Digital intelligent electric power financial reconciliation management system

Abstract

The invention relates to the technical field of electric power financial management, in particular to a digital intelligent electric power financial reconciliation management system, which comprises the following steps of: processing reconciliation data volume and regional power consumption in each fixed time period to obtain unit power consumption bill amount, obtaining an electric power prediction curve according to power grid load data and a power grid prediction model, and calculating the power consumption bill amount according to the electric power prediction curve. After a power prediction area is divided according to unit duration, bill data estimation quantity of a fixed time period can be accurately obtained through combination of unit data quantity and regional power consumption, a power grid load curve is reasonably divided into a low-load time period and a full-load time period, and continuous low-load time periods are combined to determine an account checking processing time period. By calculating and comparing the comprehensive margin value and the to-be-processed data volume in the reconciliation processing time period, the space margin of resource preparation in the reconciliation processing time period can be accurately determined, so that a power system can reasonably allocate resources according to the preset space margin, and efficient and stable operation of power financial reconciliation work is further guaranteed.

Description

Technical Field

[0001] This invention relates to the field of power financial management technology, and in particular to a digital and intelligent power financial reconciliation management system. Background Technology

[0002] In the power industry, local power companies have completed the informatization of their marketing operations, establishing a complete set of information systems, such as marketing systems, financial systems, and data collection systems.

[0003] The prior art CN112396412A discloses a method and system for monitoring anomalies during the reconciliation process between a power system and a bank, including the following steps: Step S10, the power company's marketing server sends a detailed file of electricity fee deductions to the bank's back-end host; Step S11, the bank's back-end host returns the reconciliation details file to the marketing server; Step S12, after the bank transfers the deducted fees to the power company's bank account, it sends the transaction details and electronic receipts for each transaction to the financial server; Step S13, the financial server, based on the received transaction details, performs anomaly monitoring, generates a reconciliation list, and pushes it to the marketing server; Step S14, the marketing server performs reconciliation processing on the reconciliation list; Step S15, the financial server performs financial recording processing.

[0004] However, when the power grid is under high load, a large number of power electronic financial bills will be generated at the same time. If the power electronic financial bills are reconciled in real time, the load during the high load period will be increased again, which will lead to system overload and reduce the overall operating efficiency of the system. Summary of the Invention

[0005] The purpose of this invention is to solve the problems in the background art by proposing a digital and intelligent power financial reconciliation management system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A digital and intelligent power financial reconciliation management system includes: The information analysis module is used to acquire historical data information within a valid time period. It also sets the period and fixed time period, processes the reconciliation data volume and regional power consumption in each fixed time period to obtain the unit power consumption bill volume, and then analyzes the unit power consumption bill volume to determine the unit data volume of financial reconciliation in each fixed time period. The data estimation module is used to learn and train on the power grid load data to obtain the power grid prediction model. Based on the power grid prediction model, the power grid load curve of the monitoring area on the same day is predicted to obtain the power prediction curve. Then, the power prediction area is divided according to the unit time length to obtain fixed time periods. The regional power consumption in the same fixed time period is multiplied by the unit data volume to obtain the billing data estimation volume for the fixed time period. The resource allocation module is used to obtain the power grid load curve and billing data estimation. First, based on the regional power consumption of each fixed period in the power grid load curve, the fixed period is divided into low load period and full load period. At the same time, consecutive low load periods are merged to obtain the reconciliation processing period. Then, the comprehensive margin value and the amount of data to be processed in the reconciliation processing period are calculated separately, and the comprehensive margin and the amount of data to be processed are compared to determine the space margin for resource preparation in the reconciliation processing period.

[0007] As a further aspect of the present invention, the method for obtaining the unit power consumption bill includes: S1: Using the current time as the base time, obtain historical data information within the effective time period, extract the power grid load data within the effective time period, and divide the power grid load data according to the cycle time, with the cycle time set to 1 natural day; One hour is set as the unit of time, and then the periodic time is divided again according to the unit of time to obtain n fixed time periods, where n takes the value of 24; The power grid load data is statistically analyzed according to the time node of each unit time to obtain the regional power consumption at different time nodes. Furthermore, the regional power consumption refers to the total power consumption of power users in the monitored area at different time nodes with unit time as the time length. A fixed time period corresponds to a regional power consumption. Then, obtain the financial reconciliation information from the historical data, and based on the length of the unit time, count the amount of bill information for each fixed period to obtain the amount of reconciliation data at different time nodes. Furthermore, a fixed period corresponds to a reconciliation data amount. S2: Arrange the fixed time periods within each cycle in chronological order, and assign the position number of each fixed time period to i in chronological order, where i∈[1,n]; Arbitrarily select a fixed time period i and mark this fixed time period i as the target time period. Obtain all target time periods within the valid time and extract the regional power consumption and reconciliation data volume of the target time period. Divide the amount of reconciliation data in each target time period by the regional electricity consumption, and mark the result as the unit electricity consumption bill amount ZDj, ​​where j represents the number of different target time periods, and j∈[1,J], indicating that there are a total of J target time periods within the effective time.

[0008] As a further aspect of the present invention, the method for determining the unit data volume of financial reconciliation over a fixed period includes: Once the unit electricity consumption bill ZDj is calculated, the arithmetic mean of the unit electricity consumption ZDj is calculated and the result is marked as Zp. Then, the formula is used... The standard deviation Bc is obtained; Based on the normal distribution processing method, the normal interval Q is set. In this embodiment, k is set to 2; The unit electricity consumption bill ZDj is compared with the normal interval Q. Data whose unit electricity consumption bill ZDj belongs to the normal interval Q are selected and marked as normal data ZDm, m∈[1,M], M represents the total number of normal data, and M≤J. Among them, the normal data ZDm are all greater than 0. Extract normal data ZDm and use the formula The unit data volume Gi for the target time period i is obtained. Furthermore, the unit data volume Gi is the geometric mean of the unit electricity consumption bill for the target time period i.

[0009] As a further aspect of the present invention, the method for obtaining the power prediction curve includes: Extract the grid load data within the effective time period from historical data information, set one cycle time as a loop, and integrate the grid load data in one cycle time into a set of data; Time was set as the independent variable, and power grid load data was set as the dependent variable. The J sets of data within the effective time period were divided into an experimental group and a control group. Then, based on the data in the experimental group and the control group, an artificial neural network method was used for learning and training to obtain a power grid prediction model.

[0010] As a further aspect of the present invention, the method for obtaining the estimated amount of billing data for a fixed time period includes: Based on the power grid forecasting model, the power grid load curve of the monitoring area on that day is predicted to obtain the power forecast curve; Based on the power forecast curve, the real-time regional power consumption of the monitored area for each fixed time period i on the same day is obtained, and the real-time regional power consumption of each fixed time period i is marked as HSi. At the same time, the unit data volume Gi of each fixed time period i is obtained. Based on the formula ZSi=Gi×HSi, the estimated amount of billing data ZSi for the fixed time period i on the same day is obtained.

[0011] As a further aspect of the present invention, the method for obtaining low-load periods and full-load periods includes: Obtain the load baseline value of the system in the monitored area. The load baseline value refers to the standard load capacity of the power equipment during operation. Set a lower limit threshold and multiply it by the load base value to obtain the load lower limit value Fmin. The lower limit threshold is set to 0.85. Obtain the real-time regional power consumption HSi for each fixed time period i in the power grid prediction curve. Compare the regional power consumption HSi with the load lower limit Fmin. If HSi≤Fmin, mark the corresponding fixed time period i as a low load period. Conversely, if HSi>Fmin, mark the corresponding fixed time period i as a full load period.

[0012] As a further aspect of the present invention, the method for obtaining the comprehensive margin value includes: Once all fixed time periods in the power grid prediction curve are marked, the low-load period in fixed time period i is identified. At the same time, based on the regional power consumption during the low-load period, the system load value for the corresponding low-load period is obtained. The load base value is subtracted from the regional power consumption during the low-load period, and the resulting difference is marked as the load margin value.

[0013] As a further aspect of the present invention, based on the low-load period, consecutive low-load periods are merged, and the merged period area is marked as the reconciliation processing period. The load margin value of the low-load periods participating in the merger is obtained, and the corresponding load margin values ​​are accumulated to obtain the comprehensive margin value. Furthermore, if both sides of the low-load period are full-load periods, then this independent low-load period is directly marked as the reconciliation processing period, and the load margin value of this independent low-load period is directly marked as the comprehensive margin value.

[0014] As a further aspect of the present invention, the method for determining the resource reserve space during the reconciliation processing period includes: According to the time sequence, identify the reconciliation processing period, take the first fixed period in the reconciliation processing period as the base time, obtain all fixed periods before this base time, and accumulate the estimated amount of bill data for the corresponding fixed periods to obtain the comprehensive data volume A1. Then, subtract the processing completion amount Wc from the comprehensive data volume A1 to obtain the data volume Dc to be processed. Here, the processing completion amount Wc refers to the financial reconciliation management data that has been completed in the reconciliation processing periods before this reconciliation processing period. The amount of data to be processed, Dc, is compared with the comprehensive reserve value for the corresponding reconciliation processing period. If the amount of data to be processed, Dc, is less than or equal to the comprehensive reserve value, then the amount of data to be processed, Dc, is set as the space reserve, and resources are prepared for the financial reconciliation system in advance. Conversely, if the amount of data to be processed, Dc, is greater than the comprehensive reserve value, then the comprehensive reserve value is set as the space reserve, and resources are prepared for the financial reconciliation system.

[0015] As a further embodiment of the present invention, it also includes a power acquisition module and an information storage module; The power acquisition module is used to monitor and collect the power consumption of the monitored area in real time and transmit it to the information storage module; The information storage module is used to store historical data information of the monitored area, and the information storage module and the information analysis module have a one-way communication connection.

[0016] Compared with existing technologies, the advantages of this invention are: This invention processes the reconciliation data volume and regional power consumption in each fixed time period to obtain the unit power consumption bill volume, thereby accurately determining the unit data volume of financial reconciliation in each fixed time period. Based on power grid load data and power grid prediction models, a power prediction curve is obtained. After dividing the power prediction area by unit time length, the estimated bill data volume for a fixed time period is obtained by combining the unit data volume with the regional power consumption. This provides a basis for resource allocation and power operation management, helps to rationally arrange power production and financial resource allocation, and improves the economic benefits of enterprises. By rationally dividing the power grid load curve into low-load and full-load periods, and merging consecutive low-load periods to determine the reconciliation processing period, the comprehensive margin value and the amount of data to be processed in the reconciliation processing period can be calculated and compared to accurately determine the resource reserve margin for the reconciliation processing period. This allows the power system to rationally allocate resources based on the preset margin, improve resource utilization efficiency, and further ensure the efficient and stable operation of power financial reconciliation. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0019] Reference Figure 1 A digital and intelligent power financial reconciliation management system includes an information storage module, a power acquisition module, an information analysis module, a data estimation module, and a resource allocation module; The power acquisition module is used to monitor and collect the power consumption of the monitored area in real time. After that, the power acquisition module transmits the real-time power consumption data to the information storage module. The information storage module is used to store regional information and historical data information of the monitoring area. The monitoring area refers to the power signal area covered by the system. The regional information includes the number of power users and their distribution in the monitoring area. The historical data information includes the power grid load data generated in the monitoring area in the past and the corresponding electronic billing data. After that, the information storage module and the information analysis module have a one-way communication connection. The information analysis module receives historical data and, using the current time as the base time, acquires historical data within a valid time period. The specific length of the valid time period is set by those skilled in the art based on big data experience. Then, inertial analysis is performed on the historical data within the valid time period to determine the unit data volume of the electronic billing data. Specifically, the method for determining the unit data volume includes: S1: First, extract the power grid load data within the effective time period, and divide the power grid load data according to the cycle time. The specific duration of the cycle time is set by those skilled in the art based on big data experience. In this embodiment, the cycle time is set to 1 natural day. One hour is set as the unit of time, and then the periodic time is divided again according to the unit of time to obtain n fixed time periods, where n takes the value of 24; The power grid load data is statistically analyzed according to the time node of each unit time to obtain the regional power consumption at different time nodes. Furthermore, the regional power consumption refers to the total power consumption of power users in the monitored area at different time nodes with unit time as the time length. A fixed time period corresponds to a regional power consumption. Then, obtain the financial reconciliation information from the historical data, and based on the length of the unit time, count the amount of bill information for each fixed period to obtain the amount of reconciliation data at different time nodes. Furthermore, a fixed period corresponds to a reconciliation data amount. S2: Arrange the fixed time periods within each cycle in chronological order, and assign the position number of each fixed time period to i in chronological order, where i∈[1,n]; Arbitrarily select a fixed time period i and mark this fixed time period i as the target time period. Obtain all target time periods within the valid time and extract the regional power consumption and reconciliation data volume of the target time period. Divide the amount of reconciliation data in each target time period by the regional power consumption, and mark the result as the unit power consumption bill amount ZDj, ​​where j represents the number of different target time periods and j∈[1,J], indicating that there are J target time periods in the effective time, that is, there are J periodic times in the effective time. S3: After the unit electricity consumption bill ZDj is calculated, first calculate the arithmetic mean of the unit electricity consumption ZDj, ​​and mark the result as Zp. Then use the formula... The standard deviation Bc is obtained; Based on the normal distribution processing method, the normal interval Q is set. In this embodiment, k is set to 2; The unit electricity consumption bill ZDj is compared with the normal interval Q. Data whose unit electricity consumption bill ZDj belongs to the normal interval Q are selected and marked as normal data ZDm, m∈[1,M], M represents the total number of normal data, and M≤J. It should be further noted that all normal data ZDm are greater than 0. Extract normal data ZDm and use the formula The unit data volume Gi for the target time period i is obtained. Furthermore, the unit data volume Gi is the geometric mean of the unit electricity consumption bill for the target time period i. Then, the remaining fixed time periods are sequentially set as target time periods and processed according to the above method to obtain the unit data volume Gi of electronic billing data for each fixed time period. The information analysis module then transmits the unit data volume of the electronic billing data to the data estimation module; The data estimation module receives the unit data volume of electronic billing data and the power grid load data from historical data. Based on the power grid load data, it predicts the power grid load curve for the monitored area for the day, obtaining a power forecast curve. Then, based on the power forecast curve, it combines the power forecast curve with the unit data volume to calculate and determine the estimated billing data volume for each fixed time period of the day. Specific methods for obtaining the power forecast curve include: SS1: Extract the grid load data within the valid time period from the historical data information again, set one cycle time as a loop, and integrate the grid load data in one cycle time into a set of data; Time is set as the independent variable, and power grid load data is set as the dependent variable. At the same time, the J groups of data within the effective time period are divided into experimental group and control group. Then, based on the data in the experimental group and control group, artificial neural network method is used for learning and training to obtain power grid prediction model. The process of artificial neural network method is existing technology and will not be described in detail here. SS2: Based on the power grid forecasting model, the power grid load curve of the monitoring area is predicted for the day to obtain the power forecast curve; Based on the power forecast curve, the real-time regional power consumption of each fixed time period i in the monitoring area is obtained, and the real-time regional power consumption of each fixed time period i is marked as HSi. At the same time, the unit data volume Gi of each fixed time period i is obtained. Based on the formula ZSi=Gi×HSi, the estimated amount of bill data ZSi of the fixed time period i in the day is obtained. The data estimation module then transmits the estimated billing data and power forecast to the resource allocation module; The resource allocation module is used to obtain the power grid forecast curve and, together with the estimated quantity ZSi from the billing data, allocate system resources for the daily electricity financial reconciliation. These system resources include processing time and service computing resources. Specifically, the allocation methods for system resources include: Obtain the load baseline value of the system in the monitoring area. The specific load baseline value of the system is determined by the factory parameters of the load equipment in the monitoring area. Furthermore, the load baseline value refers to the standard load capacity of the power equipment during operation. Set a lower limit threshold and multiply the lower limit threshold by the load base value to obtain the load lower limit value Fmin. The specific value of the lower limit threshold is set by those skilled in the art based on big data experience. In this embodiment, the lower limit threshold is set to 0.85. Obtain the real-time regional power consumption HSi for each fixed time period i in the power grid prediction curve, compare the regional power consumption HSi with the load lower limit Fmin. If HSi≤Fmin, mark the corresponding fixed time period i as a low load period; otherwise, if HSi>Fmin, mark the corresponding fixed time period i as a full load period. Once all fixed time periods in the power grid prediction curve are marked, identify the low-load period in fixed time period i. At the same time, based on the regional power consumption during the low-load period, obtain the corresponding system load value for the low-load period, subtract the regional power consumption during the low-load period from the load base value, and mark the resulting difference as the load margin value. Then, based on the low-load period, consecutive low-load periods are merged, and the merged period area is marked as the reconciliation processing period. Then, the load margin value of the low-load periods participating in the merger is obtained, and the corresponding load margin values ​​are accumulated to obtain the comprehensive margin value. Furthermore, if both sides of the low-load period are full-load periods, then this independent low-load period is directly marked as the reconciliation processing period, and the load margin value of this independent low-load period is directly marked as the comprehensive margin value. Then, in chronological order, the reconciliation processing period is identified. Taking the first fixed period in the reconciliation processing period as the base time, all fixed periods before this base time are obtained, and the estimated amount of bill data for the corresponding fixed periods is accumulated to obtain the comprehensive data volume A1. At the same time, the processing completion amount Wc is subtracted from the comprehensive data volume A1 to obtain the data volume Dc to be processed. Here, the processing completion amount Wc refers to the financial reconciliation management data that has been completed in the reconciliation processing periods before this reconciliation processing period. The amount of data to be processed, Dc, is compared with the comprehensive reserve value for the corresponding reconciliation processing period. If the amount of data to be processed, Dc, is less than or equal to the comprehensive reserve value, then the amount of data to be processed, Dc, is set as the space reserve, and resources are prepared for the financial reconciliation system in advance. Conversely, if the amount of data to be processed, Dc, is greater than the comprehensive reserve value, then the comprehensive reserve value is set as the space reserve, and resources are prepared for the financial reconciliation system.

[0020] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A digital and intelligent power financial reconciliation management system, characterized in that, include: The information analysis module is used to acquire historical data information within a valid time period. It also sets the period and fixed time period, processes the reconciliation data volume and regional power consumption in each fixed time period to obtain the unit power consumption bill volume, and then analyzes the unit power consumption bill volume to determine the unit data volume of financial reconciliation in each fixed time period. The data estimation module is used to learn and train on the power grid load data to obtain the power grid prediction model. Based on the power grid prediction model, the power grid load curve of the monitoring area on the same day is predicted to obtain the power prediction curve. Then, the power prediction area is divided according to the unit time length to obtain fixed time periods. The regional power consumption in the same fixed time period is multiplied by the unit data volume to obtain the billing data estimation volume for the fixed time period. The resource allocation module is used to obtain the power grid load curve and billing data estimation. First, based on the regional power consumption of each fixed period in the power grid load curve, the fixed period is divided into low load period and full load period. At the same time, consecutive low load periods are merged to obtain the reconciliation processing period. Then, the comprehensive margin value and the amount of data to be processed in the reconciliation processing period are calculated separately, and the comprehensive margin and the amount of data to be processed are compared to determine the space margin for resource preparation in the reconciliation processing period.

2. The intelligent power financial reconciliation management system according to claim 1, characterized in that, Methods for obtaining unit electricity consumption bills include: S1: Using the current time as the base time, obtain historical data information within the effective time period, extract the power grid load data within the effective time period, and divide the power grid load data according to the cycle time, with the cycle time set to 1 natural day; One hour is set as the unit of time, and then the periodic time is divided again according to the unit of time to obtain n fixed time periods, where n takes the value of 24; The power grid load data is statistically analyzed according to the time node of each unit time to obtain the regional power consumption at different time nodes. Regional power consumption refers to the total power consumption of power users in the monitored area at different time nodes with unit time as the time length. A fixed time period corresponds to a regional power consumption. Then, obtain the financial reconciliation information from the historical data, and based on the length of the unit time, count the amount of bill information for each fixed period to obtain the amount of reconciliation data at different time nodes. Furthermore, a fixed period corresponds to a reconciliation data amount. S2: Arrange the fixed time periods within each cycle in chronological order, and assign the position number of each fixed time period to i in chronological order, where i∈[1,n]; Arbitrarily select a fixed time period i and mark this fixed time period i as the target time period. Obtain all target time periods within the valid time and extract the regional power consumption and reconciliation data volume of the target time period. Divide the amount of reconciliation data in each target time period by the regional electricity consumption, and mark the result as the unit electricity consumption bill amount ZDj, ​​where j represents the number of different target time periods, and j∈[1,J], indicating that there are a total of J target time periods within the effective time.

3. The intelligent power financial reconciliation management system according to claim 2, characterized in that, Methods for determining the unit data volume for fixed-period financial reconciliation include: Once the unit electricity consumption bill ZDj is calculated, the arithmetic mean of the unit electricity consumption ZDj is calculated and the result is marked as Zp. Then, the formula is used... The standard deviation Bc is obtained; Based on the normal distribution processing method, the normal interval Q is set. Where k is set to 2; The unit electricity consumption bill ZDj is compared with the normal interval Q. Data whose unit electricity consumption bill ZDj belongs to the normal interval Q are selected and marked as normal data ZDm, m∈[1,M], M represents the total number of normal data, and M≤J. Among them, the normal data ZDm are all greater than 0. Extract normal data ZDm and use the formula The unit data volume Gi for the target time period i is obtained. Furthermore, the unit data volume Gi is the geometric mean of the unit electricity consumption bill for the target time period i.

4. The intelligent power financial reconciliation management system according to claim 1, characterized in that, Methods for obtaining power forecast curves include: Extract the grid load data within the effective time period from historical data information, set one cycle time as a loop, and integrate the grid load data in one cycle time into a set of data; Time was set as the independent variable, and power grid load data was set as the dependent variable. The J sets of data within the effective time period were divided into an experimental group and a control group. Then, based on the data in the experimental group and the control group, an artificial neural network method was used for learning and training to obtain a power grid prediction model.

5. The intelligent power financial reconciliation management system according to claim 4, characterized in that, Methods for obtaining estimates of billing data for a fixed period include: Based on the power grid forecasting model, the power grid load curve of the monitoring area on that day is predicted to obtain the power forecast curve; Based on the power forecast curve, the real-time regional power consumption of the monitored area for each fixed time period i on the same day is obtained, and the real-time regional power consumption of each fixed time period i is marked as HSi. At the same time, the unit data volume Gi of each fixed time period i is obtained. Based on the formula ZSi=Gi×HSi, the estimated amount of billing data ZSi for the fixed time period i on the same day is obtained.

6. The intelligent power financial reconciliation management system according to claim 1, characterized in that, Methods for obtaining low-load and full-load periods include: Obtain the load baseline value of the system in the monitored area. The load baseline value refers to the standard load capacity of the power equipment during operation. Set a lower limit threshold and multiply it by the load base value to obtain the load lower limit value Fmin. The lower limit threshold is set to 0.

85. Obtain the real-time regional power consumption HSi for each fixed time period i in the power grid prediction curve. Compare the regional power consumption HSi with the load lower limit Fmin. If HSi≤Fmin, mark the corresponding fixed time period i as a low load period. Conversely, if HSi>Fmin, mark the corresponding fixed time period i as a full load period.

7. The intelligent power financial reconciliation management system according to claim 6, characterized in that, Methods for obtaining the overall margin value include: Once all fixed time periods in the power grid prediction curve are marked, the low-load period in fixed time period i is identified. At the same time, based on the regional power consumption during the low-load period, the system load value for the corresponding low-load period is obtained. The load base value is subtracted from the regional power consumption during the low-load period, and the resulting difference is marked as the load margin value.

8. The intelligent power financial reconciliation management system according to claim 7, characterized in that, Based on the low-load period, consecutive low-load periods are merged, and the merged period area is marked as the reconciliation processing period. The load margin value of the low-load periods involved in the merger is obtained, and the corresponding load margin values ​​are accumulated to obtain the comprehensive margin value. If both sides of the low-load period are full-load periods, then this independent low-load period is directly marked as the reconciliation processing period, and the load margin value of this independent low-load period is directly marked as the comprehensive margin value.

9. The intelligent power financial reconciliation management system according to claim 8, characterized in that, The methods for determining the resource reserve margin during the reconciliation processing period include: According to the time sequence, identify the reconciliation processing period, take the first fixed period in the reconciliation processing period as the base time, obtain all fixed periods before this base time, and accumulate the estimated amount of bill data for the corresponding fixed periods to obtain the comprehensive data volume A1. Then, subtract the processing completion amount Wc from the comprehensive data volume A1 to obtain the data volume Dc to be processed. Here, the processing completion amount Wc refers to the financial reconciliation management data that has been completed in the reconciliation processing periods before this reconciliation processing period. The amount of data to be processed, Dc, is compared with the comprehensive reserve value for the corresponding reconciliation processing period. If the amount of data to be processed, Dc, is less than or equal to the comprehensive reserve value, then the amount of data to be processed, Dc, is set as the space reserve, and resources are prepared for the financial reconciliation system in advance. Conversely, if the amount of data to be processed, Dc, is greater than the comprehensive reserve value, then the comprehensive reserve value is set as the space reserve, and resources are prepared for the financial reconciliation system.

10. The intelligent power financial reconciliation management system according to claim 1, characterized in that, It also includes a power acquisition module and an information storage module; The power acquisition module is used to monitor and collect the power consumption of the monitored area in real time and transmit it to the information storage module; The information storage module is used to store historical data information of the monitored area, and the information storage module and the information analysis module have a one-way communication connection.

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