Energy statistical method based on multi-scale tree-shaped organization structure

Abstract

The invention discloses an energy statistical method based on a multi-scale tree-shaped organization structure. The method comprises the following steps: taking different energy sources as statistical scales according to energy source types; based on the hierarchical relationship of the energy consumption subitems, energy consumption subitem statistical trees of different energy types are constructed, and each energy consumption subitem statistical tree corresponds to one energy type; constructing an energy consumption unit statistical tree based on the hierarchical relationship of the energy statistical units; configuring an affiliation relationship between the statistical instrument and the energy type, the energy sub-item and the energy statistical unit, and establishing a relationship model; and finally, on the basis of data acquired by the statistical instrument, respectively counting the energy consumption data of each energy consumption unit for any energy type and the energy consumption data of different energy sub-items in any set range by a calculation unit according to the relation model, and generating different energy consumption statistical meters. According to the invention, multi-scale statistics of energy consumption can be realized, and the fineness and accuracy of a statistical result are improved.

Description

Technical Field

[0001] The invention relates to an energy statistics method, in particular to an energy statistics method based on a multi-scale tree-like organizational structure. Background Art

[0002] With the society's increasing attention to energy-saving policies and energy consumption, the current energy consumption management methods of enterprises and municipalities also need to develop in the direction of comprehensiveness, accuracy and refinement. The traditional method of directly collecting energy consumption of specified units or ranges through statistical instruments can no longer meet their management needs. On this basis, Patent 202110542239.1 discloses an energy statistics method, which constructs energy unit statistical trees and energy pedigree tree factory models based on the hierarchical relationship of energy statistical units and the distribution of energy statistical instruments, so that it can calculate the energy consumption data of energy consumption units at different levels based on the energy consumption data collected by each statistical instrument, that is, to achieve refined management of energy consumption data.

[0003] However, the defects of the above statistical method are that, first of all, this statistical method can only count the overall energy consumption data of energy-consuming units, but cannot separately calculate the energy consumption data of each energy-consuming unit for different energy consumption items such as lighting electricity consumption, electricity consumption for specific equipment, and electricity consumption for air conditioning. As a result, there are problems of single statistical scale and poor precision.

[0004] Secondly, the scalability of this statistical method and its effect when used for energy consumption analysis are relatively poor. The reason is that energy-consuming units will continuously upgrade their equipment during the development process, which requires manufacturers to modify their algorithms based on new facilities, and the final statistical results are also difficult to reflect the energy consumption of these new facilities.

[0005] In addition, unreasonable energy consumption data often occurs during the collection process of statistical instruments. This is partly due to inaccurate collection data caused by failure of statistical instruments, partly due to abnormal energy consumption caused by equipment failure, and partly due to normal changes in energy consumption due to special circumstances such as holidays. The above statistical methods are unable to analyze and process this part of energy consumption data, thereby reducing the accuracy of the statistical results.

[0006] Therefore, existing statistical methods have problems of single statistical scale, poor precision and poor accuracy. Summary of the invention

[0007] The purpose of the present invention is to provide an energy statistics method based on a multi-scale tree-like organizational structure, which can realize multi-scale statistics of energy consumption and improve the precision and accuracy of statistical results.

[0008] The technical solution of the present invention is an energy statistics method based on a multi-scale tree-like organizational structure, comprising the following steps:

[0009] A. Use different energy sources as statistical scales according to energy types;

[0010] B. Based on the hierarchical relationship of energy consumption sub-items, energy consumption sub-item statistical trees of different energy types are constructed respectively, and each energy consumption sub-item statistical tree corresponds to one energy type;

[0011] C. Based on the hierarchical relationship of energy statistical units, construct an energy consumption unit statistical tree;

[0012] D. Configure the relationship between statistical instruments and energy types, energy sub-items and energy statistical units, and establish a relationship model;

[0013] E. Based on the data collected by the statistical instrument, the calculation unit counts the energy consumption data of each energy-consuming unit for any energy type and the energy consumption data of different energy items within any set range according to the relational model, and generates different energy consumption statistical tables.

[0014] In the aforementioned energy statistics method based on a multi-scale tree-like organizational structure, the nodes in the energy consumption sub-item statistics tree are set as sub-item nodes, and the nodes in the energy consumption unit statistics tree are set as unit nodes; each statistical instrument corresponds to a sub-item node and a unit node respectively.

[0015] In the aforementioned energy statistics method based on a multi-scale tree-like organizational structure, any sub-item node and any unit node generate an energy consumption statistics table after combination, and the content of the energy consumption statistics table is the energy consumption data of the specified energy consumption unit for the specified energy consumption sub-item.

[0016] In the aforementioned energy statistics method based on a multi-scale tree-like organizational structure, when the unit node corresponding to the energy consumption statistics table is a leaf unit node of the energy consumption unit statistics tree,

[0017] If the sub-item node corresponding to the energy consumption statistical table is a leaf sub-item node of the energy consumption statistical tree, the content of the energy consumption statistical table is the collected data of the statistical instrument;

[0018] If the sub-item node corresponding to the energy consumption statistical meter is the parent sub-item node of the energy consumption sub-item statistical tree, and the parent sub-item node is not bound to a statistical instrument, the content of the energy consumption statistical meter is the sum of the statistical instrument collection data of the child sub-item nodes corresponding to the parent sub-item node;

[0019] If the sub-item node corresponding to the energy consumption statistical meter is the parent sub-item node of the energy consumption sub-item statistical tree, and the parent sub-item node is bound to a statistical instrument, the content of the energy consumption statistical meter is the data collected by the statistical instrument corresponding to the parent sub-item node.

[0020] In the aforementioned energy statistics method based on a multi-scale tree-like organizational structure, when the unit node corresponding to the energy consumption statistics table is the parent unit node of the energy consumption unit statistics tree,

[0021] If the parent unit node is bound to any statistical instrument of energy consumption sub-item, the content of the energy consumption statistical table generated by the sub-item node and the parent unit node is the collected data of the statistical instrument;

[0022] If the parent unit node is not bound to any statistical instrument for any energy consumption sub-item, the energy consumption statistical table content generated by the sub-item node and the parent unit node is the sum of the energy consumption statistical table content of each child unit node corresponding to the parent unit node under the energy consumption sub-item.

[0023] In the aforementioned energy statistics method based on a multi-scale tree-like organizational structure, the method for generating an energy consumption statistics table in step E comprises the following steps:

[0024] E11. The calculation unit obtains the binding relationship between each energy consumption statistical table and energy sub-item and energy statistical unit in the current state through the relational model;

[0025] E12. Construct the tree structure of energy consumption units into a unit tree structure storage, and inject table tool mapping into each organizational statistical unit;

[0026] E13. Construct the tree structure of energy consumption sub-items into a sub-item tree structure for storage, and inject the meter mapping into each sub-item statistical unit;

[0027] E14. The calculation unit traverses the unit tree structure storage and the item tree structure storage respectively to generate an energy consumption statistical table with each energy consumption unit and energy consumption item.

[0028] In the aforementioned energy statistics method based on a multi-scale tree-like organizational structure, the unit tree structure storage and the item tree structure storage are constructed from the bottom up using a recursive method.

[0029] In the aforementioned energy statistics method based on a multi-scale tree-like organizational structure, the statistical instrument in step E collects data through the following steps:

[0030] E21. Calculate the energy consumption of each statistical instrument separately;

[0031] E22. Based on the energy quantity of the statistical instrument, obtain the collected data at the start and end of a cycle. If the collected data is greater than 0, go directly to the next step. If the collected data is not greater than 0, use the second value in reverse order;

[0032] E23. Calculate the increment of the statistical instrument within the period. If the calculation result is not a negative value, directly obtain the collected data. If the calculation result is a negative value, assign the calculation result increment to 0 and then recalculate the increment of the statistical instrument within the period to obtain the collected data.

[0033] Compared with the prior art, the present invention has the following characteristics:

[0034] (1) The present invention sets up an energy consumption unit statistical tree and an energy consumption sub-item statistical tree in coordination, and generates an energy consumption statistical table on this basis, so that the energy consumption statistical table can correspond to a sub-item node and a unit node respectively; on this basis, not only the overall energy consumption data of any energy consumption unit can be generated, but also the energy consumption data of any energy consumption sub-item within a specified range can be generated, thereby effectively improving the precision and accuracy of its statistical results;

[0035] (2) By limiting the structure of the energy consumption statistics table generation method under different unit nodes and sub-item node conditions, it is also possible to optimize the priority of the energy consumption statistics table generation method, thereby further improving the accuracy of the energy consumption statistics table;

[0036] (3) By limiting the data collection method of the statistical instrument, it is also possible to judge and filter abnormal data, thereby further improving the statistical accuracy of the present invention;

[0037] Therefore, the present invention can realize multi-scale statistics of energy consumption and improve the precision and accuracy of the statistical results. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is the workflow diagram when the energy consumption statistics table is generated;

[0039] Figure 2 It is the workflow diagram of the statistical instrument when collecting data;

[0040] Figure 3 It is the structure diagram of the statistical tree of energy consumption units in the experimental example;

[0041] Figure 4 It is the structural diagram of the energy consumption sub-item statistical tree in the experimental example. DETAILED DESCRIPTION

[0042] The present invention is further described below in conjunction with the accompanying drawings and embodiments, but they are not intended to limit the present invention.

[0043] Embodiment: An energy statistics method based on a multi-scale tree-like organizational structure comprises the following steps:

[0044] A. Use different energy sources as statistical scales according to energy types;

[0045] B. Based on the hierarchical relationship of energy consumption sub-items, energy consumption sub-item statistical trees of different energy types are constructed respectively, and each energy consumption sub-item statistical tree corresponds to one energy type;

[0046] C. Based on the hierarchical relationship of energy statistical units, construct an energy consumption unit statistical tree;

[0047] D. Configure the relationship between statistical instruments and energy types, energy sub-items and energy statistical units, and establish a relationship model;

[0048] E. Based on the data collected by the statistical instrument, the calculation unit counts the energy consumption data of each energy-consuming unit for any energy type and the energy consumption data of different energy items within any set range according to the relational model, and generates different energy consumption statistical tables.

[0049] The nodes in the energy consumption sub-item statistical tree are assumed to be sub-item nodes, and the nodes in the energy consumption unit statistical tree are assumed to be unit nodes; each statistical instrument corresponds to a sub-item node and a unit node respectively.

[0050] Any sub-item node and any unit node are combined to generate an energy consumption statistics table, the content of which is the energy consumption data of the specified energy consumption unit for the specified energy consumption sub-item.

[0051] When the unit node corresponding to the energy consumption statistics table is the leaf unit node of the energy consumption unit statistics tree,

[0052] If the sub-item node corresponding to the energy consumption statistical table is a leaf sub-item node of the energy consumption statistical tree, the content of the energy consumption statistical table is the collected data of the statistical instrument;

[0053] If the sub-item node corresponding to the energy consumption statistical meter is the parent sub-item node of the energy consumption sub-item statistical tree, and the parent sub-item node is not bound to a statistical instrument, the content of the energy consumption statistical meter is the sum of the statistical instrument collection data of the child sub-item nodes corresponding to the parent sub-item node;

[0054] If the sub-item node corresponding to the energy consumption statistical meter is the parent sub-item node of the energy consumption sub-item statistical tree, and the parent sub-item node is bound to a statistical instrument, the content of the energy consumption statistical meter is the data collected by the statistical instrument corresponding to the parent sub-item node.

[0055] When the unit node corresponding to the energy consumption statistics table is the parent unit node of the energy consumption unit statistics tree,

[0056] If the parent unit node is bound to any statistical instrument of energy consumption sub-item, the content of the energy consumption statistical table generated by the sub-item node and the parent unit node is the collected data of the statistical instrument;

[0057] If the parent unit node is not bound to any statistical instrument for any energy consumption sub-item, the energy consumption statistical table content generated by the sub-item node and the parent unit node is the sum of the energy consumption statistical table content of each child unit node corresponding to the parent unit node under the energy consumption sub-item.

[0058] The method for generating the energy consumption statistics table in step E is as follows: Figure 1 As shown, the following steps are included:

[0059] E11. The calculation unit obtains the binding relationship between each energy consumption statistical table and energy sub-item and energy statistical unit in the current state through the relational model;

[0060] E12. Construct the tree structure of energy consumption units into a unit tree structure storage, and inject table tool mapping into each organizational statistical unit;

[0061] E13. Construct the tree structure of energy consumption sub-items into a sub-item tree structure for storage, and inject the meter mapping into each sub-item statistical unit;

[0062] E14. The calculation unit traverses the unit tree structure storage and the item tree structure storage respectively to generate an energy consumption statistical table with each energy consumption unit and energy consumption item.

[0063] The unit tree structure storage and the item tree structure storage are constructed from bottom to top using a recursive method; if the computing unit itself is bound to an energy consumption statistics table, the energy consumption statistics table is directly mapped, and the formula is:

[0064]

[0065] Among them, [1,n] represents the table instrument bound to itself, and [n+1,m] represents the table instrument bound to its children.

[0066] In step E, the statistical instrument collects data through the following steps: Figure 2 As shown:

[0067] E21. Calculate the energy consumption of each statistical instrument separately;

[0068] E22. Based on the energy quantity of the statistical instrument, obtain the collected data at the start and end of a cycle. If the collected data is greater than 0, directly proceed to the next step. If the collected data is not greater than 0, use the reverse second value, which is the reverse second collection value of the historical data;

[0069] E23. Calculate the increment of the statistical instrument within the period. If the calculation result is not a negative value, directly obtain the collected data. If the calculation result is a negative value, assign the calculation result increment to 0 and then recalculate the increment of the statistical instrument within the period to obtain the collected data. Assigning an increment value of 0 means assigning the calculation result to 0.

[0070] Manufacturers can also set corresponding data collection thresholds based on different dates. If the collected data exceeds the set threshold, an abnormal alarm will be issued.

[0071] Working principle of the present invention: The present invention coordinates the statistical instrument, the energy consumption unit statistical tree and the energy consumption sub-item statistical tree, so that the calculation unit calculates the energy consumption data of any energy-consuming unit for any energy sub-item based on the energy consumption unit statistical tree and the energy consumption sub-item statistical tree, thereby effectively improving its statistical comprehensiveness and precision compared with the existing energy statistical methods.

[0072] Experimental example: Set energy consumption unit A, energy consumption unit B, energy consumption unit A-1, energy consumption unit A-2, energy consumption unit B-1, and energy consumption unit B-2; where energy consumption unit A is area A, energy consumption unit A-1 is area A1, energy consumption unit A-2 is area A2, energy consumption unit B is household B, energy consumption unit B-1 is household B1, and energy consumption unit B-2 is household B2. Based on the above, an energy consumption unit statistical tree is established. The energy consumption unit statistical tree is as follows: Figure 3 shown.

[0073] Take electric energy as the energy type, set the energy consumption sub-item as electric energy sub-item, set electric energy sub-item H, electric energy sub-item I, electric energy sub-item H-1, electric energy sub-item H-2, electric energy sub-item I-1, and electric energy sub-item I-2; among which electric energy sub-item H is lighting, electric energy sub-item H-1 is lighting 1, electric energy sub-item H-2 is lighting 2, electric energy sub-item I is socket, electric energy sub-item I-1 is socket 1, and electric energy sub-item I-2 is socket 2. Based on the above, an energy consumption sub-item statistical tree is established. The energy consumption sub-item statistical tree is as follows: Figure 4 shown.

[0074] Bind the statistical instruments to the electric energy sub-items of energy consumption unit A, energy consumption unit A-1, energy consumption unit A-2, energy consumption unit B-1 and energy consumption unit B-2, electric energy sub-item H, electric energy sub-item H-1, electric energy sub-item H-2, electric energy sub-item I-1 and electric energy sub-item I-2 respectively.

[0075] When it is necessary to count the lighting 1 power consumption of household A1 per unit time, the calculation unit generates the corresponding energy consumption statistical table according to the relational model, with the energy consumption unit B-1 as the unit node and the electric energy sub-item H-1 as the sub-item node. The content of the energy consumption statistical table is the collected data of the statistical instrument corresponding to the electric energy sub-item H-1 in the energy consumption unit B-1.

[0076] When it is necessary to count the lighting power consumption of household B1 per unit time, the calculation unit generates the corresponding energy consumption statistical table according to the relational model, with energy consumption unit B-1 as the unit node and electric energy sub-item H as the sub-item node. The content of the energy consumption statistical table is the collected data of the statistical instrument corresponding to the electric energy sub-item H in the energy consumption unit B-1.

[0077] When it is necessary to count the socket power consumption of household B1 per unit time, the calculation unit generates the corresponding energy consumption statistical meter according to the relational model, with the energy consumption unit B-1 as the unit node and the electric energy sub-item I as the sub-item node. The content of the energy consumption statistical meter is the sum of the collected data of the statistical meter corresponding to the electric energy sub-item I-1 and the statistical meter corresponding to the electric energy sub-item I-2 in the energy consumption unit B-1.

[0078] When it is necessary to count the lighting power consumption of area A per unit time, the calculation unit generates a corresponding energy consumption statistical table based on the relationship model, with energy consumption unit A as the unit node and electric energy sub-item H as the sub-item node. The content of the energy consumption statistical table is the collected data of the statistical instrument corresponding to the electric energy sub-item H in the energy consumption unit A.

[0079] When it is necessary to count the socket power consumption of household B in a unit time, the calculation unit generates a corresponding energy consumption statistical table according to the relational model, with energy consumption unit B as the unit node and electric energy sub-item I as the sub-item node. The content of the energy consumption statistical table is the sum of the energy consumption data of electric energy sub-item I in energy consumption unit B-1 and the energy consumption data of electric energy sub-item I in energy consumption unit B-2. The energy consumption data of electric energy sub-item I in energy consumption unit B-1 is the sum of the collected data of the statistical instrument corresponding to electric energy sub-item I-1 in energy consumption unit B-1 and the collected data of the statistical instrument corresponding to electric energy sub-item I-2 in energy consumption unit B-1; the energy consumption data of electric energy sub-item I in energy consumption unit B-2 is the sum of the collected data of the statistical instrument corresponding to electric energy sub-item I-1 in energy consumption unit B-2 and the collected data of the statistical instrument corresponding to electric energy sub-item I-2 in energy consumption unit B-2. That is, the collected data of the four statistical instruments are added to obtain the statistical socket power consumption of household B in a unit time.

Claims

1. An energy statistics method based on a multi-scale tree-like organizational structure, characterized in that: The following steps are involved: A. Use different energy sources as statistical scales according to energy types; B. Based on the hierarchical relationship of energy consumption sub-items, energy consumption sub-item statistical trees of different energy types are constructed respectively, and each energy consumption sub-item statistical tree corresponds to one energy type; C. Based on the hierarchical relationship of energy statistical units, construct an energy consumption unit statistical tree; D. Configure the relationship between statistical instruments and energy types, energy sub-items and energy statistical units, and establish a relationship model; E. Based on the data collected by the statistical instrument, the calculation unit counts the energy consumption data of each energy-consuming unit for any energy type and the energy consumption data of different energy items within any set range according to the relational model, and generates different energy consumption statistical tables.

2. The energy statistics method based on a multi-scale tree-like organizational structure according to claim 1 is characterized by: The nodes in the energy consumption sub-item statistical tree are assumed to be sub-item nodes, and the nodes in the energy consumption unit statistical tree are assumed to be unit nodes; each statistical instrument corresponds to a sub-item node and a unit node respectively.

3. The energy statistics method based on a multi-scale tree-like organizational structure according to claim 2 is characterized by: Any sub-item node and any unit node are combined to generate an energy consumption statistics table, the content of which is the energy consumption data of the specified energy consumption unit for the specified energy consumption sub-item.

4. The energy statistics method based on a multi-scale tree-like organizational structure according to claim 3 is characterized by: When the unit node corresponding to the energy consumption statistics table is the leaf unit node of the energy consumption unit statistics tree, If the sub-item node corresponding to the energy consumption statistical table is a leaf sub-item node of the energy consumption statistical tree, the content of the energy consumption statistical table is the collected data of the statistical instrument; If the sub-item node corresponding to the energy consumption statistical meter is the parent sub-item node of the energy consumption sub-item statistical tree, and the parent sub-item node is not bound to a statistical instrument, the content of the energy consumption statistical meter is the sum of the statistical instrument collection data of the child sub-item nodes corresponding to the parent sub-item node; If the sub-item node corresponding to the energy consumption statistical meter is the parent sub-item node of the energy consumption sub-item statistical tree, and the parent sub-item node is bound to a statistical instrument, the content of the energy consumption statistical meter is the data collected by the statistical instrument corresponding to the parent sub-item node.

5. The energy statistics method based on a multi-scale tree-like organizational structure according to claim 3 is characterized by: When the unit node corresponding to the energy consumption statistics table is the parent unit node of the energy consumption unit statistics tree, If the parent unit node is bound to any statistical instrument of energy consumption sub-item, the content of the energy consumption statistical table generated by the sub-item node and the parent unit node is the collected data of the statistical instrument; If the parent unit node is not bound to any statistical instrument for any energy consumption sub-item, the energy consumption statistical table content generated by the sub-item node and the parent unit node is the sum of the energy consumption statistical table content of each child unit node corresponding to the parent unit node under the energy consumption sub-item.

6. The energy statistics method based on a multi-scale tree-like organizational structure according to claim 1 is characterized in that: The method for generating the energy consumption statistics table in step E comprises the following steps: E11. The calculation unit obtains the binding relationship between each energy consumption statistical table and energy sub-item and energy statistical unit in the current state through the relational model; E12. Construct the tree structure of energy consumption units into a unit tree structure storage, and inject table tool mapping into each organizational statistical unit; E13. Construct the tree structure of energy consumption sub-items into a sub-item tree structure for storage, and inject the meter mapping into each sub-item statistical unit; E14. The calculation unit traverses the unit tree structure storage and the item tree structure storage respectively to generate an energy consumption statistical table with each energy consumption unit and energy consumption item.

7. The energy statistics method based on a multi-scale tree-like organizational structure according to claim 6 is characterized by: The unit tree structure storage and the item tree structure storage are constructed from bottom to top using a recursive method.

8. The energy statistics method based on a multi-scale tree-like organizational structure according to claim 1 is characterized in that: In step E, the statistical instrument collects data by the following steps: E21. Calculate the energy consumption of each statistical instrument separately; E22. Based on the energy quantity of the statistical instrument, obtain the collected data at the start and end of a cycle. If the collected data is greater than 0, go directly to the next step. If the collected data is not greater than 0, use the second value in reverse order; E23. Calculate the increment of the statistical instrument within the period. If the calculation result is not a negative value, directly obtain the collected data. If the calculation result is a negative value, assign the calculation result increment to 0 and then recalculate the increment of the statistical instrument within the period to obtain the collected data.

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