CN116051216A - Energy cost statistical method, device, equipment and medium based on graph data model - Google Patents
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Abstract
The present invention relates to the field of energy statistics technologies, and in particular, to a method, an apparatus, a device, and a medium for energy cost statistics based on a graph data model. The method comprises the steps of obtaining a user with a subordinate relation with a target client, using the target client as a first node, using the user as a second node, connecting the first node with the second node to form initial relation graph data, obtaining a metering point associated with the user, using the metering point as a third node, using the metering value as node information of the third node, connecting the second node with the third node to obtain updated relation graph data, performing first-order aggregation processing twice by using the node information of the third node to obtain charging information of the target client, and using the graph data to evaluate the energy relation, so that richer user relation information can be saved simultaneously, parallel energy cost statistics calculation is performed, the structure of the energy relation is clearer, the occurrence of calculation errors in energy cost statistics is avoided, and the efficiency and accuracy of an energy cost statistics process are improved.
Description
Technical Field
The present invention relates to the field of energy statistics technologies, and in particular, to a method, an apparatus, a device, and a medium for energy cost statistics based on a graph data model.
Background
At present, the traditional energy statistics method adopts a charging model taking an energy utilization address as a center and taking a payment main body and a customer as assistance, when the energy cost of the customer is counted, a plurality of charging models aiming at the energy utilization address are required to be counted, in the existing energy statistics method, the publication number is CN115271532A, the invention named as comprehensive energy utilization data analysis method is divided into a plurality of acquisition areas according to the principles of the application function, the geographic position or the security level of an area energy station server, each data acquisition area consists of a plurality of data acquisition nodes, the energy service data on each data acquisition node is acquired, the energy analysis is performed according to the acquired energy service data, the area division is performed according to the energy utilization address, the energy statistics and the analysis are performed, and the energy utilization address is still taken as the center.
However, as the energy consumption relationship becomes more and more complex, the energy consumption address corresponding to the same customer is also increased gradually, and when the energy consumption statistics of the customer is performed by adopting a statistical model or a charging model with the energy consumption address as a center, a plurality of energy consumption addresses are often involved, that is, a plurality of statistical models based on the energy consumption addresses need to be integrated, the operation process is complex, the consumed computing resources are also more, the efficiency of the energy cost statistics process is lower, and the computing errors are easy to occur, so how to improve the efficiency and the accuracy of the energy cost statistics becomes a problem to be solved.
Disclosure of Invention
In view of the above, the embodiment of the invention provides an energy cost statistics method, device, equipment and medium based on a graph data model, so as to solve the problems of low efficiency and low accuracy of energy cost statistics.
In a first aspect, an embodiment of the present invention provides an energy cost statistics method based on a graph data model, where the energy cost statistics method includes:
acquiring at least one user with a subordinate relation with any target client, taking the target client as a first node, taking each user as a second node, respectively connecting the first node with each second node, traversing all target clients, and forming initial relation graph data;
traversing each user, and acquiring at least one metering point associated with the user to obtain metering points respectively associated with each user;
taking each metering point as a third node, taking the metering value of the corresponding metering point as node information of the corresponding third node, respectively connecting each second node with each corresponding third node according to the association relation between a user and each metering point, and updating the initial relation graph data to obtain updated relation graph data;
Performing first-order aggregation processing by using node information of a third node connected with each second node, and updating the node information of the corresponding second node by using a first-order aggregation result;
and carrying out first-order aggregation processing by using updated node information of a second node connected with each first node, updating the node information of the corresponding first node by using a first-order aggregation result, and determining the updated node information of each first node as charging information of the corresponding target client.
In a second aspect, an embodiment of the present invention provides an energy cost statistics apparatus based on a graph data model, the energy cost statistics apparatus including:
the data modeling module is used for acquiring at least one user with a subordinate relation with any target client, taking the target client as a first node, taking each user as a second node, respectively connecting the first node with each second node, traversing all target clients and forming initial relation graph data;
the metering point acquisition module is used for traversing each user, acquiring at least one metering point associated with the user, and obtaining the metering point respectively associated with each user;
the model updating module is used for taking each metering point as a third node, taking the metering value of the corresponding metering point as node information of the corresponding third node, and respectively connecting each second node with each corresponding third node according to the association relation between a user and each metering point to update the initial relation diagram data to obtain updated relation diagram data;
The first aggregation module is used for carrying out first-order aggregation processing by utilizing node information of a third node connected with each second node, and updating the node information of the corresponding second node by a first-order aggregation result;
and the second aggregation module is used for carrying out first-order aggregation processing by using updated node information of the second nodes connected with each first node, updating the node information of the corresponding first nodes by using a first-order aggregation result, and determining that the updated node information of each first node is charging information of the corresponding target client.
In a third aspect, an embodiment of the present invention provides a computer device, where the computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, where the processor implements the graph data model-based energy cost statistics method according to the first aspect when the computer program is executed.
In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium storing a computer program, which when executed by a processor implements the graph data model-based energy cost statistics method according to the first aspect.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
at least one user with a subordinate relation with any target client is obtained, the target client is used as a first node, each user is used as a second node, the first node is respectively connected with each second node, all target clients are traversed to form initial relation graph data, each user is traversed to obtain at least one metering node associated with the user, metering nodes respectively associated with each user are obtained, each metering node is used as a third node, a metering value of the corresponding metering node is used as node information of the corresponding third node, each second node is respectively connected with each third node corresponding to the second node according to the association relation between the user and each metering node, initial relation graph data are updated, the node information of the third node connected with each second node is utilized to conduct first-order aggregation processing, the node information of the second node connected with each first node is utilized to conduct first-order aggregation processing, the first-order aggregation processing is utilized to update the node information of the second node connected with each first node, the first-order aggregation processing is utilized to update the node information of the first node, the statistics of the first-node is utilized to calculate the cost-average value of the corresponding relation graph, the cost-price-for the user is more accurate, the statistics of the cost-price-for the corresponding relation graph is avoided, and the cost-for the user can be calculated, the statistics of the cost-price-for the corresponding relation is more accurate, and the cost-price-for the user-statistics is calculated.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an application environment of an energy cost statistics method based on a graph data model according to an embodiment of the present invention;
FIG. 2 is a flow chart of an energy cost statistics method based on a graph data model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of initial relationship graph data in an energy cost statistics method based on a graph data model according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of updating relationship graph data in an energy cost statistics method based on a graph data model according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a relationship diagram data in an energy cost statistics method based on a graph data model according to a second embodiment of the present invention;
fig. 6 is a schematic structural diagram of an energy cost statistics method device based on a graph data model according to a third embodiment of the present invention;
Fig. 7 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in the present description and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the invention. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
It should be understood that the sequence numbers of the steps in the following embodiments do not mean the order of execution, and the execution order of the processes should be determined by the functions and the internal logic, and should not be construed as limiting the implementation process of the embodiments of the present invention.
In order to illustrate the technical scheme of the invention, the following description is made by specific examples.
The energy cost statistics method based on the graph data model provided by the embodiment of the invention can be applied to an application environment as shown in fig. 1, wherein a client communicates with a server. The client includes, but is not limited to, a palm top computer, a desktop computer, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a cloud terminal device, a personal digital assistant (personal digital assistant, PDA), and other computer devices. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 2, a flow chart of an energy cost statistics method based on a graph data model according to an embodiment of the present invention is shown, where the energy cost statistics method can be applied to a client in fig. 1, and a computer device corresponding to the client is connected to a server to obtain a target client, a user, a metering point, a metering value of the metering point, and the like related to energy cost statistics, and each metering point is connected to the server to upload the metering value of the corresponding metering point to the server for storage. As shown in fig. 2, the energy cost statistics method may include the steps of:
step S201, at least one user with subordinate relation with any target client is obtained, the target client is used as a first node, each user is used as a second node, the first node is respectively connected with each second node, and all target clients are traversed to form initial relation diagram data.
The target client may be a client needing to perform energy cost statistics, the client may be the only natural person or legal person, the user may be a terminal for using properties, a charging interface, etc., in this embodiment, the property may be a building structure providing a usage space for a production activity, the dependency relationship may be a property relationship, and a user and a target client have a dependency relationship, that is, the property right corresponding to the user belongs to the target client, the energy involved in energy cost statistics may be various energy sources such as electric energy, photovoltaic energy, new energy, hydraulic energy, thermal energy, wind energy, etc., for example, the new energy may be a charging pile energy, biomass energy, etc., and the thermal energy source may include a gas energy, a coal-fired energy, a geothermal energy, etc.
The first node may refer to a structured representation of the target client and the second node may refer to a structured representation of the user, and the initial relationship graph data may be used to characterize structured information between the target client and all users that have a affiliation with the target client.
In particular, a single target client may correspond to at least one user, but each user may correspond to only one target client, with one-to-many relationships between target clients and users.
After the first node and the second node are constructed, node information of the first node and the second node may be set, and the node information may include identification information, which may be name information, identification information, etc. of the target client or the user.
It should be noted that, the above-mentioned multiple forms of energy cost statistics can be parallelly constructed, for example, for the same customer, a photovoltaic energy cost statistics model and a new energy electric energy charging pile energy cost statistics model can be simultaneously built, so as to realize the effects of simultaneously counting multiple forms of energy and respectively performing independent calculation according to different forms, so that the application range of energy cost statistics is wider and the application is more convenient.
Referring to fig. 3, fig. 3 shows an example graph of initial relationship graph data provided by the embodiment of the present invention, where the target client 3-a and the target client 3-B are both first nodes, the user 3-a, the user 3-B and the user 3-C are all second nodes, the target client 3-a and the user 3-a have a connection relationship, where a single target client corresponds to a single user, and the target client 3-B has a connection relationship with the user 3-B and the user 3-C, respectively, where a single target client may correspond to two users, that is, one-to-many relationship.
And the step of obtaining at least one user with a subordinate relation with any target client, taking the target client as a first node, taking each user as a second node, connecting the first node with each second node respectively, traversing all the target clients to form initial relation graph data, and carrying out structural representation on the target clients and the users related to energy cost statistics, thereby being convenient for storing and processing a plurality of target client information in parallel and improving the efficiency of subsequent energy cost statistics.
Step S202, each user is traversed, at least one metering point associated with the user is obtained, and the metering point respectively associated with each user is obtained.
The metering point may refer to an energy consumption statistics device, in this embodiment, may refer to an energy consumption statistics device such as an electric energy metering device and a thermal energy metering device, and the association relationship between the metering point and the user may refer to that the metering point is used for metering part or all of energy consumption information of the user.
Specifically, the same user may correspond to at least one metering point, but each metering point may correspond to only one target client, and the relationship between the user and the metering point is one-to-many.
And traversing each user, acquiring at least one metering point associated with the user, and obtaining the metering point respectively associated with each user, wherein the relationship between the user and the metering point is determined, so that the subsequent statistics of the energy utilization information of the user is facilitated, and the efficiency of energy cost statistics is improved.
In step S203, each metering point is used as a third node, the metering value of the corresponding metering point is used as node information of the corresponding third node, and each second node is respectively connected with each corresponding third node according to the association relationship between the user and each metering point, so as to update the initial relationship graph data and obtain updated relationship graph data.
The third node may refer to a structured representation of the metering point, the metering value may refer to a sum of energy consumption information counted by the metering point in a preset time period, and the node information of the third node may include identification information and energy consumption information, that is, the metering value.
The updated relationship graph data may be used to characterize structured information between a target client, all users that have a affiliation with the target client, and all metering points that have an association with each user.
Specifically, third nodes corresponding to metering points are constructed on the basis of initial relation diagram data, each second node is respectively connected with all third nodes with association relation to the second nodes, relation diagram data containing new nodes and new connection relation are obtained, namely the initial relation diagram data is updated, and updated relation diagram data are obtained.
Referring to fig. 4, fig. 4 shows an example graph of updated relationship graph data provided by the embodiment of the present invention, where a target client 4-a and a target client 4-B are both first nodes, a user 4-a, a user 4-B and a user 4-C are both second nodes, a measurement point 4-a, a measurement point 4-B, a measurement point 4-C and a measurement point 4-D are both third nodes, a connection relationship exists between the target client 4-a and the user 4-a, a connection relationship exists between the target client 4-B and the user 4-B, a connection relationship exists between the user 4-a and the measurement point 4-a, and a connection relationship exists between the user 4-B and the measurement point 4-B, at this time, a single user corresponds to a single measurement point, and a connection relationship exists between the user 4-C and the measurement point 4-D, and a single user corresponds to a plurality of measurement points.
And the step of updating the initial relationship diagram data by taking each metering point as a third node and taking the metering value of the corresponding metering point as node information of the corresponding third node, respectively connecting each second node with each corresponding third node according to the association relation between the user and each metering point, and updating the initial relationship diagram data, thereby updating the initial relationship diagram data through the association relation between each user and each metering point, improving the information characterization capability of the relationship diagram data, facilitating the acquisition of the energy information of a target client in a multi-stage statistical mode, and realizing a charging model taking the target client as a center, and further improving the efficiency and the accuracy of charging statistics.
Step S204, using the node information of the third node connected with each second node to perform first order aggregation processing, and updating the node information of the corresponding second node with the first order aggregation result.
Wherein, the first-order aggregation may refer to providing neighborhood information for the processed node by using all nodes of the processed node having connection relations.
Specifically, the node information may include attribute identification information, node identification information and energy consumption value, where the attribute identification information may be used to characterize attribute type information corresponding to a node, the attribute type information corresponding to a node may include three types of a target client attribute corresponding to a first node, a user attribute corresponding to a second node, and a metering point attribute corresponding to a third node, the node identification information may be used to represent a label of a node in the relationship graph data, so as to distinguish different nodes, the energy consumption value may be a statistical value of energy consumption of a user corresponding to the node, and the node information may be represented as [ I, J, K ], where I may be node identification information, in this embodiment, a value range of I is [1,2,3], respectively corresponding to three different attribute types, J may be node identification information, J is an integer greater than zero, K is a value greater than or equal to zero, for example, [2,4, 10] may represent a value of energy consumption of a user corresponding to the second node in the relationship graph data, and 4 is 10.
Before the first-order aggregation processing is performed, the node information of each first node and each second node needs to be initialized, the initialization object is the energy value in the node information, and the energy value in the node information of each first node and each second node is initialized to be zero. The energy information of the target client and the user is unknown before the first-order aggregation treatment is not performed, that is, before the charging statistics are not performed, and the energy information is represented by zero.
When the first-order aggregation processing is performed, the energy value of the other node connected with the node is used as neighborhood information for any node of the initialized initial relation diagram data, and the energy statistics value of the node is determined.
Optionally, performing the first-order aggregation processing by using node information of a third node connected to each second node, and updating the node information of the corresponding second node with the first-order aggregation result includes:
for any second node, determining all third nodes connected with the second node, carrying out weighted addition on node information of all third nodes, and determining a weighted addition result as a first aggregation result;
Updating the node information of the second nodes by the first aggregation result, traversing each second node, and obtaining the updated node information of each second node.
And determining that the connecting edge between one third node and one second node is a second connecting edge, giving a preset weight to each second connecting edge, wherein the preset weight can be 1, carrying out weighted addition according to the energy consumption values of all the third nodes and the weights of the related second connecting edges, and the first aggregation result can be used for representing the energy consumption statistical value of the second node to the application user.
Specifically, for the first
Second node, and->
The third nodes connected with the second nodes are N in total, the second node
The first aggregate result for the second nodes may be expressed as:
；
Wherein,,
can represent->
First aggregation result of second node +.>
The energy value of the second node is +.>
Can represent the energy value of the nth third node connected with the jth second node, and the value range of n is [1, N]Integer in>
Can represent and->
The weight of the second connecting edge related to the nth third node connected with the second nodes is set to be 1.
Updating the energy value of the second node by means of assignment, i.e. order 
。
According to the method, the first-order aggregation processing of the energy values for the second nodes is performed in a weighted calculation mode, so that the calculation efficiency is high, errors are not prone to occurring, and the efficiency and the accuracy of the energy cost statistics process are improved.
And the step of updating the node information of the corresponding second node by using the node information of the third node connected with each second node, and counting the energy utilization information of the metering point by using the first-order aggregation result, thereby updating the energy utilization information of the user, facilitating the subsequent provision of neighborhood information for the energy utilization information of the target client, ensuring clear structure of energy utilization statistics, being not easy to make mistakes, and improving the efficiency and accuracy of energy utilization statistics.
Step S205, the updated node information of the second node connected with each first node is utilized to perform first order aggregation processing, the node information of the corresponding first node is updated according to the first order aggregation result, and the updated node information of each first node is determined to be the charging information of the corresponding target client.
The updated node information of the second node may refer to the updated energy value of the second node after the first-order aggregation processing in the above step.
Specifically, when the node information corresponding to the first node is updated with the first-order aggregation result, the updated object is also the energy value in the node information of the first node. When the updated node information of each first node is determined to be the charging information of the corresponding target client, the charging information of the target client can be obtained by multiplying the updated energy value of the first node by the energy price.
Optionally, the first-order aggregation processing is performed by using updated node information of a second node connected to each first node, and updating the node information of the corresponding first node with the first-order aggregation result includes:
for any first node, determining all second nodes connected with the first node, carrying out weighted addition on node information updated by all second nodes, and determining a weighted addition result as a second polymerization result;
updating the node information of the first nodes by using the second aggregation result, traversing each first node, and obtaining the updated node information of each first node.
The method comprises the steps of determining that a connecting edge between one second node and one first node is a first connecting edge, giving a preset weight to each first connecting edge, wherein the preset weight can be 1, carrying out weighted addition according to the energy consumption values of all the second nodes and the weights of the first connecting edges involved by the energy consumption values, and the second polymerization result can be used for representing the energy consumption statistical value of a target client corresponding to the first node.
Specifically, for the first
First node, and->
M second nodes connected with the first node, the first node
The second aggregation result of the first nodes may be expressed as: / >
；
Wherein,,
can represent->
Second polymerization result of the first node +.>
The energy value of the first node is +.>
Can represent and->
The updated energy value of the mth second node connected with the first node, and the value range of m is [1, M]Integer in>
Can represent and->
The weight of the first connection edge related to the mth second node connected with the first node is set to be 1.
Updating the energy value of the first node by means of assignment, i.e. order
。/>
According to the method, the first-order aggregation processing of the energy values of the first node is performed in a weighted calculation mode, so that the calculation efficiency is high, errors are not prone to occurring, and the efficiency and the accuracy of the energy cost statistics process are improved.
Optionally, the energy cost statistics method in this embodiment further includes:
acquiring at least one settlement house with settlement relation with any target customer;
and taking the settlement account as a fourth node, taking the settlement value corresponding to the settlement account as node information corresponding to the fourth node, respectively connecting the first node with each fourth node according to the settlement relation between the settlement account and the target clients, and traversing all the target clients to obtain second updated relation graph data.
The settlement account may refer to a user who pays the energy cost, the fourth node may refer to a structural representation of the settlement account, the settlement value may refer to a sum of the energy cost paid by the settlement account in a preset time period, and the settlement relationship may refer to that the energy cost of the target customer is paid by the settlement account.
Specifically, the same target client may correspond to at least one settlement house, and the same settlement house may also correspond to different target clients.
On the basis of updating the relationship graph data, fourth nodes corresponding to the settlement house are constructed, each first node is respectively connected with all fourth nodes with settlement relationship, the relationship graph data containing new nodes and new connection relationship is obtained, namely, the relationship graph data is updated again, and second updated relationship graph data is obtained.
According to the embodiment, the information representation capability of the relational graph data is improved, the payment information of the target client is conveniently obtained in a multi-level statistical mode, the charging model with the target client as the center is realized, and therefore the efficiency and the accuracy of charging statistics and accounting are improved.
Optionally, after obtaining the second updated relationship diagram data, the method further includes:
Performing first-order aggregation processing by using node information of a fourth node connected with each first node, and adding a first-order aggregation result to the node information of the corresponding first node;
and determining the added node information corresponding to the first node as settlement information corresponding to the target user.
And determining that the connecting edge between one fourth node and one first node is a third connecting edge, giving a preset weight to each third connecting edge, wherein the preset weight can also be 1, and carrying out weighted addition according to the payment values of all the fourth nodes and the weights of the related third connecting edges.
Specifically, the node information of the first node after the addition may be represented as [ I, J, K, L ], where L may refer to a payment value, and L is a value greater than or equal to zero. Before the first-order aggregation processing is performed, initializing each first node information, wherein an initialization object is a payment value in the node information, and initializing the payment value in the node information of each first node to be zero.
According to the embodiment, the payment information of the user is counted, the payment information of the target client is updated, the structure of the user can be counted is clear, errors are not prone to happening, and the efficiency and the accuracy of the user can pay are improved.
Optionally, performing the first-order aggregation processing by using node information of a fourth node connected to each first node, and adding the first-order aggregation result to the node information of the corresponding first node includes:
determining a corresponding payment relationship between each settlement house and each metering point, and respectively connecting each fourth node with a third node with the payment relationship according to the payment relationship to obtain third updating diagram data;
acquiring payment information of each settlement user, and adding a payment value to node information of a third node of the corresponding settlement user according to the payment information of the corresponding settlement user;
in the third updating graph data, performing first-order aggregation processing by using node information added by a third node connected with each second node, and adding a first-order aggregation result to the node information of the corresponding second node;
and determining the node information added by the corresponding first node according to the node information added by all the second nodes.
The payment relationship may refer to that a settlement user is a target customer and can pay the fee, and the settlement user may be the target customer, the user or other users, and the payment information includes payment values corresponding to metering points.
And carrying out weighted addition according to the payment values of all the third nodes and the weights of the related second connecting edges, and adding the weighted addition result to the node information of the corresponding second nodes.
Specifically, the node information of the second node after the addition may be represented as [ I, J, K, L ], where L may refer to a payment statistics value, and L is a value greater than or equal to zero. Before the first-order aggregation processing is performed, initializing each second node information, wherein an initialization object is a payment statistical value in the node information, and initializing the payment statistical value in the node information of each second node to be zero.
Referring to fig. 5, fig. 5 shows an exemplary diagram of a relationship diagram data provided by an embodiment of the present invention, where each of a target client 5-a, a target client 5-B, and a target client 5-C is a first node, each of a user 5-a, a user 5-B, a user 5-C, and a user 5-D is a second node, each of a metering point 5-a, a metering point 5-B, a metering point 5-C, a metering point 5-D, and a metering point 5-E is a third node, and each of a settlement user 5-a, a settlement user 5-B, and a settlement user 5-C is a fourth node;
the target client 5-A has a connection relation with the user 5-A, the target client 5-B has a connection relation with the user 5-B, and the target client 5-C has a connection relation with the user 5-C and the user 5-D respectively, namely, the target client can correspond to at least one user;
The user 5-A has a connection relation with the metering point 5-A, the user 5-B has a connection relation with the metering point 5-B, the user 5-C has a connection relation with the metering point 5-C, and the user 5-D has a connection relation with the metering point 5-D and the metering point 5-E respectively, namely, the user can correspond to at least one metering point;
the metering point 5-A and the settlement house 5-A have a connection relationship, the metering point 5-B and the metering point 5-C have a connection relationship with the settlement house 5-B, the settlement house 5-A and the target customer 5-A have a payment relationship, and the settlement house 5-B and the target customer 5-C have a payment relationship respectively, namely the same settlement house can correspond to at least one target customer;
the metering point 5-D and the metering point 5-E are connected with the settlement house 5-C, so that the target customer 5-C has a payment relationship with the settlement house 5-B and the settlement house 5-C respectively, i.e. the same target customer can also correspond to at least one settlement house.
In the embodiment, the first-order aggregation processing of the second node payment value is performed in a weighted calculation mode, so that the calculation efficiency is high, errors are not prone to occurring, and the efficiency and the accuracy of the energy consumption payment statistical process are improved.
Optionally, determining the node information added by the corresponding first node according to the node information added by all the second nodes includes:
Carrying out weighted addition on node information added by all the second nodes, and determining a weighted addition result as a third polymerization result;
and determining that the third polymerization result is the node information added corresponding to the first node, and adding the third polymerization result to the node information of the first node.
And carrying out weighted addition according to the payment values of all the second nodes and the weights of the first connecting edges involved by the second nodes, wherein the third polymerization result can be used for representing the payment statistical values of the first nodes to the application energy user.
Specifically, after the payment value of the first node is obtained, that is, the payment value of the target client is obtained, the payment condition of the target client can be calculated according to the payment value of the target client and the charging value of the energy consumption, and according to the charging model established in the embodiment, the tracing of the unpaid settlement user is facilitated, and the efficiency of the energy consumption payment accounting process with the client as the center is effectively improved.
In the embodiment, the first-order aggregation processing of the first node payment value is performed in a weighted calculation mode, so that the calculation efficiency is high, errors are not prone to occurring, and the efficiency and the accuracy of the energy consumption payment statistical process with customers as centers are improved.
And the step of determining the updated node information of each first node as the charging information of the corresponding target client by using the updated node information of the second node connected with each first node to perform first-order aggregation processing and updating the node information of the corresponding first node according to the first-order aggregation result, and the step of counting the energy utilization information of the user, updating the energy utilization information of the target client, wherein the energy utilization statistics structure is clear and is not easy to make mistakes, and the efficiency and the accuracy of the energy utilization statistics are improved.
In the embodiment, the energy consumption relation is represented by the form of the graph data, so that the user relation information of a plurality of clients can be stored simultaneously, the calculation of the energy cost statistics is performed in parallel, the structure of the energy consumption relation is clearer, the calculation error of the energy cost statistics is avoided, and the efficiency and the accuracy of the energy cost statistics process are improved.
Fig. 6 shows a block diagram of an energy cost statistics device based on a graph data model according to a third embodiment of the present invention, where the energy cost statistics device is applied to a client, and a computer device corresponding to the client is connected to a server to obtain a target client, a user, a metering point, and metering values of the metering point related to energy cost statistics, and each metering point is connected to the server to upload the metering value of the corresponding metering point to the server for storage. For convenience of explanation, only portions relevant to the embodiments of the present invention are shown.
Referring to fig. 6, the energy cost statistics apparatus includes:
the data modeling module 61 is configured to obtain at least one user having a subordinate relationship with any target client, take the target client as a first node, each user as a second node, connect the first node with each second node respectively, and traverse all the target clients to form initial relationship graph data;
The metering point obtaining module 62 is configured to traverse each user, obtain at least one metering point associated with the user, and obtain metering points associated with each user respectively;
the model updating module 63 is configured to use each measurement point as a third node, use a measurement value of the corresponding measurement point as node information of the corresponding third node, and connect each second node with each corresponding third node according to an association relationship between the user and each measurement point, update the initial relationship graph data, and obtain updated relationship graph data;
a first aggregation module 64, configured to perform a first-order aggregation process using node information of a third node connected to each second node, and update the node information of the corresponding second node with a first-order aggregation result;
and the second aggregation module 65 is configured to perform first-order aggregation processing by using updated node information of the second node connected to each first node, update the node information of the corresponding first node with a first-order aggregation result, and determine that the updated node information of each first node is charging information of the corresponding target client.
Optionally, the first aggregation module 64 includes:
the first weighting unit is used for determining all third nodes connected with the second node aiming at any second node, carrying out weighted addition on node information of all third nodes, and determining a weighted addition result as a first aggregation result;
The first updating unit is used for updating the node information of the second nodes according to the first aggregation result, traversing each second node and obtaining the updated node information of each second node.
Optionally, the second polymerization module 65 includes:
the second weighting unit is used for determining all second nodes connected with the first node aiming at any first node, carrying out weighted addition on node information updated by all second nodes, and determining a result after weighted addition as a second polymerization result;
and the second updating unit is used for updating the node information of the first nodes according to the second aggregation result, traversing each first node and obtaining the updated node information of each first node.
Optionally, the energy cost statistics device further includes:
the settlement house acquisition module is used for acquiring at least one settlement house with settlement relation with any target customer;
and the graph data updating module is used for taking the settlement house as a fourth node, taking the settlement value corresponding to the settlement house as node information corresponding to the fourth node, respectively connecting the first node with each fourth node according to the settlement relation between the settlement house and the target clients, and traversing all the target clients to obtain second updated relation graph data.
Optionally, the energy cost statistics device further includes:
the third aggregation module is used for performing first-order aggregation processing by using node information of a fourth node connected with each first node, and adding a first-order aggregation result to the node information of the corresponding first node;
and the settlement information determining module is used for determining the added node information corresponding to the first node as settlement information corresponding to the target user.
Optionally, the third mold block includes:
the payment relation determining unit is used for determining the corresponding payment relation between each settlement house and each metering point, and according to the payment relation, each fourth node is connected with a third node with the payment relation, so as to obtain third updating diagram data;
the payment value adding unit is used for obtaining payment information of each settlement user and adding the payment value into node information of a third node of the corresponding settlement user according to the payment information of the corresponding settlement user;
a fourth aggregation unit, configured to perform first-order aggregation processing by using node information added by a third node connected to each second node in the third update map data, and add a first-order aggregation result to the node information of the corresponding second node;
And the node information adding unit is used for determining the node information added by the corresponding first node according to the node information added by all the second nodes.
Optionally, the node information adding unit includes:
a weighted addition subunit, configured to perform weighted addition on node information added by all the second nodes, and determine that a result after weighted addition is a third polymerization result;
an addition information determining subunit, configured to determine that the third polymerization result is node information added corresponding to the first node, and add the third polymerization result to the node information of the first node.
It should be noted that, because the content of information interaction, execution process and the like between the modules, units and sub-units is based on the same concept as the method embodiment of the present invention, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein.
Fig. 7 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention. As shown in fig. 7, the computer device of this embodiment includes: at least one processor (only one shown in fig. 7), a memory, and a computer program stored in the memory and executable on the at least one processor, the processor executing the computer program to perform the steps of any of the various energy cost statistics method embodiments described above.
The computer device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that fig. 7 is merely an example of a computer device and is not intended to be limiting, and that a computer device may include more or fewer components than shown, or may combine certain components, or different components, such as may also include a network interface, a display screen, an input device, and the like.
The processor may be a CPU, but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory includes a readable storage medium, an internal memory, etc., where the internal memory may be the memory of the computer device, the internal memory providing an environment for the execution of an operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments may be an external storage device of the computer device, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. that are provided on the computer device. Further, the memory may also include both internal storage units and external storage devices of the computer device. The memory is used to store an operating system, application programs, boot loader (BootLoader), data, and other programs such as program codes of computer programs, and the like. The memory may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present invention. The specific working process of the units and modules in the above device may refer to the corresponding process in the foregoing method embodiment, which is not described herein again. The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above-described embodiment, and may be implemented by a computer program to instruct related hardware, and the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of the method embodiment described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code, a recording medium, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a U-disk, removable hard disk, magnetic or optical disk, etc. In some jurisdictions, computer readable media may not be electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The present invention may also be implemented as a computer program product for implementing all or part of the steps of the method embodiments described above, when the computer program product is run on a computer device, causing the computer device to execute the steps of the method embodiments described above.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided by the present invention, it should be understood that the disclosed apparatus/computer device and method may be implemented in other manners. For example, the apparatus/computer device embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (10)
1. The energy cost statistical method based on the graph data model is characterized by comprising the following steps of:
acquiring at least one user with a subordinate relation with any target client, taking the target client as a first node, taking each user as a second node, respectively connecting the first node with each second node, traversing all target clients, and forming initial relation graph data;
Traversing each user, and acquiring at least one metering point associated with the user to obtain metering points respectively associated with each user;
taking each metering point as a third node, taking the metering value of the corresponding metering point as node information of the corresponding third node, respectively connecting each second node with each corresponding third node according to the association relation between a user and each metering point, and updating the initial relation graph data to obtain updated relation graph data;
performing first-order aggregation processing by using node information of a third node connected with each second node, and updating the node information of the corresponding second node by using a first-order aggregation result;
and carrying out first-order aggregation processing by using updated node information of a second node connected with each first node, updating the node information of the corresponding first node by using a first-order aggregation result, and determining the updated node information of each first node as charging information of the corresponding target client.
2. The energy cost statistics method according to claim 1, wherein the performing a first order aggregation process using node information of a third node connected to each of the second nodes, and updating the node information of the corresponding second node with the first order aggregation result comprises:
For any second node, determining all third nodes connected with the second node, carrying out weighted addition on node information of all third nodes, and determining a weighted addition result as a first aggregation result;
updating the node information of the second nodes by the first aggregation result, traversing each second node, and obtaining the updated node information of each second node.
3. The energy cost statistics method according to claim 1, wherein the performing a first order aggregation process using updated node information of the second node connected to each first node, and updating the node information of the corresponding first node with the first order aggregation result comprises:
for any first node, determining all second nodes connected with the first node, carrying out weighted addition on node information updated by all second nodes, and determining a weighted addition result as a second polymerization result;
updating the node information of the first nodes by the second aggregation result, traversing each first node, and obtaining the updated node information of each first node.
4. A method of energy cost statistics according to any one of claims 1 to 3, further comprising:
Acquiring at least one settlement house with settlement relation with any target customer;
and taking the settlement account as a fourth node, taking the settlement value corresponding to the settlement account as node information corresponding to the fourth node, respectively connecting the first node with each fourth node according to the settlement relation between the settlement account and the target clients, and traversing all the target clients to obtain second updated relation graph data.
5. The energy cost statistics method according to claim 4, further comprising, after said obtaining the second updated map data:
performing first-order aggregation processing by using node information of a fourth node connected with each first node, and adding a first-order aggregation result to the node information of the corresponding first node;
and determining the added node information corresponding to the first node as settlement information corresponding to the target user.
6. The energy cost statistics method according to claim 5, wherein the performing a first order aggregation process using node information of a fourth node connected to each first node, and adding the first order aggregation result to the node information of the corresponding first node comprises:
determining a corresponding payment relationship between each settlement house and each metering point, and connecting each fourth node with a third node with the payment relationship according to the payment relationship to obtain third updating diagram data;
Acquiring payment information of each settlement user, and adding a payment value to node information of a third node of the corresponding settlement user according to the payment information of the corresponding settlement user;
in the third updating graph data, performing first-order aggregation processing by using node information added by a third node connected with each second node, and adding a first-order aggregation result to the node information of the corresponding second node;
and determining the node information added by the corresponding first node according to the node information added by all the second nodes.
7. The energy cost statistics method according to claim 6, wherein the determining the node information added to the corresponding first node according to the node information added to all the second nodes includes:
carrying out weighted addition on node information added by all the second nodes, and determining a weighted addition result as a third polymerization result;
and determining the third polymerization result as the node information added by the corresponding first node, and adding the third polymerization result to the node information of the first node.
8. An energy cost statistics device based on a graph data model, characterized in that the energy cost statistics device comprises:
The data modeling module is used for acquiring at least one user with a subordinate relation with any target client, taking the target client as a first node, taking each user as a second node, respectively connecting the first node with each second node, traversing all target clients and forming initial relation graph data;
the metering point acquisition module is used for traversing each user, acquiring at least one metering point associated with the user, and obtaining the metering point respectively associated with each user;
the model updating module is used for taking each metering point as a third node, taking the metering value of the corresponding metering point as node information of the corresponding third node, and respectively connecting each second node with each corresponding third node according to the association relation between a user and each metering point to update the initial relation diagram data to obtain updated relation diagram data;
the first aggregation module is used for carrying out first-order aggregation processing by utilizing node information of a third node connected with each second node, and updating the node information of the corresponding second node by a first-order aggregation result;
and the second aggregation module is used for carrying out first-order aggregation processing by using updated node information of the second nodes connected with each first node, updating the node information of the corresponding first nodes by using a first-order aggregation result, and determining that the updated node information of each first node is charging information of the corresponding target client.
9. A computer device comprising a processor, a memory and a computer program stored in the memory and executable on the processor, the processor implementing the energy cost statistics method according to any of claims 1 to 7 when the computer program is executed by the processor.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the energy cost statistics method according to any one of claims 1 to 7.
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