CN111198891A - Data source fusion method, electronic device and non-transitory computer readable storage medium - Google Patents

Data source fusion method, electronic device and non-transitory computer readable storage medium Download PDF

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CN111198891A
CN111198891A CN201911235779.4A CN201911235779A CN111198891A CN 111198891 A CN111198891 A CN 111198891A CN 201911235779 A CN201911235779 A CN 201911235779A CN 111198891 A CN111198891 A CN 111198891A
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CN111198891B (en
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王维洲
刘道新
胡航海
李文娟
拜润卿
刘福潮
史玉杰
何欣
陈仕彬
邢延东
张震雷
刘文飞
张海龙
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State Grid Corp of China SGCC
State Grid Information and Telecommunication Co Ltd
Electric Power Research Institute of State Grid Gansu Electric Power Co Ltd
Beijing Guodiantong Network Technology Co Ltd
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State Grid Information and Telecommunication Co Ltd
Electric Power Research Institute of State Grid Gansu Electric Power Co Ltd
Beijing Guodiantong Network Technology Co Ltd
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Abstract

The invention provides a data source fusion method for synchronous line loss, electronic equipment and a non-transitory computer readable storage medium. The data source fusion method is based on a data processing angle, provides a multi-service data source fusion method facing to the synchronous line loss, fully utilizes the acquired data of each service system and the relationship between the frequency and the precision of the data acquired by each power distribution service system, and achieves the purpose that the data of the multi-service system meets the synchronous line loss statistics through data judgment, fusion and matching correction, thereby solving the problems that the existing power distribution system has incomplete data, non-uniform acquisition frequency, non-uniform time scale, poor data quality and difficult strong support on the synchronous line loss service in a short period.

Description

Data source fusion method, electronic device and non-transitory computer readable storage medium
Technical Field
The invention relates to the technical field of power grid data processing, in particular to technologies for line loss-oriented data management, fusion of power grid multi-service system source data, quality management and restoration and the like, and specifically relates to a synchronization line loss-oriented data source fusion method, electronic equipment and a non-transitory computer-readable storage medium.
Background
The power supply quantity and the power selling quantity of the power system have certain difference in synchronization statistics, and the specific difference is called synchronization line loss. The ratio of the loss of electrical energy to the amount of power delivered during the transfer of electrical energy from the power plant to the consumer is known as the line loss rate. The line loss rate is an important index for judging the operation management level and the production technology of the power grid enterprise. When the line loss management work is carried out, line loss four-point management is generally adopted. The line loss quartering management is a mode that power grid enterprises manage the regions governed by the power grid enterprises according to voltage levels, power supply regions, public lines and public power distribution areas, the line loss rates of the four modes are counted and analyzed, and finally measures for reducing the synchronous line loss are taken pertinently according to judgment results. When line loss statistics is carried out on a public power distribution station area at the present stage, due to the influence of statistics on different periods between power supply quantity and power selling quantity, the statistical result is often greatly different from the actual line loss, and the improvement of a power grid enterprise on the line loss management level is severely restricted to a great extent.
With the rapid promotion of the construction of the power internet of things and the gradual maturity of a power big data technology, data of various service systems of a power distribution network system are fused and used for management of the power distribution network, the management capability of a power company on services such as power production, operation and maintenance, scheduling and marketing is intensively reflected for the contemporaneous line loss statistics, but the existing power distribution system has incomplete data, non-uniform acquisition frequency, non-uniform time scale and poor data quality, is difficult to powerfully support contemporaneous line loss services in a short term, and how to realize the organic integration and fusion of the data of each service system is a key problem faced by the existing power company.
Disclosure of Invention
In view of the above, the present invention aims to provide a data source fusion method for a contemporaneous line loss, so as to solve the problems that the existing power distribution system has incomplete data, non-uniform acquisition frequency, non-uniform time scale, poor data quality, and difficulty in strongly supporting a contemporaneous line loss service in a short period.
Based on the above purpose, the present invention provides a data source fusion method for synchronous line loss, which includes:
acquiring data of a multi-service system acquisition node, obtaining a first service system with target frequency and a second service system with target precision through data screening, and performing fusion matching on the data of the first service system and the data of the second service system;
acquiring data of a target acquisition node and acquisition nodes connected with the target acquisition node, and estimating and matching the data of the target acquisition node;
and acquiring data of the target time of the same acquisition node and the time adjacent to the target time, and judging and correcting the correctness of the target data by utilizing the data consistency of the target time of the same acquisition node and the time adjacent to the target time.
Further, the fusing and matching the data of the first service system and the data of the second service system includes:
when the same acquisition node simultaneously has data of a first service system and data of a second service system at the same time, the data of the first service system are subjected to reference calibration through the data of the second service system; alternatively, the first and second electrodes may be,
and when the data of the first service system and the second service system of the same acquisition node are not uniform at the moment, matching the data of the first service system and the second service system by adopting an average interpolation method.
In the invention, the average interpolation method comprises the following steps: if it is to be at xiAnd xi+1N values are inserted between the two, and then the N values are respectively:
Figure BDA0002304841190000021
wherein the variable x is data collected by the service system.
In the invention, the target frequency refers to the acquisition frequency of the service system with the highest acquisition frequency in the multi-service system; the target precision refers to the collection precision of the service system with the highest collection precision in the multi-service system; the data of the first service system is calibrated by using the data of the second service system, that is, the data of the first service system is calibrated by using the data of the second service system as a reference.
Furthermore, the multi-service system comprises a power utilization information acquisition system, a marketing management system, a distribution network scheduling system, a distribution network operation and maintenance system, a communication system, a power quality monitoring system and a scheduling automation system.
Furthermore, the first service system is an electricity consumption information acquisition system, and the second service system is a marketing management system; the data of the electricity consumption information acquisition system is subjected to benchmark calibration through the data of the marketing management system, and the method comprises the following steps:
Figure BDA0002304841190000031
Figure BDA0002304841190000032
Figure BDA0002304841190000033
wherein the content of the first and second substances,
Figure BDA0002304841190000034
acquiring a theoretical power acquisition value of an acquisition node i for the electricity utilization information acquisition system; mu.s1Acquiring error rate for data of the electricity consumption information acquisition system;
Figure BDA0002304841190000035
the difference value between the real collection value and the theoretical collection value of the power utilization information collection system is obtained; mu.s2Error rate for data acquisition of the marketing management system;
Figure BDA0002304841190000036
the difference value between the real acquisition value and the theoretical acquisition value of the marketing management system is obtained; mu | ofCI is the measurement precision of the power consumption information acquisition system; mu | ofXAnd | is the measurement accuracy of the marketing management system.
Furthermore, the first service system is an electricity consumption information acquisition system, and the second service system is a marketing management system; the data existence moments of the electricity utilization information acquisition system and the marketing management system of the same acquisition node are not unified to form the following formula:
Figure BDA0002304841190000037
and further, the data of the multi-service system is subjected to priority sequencing according to the acquisition frequency and the acquisition precision, two different service systems with the highest acquisition frequency and the highest acquisition precision are obtained for each acquisition node through sequencing, and the data of the two different service systems are subjected to fusion matching.
Further, the acquiring of the data of the target acquisition node and the acquisition nodes connected thereto, and the estimating and matching of the data of the target acquisition node include that the power of the target acquisition node is obtained by adding the power of one or more acquisition nodes connected thereto and the line loss, and the following formula is given:
Figure BDA0002304841190000038
Figure BDA0002304841190000039
wherein n is the number of acquisition nodes connected with the s acquisition node; j ranges from 1 to n; p is a radical ofmjThe power of the j collecting node connected with the s collecting node; i isjCollecting current data between the node s and the node j; rjCollecting resistance value data between the s collecting node and the j collecting node;
Figure BDA00023048411900000310
collecting a theoretical power collection value of the node for j; mu.ssMeasuring the data precision of the s acquisition node; mu.smjAnd measuring the data precision of the j acquisition node connected with the s acquisition node.
Further, when the data of the s acquisition node and each acquisition node connected with the s acquisition node appear
Figure BDA0002304841190000041
And then, the s acquisition node updates the data of the corresponding acquisition node at the corresponding moment by using the data calculated by each acquisition node connected with the s acquisition node, and matches the acquired data of the acquisition node by adopting an average interpolation method.
Further, the correctness of the target data is judged and corrected, and the data including the target time of the same acquisition node and the time adjacent to the target time meets at least one of the following formula (9) and formula (10) and the formula (8), so that the target data is accurate:
pk-Δt≈pk≈pk+Δt(8)
Figure BDA0002304841190000042
Figure BDA0002304841190000043
wherein p iskCollecting a power collection value of a node k for k; and delta t is the acquisition interval of the acquisition nodes.
Further, when the data of the target time of the same acquisition node and the data of the time adjacent to the target time do not satisfy the following formula (9) and formula (10), the target data is preliminarily judged to be incorrect, and the matching correction is performed by adopting an average interpolation method.
Furthermore, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the data source fusion method as described above.
Furthermore, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the above-described data source fusion method.
From the above, it can be seen that the data source fusion method for the contemporaneous line loss provided by the present invention provides a multiservice data source fusion method for the contemporaneous line loss from the data processing perspective, and the method comprises the steps of firstly, fully utilizing the relationship between the frequency and the precision of data acquisition of each service system of power distribution, respectively screening out two service systems with the highest data acquisition frequency and the best precision for each acquisition node or acquisition partition, and performing fusion matching on the data of the two service systems, wherein the data of the other service systems are used as necessary backup and reference; secondly, estimating and matching the data of the acquired nodes by using the data of the acquired nodes and the data of each adjacent acquired node; and judging and correcting the correctness of the data by utilizing the consistency of the data collected before and after the data of the same collection node. The data collected by each service system and the relation between the frequency and the precision of the data collected by each power distribution service system are fully utilized, the data of the multi-service system are judged, fused and matched to be corrected, the purpose that the data of the multi-service system meet the synchronous line loss statistics is achieved, and the problems that the existing power distribution system is incomplete in data, non-uniform in collection frequency, non-uniform in time scale, poor in data quality and difficult to powerfully support the synchronous line loss service in a short period are solved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a data source fusion method for synchronous line loss according to an embodiment of the present invention;
FIG. 2 is a flow chart of data matching for a business system in an embodiment of the present invention;
fig. 3 is a schematic diagram of a specific hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
It is to be noted that technical terms or scientific terms used in the embodiments of the present invention should have the ordinary meanings as understood by those having ordinary skill in the art to which the present disclosure belongs, unless otherwise defined. The use of "first," "second," and similar terms in this disclosure is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
As shown in fig. 1, the present invention provides a data source fusion method for contemporaneous line loss, including:
s1, acquiring data of a multi-service system acquisition node, obtaining a first service system with target frequency and a second service system with target precision through data screening, and fusing and matching the data of the first service system and the data of the second service system; s2, acquiring data of the target acquisition node and the acquisition nodes connected with the target acquisition node, and estimating and matching the data of the target acquisition node; and S3, acquiring data of the target time of the same acquisition node and the time adjacent to the target time, and judging and correcting the correctness of the target data by utilizing the data consistency of the target time of the same acquisition node and the time adjacent to the target time.
Furthermore, when the data of the first service system and the data of the second service system are fused and matched, including the data of the first service system and the data of the second service system which are simultaneously provided by the same acquisition node at the same time, the data of the first service system is subjected to reference calibration through the data of the second service system; or when the data of the first service system and the second service system of the same acquisition node exist at different times, matching the data of the first service system and the data of the second service system by adopting an average interpolation method.
In the invention, the average interpolation method comprises the following steps: if it is to be at xiAnd xi+1N values are inserted between the two, and then the N values are respectively:
Figure BDA0002304841190000061
wherein the variable x is data collected by the service system.
In the invention, the target frequency refers to the acquisition frequency of the service system with the highest acquisition frequency in the multi-service system; the target precision refers to the collection precision of the service system with the highest collection precision in the multi-service system; the data of the first service system is calibrated by using the data of the second service system, that is, the data of the first service system is calibrated by using the data of the second service system as a reference.
Furthermore, the multi-service system comprises a power utilization information acquisition system, a marketing management system, a distribution network scheduling system, a distribution network operation and maintenance system, a communication system, a power quality monitoring system and a scheduling automation system.
Furthermore, the first service system is an electricity consumption information acquisition system, and the second service system is a marketing management system; the data of the electricity consumption information acquisition system is subjected to benchmark calibration through the data of the marketing management system, and the method comprises the following steps:
Figure BDA0002304841190000062
Figure BDA0002304841190000063
Figure BDA0002304841190000064
wherein the content of the first and second substances,
Figure BDA0002304841190000065
acquiring a theoretical power acquisition value of an acquisition node i for the electricity utilization information acquisition system; mu.s1Acquiring error rate for data of the electricity consumption information acquisition system;
Figure BDA0002304841190000071
the difference value between the real collection value and the theoretical collection value of the power utilization information collection system is obtained; mu.s2Error rate for data acquisition of the marketing management system;
Figure BDA0002304841190000072
the difference value between the real acquisition value and the theoretical acquisition value of the marketing management system is obtained; mu | ofCI is the measurement precision of the power consumption information acquisition system; mu | ofXAnd | is the measurement accuracy of the marketing management system.
Furthermore, the first service system is an electricity consumption information acquisition system, and the second service system is a marketing management system; the data existence moments of the electricity utilization information acquisition system and the marketing management system of the same acquisition node are not unified to form the following formula:
Figure BDA0002304841190000073
and further, the data of the multi-service system is subjected to priority sequencing according to the acquisition frequency and the acquisition precision, two different service systems with the highest acquisition frequency and the highest acquisition precision are obtained for each acquisition node through sequencing, and the data of the two different service systems are subjected to fusion matching.
Further, the acquiring of the data of the target acquisition node and the acquisition nodes connected thereto, and the estimating and matching of the data of the target acquisition node include that the power of the target acquisition node is obtained by adding the power of one or more acquisition nodes connected thereto and the line loss, and the following formula is given:
Figure BDA0002304841190000074
Figure BDA0002304841190000075
wherein n is the number of acquisition nodes connected with the s acquisition node; j ranges from 1 to n; p is a radical ofmjThe power of the j collecting node connected with the s collecting node; i isjCollecting current data between the node s and the node j; rjCollecting resistance value data between the s collecting node and the j collecting node;
Figure BDA0002304841190000076
collecting a theoretical power collection value of the node for j; mu.ssMeasuring the data precision of the s acquisition node; mu.smjAnd measuring the data precision of the j acquisition node connected with the s acquisition node.
Further, when the data of the s acquisition node and each acquisition node connected with the s acquisition node appear
Figure BDA0002304841190000077
And then, the s acquisition node updates the data of the corresponding acquisition node at the corresponding moment by using the data calculated by each acquisition node connected with the s acquisition node, and matches the acquired data of the acquisition node by adopting an average interpolation method.
Further, the correctness of the target data is judged and corrected, and the data including the target time of the same acquisition node and the time adjacent to the target time meets at least one of the following formula (9) and formula (10) and the formula (8), so that the target data is accurate:
pk-Δt≈pk≈pk+Δt(8)
Figure BDA0002304841190000081
Figure BDA0002304841190000082
wherein p iskCollecting a power collection value of a node k for k; and delta t is the acquisition interval of the acquisition nodes.
Further, when the data of the target time of the same acquisition node and the data of the time adjacent to the target time do not satisfy the following formula (9) and formula (10), the target data is preliminarily judged to be incorrect, and the matching correction is performed by adopting an average interpolation method.
From the above, it can be seen that the data source fusion method for the contemporaneous line loss provided by the present invention provides a multiservice data source fusion method for the contemporaneous line loss from the data processing perspective, and the method comprises the steps of firstly, fully utilizing the relationship between the frequency and the precision of data acquisition of each service system of power distribution, respectively screening out two service systems with the highest data acquisition frequency and the best precision for each acquisition node or acquisition partition, and performing fusion matching on the data of the two service systems, wherein the data of the other service systems are used as necessary backup and reference; secondly, estimating and matching the data of the acquired nodes by using the data of the acquired nodes and the data of each adjacent acquired node; and judging and correcting the correctness of the data by utilizing the consistency of the data collected before and after the data of the same collection node. The data collected by each service system and the relation between the frequency and the precision of the data collected by each power distribution service system are fully utilized, the data of the multi-service system are judged, fused and matched to be corrected, the purpose that the data of the multi-service system meet the synchronous line loss statistics is achieved, and the problems that the existing power distribution system is incomplete in data, non-uniform in collection frequency, non-uniform in time scale, poor in data quality and difficult to powerfully support the synchronous line loss service in a short period are solved.
To specifically illustrate the technical solution of the present invention, the detailed description is as follows:
step 1: data matching based on multi-service system sources: in the process of power distribution operation management, a plurality of service systems exist, and cross redundancy exists on power distribution network data to a great extent, such as a power consumption information acquisition system, a marketing management system, a distribution network scheduling system, a distribution network operation and maintenance system, a communication system, a power quality monitoring system and the like, wherein the power consumption information acquisition system has higher acquisition frequency and can reflect transient data, the marketing management system has lower acquisition frequency and higher accuracy, when the same acquisition point simultaneously has the power consumption information acquisition system data and the marketing system acquisition data, the power consumption information acquisition system data can be subjected to reference calibration through the marketing system acquisition data, and when the power consumption information acquisition system data and the marketing system data have different time, the power consumption information acquisition system data and the marketing system data are matched by adopting an average interpolation method;
step 2: and data matching based on the collected information of the adjacent nodes: according to the energy conservation theorem, the power of a certain acquisition node can be obtained by adding the power of one or more nodes connected with the certain acquisition node and the line loss, and if a more accurate result can be obtained by the method, the method can be used for making up partial missing or inaccurate data;
and step 3: and data matching based on the collected information at adjacent moments: for the acquisition systems with high acquisition frequency, such as a power utilization information acquisition system, a dispatching automation system and the like, when the load of a power distribution system is stable and sudden increase and decrease do not occur, the data difference of the adjacent time of the data acquired by the same acquisition node is not large, and if the data are not satisfied, the data acquired by the same acquisition node can be preliminarily judged to be incorrect. The matching correction can be further carried out by adopting the two methods. Compared with the prior art, the method has the advantages that the data volume is small, the algorithm is easy to realize, the service system data with high data accuracy is selected, the service system data with high collection frequency is subjected to matching correction, the data accuracy is preliminarily judged through the data of the collected nodes at different moments, if the judgment has errors or fluctuation, the continuous judgment and the matching can be carried out through the data of the adjacent nodes, and the data correction value with high accuracy is given when the data is wrong.
For example, one specific embodiment is as follows:
step 1: data matching based on multi-service system sources: in the process of power distribution operation management, various service systems exist, cross redundancy exists in power distribution network data to a great extent, such as a power utilization information acquisition system, a marketing management system, a distribution network scheduling system, a distribution network operation and maintenance system, a communication system, a power quality monitoring system and the like, wherein the power utilization information acquisition system has higher acquisition frequency and can reflect transient data, the marketing system has lower acquisition frequency and higher precision, when the same acquisition point simultaneously has the power utilization information acquisition system data and the marketing system acquisition data, the power utilization information acquisition system data can be subjected to benchmark calibration through the marketing system acquisition data, and when the power utilization information acquisition system data and the marketing system data have different time, the power utilization information acquisition system data and the marketing system data are matched by adopting an average interpolation method.
The power collected by the power consumption information collection system at a certain moment collection point i is assumed as follows:
Figure BDA0002304841190000101
wherein
Figure BDA0002304841190000102
For the power consumption information acquisition system to acquire the theoretical power acquisition value mu of the point i1For the data acquisition error rate of the electricity consumption information acquisition system,
Figure BDA0002304841190000103
the difference value between the real collection value and the theoretical collection value of the power utilization information collection system is obtained.
At the same acquisition time, the acquisition power of the marketing management system to the acquisition point i is as follows:
Figure BDA0002304841190000104
wherein mu2To collect error rates with the data of the marketing management system,
Figure BDA0002304841190000105
for real and theoretical collected values of marketing management systemThe difference value.
When p isi1And pi2All within the precision allowable range of the respective system acquisition terminal
Figure BDA0002304841190000106
Wherein, | muCThe | is the measurement precision (generally +/-1%) of the power consumption information acquisition system, and the | mu isXI is the measurement precision (generally +/-0.2%) of the marketing management system, and the data p of the current power utilization information acquisition systemi1When the error exceeds the measurement precision of each system, the following generally occurs:
Figure BDA0002304841190000107
the invention takes the situation as a condition, when the situation occurs, the data of the corresponding node of the electricity utilization information acquisition system at the corresponding moment is updated by using the data of the corresponding node of the marketing management system at the corresponding moment, and the electricity utilization information acquisition data is matched by adopting an average interpolation method.
Similarly, the data of the multi-service system which can be used for the line loss statistics at the same time are subjected to priority sequencing according to the acquisition frequency and the acquisition precision, and the service system participating in the sequencing comprises: the system comprises a power utilization information acquisition system, a distribution network scheduling system, a communication system, a distribution network operation and maintenance system, a marketing management system and a power quality monitoring system, wherein a service system with the highest acquisition frequency and a service system with the highest acquisition precision (the two service systems are not the same system) are obtained for each acquisition node through sequencing, and data of the two service systems are matched. The flow chart is shown in fig. 2.μ in FIG. 2CCFor collecting the business system and mu with the highest frequencyXXThe method is a business system with the highest acquisition precision.
Step 2: and data matching based on the collected information of the adjacent nodes: according to the energy conservation theorem, the power of a certain collection node can be obtained by adding the power of one or more nodes connected with the certain collection node and the line loss, and if a more accurate result can be obtained by the method, partial missing or inaccurate data can be made up. The basic formula of the power calculation method of the s acquisition node is as follows:
Figure BDA0002304841190000111
wherein n is the number of nodes connected with the s node, j has the value range of 1-n, and pmjFor the j-th node power, I, connected to the s-nodejAs current data between the s-node and the j-node, RjIs the resistance value data between the s node and the j node.
According to the principle of the step (1), for the s node, when the s node and the data of each node connected with the s node are within the precision allowable range, the requirement is met
Figure BDA0002304841190000112
Wherein, therein
Figure BDA0002304841190000113
Is the theoretical power acquisition value of the j node, musData measurement accuracy of s-node, mumjThe data measurement accuracy of the j node connected with the s node is obtained.
μsAnd mumjIs an acquisition system in principle, has consistent precision when acquiring data p of a node jsErrors that exceed the accuracy of the data calculated by each connected node typically occur:
Figure BDA0002304841190000114
the invention takes the situation as a condition, when the situation occurs, the data obtained by calculation of each connecting node is used for updating the data of the corresponding node at the corresponding moment, and the average interpolation method is adopted for matching the data acquired by the node.
And step 3: and data matching based on the collected information at adjacent moments: for the collection system with higher collection frequency, such as a power utilization information collection system, a dispatching automation system and the like, when the load of a power distribution system is relatively stable and the situation of sudden increase and sudden decrease does not occur, the data difference between the adjacent moments of the collected data of the same node is not large, and the following requirements are generally met:
pk-Δt≈pk≈pk+Δt(8)
and delta t is a node acquisition interval.
According to the principle of the step (1), for the k node, when the data of each adjacent acquisition time is within the precision allowable range, the following requirements are met:
Figure BDA0002304841190000121
Figure BDA0002304841190000122
therefore, when at least one of the data at each acquisition time meets the above formula, the result of the acquired data is accurate, and when the two formulas do not meet, the acquired data can be preliminarily judged to be incorrect. The method of average mean value can be further adopted for matching correction.
It should be noted that the method of the embodiment of the present invention may be executed by a single device, such as a computer or a server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In the case of such a distributed scenario, one of the multiple devices may only perform one or more steps of the method according to the embodiment of the present invention, and the multiple devices interact with each other to complete the method.
In addition, the present invention further provides an electronic device, and specifically, fig. 3 shows a more specific hardware structure diagram of the electronic device provided in this embodiment, where the electronic device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.
The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
Bus 1050 includes a path that transfers information between various components of the device, such as processor 1010, memory 1020, input/output interface 1030, and communication interface 1040.
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
Computer-readable media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the idea of the invention, also features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
In addition, well known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures for simplicity of illustration and discussion, and so as not to obscure the invention. Furthermore, devices may be shown in block diagram form in order to avoid obscuring the invention, and also in view of the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the present invention is to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the invention, it should be apparent to one skilled in the art that the invention can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
The embodiments of the invention are intended to embrace all such alternatives, modifications and variances that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements and the like that may be made without departing from the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (12)

1. A data source fusion method facing to synchronous line loss comprises the following steps:
acquiring data of a multi-service system acquisition node, obtaining a first service system with target frequency and a second service system with target precision through data screening, and performing fusion matching on the data of the first service system and the data of the second service system;
acquiring data of a target acquisition node and acquisition nodes connected with the target acquisition node, and estimating and matching the data of the target acquisition node;
and acquiring data of the target time of the same acquisition node and the time adjacent to the target time, and judging and correcting the correctness of the target data by utilizing the data consistency of the target time of the same acquisition node and the time adjacent to the target time.
2. The data source fusion method according to claim 1, wherein the performing fusion matching on the data of the first service system and the data of the second service system comprises:
when the same acquisition node simultaneously has data of a first service system and data of a second service system at the same time, the data of the first service system are subjected to reference calibration through the data of the second service system; alternatively, the first and second electrodes may be,
and when the data of the first service system and the second service system of the same acquisition node are not uniform at the moment, matching the data of the first service system and the second service system by adopting an average interpolation method.
3. The data source fusion method of claim 2, wherein the multi-service system comprises a power consumption information acquisition system, a marketing management system, a distribution network scheduling system, a distribution network operation and maintenance system, a communication system, a power quality monitoring system and a scheduling automation system.
4. The data source fusion method of claim 3, wherein the first business system is a power consumption information acquisition system, and the second business system is a marketing management system; the data of the electricity consumption information acquisition system is subjected to benchmark calibration through the data of the marketing management system, and the method comprises the following steps:
Figure FDA0002304841180000011
Figure FDA0002304841180000012
Figure FDA0002304841180000013
wherein the content of the first and second substances,
Figure FDA0002304841180000014
acquiring a theoretical power acquisition value of an acquisition node i for the electricity utilization information acquisition system; mu.s1Acquiring error rate for data of the electricity consumption information acquisition system;
Figure FDA0002304841180000015
the difference value between the real collection value and the theoretical collection value of the power utilization information collection system is obtained; mu.s2Error rate for data acquisition of the marketing management system;
Figure FDA0002304841180000021
the difference value between the real acquisition value and the theoretical acquisition value of the marketing management system is obtained; mu | ofCI is the measurement precision of the power consumption information acquisition system; mu | ofXAnd | is the measurement accuracy of the marketing management system.
5. The data source fusion method of claim 3, wherein the first business system is a power consumption information acquisition system, and the second business system is a marketing management system; the data existence moments of the electricity utilization information acquisition system and the marketing management system of the same acquisition node are not unified to form the following formula:
Figure FDA0002304841180000022
6. the data source fusion method according to claim 3, further comprising prioritizing data of the multi-service systems according to acquisition frequency and acquisition precision, obtaining two different service systems with highest acquisition frequency and highest acquisition precision for each acquisition node by ranking, and performing fusion matching on the data of the two different service systems.
7. The data source fusion method according to any one of claims 1 to 3, wherein the data of the target collection node and the collection nodes connected thereto are obtained, and the data of the target collection node is estimated and matched, including that the power of the target collection node is obtained by adding the power and the line loss of one or more collection nodes connected thereto, and the following formula is given:
Figure FDA0002304841180000023
Figure FDA0002304841180000024
wherein n is the number of acquisition nodes connected with the s acquisition node; j ranges from 1 to n; p is a radical ofmjThe power of the j collecting node connected with the s collecting node; i isjCollecting current data between the node s and the node j; rjCollecting resistance value data between the s collecting node and the j collecting node;
Figure FDA0002304841180000025
collecting a theoretical power collection value of the node for j; mu.ssMeasuring the data precision of the s acquisition node; mu.smjAnd measuring the data precision of the j acquisition node connected with the s acquisition node.
8. The data source fusion method of claim 7 wherein the data is generated when the s-collection node and each collection node connected thereto appear
Figure FDA0002304841180000026
And then, the s acquisition node updates the data of the corresponding acquisition node at the corresponding moment by using the data calculated by each acquisition node connected with the s acquisition node, and matches the acquired data of the acquisition node by adopting an average interpolation method.
9. The data source fusion method according to any one of claims 1 to 3, wherein the judgment and correction of the correctness of the target data include that data of the target time of the same acquisition node and a time adjacent to the target time satisfy at least one of the following formula (9) and formula (10) and formula (8), and the target data is accurate:
pk-Δt≈pk≈pk+Δt(8)
Figure FDA0002304841180000031
Figure FDA0002304841180000032
wherein p iskCollecting a power collection value of a node k for k; and delta t is the acquisition interval of the acquisition nodes.
10. The data source fusion method of claim 9, wherein when the data of the target time of the same collection node and the data of the time adjacent to the target time do not satisfy the following formula (9) and formula (10), the preliminary judgment that the target data is incorrect is performed, and the matching correction is performed by using an average interpolation method.
11. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the data source fusion method of any one of claims 1 to 10 when executing the program.
12. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the data source fusion method of any one of claims 1 to 10.
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