, is the end time of the first function body, is the mapping parameter for the first time period, Map parameters for the first time period.

[0062] The second preset equation is ; is the initial time period mapping relationship between the first time period to be matched and the first function body, , is the mapping function to be matched, For electric energy data, is the starting time of the first time period to be matched, is the end time of the first time period to be matched, is the time point of the actual inflection point data, is the value of the actual inflection point data, is the value of the preset inflection point data, The time point for the preset inflection point data.

[0063] Taking the first function body as a sine function as an example, the generated mapping function to be matched is , whose mapping parameters include 、 、 、 and .

[0064] In a specific application scenario, the trend difference is ; The numerical difference is ;in, is the mapping function to be matched, For electric energy data, is the derivative of the electric energy data, is the derivative of the first function to be matched, is the starting time of the first time period to be matched, is the end time of the first time period to be matched, is the initial time period mapping relationship between the first time period to be matched and the first function body, is the time offset parameter.

[0065] In actual use, in addition to the function body in the historical mapping function set, each relay device also stores a benchmark function body, namely ;in, is a fixed value (such as ), the function body of the benchmark is used to match the gap time periods between adjacent matching time periods after calculating each matching time period. Specifically, the actual total electric energy of the gap time period is calculated, and the actual total electric energy is used to calculate the Value, specifically: To solve, is the start time of the gap time period, is the end time of the gap time period, For electrical energy data.

[0066] In matching After the value, the function identifier of the function body of the gap function, The value is sent to the cloud platform as a substitute for the electric energy data in the gap time period. This implementation is based on the same concept as the above-mentioned sending the function identifier of the first function body and the matching generated mapping parameters as substitutes for each electric energy data in the matching time period to the cloud platform, and will not be elaborated here.

[0067] Example 2: by Figure 1 The power grid communication transmission architecture shown is a scenario. The underlying data reporting optimization method based on the cloud platform provided in this embodiment specifically includes: (1) The power grid data relay device starts requesting the historical mapping function set; when each power grid data relay device (including collectors, concentrators and front-end processors) is first started, it will search locally to see if there is a corresponding mapping function set for collecting power data. If there is no corresponding mapping function set, it will send an access request message to the power grid cloud platform.

[0068] (2) After receiving the access request message, the power grid cloud platform obtains the historical mapping function set of the area under the jurisdiction of the power grid data relay device that initiated the request from its database. When a corresponding historical mapping function set is obtained, it is sent to the corresponding power grid data relay device.

[0069] (3) If there is no historical mapping function set corresponding to the power grid data relay device in the database, then the historical power grid data is collected and sorted according to the area that the corresponding power grid data relay device is responsible for, thereby generating a set of mapping function sets. Among them, the reason for the absence of a historical mapping function set may be that this function set system has just been launched, resulting in it not yet forming a historical mapping function set; it may also be that the corresponding power grid division area has changed. For example, the power grid area that the power grid data relay device that currently sends the request is responsible for has changed compared to the historical area range. In this case, the cloud platform will not be able to find the area because of the regional element matching.

[0070] The generating of a set of mapping functions specifically includes: (3.1) Determine the electricity data classification for the area under the responsibility of the power grid data relay device, including: residential electricity, commercial street electricity, factory electricity, charging pile electricity, substation data, hydropower generation, photovoltaic power generation, wind power generation, and transformer data in each city; among them, residential electricity, commercial street electricity, industrial electricity, hydropower generation, photovoltaic power generation, and wind power generation are used as strongly regularized electricity consumption themes. If the area under the responsibility of the corresponding power grid data relay device is one or more of these combinations, then the corresponding power grid data relay device is deemed to have the ability to generate a mapping function set.

[0071] (3.2) After confirming the classification of the electricity data, the data is divided into multiple time periods based on the operational characteristics of each time period. A corresponding mapping function is generated for each time period through linear fitting to characterize the changes in electricity consumption data within the corresponding time period. The mapping function includes a function body, which is generally one or more combinations of sinusoidal, linear, exponential, and logarithmic relationships.

[0072] (3.3) The time periods are divided based on the corresponding statistical historical big data. The mapping functions within the divided time periods are highly reproducible with the actual power data during the change process and are universal at the peak / turning points. Here, when calculating, the cloud platform will prioritize excluding the power data of a specific day (identifying it as irregular data) based on the date, weather, holidays, etc. For example, for photovoltaic power generation, cloudy and rainy days will be excluded. For commercial street electricity consumption, special holidays will be differentiated from ordinary Monday to Friday for analysis. For user electricity consumption, the peak / turning point of the corresponding mapping function will be shifted according to the changes in solar terms, and a separate mapping function analysis will be performed for each season (especially summer data).

[0073] (4) The power grid data relay equipment collects electric energy data (including electricity consumption or power generation) in units of days. After confirming the environmental information in advance, it obtains a floating range (the environmental information here includes dates, weather, holidays, etc. in different scenarios), and analyzes the matching degree with the electric energy data of one day based on the time period carried by each mapping function.

[0074] There are two core elements to using a mapping function: 1. Prioritizing peak value matching to determine the time offset; 2. Determining the weighting value to assign to the mapping function based on the total power consumed during the function period, so that the total power calculated by the mapping function, when the weighting value is included in the calculation, matches the actual total power generated. 3. There is also a universal mapping function (i.e., the benchmark function body in Example 1) that serves as a transition between the various mapping functions. This mapping function is a horizontal line, and its use only requires that the total power calculated using the function within the corresponding time period be the same as the actual total power consumed during that time period.

[0075] Using these three points, we can establish a functional mapping for overall electricity consumption. Based on the various energy models categorized above, analysis is performed. Because the time-based mapping functions are calculated by the cloud platform based on historical data for the corresponding region, their reproducibility and availability are highly reliable. Energy data for regions not involved in the mapping function is recorded using a standard table.

[0076] Because the mapping functions are transparent between the cloud platform and each power grid data relay device (i.e., a pre-defined protocol specifies which function identifier corresponds to which mapping function), the cloud platform only needs to send the mapping function to the power grid data relay device once. Subsequent data transmission between the two devices can be achieved by simply replacing each mapping function with a one-byte or multi-byte number (i.e., function identifier) for each mappable function segment. This number is included in the message sent from the cloud platform to the power grid data relay device, significantly reducing network data volume. Furthermore, mapping matching rules show that after a period of real energy data is represented by a mapping function, its core elements are guaranteed: 1. The peak time point is accurate; 2. The total energy data for that period is accurate; and 3. The energy trend within that period is consistent with the trend of the mapping function. These three points ensure the validity of this data segment, thus meeting the requirements of power grid data usage.

[0077] Example 3: like Figure 8 , is a schematic diagram of the architecture of the underlying data reporting optimization device based on the cloud platform according to an embodiment of the present invention. The underlying data reporting optimization device based on the cloud platform according to this embodiment includes one or more processors 21 and a memory 22. Figure 8 A processor 21 is taken as an example.

[0078] The processor 21 and the memory 22 may be connected via a bus or other means. Figure 8 The bus connection is taken as an example.

[0079] The memory 22 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs and non-volatile computer executable programs, such as the cloud platform-based underlying data reporting optimization method in Example 1. The processor 21 executes the cloud platform-based underlying data reporting optimization method by running the non-volatile software programs and instructions stored in the memory 22.

[0080] The memory 22 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. In some embodiments, the memory 22 may optionally include a memory remotely located relative to the processor 21, and such remote memory may be connected to the processor 21 via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0081] The program instructions / modules are stored in the memory 22 , and when executed by the one or more processors 21 , the underlying data reporting optimization method based on the cloud platform in the above-mentioned embodiment 1 is executed.

[0082] It is worth noting that the information interaction, execution process, etc. between the modules and units within the above-mentioned devices and systems are based on the same concept as the processing method embodiment of the present invention. The specific content can be found in the description of the method embodiment of the present invention and will not be repeated here.

[0083] Those skilled in the art will understand that all or part of the steps in the various methods of the embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a disk or an optical disk, etc.

[0084] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing bottom-level data reporting based on a cloud platform, characterized in that: Applied to a power grid communication transmission system, the power grid communication transmission system includes a cloud platform, relay equipment, and data acquisition equipment; the method includes: When the relay device accesses the cloud platform, it sends an access request message to the cloud platform; wherein the access request message carries the device identifier of the relay device; The cloud platform determines, based on the device identifier of the relay device, a first jurisdiction corresponding to the relay device, and searches for a corresponding historical mapping function set in the first jurisdiction; If a first historical mapping function set corresponding to the first jurisdiction is found, the first historical mapping function set is sent to the relay device; If the search shows that there is no historical mapping function set corresponding to the first jurisdiction area, generating a first historical mapping function set according to the historical electric energy data within the first jurisdiction area, and sending the first historical mapping function set to the relay device; When the relay device subsequently receives the electric energy data from the data acquisition device, it matches the corresponding first function body from the first historical mapping function set according to the acquisition time of the electric energy data, and sends the function identifier of the first function body and the mapping parameters generated by the matching to the cloud platform; The cloud platform uses the function identifier of the first function body and the mapping parameters to restore the change trend of the electric energy data and the key electric energy indicators; A historical mapping function set includes each historical time period and a function body corresponding to each historical time period, and the key electric energy indicators include actual inflection point data and total power.

2. The method for optimizing underlying data reporting based on a cloud platform according to claim 1, characterized in that: The step of matching a corresponding first function body from the first historical mapping function set according to the collection time of the electric energy data, and sending a function identifier of the first function body and a mapping parameter generated by the matching to the cloud platform specifically includes: The cloud platform also regularly sends the time period offset coefficient, time period compensation coefficient and value floating range to the relay device; Compensating the first historical time period using the time period offset coefficient and the time period compensation coefficient to obtain a first time period to be matched; Adding the electric energy data in the first time period to be matched to obtain the actual total electric energy in the first time period to be matched; and finding the actual inflection point data from the electric energy data in the first time period to be matched; Generate mapping parameters using the actual total electric energy, the actual inflection point data, and a first function body corresponding to the first historical time period, and substitute the mapping parameters into the first function body to obtain a mapping function to be matched; Calculating the trend difference and the value difference between the electric energy data in the first to-be-matched time period and the mapping function to be matched; If the trend difference is less than or equal to the preset threshold, and the numerical difference is within the numerical floating range, the function identifier and each mapping parameter of the first function body are sent to the cloud platform.

3. The method for optimizing underlying data reporting based on a cloud platform according to claim 2, characterized in that: The step of using the actual total electric energy, the actual inflection point data, and a first function body corresponding to the first historical time period to generate mapping parameters, and substituting the mapping parameters into the first function body to obtain a mapping function to be matched specifically includes: Solve the first preset equation to obtain the first time period mapping parameter and the second time period mapping parameters ; Solve the second preset equation to get the time offset parameter and other mapping parameters; Among them, the first preset equation is ; is the start time of the first function body, is the end time of the first function body, is the mapping parameter for the first time period, Mapping parameters for the first time period; The second preset equation is ; is the initial time period mapping relationship between the first time period to be matched and the first function body, , is the mapping function to be matched, For electric energy data, is the starting time of the first time period to be matched, is the end time of the first time period to be matched, is the time point of the actual inflection point data, is the value of the actual inflection point data, is the value of the preset inflection point data, The time point for the preset inflection point data.

4. The method for optimizing bottom-level data reporting based on a cloud platform according to claim 2, characterized in that: The trend difference is ; The numerical difference is ; in, is the mapping function to be matched, For electric energy data, is the derivative of the electric energy data, is the derivative of the first function to be matched, is the starting time of the first time period to be matched, is the end time of the first time period to be matched, is the initial time period mapping relationship between the first time period to be matched and the first function body, is the time offset parameter.

5. The method for optimizing bottom-level data reporting based on a cloud platform according to claim 1, characterized in that: The searching whether the first jurisdiction area has a corresponding historical mapping function set specifically includes: Pre-setting a plurality of jurisdiction types, and pre-analyzing each jurisdiction type to obtain a corresponding historical mapping function set; wherein the plurality of jurisdiction types include one or more of a residential electricity usage jurisdiction, a commercial street electricity usage jurisdiction, a factory electricity usage jurisdiction, a charging pile electricity usage jurisdiction, a substation data jurisdiction, a hydropower generation jurisdiction, a photovoltaic power generation jurisdiction, a wind power generation jurisdiction, and a transformer data jurisdiction; According to the first jurisdiction type corresponding to the first jurisdiction area, selecting a historical mapping function set corresponding to the first jurisdiction type as a default mapping function set; Check whether the cloud platform stores a custom function body for the first jurisdiction. If so, use the custom function body to replace the corresponding function body in the default mapping function set to obtain a historical mapping function set corresponding to the first jurisdiction.

6. The method for optimizing bottom-level data reporting based on a cloud platform according to claim 1, characterized in that: The access request message includes a capability identifier, a device identifier of the relay device, the total number of data acquisition devices under the control of the relay device, and a device identifier of each data acquisition device under the control of the relay device; The first historical mapping function set is carried in an access response message and sent to the relay device, and the access response message includes a capability identifier, a device identifier of the relay device, the total number of function bodies in the first historical mapping function set, each historical time period in the first historical mapping function set, and a function identifier of each function body in the first historical mapping function set.

7. The method for optimizing bottom-level data reporting based on a cloud platform according to claim 1, characterized in that: The function identifier of the first function body and the mapping parameters generated by matching are carried in a data reporting message and sent to the cloud platform. The data reporting message includes a capability identifier, the number of types of electric energy data, and compressed data of each type of electric energy data; Among them, the compressed data includes data identifiers of various types of electric energy data, time period serial numbers, start time of the matching time period, end time of the matching time period, function identifier of the first function body corresponding to the matching time period, the total number of mapping parameters and each mapping parameter.

8. The method for optimizing underlying data reporting based on a cloud platform according to any one of claims 1 to 7, characterized in that: The function body in each history mapping function set is a combination of one or more of a sine function, a linear function, an exponential function and a logarithmic function.

9. A bottom layer data reporting optimization device based on a cloud platform, characterized in that: include: at least one processor; And, a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to execute the cloud platform-based underlying data reporting optimization method described in any one of claims 1-8.

10. A non-volatile computer storage medium, characterized in that The computer storage medium stores computer-executable instructions, which are executed by one or more processors to complete the underlying data reporting optimization method based on the cloud platform as described in any one of claims 1-8.

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