CN118095655A - Water affair data calculation method and system based on unit matrix group - Google Patents
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Abstract
The application relates to a water service data calculation method and system based on a unit matrix group, wherein the method comprises the following steps: constructing a water affair data calculation frame based on the time scale and the attribution relation of the water affair data index; constructing a directed graph adjacent matrix of the water service data index based on the water service data calculation frame, wherein the directed graph adjacent matrix is used for calculating the water service data of the hydropower station; based on the directed graph adjacency matrix, the calculation of the water service data in the preset calculation task is completed through a directed graph search algorithm. According to the method and the device, a computing framework is built according to the time scale and the attribution relation of the water service data indexes, the multi-dimensional associated index directed graph adjacent matrix is obtained through division, the index relation is accurately captured through a depth search algorithm, the relevant computing matrix is located, the computing efficiency is improved, the accuracy of the data can be ensured, and the problems of how to improve the accuracy and timeliness of the water service data computation are solved.
Description
Technical Field
The application relates to the technical field of hydroelectric generation, in particular to a water service data calculation method and system based on a unit matrix group.
Background
The roles of the conventional hydropower station are gradually changed from an electric energy supplier to an electric energy supplier and a flexible regulator, and the fast-paced regulation requirement has higher requirements on the correctness, timeliness and reliability of hydropower station water regime data.
The traditional water affair computing system or functional module is applied to hydropower stations and watershed centralized control for many years, acquires data in real time by accessing water and rain conditions, and calculates scheduling operation data under different time scales based on a water balance principle, but under the scene of fast-paced adjustment requirements, the problems of catching the front part, checking the elbow, losing the number and the like are outstanding, accurate manufacture of a scheduling plan and efficient utilization of water resources are not facilitated, and the increasingly high data requirements of water and wind complementation scheduling, spot market optimization and the like are difficult to meet.
At present, an effective solution is not proposed for solving the problem of how to improve the accuracy and timeliness of water affair data calculation in the related technology.
Disclosure of Invention
The embodiment of the application provides a water service data calculation method and a system based on a unit matrix group, which at least solve the problem of how to improve the accuracy and timeliness of water service data calculation in the related technology.
In a first aspect, an embodiment of the present application provides a method for calculating water service data based on a unit matrix group, where the method includes:
constructing a water affair data calculation frame based on the time scale and the attribution relation of the water affair data index;
constructing a directed graph adjacency matrix of the water service data index based on the water service data calculation frame, wherein the directed graph adjacency matrix is used for calculating water service data of the hydropower station;
Based on the directed graph adjacent matrix, the calculation of the water service data in the preset calculation task is completed through a directed graph search algorithm.
In some of these embodiments, constructing the water affair data calculation framework based on the time scale and the attribution relation of the water affair data index comprises:
Obtaining an attribution relation of water service data indexes based on the internal structure and the attribute of the hydropower station, wherein the attribution relation comprises an equipment level, a power station level and a river basin level;
Obtaining a time scale of water affair data indexes based on the running time of a hydropower station, wherein the time scale comprises real-time, minutes, hours, days, ten days, months and years;
and constructing a water service data computing frame based on the time scale and the attribution relation, wherein the time scale is the longitudinal association of the water service data computing frame, and the attribution relation is the transverse association of the water service data computing frame.
In some of these embodiments, constructing the directed graph adjacency matrix of the water data indicator based on the water data computing framework comprises:
Constructing a first directed graph adjacency matrix of the water service data index corresponding to a first calculation relation based on the first calculation relation of the water service data index in the water service data calculation framework;
constructing a second directed graph adjacency matrix of the water service data index corresponding to a second calculation relation based on the second calculation relation of the water service data index in the water service data calculation framework;
and constructing a third directed graph adjacency matrix of the water service data index corresponding to a third calculation relation based on the third calculation relation of the water service data index in the water service data calculation framework.
In some of these embodiments, the method comprises:
the first calculation relation is the calculation relation of the same water service data index under different time scales;
the second calculation relation is a calculation relation among different water service data indexes under the same time scale;
the third calculation relation is a calculation relation among different water affair data indexes under different time scales.
In some of these embodiments, constructing the directed graph adjacency matrix of the water data indicator based on the water data computing framework comprises:
and based on the attribution relation of the water service data indexes in the water service data calculation frame, sequentially constructing a plurality of directed graph adjacency matrixes of the water service data indexes, wherein the attribution relation comprises a device level, a power station level and a river basin level.
In some of these embodiments, after constructing several directed graph adjacency matrices of the water data index in turn, the method comprises:
extracting water service data indexes in adjacent matrixes of different directed graphs;
Based on the water service data index, completing matrix combination among adjacent matrixes of different directed graphs through a preset matrix combination formula to obtain a target matrix.
In some embodiments, based on the directed graph adjacency matrix, completing the calculation of the water service data in the preset calculation task through a directed graph search algorithm includes:
the preset computing tasks comprise an instant computing task and a timing computing task;
Starting the instant calculation task, and identifying a water service data index corresponding to the water service data under the condition that the water service data is detected to change;
traversing to obtain an associated index influenced by the water service data index change through a depth-first search algorithm based on the directed graph adjacency matrix;
and calculating and updating water service data corresponding to the directed graph adjacent matrix related to the association index.
In some embodiments, based on the directed graph adjacency matrix, completing the calculation of the water service data in the preset calculation task through a directed graph search algorithm includes:
And starting the timing calculation task, and periodically traversing and updating water service data corresponding to the directed graph adjacency matrix through a depth-first search algorithm, wherein the timing calculation task comprises a minute timing calculation task, an hour timing calculation task, a day timing calculation task, a ten-day timing calculation task, a month timing calculation task and a year timing calculation task.
In some of these embodiments, the method comprises:
Collecting real-time water service data of a hydropower station;
determining abnormal real-time water service data through presetting an abnormal judgment logic;
and reassigning the abnormal real-time water service data.
In a second aspect, an embodiment of the present application provides a water service data computing system based on a unit matrix group, where the system is configured to perform the method described in the first aspect, and the system includes a frame construction module, a matrix construction module, and a task computing module;
The frame construction module is used for constructing a water affair data calculation frame according to the time scale and the attribution relation of the water affair data indexes;
The matrix construction module is used for constructing a directed graph adjacent matrix of the water service data index according to the water service data calculation frame, wherein the directed graph adjacent matrix is used for calculating water service data of the hydropower station;
And the task calculation module is used for completing the calculation of the water service data in a preset calculation task through a directed graph search algorithm according to the directed graph adjacent matrix.
Compared with the related art, the water affair data calculation method and system based on the unit matrix group provided by the embodiment of the application are characterized in that the method constructs a water affair data calculation frame based on the time scale and the attribution relation of water affair data indexes; constructing a directed graph adjacent matrix of the water service data index based on the water service data calculation frame, wherein the directed graph adjacent matrix is used for calculating the water service data of the hydropower station; based on the directed graph adjacent matrix, the calculation of the water service data in the preset calculation task is completed through a directed graph search algorithm, a calculation frame is built according to the time scale and the attribution relation of the water service data indexes, the multi-dimensional associated index directed graph adjacent matrix is obtained through division, the index relation is accurately captured through a depth search algorithm, the relevant calculation matrix is positioned, the calculation efficiency is improved, the accuracy of the data can be ensured, and the problems of how to improve the accuracy and timeliness of the water service data calculation are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flow chart of steps of a water service data calculation method based on a cell matrix group according to an embodiment of the present application;
FIG. 2 is a flow chart of a water service data calculation method based on a cell matrix group according to an embodiment of the application;
FIG. 3 is a schematic diagram of a water service data computing framework in accordance with an embodiment of the present application;
FIG. 4 is a block diagram of a water service data computing system based on a cell matrix group in accordance with an embodiment of the present application;
fig. 5 is a schematic view of an internal structure of an electronic device according to an embodiment of the present application.
Detailed Description
The present application will be described and illustrated with reference to the accompanying drawings and examples in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application. All other embodiments, which can be made by a person of ordinary skill in the art based on the embodiments provided by the present application without making any inventive effort, are intended to fall within the scope of the present application.
It is apparent that the drawings in the following description are only some examples or embodiments of the present application, and it is possible for those of ordinary skill in the art to apply the present application to other similar situations according to these drawings without inventive effort. Moreover, it should be appreciated that while such a development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as having the benefit of this disclosure.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by those of ordinary skill in the art that the described embodiments of the application can be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. The terms "a," "an," "the," and similar referents in the context of the application are not to be construed as limiting the quantity, but rather as singular or plural. The terms "comprising," "including," "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in connection with the present application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. The terms "first," "second," "third," and the like, as used herein, are merely distinguishing between similar objects and not representing a particular ordering of objects.
Firstly, it should be noted that in practical application, the existing water affair data calculation mainly has the following problems:
Data timeliness: due to the fact that the data acquisition, transmission and processing efficiency is limited, minute-level lag exists when the original data of part of stations enter the central station, and if the water affair calculation needs to fully consider the time of entering the original data, timeliness of calculating the data is difficult to meet the requirement.
Data consistency and accuracy: the water affair data comprises different time scales and a plurality of data indexes, the serial complete and accurate water affair data needs scientific and reasonable automatic calculation tools and manual maintenance, and under the condition that a strong association mechanism is not established for the water affair data indexes, the problems of inconsistent multi-stage data and domino-like linkage errors in calculation after manual intervention are extremely easy to cause.
Therefore, the present application provides a water service data calculation method based on a unit matrix group, so as to solve the above-mentioned problems in the existing water service data calculation.
The embodiment of the application provides a water service data calculation method based on a unit matrix group, and fig. 1 is a step flow chart of the water service data calculation method based on the unit matrix group according to the embodiment of the application, as shown in fig. 1, the method comprises the following steps:
step S102, constructing a water affair data calculation frame based on the time scale and the attribution relation of water affair data indexes;
Step S102 specifically further includes the following steps:
Step S1021, obtaining an attribution relation of water service data indexes based on the internal structure and the attribute of the hydropower station, wherein the attribution relation comprises a device level, a power station level and a river basin level;
The internal structure of the hydropower station comprises a reservoir, a gate, a unit, a whole plant and the like, the attribute of the hydropower station comprises a water level station, a rainfall station, a power station section and the like, in other words, the equipment level and the power station level in the attribution relation of the water service data index can be divided into the reservoir, the gate, the unit and the whole plant, and the river basin level can be divided into the water level station, the rainfall station and the power station section.
Step S1022, obtaining a time scale of the water affair data index based on the running time of the hydropower station, wherein the time scale comprises real-time, minutes, hours, days, ten days, months and years;
it should be noted that, according to the running time of the hydropower station, the water service data may be divided into real-time data, minute data, hour data, day data, ten-day data, month data, year data, etc., that is, the time scale of the water service data index may be divided into real-time, minute, hour, day, ten-day, month and year, where the real-time data specifically refers to the original collected data, and the time scale may be increased or decreased according to the specific needs of the application power station.
Step S1023, constructing a water affair data calculation frame based on a time scale and a attribution relation, wherein the time scale is longitudinal association of the water affair data calculation frame, and the attribution relation is transverse association of the water affair data calculation frame.
It should be noted that, in the water affair data calculation framework, the transverse association (attribution relation) may divide different water affair data indexes, such as: dam upper water level, dam lower water level, gate opening, unit electric quantity, unit load and the like; the vertical associations (time scales) may divide the same water data index, such as: real-time water level on the dam, water level on the minute dam, water level on the hour dam, water level on the sky dam, etc.
In the longitudinal association direction, water service data of a certain water service data index under different time scales can be calculated step by step in a specific mode (such as weighted average); in the transverse association direction, water service data such as the water reject flow of the power station, the power generation flow, the ex-warehouse flow, the warehouse-in flow and the like can be calculated based on water balance, the flow data of the water level station and the river basin surface rainfall data are obtained through a water level-flow relation curve and a surface rainfall solving method (such as Thiessen polygons), and the interval flow data of the cascade power station are obtained through the ex-warehouse flow data and the time delay relation of the upper power station and the lower power station. In addition, the water consumption rate, the waste water loss electric quantity, the idle flow, the ecological flow and other derived water service data can be calculated according to the requirements of the application power station.
Step S104, constructing a directed graph adjacent matrix of the water service data index based on the water service data calculation frame, wherein the directed graph adjacent matrix is used for calculating the water service data of the hydropower station;
Step S104 specifically includes the steps of:
Step S1041, constructing a first directed graph adjacency matrix of the water service data index corresponding to a first calculation relation based on the first calculation relation of the water service data index in the water service data calculation framework, wherein the first calculation relation is the calculation relation of the same water service data index under different time scales;
Step S1042, constructing a second directed graph adjacency matrix of the water service data index corresponding to a second calculation relation based on the second calculation relation of the water service data index in the water service data calculation framework, wherein the second calculation relation is the calculation relation between different water service data indexes under the same time scale;
Step S1043, constructing a third directed graph adjacency matrix of the water service data indexes corresponding to the third calculation relation based on the third calculation relation of the water service data indexes in the water service data calculation framework, wherein the third calculation relation is a calculation relation between different water service data indexes under different time scales.
It should be noted that, the contents and the arrangement sequence of the abscissa and the ordinate of the matrix ① of the directed graph adjacent matrix constructed in the steps S1041 to S1043 should be kept consistent (the water affair data index from left to right should be kept consistent with the water affair data index from top to bottom); ② The same matrix simultaneously contains the calculated quantity and the result quantity under a certain calculated relation, the matrix is in a unidirectional relation (the calculated quantity points to the result quantity), and whether the corresponding transverse index and the corresponding longitudinal index have a point relation is indicated by 0 or 1 in the matrix; ③ Based on the water affair data, calculating the attribution relation (reservoir, unit, gate, etc.) of the water affair data index in the framework, and sequentially constructing a plurality of directed graph adjacent matrixes of the water affair data index according to the sequence from small scale to large scale (sequence of equipment level, power station level and river basin level) to obtain a matrix group.
After constructing the step S1041 to the step S1043 to obtain a plurality of directed graph adjacent matrixes, extracting water service data indexes in different directed graph adjacent matrixes; based on the water affair data index, completing matrix combination among adjacent matrixes of different directed graphs through a preset matrix combination formula to obtain a target matrix.
Specifically, define M i as the i-th matrix in several directed graph adjacency matrices (matrix groups), T (M i) is the time scale of matrix M i, L (M i) is the set of water data indicators extracted from matrix M i, M large is the target matrix, pos (lable, M) is the position of water data indicator lable in matrix M.
Step1: the matrix groups are arranged in ascending order according to T (M i);
Step2: extracting all water affair data indexes L all=UiL(Mi of the abscissa), keeping the water affair data indexes of the ordinate and the abscissa consistent according to the construction principle ① of the matrix, and not needing to extract the union of the ordinate indexes for the second time;
Step3: initializing a target matrix M large, wherein the size of the target matrix M large is |L all|*|Lall |, and initializing elements in the matrix to 0;
step4: the target matrix is populated as follows:
The time scale problem is considered in the splicing process of the matrix, so that the related indexes of smaller scale and source data can be placed preferentially during splicing.
Step S106, based on the directed graph adjacent matrix, the calculation of the water service data in the preset calculation task is completed through a directed graph search algorithm.
Since the preset computing task includes an instant computing task and a timed computing task, step S106 specifically further includes the following steps:
Step S1061, starting an instant calculation task, and identifying a water service data index corresponding to water service data under the condition that the water service data is detected to change; traversing to obtain an associated index influenced by the change of the water service data index by a depth-first search algorithm based on the directed graph adjacency matrix; and calculating water service data corresponding to the directed graph adjacent matrix related to the updated association index.
It should be noted that, fig. 2 is a flow chart of a water service data computing method based on a unit matrix group according to an embodiment of the present application, as shown in fig. 2, an instant computing task is started, when a user manually modifies certain water service data, a system identifies a water service data index corresponding to the modified water service data, and obtains an associated index affected by the index change through automatic searching of a directed graph, and the water service recalculation only recalculates a matrix group containing the associated index.
Step S1062, starting a timing calculation task, and periodically traversing and updating water service data corresponding to the directed graph adjacency matrix through a depth-first search algorithm, wherein the timing calculation task comprises a minute timing calculation task, an hour timing calculation task, a day timing calculation task, a ten-day timing calculation task, a month timing calculation task and a year timing calculation task.
In order to improve the maintenance breadth of the hydropower station water service data, as shown in fig. 2, a plurality of calculation tasks are divided according to the hydropower station requirements, each stage of data calculation of the hydropower station is completed by performing tasks at regular time, and the timing tasks include a minute timing task, an hour timing task, a day timing task and the like according to the calculation frequency. The water affair data of the ten-day scale is updated and calculated time by time through the hour timing task, and the water affair data of the ten-day and month scale is updated and calculated day by day through the day timing task. In the water affair frame, the longitudinal calculation sequence of the indexes is calculated from the original real-time scale step by step to the large time scale of hours, days, ten days and the like; the transverse calculation sequence is organized and constructed by defining association among indexes, and single-station water service indexes are covered in a mode of firstly gating and generating units and then generating stations, and full-flow water service indexes are covered in a mode of firstly single station measurement and generating stations and then flowing fields. In addition, in order to improve the convenience of data maintenance, the embodiment of the application further provides interface operation functions, such as: and the task management interface realizes the operations of checking, configuring, starting and stopping the water affair calculation task and the like. And setting the task name, the affiliated drainage basin, the power station and the execution period. In addition, the real-time water condition data is limited by the acquisition device and the communication equipment, and minute-level delay can exist, so that the requirements of timeliness and accuracy of the data can be met by repeatedly executing tasks and delaying the recalculated execution time. If the indexes are successively associated among the tasks, the calculation can be satisfied by defining different execution times. And setting an index calculation start and stop interface, configuring the subordination relation between the index and the calculation task, and realizing the functions of water service recalculation and the like in a matching way. And the log window interface outputs the task execution condition.
It should be further noted that, due to the multidimensional scale and the multiple relevance of the water affair calculation indexes, the steps of performing hierarchical adjacent traversal by using the depth-first search algorithm in the calculation tasks of step S1061 and step S1062 are as follows:
defining a G= (V, E) directed graph, wherein V is a node set and E is an edge set;
visited V- { true, false } is used to indicate whether the node is accessed; adj (v) represents returning all node sets adjacent to node v; s represents a stack data structure; t (v) represents the time scale of the return node; r represents the result output queue (for storing output nodes).
Step1: initializing, setting a virtual (V) =false for all V belonging to V, S is an empty stack, and R is an empty queue.
Step2: one non-accessed node v is selected from the node set of the smallest time scale T, marked v as accessed (v) =true. And pushing v to the stack S. For each u belonging to adj (v), step2 is recycled if visited (v) =false.
Step3: backtracking, if all adjacent nodes of v have been accessed, ejecting v from S and adding it to R; if S is not empty, the next node is removed from S and Step2 is returned.
Step3: ending, if all V belongs to V that has been accessed, or there is no optional starting node, DFS is complete, output queue R.
The DFS algorithm (Depth-FIRST SEARCH, depth-first search algorithm) in combination with hierarchical control ensures that after the nodes of small time scale are explored, they will be added first to the result queue R. Although the search process may span multiple time scales, in the final output, the results are ordered from small to large according to the time scale, and are suitable for the business application scenario of water business data calculation.
Through the steps S102 to S106 in the embodiment of the application, a calculation frame is built according to the time scale and the attribution relation of the water service data indexes, and then the multi-dimensional associated index directed graph adjacent matrix is obtained by dividing, the index relation is accurately captured through a depth search algorithm, and the relevant calculation matrix is positioned, so that the calculation efficiency is improved, the accuracy of the data is ensured, and the problems of how to improve the accuracy and timeliness of the water service data calculation are solved.
In some of these embodiments, the method includes real-time acquisition of water traffic data further including:
collecting real-time water service data of a hydropower station; determining abnormal real-time water service data through presetting an abnormal judgment logic; and reassigning the abnormal real-time water service data.
It should be noted that, in most cases, the abnormal water affair data is caused by the problem of acquisition of real-time water affair data, and the quality control of the real-time water affair data is well done to avoid the jump and error problems of the water affair data in most hours and days. The water service data is the basis of the water service dispatching operation work, a water service data shortage and wrong number fault tolerance mechanism is established, reasonable data deletion and abnormal data assignment rules are formulated, continuity, stability and accuracy of water service data can be ensured, and powerful data guarantee is provided for water and electricity participating in multi-energy complementary dispatching operation, spot market and the like.
According to main types (rainfall, water level, opening degree and load) of real-time water service data, four preset abnormality judgment logics are provided:
① The rainfall data is usually measured using a skip type rain gauge, and each turn is recorded as one rainfall unit. The real-time rainfall data is recorded in a cyclic code mode, the number is generally transmitted after one rainfall unit is accumulated, and then the current cyclic code data is retransmitted at fixed intervals to be used as a 'safe report', so that normal information transmission is ensured. Determining whether the rainfall data is broken or not by combining the 'safe report' interval duration of the rainfall station, if the rainfall data is not broken, but the difference value of the front and back cyclic codes is larger than the local maximum rainfall, namely, the rainfall is regarded as the jump of the data, and the actual rainfall is processed according to the increment of 1 rainfall unit.
② The common abnormal condition of the opening degree of the gate is zero drift, the upper limit value of the zero drift can be set according to the actual condition of the power station, and when the opening degree is smaller than the limit value, zero setting processing is carried out.
③ The abnormal conditions of the unit load and the electric quantity index mainly comprise data jump, the actual operation condition is covered up to avoid error correction of the data, the maximum output and the power generation capacity of the unit are combined, and if the maximum output and the power generation capacity are exceeded, the value is assigned according to the maximum output and the power generation capacity.
④ If the water level exceeds the check flood level (upper limit) or the water level measurement lower limit, the water level is assigned according to the upper limit and the lower limit. The abnormal data judgment can determine amplitude variation limit according to the water level history data, and if the amplitude variation limit is exceeded, the abnormal data judgment is corrected according to the variation trend of the first 6 time periods.
If the collection of the real-time water service data triggers the automatic correction of the preset abnormality judgment logic, the water service system prompts a user to manually check and confirm the automatically corrected data.
The specific embodiment of the application provides a water service data calculation method based on a unit matrix group, which takes power station-level water service data calculation with a simpler structure as an example (the water service data calculation method is correspondingly and perfectly expanded according to the actual situation of a river basin in practical application). Assuming that a hydropower station is provided with two sets #1 and #2, and two gates #1 and #2 are needed to calculate water service data of the hour, day and month of the hydropower station, the specific embodiment of the application adopts a gate opening discharge curve to calculate flood discharge flow, adopts a set NHQ curve to calculate power generation flow, calculates indexes such as power generation, flood discharge, warehouse-out and warehouse-in flow of the hydropower station based on water balance, and the like, and single index is processed into data of different time scales of the indexes through weighted average.
Fig. 3 is a schematic structural diagram of a water service data computing framework according to an embodiment of the present application, and as shown in fig. 3, the water service data computing framework is constructed based on a time scale and a attribution relation of water service data indexes.
Based on the water affair data calculation frame, constructing a directed graph adjacency matrix of the water affair data index. Based on step S1041 to step S1043 in the above embodiments, a manner of constructing the directed graph adjacency matrix is exemplified.
(1) As shown in fig. 3, the construction of the directed graph adjacency matrix a 1 of the same water service data index (real-time dam water level h Lower part(s) ,rt, hour dam water level h Lower part(s) ,h, daily dam water level h Lower part(s) ,d, month dam water level h Lower part(s) ,m) under different time scales:
(2) As shown in fig. 3, a directed graph adjacent matrix a 2 is constructed between different water service data indexes (an hour dam upper water level h upper part ,h, an hour dam lower water level h Lower part(s) ,h, an hour load N #1,h of the #1 unit and an hour power generation flow Q #1 Hair brush ,h of the #1 unit) under the same time scale:
(3) As shown in fig. 3, a directed graph adjacency matrix a 3 between different water service data indexes (water level h upper part ,h on an hour dam, #1 gate real-time opening θ #1,rt, #1 gate hour flood discharge flow Q #1 Leakage valve ,h) at different time scales is constructed:
Splicing the constructed water service data calculation frame (matrix group) into a target matrix covering all indexes, ensuring that the related indexes of smaller scale and source data are placed preferentially during splicing, and constructing the target matrix:
the depth-first search algorithm based on the directed graph performs hierarchical adjacent traversal to obtain a water service data index related to a certain water service data index, and the water service data index is used for rapidly guiding recalculation requirements, for example, the method comprises the following steps:
As shown in fig. 3, if the modified water service data index is the power generation flow Q Hair brush ,h of the hour power station, the related water service data index should be sequentially output: q Out of ,h、Q Into (I) ,h、Q Hair brush ,d、Q Hair brush ,m、Q Out of ,d、Q Out of ,m、Q Into (I) ,d、Q Into (I) ,m.
As shown in FIG. 3, if the modified water data index is the hour dam water level h Lower part(s) ,h, the related water data index should be sequentially output ：Q#1 Hair brush ,h、Q#2 Hair brush ,h、Q Hair brush ,h、Q Out of ,h、Q Into (I) ,h、h Lower part(s) ,d、h Lower part(s) ,m、Q#1 Hair brush ,d、Q#1 Hair brush ,m、Q#2 Hair brush ,d、Q#2 Hair brush ,m、Q Hair brush ,d、Q Hair brush ,m、Q Out of ,d、Q Out of ,m、Q Into (I) ,d、Q Into (I) ,m.
The method has the advantages that the associated indexes which can be influenced by the source data are definitely determined, the water affair recalculation only recalculates matrix groups containing the associated indexes, the calculation efficiency can be greatly improved on the premise that the data accuracy is met, and the timeliness of the water affair data is ensured.
It should be noted that the steps illustrated in the above-described flow or flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein.
The embodiment of the application provides a water service data computing system based on a unit matrix group, and fig. 4 is a structural block diagram of the water service data computing system based on the unit matrix group according to the embodiment of the application, as shown in fig. 4, and the system comprises a frame construction module, a matrix construction module and a task computing module;
the frame construction module is used for constructing a water affair data calculation frame according to the time scale and the attribution relation of the water affair data indexes;
The matrix construction module is used for constructing a directed graph adjacent matrix of the water service data index according to the water service data calculation frame, wherein the directed graph adjacent matrix is used for calculating the water service data of the hydropower station;
and the task calculation module is used for completing the calculation of the water service data in a preset calculation task through a directed graph search algorithm according to the directed graph adjacency matrix.
By the frame construction module, the matrix construction module and the task calculation module in the embodiment of the application, the calculation frame is constructed according to the time scale and the attribution relation of the water service data indexes, the multi-dimensional associated index directed graph adjacent matrix is obtained by dividing, the index relation is accurately captured by the depth search algorithm, and the relevant calculation matrix is positioned, so that the calculation efficiency is improved, the accuracy of the data is ensured, and the problems of how to improve the accuracy and the timeliness of the water service data calculation are solved.
The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.
The present embodiment also provides an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, where the transmission device is connected to the processor, and the input/output device is connected to the processor.
It should be noted that, specific examples in this embodiment may refer to examples described in the foregoing embodiments and alternative implementations, and this embodiment is not repeated herein.
In addition, in combination with the water service data calculation method based on the unit matrix group in the above embodiment, the embodiment of the application can be implemented by providing a storage medium. The storage medium has a computer program stored thereon; the computer program, when executed by a processor, implements any of the water service data calculation methods based on the cell matrix groups in the above embodiments.
In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a water service data calculation method based on a cell matrix group. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
In one embodiment, fig. 5 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application, and as shown in fig. 5, an electronic device, which may be a server, is provided, and an internal structure diagram thereof may be as shown in fig. 5. The electronic device includes a processor, a network interface, an internal memory, and a non-volatile memory connected by an internal bus, where the non-volatile memory stores an operating system, computer programs, and a database. The processor is used for providing computing and control capability, the network interface is used for communicating with an external terminal through network connection, the internal memory is used for providing environment for the operation of an operating system and a computer program, the computer program is executed by the processor to realize a water service data computing method based on a unit matrix group, and the database is used for storing data.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the electronic device to which the present inventive arrangements are applied, and that a particular electronic device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It should be understood by those skilled in the art that the technical features of the above-described embodiments may be combined in any manner, and for brevity, all of the possible combinations of the technical features of the above-described embodiments are not described, however, they should be considered as being within the scope of the description provided herein, as long as there is no contradiction between the combinations of the technical features.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A water service data calculation method based on a unit matrix group, the method comprising:
constructing a water affair data calculation frame based on the time scale and the attribution relation of the water affair data index;
constructing a directed graph adjacency matrix of the water service data index based on the water service data calculation frame, wherein the directed graph adjacency matrix is used for calculating water service data of the hydropower station;
Based on the directed graph adjacent matrix, the calculation of the water service data in the preset calculation task is completed through a directed graph search algorithm.
2. The method of claim 1, wherein constructing a water data computing framework based on the time scale and the attribution relationship of the water data indicators comprises:
Obtaining an attribution relation of water service data indexes based on the internal structure and the attribute of the hydropower station, wherein the attribution relation comprises an equipment level, a power station level and a river basin level;
Obtaining a time scale of water affair data indexes based on the running time of a hydropower station, wherein the time scale comprises real-time, minutes, hours, days, ten days, months and years;
and constructing a water service data computing frame based on the time scale and the attribution relation, wherein the time scale is the longitudinal association of the water service data computing frame, and the attribution relation is the transverse association of the water service data computing frame.
3. The method of claim 1, wherein constructing a directed graph adjacency matrix of the water data indicator based on the water data computing framework comprises:
Constructing a first directed graph adjacency matrix of the water service data index corresponding to a first calculation relation based on the first calculation relation of the water service data index in the water service data calculation framework;
constructing a second directed graph adjacency matrix of the water service data index corresponding to a second calculation relation based on the second calculation relation of the water service data index in the water service data calculation framework;
and constructing a third directed graph adjacency matrix of the water service data index corresponding to a third calculation relation based on the third calculation relation of the water service data index in the water service data calculation framework.
4. A method according to claim 3, characterized in that the method comprises:
the first calculation relation is the calculation relation of the same water service data index under different time scales;
the second calculation relation is a calculation relation among different water service data indexes under the same time scale;
the third calculation relation is a calculation relation among different water affair data indexes under different time scales.
5. The method of claim 1, wherein constructing a directed graph adjacency matrix of the water data indicator based on the water data computing framework comprises:
and based on the attribution relation of the water service data indexes in the water service data calculation frame, sequentially constructing a plurality of directed graph adjacency matrixes of the water service data indexes, wherein the attribution relation comprises a device level, a power station level and a river basin level.
6. The method according to claim 5, wherein after constructing several directed graph adjacency matrices of the water data index in turn, the method comprises:
extracting water service data indexes in adjacent matrixes of different directed graphs;
Based on the water service data index, completing matrix combination among adjacent matrixes of different directed graphs through a preset matrix combination formula to obtain a target matrix.
7. The method of claim 1, wherein performing the calculation of the water data in the preset calculation task by the directed graph search algorithm based on the directed graph adjacency matrix comprises:
the preset computing tasks comprise an instant computing task and a timing computing task;
Starting the instant calculation task, and identifying a water service data index corresponding to the water service data under the condition that the water service data is detected to change;
traversing to obtain an associated index influenced by the water service data index change through a depth-first search algorithm based on the directed graph adjacency matrix;
and calculating and updating water service data corresponding to the directed graph adjacent matrix related to the association index.
8. The method of claim 7, wherein performing the calculation of the water data in the preset calculation task by the directed graph search algorithm based on the directed graph adjacency matrix comprises:
And starting the timing calculation task, and periodically traversing and updating water service data corresponding to the directed graph adjacency matrix through a depth-first search algorithm, wherein the timing calculation task comprises a minute timing calculation task, an hour timing calculation task, a day timing calculation task, a ten-day timing calculation task, a month timing calculation task and a year timing calculation task.
9. The method according to claim 1, characterized in that the method comprises:
Collecting real-time water service data of a hydropower station;
determining abnormal real-time water service data through presetting an abnormal judgment logic;
and reassigning the abnormal real-time water service data.
10. A water service data computing system based on a matrix group of cells, characterized in that the system is adapted to perform the method of any of the preceding claims 1-9, the system comprising a frame construction module, a matrix construction module and a task computing module;
The frame construction module is used for constructing a water affair data calculation frame according to the time scale and the attribution relation of the water affair data indexes;
The matrix construction module is used for constructing a directed graph adjacent matrix of the water service data index according to the water service data calculation frame, wherein the directed graph adjacent matrix is used for calculating water service data of the hydropower station;
And the task calculation module is used for completing the calculation of the water service data in a preset calculation task through a directed graph search algorithm according to the directed graph adjacent matrix.
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