CN116257554A

CN116257554A - Demand statistical analysis method, device, equipment and medium based on virtual summary table

Info

Publication number: CN116257554A
Application number: CN202111461647.0A
Authority: CN
Inventors: 朱凤健; 陈建锋
Original assignee: Shanghai Standardel Co ltd
Current assignee: Shanghai Standardel Co ltd
Priority date: 2021-12-02
Filing date: 2021-12-02
Publication date: 2023-06-13

Abstract

The application provides a demand statistics analysis method, device, equipment and medium based on a virtual summary table, wherein the historical data of each physical ammeter is obtained by constructing the virtual summary table and associating a plurality of physical meters; accumulating the real-time demand of all the historical data at each time node to obtain the real-time demand of the corresponding virtual summary at each time node; screening abnormal time nodes in each historical data to be removed from the total data; and carrying out statistical analysis on the processed total data, and writing the statistical data into a cache to form an incremental cache. According to the method and the system, the total loop demand is calculated by counting the demand data of all sub-tables, and abnormal data are eliminated, so that an enterprise can measure the total demand values of a plurality of different incoming line loops, the accuracy of demand statistics is improved, the enterprise can predict the demand better, and the electricity cost is saved.

Description

Demand statistical analysis method, device, equipment and medium based on virtual summary table

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a medium for statistical analysis of demand based on virtual summary.

Background

For enterprises with high electricity demands such as markets and industrial and mining areas, enterprises with basic electricity charges charged according to the demand are more and more selected, so that the reasonable demand prediction can save the electricity cost of the enterprises to the greatest extent.

The traditional demand measurement is mainly aimed at single-loop power supply, and if the total demand of multiple loops is required to be measured, an ammeter is required to be additionally installed on the total loops of the multiple loops for measurement, however, when the actual installation is carried out, due to limited construction conditions, part of enterprises do not have the condition of installing the ammeter on the total loops, and only the ammeter can be installed on each single sub-loop, so that the enterprises cannot directly measure the total demand of the multiple loops.

Disclosure of Invention

In view of the above drawbacks of the prior art, an object of the present application is to provide a method, an apparatus, a device and a medium for statistical analysis of demand based on virtual summary, so as to solve the problem in the prior art that the total loop demand cannot be measured due to the fact that an enterprise cannot install the summary in the total loop.

To achieve the above and other related objects, the present application provides a method for statistical analysis of demand based on virtual summary, the method comprising: constructing a virtual summary table and associating a plurality of physical electric meters so as to acquire historical data of each physical electric meter; accumulating the real-time demand of all the historical data at each time node to obtain the real-time demand of the corresponding virtual summary at each time node; screening abnormal time nodes in each historical data to be removed from the total data corresponding to the virtual total table; and carrying out statistical analysis on the processed total data, and writing the statistical data into a cache to form an incremental cache.

In an embodiment of the present application, the accumulating the real-time requirements of all the historical data at each time node to obtain the real-time requirements of the corresponding virtual summary at each time node includes: acquiring all time nodes in the historical data of any one physical ammeter as a basic time node list; and acquiring the accumulated value of the real-time demand of all the physical electric meters at the same time node according to the basic time node list so as to obtain the real-time demand of the corresponding virtual summary table at each time node, and forming summary table data corresponding to the virtual summary table.

In an embodiment of the present application, the acquiring, according to the base time node list, an accumulated value of real-time requirements of all physical electric meters at the same time node includes: traversing all time nodes in the basic time node list, and finding out the historical data of each physical ammeter in each time node by using a binary search method; and adding the demand data of all the physical electric meters at the same time node.

In an embodiment of the present application, the filtering the abnormal time node in each of the historical data for being removed from the summary data corresponding to the virtual summary includes: judging whether all the historical data exist on the same time node and are valid values; if all the historical data on a certain time node exist and are valid values, the time node is regarded as a valid time node, and the valid time node is added into the total data of the virtual total table; if the historical data is truly or partially invalid, the time node is regarded as invalid time node and is not added to the total data of the virtual total table.

In an embodiment of the present application, the performing statistical analysis on the processed total data includes: traversing the processed total data, and finding out the maximum value and the minimum value of the required quantity through cyclic comparison; recording the time node, the total data number and the time of the last data corresponding to the maximum value and the minimum value; and accumulating all the processed total data to calculate the total demand value, and dividing the total demand value by the total number of the data to obtain the average demand value.

In one embodiment of the present application, the statistics include: the time of the demand maximum, the demand minimum, the time of the demand maximum, the time of the demand minimum, the total number of pieces of data, the time of the last piece of data, the total cumulative value of all data, and the average value of all data.

In an embodiment of the present application, the writing the statistics into the cache to obtain the cached data forms an incremental cache, including: writing the statistical data into a cache to obtain cache data; judging whether the cached data is the final time of the month end according to the time of the last piece of data; if yes, caching data does not need to be modified; if not, carrying out statistical analysis on all data which are not statistically analyzed before the final time of the month end, and carrying out incremental modification on the cached data.

To achieve the above and other related objects, the present application provides a demand statistics analysis apparatus based on a virtual summary, the apparatus comprising: the virtual summary module is used for constructing a virtual summary and associating a plurality of physical electric meters so as to acquire historical data of each physical electric meter; the statistical analysis module is used for accumulating the real-time demand of all the historical data at each time node to obtain the real-time demand of the corresponding virtual summary at each time node; screening abnormal time nodes in each historical data to be removed from the total data corresponding to the virtual total table; and carrying out statistical analysis on the processed total data, and writing the statistical data into a cache to form an incremental cache.

To achieve the above and other related objects, the present application provides a computer apparatus comprising: a memory, a processor, and a communicator; the memory is used for storing computer instructions; the processor executing computer instructions to implement the method as described above; the communicator is used for external communication.

To achieve the above and other related objects, the present application provides a computer-readable storage medium storing computer instructions that, when executed, perform a method as described above.

In summary, the method, the device, the equipment and the medium for demand statistics analysis based on the virtual summary are provided, and the historical data of each physical ammeter is obtained by constructing the virtual summary and associating a plurality of physical meters; accumulating the real-time demand of all the historical data at each time node to obtain the real-time demand of the corresponding virtual summary at each time node; screening abnormal time nodes in each historical data to be removed from the total data corresponding to the virtual total table; and carrying out statistical analysis on the processed total data, and writing the statistical data into a cache to form an incremental cache.

Has the following beneficial effects:

according to the method and the device, the total loop demand is calculated by counting the demand data of all sub-meters, and abnormal data are eliminated, so that the enterprise can measure the demand without additionally installing an ammeter on the total loop, the power construction difficulty and the construction cost of the enterprise are reduced, the accuracy of demand statistics is improved, the enterprise can predict the demand better, and the purpose of saving the electricity cost is achieved.

Drawings

FIG. 1 is a flow chart of a method for analyzing demand statistics based on virtual summary in an embodiment of the present application.

FIG. 2 is a diagram showing the accumulation of sub-table demand data in one embodiment of the present application.

FIG. 3 is a diagram illustrating a data structure of statistics in a cache according to one embodiment of the present application.

FIG. 4 is a schematic block diagram of a device for analyzing demand statistics based on virtual summary in an embodiment of the present application.

Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application.

Detailed Description

Other advantages and effects of the present application will become apparent to those skilled in the art from the present disclosure, when the following description of the embodiments is taken in conjunction with the accompanying drawings. The present application may be embodied or carried out in other specific embodiments, and the details of the present application may be modified or changed from various points of view and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.

It should be noted that, the illustrations provided in the following embodiments merely illustrate the basic concepts of the application by way of illustration, and although only the components related to the application are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.

As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes," and/or "including" specify the presence of stated features, steps, operations, elements, components, items, categories, and/or groups, but do not preclude the presence, presence or addition of one or more other features, steps, operations, elements, components, items, categories, and/or groups. The terms "or" and/or "as used herein are to be construed as inclusive, or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: a, A is as follows; b, a step of preparing a composite material; c, performing operation; a and B; a and C; b and C; A. b and C). An exception to this definition will occur only when a combination of elements, functions, steps or operations are in some way inherently mutually exclusive.

FIG. 1 is a flow chart of a method for performing a virtual summary-based demand statistical analysis according to an embodiment of the present application. As shown, the method includes:

step S101: and constructing a virtual summary table and associating a plurality of physical electric meters so as to acquire historical data of each physical electric meter.

In an embodiment of the present application, in order to solve the problem in the prior art that the total loop demand cannot be measured due to the fact that the enterprise cannot install the total meter in the total loop, the present application associates a plurality of physical electric meters to a manually created virtual total meter, so as to obtain real-time demand historical data of the assigned months of all the physical electric meters under the virtual total meter.

Briefly, a virtual summary table is first created on the platform, and then the physical electricity meters of the multiple loops are associated with the virtual summary table, which is logically one summary table on the sub-table total loops. For example, there are three independent circuits, and table a, table B, and table C are installed respectively, but these three circuits do not have the condition of installing electricity meters under one total circuit or on the total circuit, and a virtual table may be created on the platform, and then table a, table B, and table C are associated with the virtual table, and then the virtual table is logically the electricity meters on the total circuit of table a, table B, and table C.

In an embodiment of the present application, the history data at least includes: the data time nodes and the real-time demand values, and each historical data is ordered from small to large according to the time nodes.

In some examples, the platform will collect real-time demand data in the sub-table at set time intervals by the collection device, and these data will be stored in a designated database, and these data are the historical data of the sub-table. It should be noted that, due to factors such as on-site acquisition equipment, communication equipment, on-site environmental interference, etc., problems such as insufficient data acquisition or abnormal data may occur.

In this application, the historical data includes at least two values, time and real-time demand values, respectively. Wherein, the time is the data acquisition time, and is accurate to seconds; the real-time demand value is the real-time demand value in the physical ammeter collected by the collecting device, the real-time demand is generally the average value of the power of 15 minutes, for example, the collecting time is 10:30, and the real-time demand is the average value of the power of 10:15 to 10:30. In addition, the obtained historical data are all ordered according to the order from small to large when the database is queried, so that the later merging and searching can be facilitated.

Step S102: and accumulating the real-time demand of all the historical data at each time node to obtain the real-time demand of the corresponding virtual summary at each time node.

In the embodiment of the invention, the obtaining of the real-time demand of the total table through the real-time demand of the sub-table is a key step, wherein the related data volume is larger, and the calculated volume is also larger.

Specifically, the step S102 includes:

A. acquiring all time nodes in the historical data of any one physical ammeter as a basic time node list;

in short, the historical data of any one of all the physical electric meters is found out to be used as the basic data, and all the time nodes of the meter are obtained to be used as a basic time node list. For example, the history data of table a is used as the base data, and the list of all time nodes of the history data of table a is used as the base time list.

B. And acquiring the accumulated value of the real-time demand of all the physical electric meters at the same time node according to the basic time node list so as to obtain the real-time demand of the corresponding virtual summary table at each time node, and forming summary table data corresponding to the virtual summary table.

Specifically, traversing all time nodes in the basic time node list, and finding out the historical data of each physical ammeter at each time node by using a binary search method; and adding the demand data of all the physical electric meters at the same time node.

The demand is the average value of the power in the specified period, and the direct addition is the average value of the total power of the plurality of physical electric meters in the specified period, namely the demand data of the virtual total table in the corresponding time node.

For example, the time node of table a is traversed, and then a binary search is used to find the real-time demand value of the same time node in the historical data of physical electricity meter B and physical electricity meter C according to the time node. For example, if the current time node is 31 minutes and 0 seconds at 12 th point of 8/15 in 2020, the data of this time node needs to be found in the history data of table B and table C. As shown in fig. 2, the real-time demand of all the physical electric meters of the same time node is accumulated, so that the total demand data of the virtual total table on each time node can be obtained.

In the application, the real-time demand values of the table A, the table B and the table C of the same time node are searched and added to be the real-time demand value of the virtual total table at the time node, and the real-time demand values of all the time nodes of the virtual total table are the total table data.

Step S103: and screening abnormal time nodes in each historical data to be removed from the total table data corresponding to the virtual total table.

In some embodiments, the accuracy of the data may be improved by deleting the time nodes of the anomalies in each of the historical data. Specifically, the step S103 includes:

A. and judging whether all the historical data on the same time node exist and are valid values.

B. If all the historical data on a certain time node exist and are valid values, the time node is regarded as a valid time node, and the valid time node is added to the total data of the virtual total table.

C. If the historical data is truly or partially invalid, the time node is regarded as invalid time node and is not added to the total data of the virtual total table.

For example: on the time node of 31 min 0 s at 12 th point of 8 th month 15 of the time node 2020, only the data of the table a and the table B are found, and the data of the table C is not found, then the time node data is regarded as invalid data; for example, at the time node of 43 minutes 0 seconds at 13 th 8/15 in the time node 2020, the data of table a and table B are normal, but the data of table C is not a number, and the time node data is regarded as invalid data.

It should be noted that, the time node demand value for the present application is an average value of power in a specified period, and is different from a common sub-table or a total table, and the total power consumption is required to be accurate or complete at each time point, because it is mostly different from the time point for finding electric leakage or problems. The data of each time node is not needed, namely, certain time nodes are lack of electricity data, and the average value can be obtained through the number of the time nodes, so that the data of the ammeter can be filtered, and the data of abnormal time nodes and the like can be removed. Therefore, the power consumption data and the processing using the power consumption data to which the present application is directed are different from the prior art.

Step S104: and carrying out statistical analysis on the processed total data, and writing the statistical data into a cache to form an incremental cache.

In the embodiment of the invention, a large amount of historical data is required to be subjected to statistical analysis, and the data after the statistical analysis is written into the cache, so that the size of the cache data is reduced, and the memory space is saved.

Specifically, step S104 includes:

A. traversing the processed total data, and finding out the maximum value and the minimum value of the required quantity through cyclic comparison;

B. recording the time node, the total data number and the time of the last data corresponding to the maximum value and the minimum value;

C. accumulating all processed total data to calculate total required value, dividing the total required value by the total number of data to obtain average required value

In short, traversing the processed total data, finding out the maximum value and the minimum value of the required quantity through cyclic comparison, recording the corresponding time nodes of the maximum value and the minimum value, additionally recording the total number of the data, the time of the last piece of data, accumulating all the processed data, calculating the total required quantity value, and dividing the total number of the data obtained by the previous step to obtain the average required quantity.

In one embodiment of the present application, for better analysis of demand information by an enterprise, more comprehensive statistics are presented herein, including but not limited to: the time of the demand maximum, the demand minimum, the time of the demand maximum, the time of the demand minimum, the total number of pieces of data, the time of the last piece of data, the total cumulative value of all data, and the average value of all data. The total number of data and the time of the last data are updated for the subsequent increment. And finally, writing all the statistical data into a cache in json format.

In some examples, the final statistics are packaged into json format; after the data is written into the cache, the query speed is higher, so that the situation that the analysis result needs to be counted again in each query is avoided, and the pressure of the server is relieved. The statistical data format is shown in fig. 3, and the statistical data format is stored in a buffer memory in the form of key value pairs, wherein the key format is virtual total table ID, namely, year and month, for example, the virtual total table ID is 1001, the statistical data is 10 months in 2020, and the key value is 1001:202010; the format of the value is json character string of statistical data

In an embodiment of the present application, writing the statistics into the cache to obtain the cached data forms an incremental cache, including:

A. writing the statistical data into a cache to obtain cache data;

B. and judging whether the cached data is the final time of the month end according to the time of the last piece of data.

The demand information is generally counted by month, so that data of the whole month is required to accurately count data such as the maximum demand, the minimum demand, the average demand and the like.

In some embodiments, the collector may fail to collect all data of a whole month due to a communication or network problem, for example, the demand statistics are made at the end of month 8 in 2020, but the collector only collects data from month 1 in 2020 to month 20 in 2020 due to a communication or network problem, and then the time 2020 of the last data is recorded; for example, the current time is No. 9/18 in 2020, but the enterprise needs to check the current month requirement information, only the data up to No. 18 can be counted, and the time for recording the last data is No. 18/9 in 2020. If the acquired data is the data of a whole month, the time of recording the last data is the month end time, the acquisition period of the required data is 1 minute, and if the statistics is the data of 10 months in 2020, the time of the last data is the ten-bit time stamp of 23 points 59 minutes in 31 of 10 months in 2020.

C. If yes, caching data does not need to be modified; if not, carrying out statistical analysis on all data which are not statistically analyzed before the final time of the month end, and carrying out incremental modification on the cached data.

In short, if the last data time of the cached data is the month end time when the cached information of the required quantity of a certain month is queried, the queried cached data is directly returned without other processing. If the time of the last piece of data is not the final time of the month end, the data of the period of time which is not counted is needed to be counted again, and the counted data and the cached data are combined into the latest counted data to be rewritten into the cache.

In an embodiment of the present application, if the last data time of the cached data is not the month end time when the demand statistical information of a certain month is queried, it is indicated that the data of the still time period is to be statistically analyzed, and at this time, the data of the sub-table needs to be queried again to be statistically analyzed and then combined with the original cached data, and then the latest statistical result is written into the cache.

For example, it is necessary to check the statistical information of the total table requirement in month 8 of 2020, the last data time in the checked cache data is month 8 of 2020, which means that the data from month 8 of 2020 to month 8 of 2020 is not yet statistically analyzed, at this time, it is necessary to acquire the historical data of sub-table a, sub-table B, sub-table C between 19 and 31, re-statistically analyze, and finally combine the statistical result with the previously checked cache statistical result.

It should be noted that, performing incremental modification on the cached data further includes performing corresponding modification on the maximum demand value, the minimum demand value, the time of the maximum demand value, the time of the minimum demand value, the total number of data pieces, the time of the last data piece, the total accumulated value of all data pieces, and the average value data of all data pieces.

For example, comparing the maximum value and the minimum value in the two statistics data respectively, determining whether the maximum value, the minimum value, the maximum value time and the minimum value time need to be updated, adding the total data count and the demand accumulation value of the two statistics data to obtain the latest demand accumulation value and the total data count, calculating the latest average value according to the latest demand accumulation value and the total data count, updating the time of the last data, and finally rewriting the latest result into the cache to cover the previous cache data.

In this embodiment, when the information of the required amount of the total data is queried each time, whether the data needs to be updated is determined according to the last data time, so as to ensure that the latest statistical data is queried each time.

FIG. 4 is a schematic block diagram of a device for analyzing demand statistics based on virtual summary in an embodiment of the present application. As shown, the apparatus 400 includes:

the virtual summary module 401 is configured to construct a virtual summary and associate a plurality of physical electric meters, so as to obtain historical data of each physical electric meter;

a statistical analysis module 402, configured to accumulate real-time requirements of all the historical data at each time node, so as to obtain real-time requirements of the corresponding virtual summary table at each time node; screening abnormal time nodes in each historical data to be removed from the total data corresponding to the virtual total table; and carrying out statistical analysis on the processed total data, and writing the statistical data into a cache to form an incremental cache.

It should be noted that, because the content of information interaction and execution process between the modules/units of the above system is based on the same concept as the method embodiment described in the present application, the technical effects brought by the content are the same as the method embodiment described in the present application, and specific content can be referred to the description in the method embodiment described in the foregoing application, which is not repeated here.

It should be further noted that the division of the modules in the above system is merely a division of logic functions, and may be fully or partially integrated into one physical entity or may be physically separated. And these units may all be implemented in the form of software calls through the processing element; or can be realized in hardware; the method can also be realized in a form of calling software by a processing element, and the method can be realized in a form of hardware by a part of modules. For example, the statistical analysis module 402 may be a processing element that is set up separately, may be implemented in a chip of the system, or may be stored in a memory of the system in the form of a program code, and the function of the statistical analysis module 402 may be called and executed by a processing element of the system. The implementation of the other modules is similar. In addition, all or part of the modules can be integrated together or can be independently implemented. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in a software form.

For example, the modules above may be one or more integrated circuits configured to implement the methods above, such as: one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), or one or more microprocessors (digital signal processor, abbreviated as DSP), or one or more field programmable gate arrays (Field Programmable Gate Array, abbreviated as FPGA), or the like. For another example, when a module above is implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a central processing unit (Central Processing Unit, CPU) or other processor that may invoke the program code. For another example, the modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

As shown in fig. 5, a schematic structural diagram of a computer device in an embodiment of the present application is shown. As shown, the computer device 500 includes: memory 501, processor 502, and communicator 503; the memory 501 is used to store computer instructions; the processor 502 executes computer instructions to implement the method as described in fig. 1; the communicator 503 is used to communicate with, for example, a physical electricity meter or a server.

In some embodiments, the number of the memories 501 in the computer device 500 may be one or more, the number of the processors 502 may be one or more, and the number of the communicators 503 may be one or more, and one is exemplified in fig. 5.

In an embodiment of the present application, the processor 502 in the computer device 500 loads one or more instructions corresponding to the process of the application program into the memory 501 according to the steps described in fig. 1, and the processor 502 executes the application program stored in the memory 501, so as to implement the method described in fig. 1.

The memory 501 may include a random access memory (Random Access Memory, simply referred to as RAM) or may include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. The memory 501 stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof, wherein the operating instructions may include various operating instructions for performing various operations. The operating system may include various system programs for implementing various underlying services and handling hardware-based tasks.

The processor 502 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but also digital signal processors (Digital Signal Processing, DSP for short), application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), field-programmable gate arrays (Field-Programmable Gate Array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

The communicator 503 is configured to implement a communication connection between the database access apparatus and other devices (e.g., a client, a read-write library, and a read-only library). The communicator 503 may comprise one or more sets of modules of different communication means, for example CAN communication modules communicatively coupled to a CAN bus. The communication connection may be one or more wired/wireless communication means and combinations thereof. The communication mode comprises the following steps: any one or more of the internet, CAN, intranet, wide Area Network (WAN), local Area Network (LAN), wireless network, digital Subscriber Line (DSL) network, frame relay network, asynchronous Transfer Mode (ATM) network, virtual Private Network (VPN), and/or any other suitable communication network. For example: any one or more of WIFI, bluetooth, NFC, GPRS, GSM, and ethernet.

In some specific applications, the various components of the computer device 500 are coupled together by a bus system, which may include a power bus, control bus, status signal bus, and the like, in addition to a data bus. But for purposes of clarity of illustration the various buses are referred to in fig. 5 as a bus system.

In one embodiment of the present application, a computer readable storage medium is provided, on which a computer program is stored, which when executed by a processor, implements the method as described in fig. 1.

The present application may be a system, method, and/or computer program product at any possible level of technical detail. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present application.

The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.

The computer readable program described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.

Computer program instructions for carrying out operations of the present application may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and a procedural programming language such as the "C" language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present application are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information for computer readable program instructions, which may execute the computer readable program instructions.

The method effectively overcomes various defects in the prior art and has high industrial utilization value.

The foregoing embodiments are merely illustrative of the principles of the present application and their effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those of ordinary skill in the art without departing from the spirit and scope of the present application. Accordingly, it is intended that all equivalent modifications and variations which a person of ordinary skill in the art could accomplish without departing from the spirit and technical spirit of the present disclosure be covered by the claims of this application.

Claims

1. A method of statistical analysis of demand based on a virtual summary, the method comprising:

constructing a virtual summary table and associating a plurality of physical electric meters so as to acquire historical data of each physical electric meter;

accumulating the real-time demand of all the historical data at each time node to obtain the real-time demand of the corresponding virtual summary at each time node;

screening abnormal time nodes in each historical data to be removed from the total data corresponding to the virtual total table; and carrying out statistical analysis on the processed total data, and writing the statistical data into a cache to form an incremental cache.

2. The method of claim 1, wherein said accumulating the real-time demand of all of said historical data at each time node to obtain the real-time demand of the corresponding virtual summary at each time node comprises:

acquiring all time nodes in the historical data of any one physical ammeter as a basic time node list;

and acquiring the accumulated value of the real-time demand of all the physical electric meters at the same time node according to the basic time node list so as to obtain the real-time demand of the corresponding virtual summary table at each time node, and forming summary table data corresponding to the virtual summary table.

3. The method according to claim 2, wherein the obtaining, from the base time node list, an accumulated value of real-time demand of all physical electricity meters at the same time node includes:

traversing all time nodes in the basic time node list, and finding out the historical data of each physical ammeter in each time node by using a binary search method;

and adding the demand data of all the physical electric meters at the same time node.

4. The method of claim 1, wherein said screening the time nodes of anomalies in each of said historical data for culling from the summary data corresponding to said virtual summary comprises:

judging whether all the historical data exist on the same time node and are valid values;

if all the historical data on a certain time node exist and are valid values, the time node is regarded as a valid time node, and the valid time node is added into the total data of the virtual total table;

if the historical data is truly or partially invalid, the time node is regarded as invalid time node and is not added to the total data of the virtual total table.

5. The method of claim 1, wherein said statistically analyzing said processed summary data comprises:

traversing the processed total data, and finding out the maximum value and the minimum value of the required quantity through cyclic comparison;

recording the time node, the total data number and the time of the last data corresponding to the maximum value and the minimum value;

and accumulating all the processed total data to calculate the total demand value, and dividing the total demand value by the total number of the data to obtain the average demand value.

6. The method of claim 5, wherein the statistics comprise: the time of the demand maximum, the demand minimum, the time of the demand maximum, the time of the demand minimum, the total number of pieces of data, the time of the last piece of data, the total cumulative value of all data, and the average value of all data.

7. The method of claim 5, wherein writing the statistics to the cache to obtain the cached data forms an incremental cache, comprising:

writing the statistical data into a cache to obtain cache data;

judging whether the cached data is the final time of the month end according to the time of the last piece of data;

if yes, caching data does not need to be modified; if not, carrying out statistical analysis on all data which are not statistically analyzed before the final time of the month end, and carrying out incremental modification on the cached data.

8. A virtual summary-based demand statistics analysis apparatus, the apparatus comprising:

the virtual summary module is used for constructing a virtual summary and associating a plurality of physical electric meters so as to acquire historical data of each physical electric meter;

the statistical analysis module is used for accumulating the real-time demand of all the historical data at each time node to obtain the real-time demand of the corresponding virtual summary at each time node; screening abnormal time nodes in each historical data to be removed from the total data corresponding to the virtual total table; and carrying out statistical analysis on the processed total data, and writing the statistical data into a cache to form an incremental cache.

9. A computer device, the device comprising: a memory, a processor, and a communicator; the memory is used for storing computer instructions; the processor executing computer instructions to implement the method of any one of claims 1 to 7; the communicator is used for external communication.

10. A computer readable storage medium, characterized in that computer instructions are stored, which when executed perform the method of any of claims 1 to 7.