An HPLC module of an electric energy meter with load curve acquisition and storage and an acquisition and storage method

Abstract

The present invention provides an HPLC module for an electric energy meter with load curve acquisition and storage, and an acquisition and storage method, which relates to the technical field of load curve data processing. The method includes obtaining real-time segmented index data of an acquisition period, and updating the time period interval of segmented acquisition according to the real-time segmented index data to form initial segmented acquisition time period data; performing segmented adjustment analysis based on the aggregated quantity of acquisition points according to the load point data in the segmented acquisition time period interval in the initial segmented acquisition time period data to form adjusted segmented acquisition time period data; performing fitting processing on the load curve for each adjusted time period interval according to the adjusted segmented acquisition time period data to form interval load curve information; and performing deferred storage processing based on information transmission confirmation on the interval load curve information in sequence. This method can fully combine the characteristics of HPLC technology for the acquisition and storage of load curve data, so as to optimize the entire load curve data processing system and improve the resource utilization status of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of load curve data processing, and in particular, to an electric energy meter HPLC module with load curve acquisition and storage and an acquisition and storage method. Background Art

[0002] HPLC is high-speed power line carrier, also known as broadband power line carrier, which is a broadband power line carrier technology for data transmission on low-voltage power lines. The broadband power line carrier communication network uses the power line as the communication medium to realize the aggregation, transmission, and interaction of electricity consumption information of low-voltage power users. Compared with the traditional low-speed narrowband power line carrier technology, the HPLC technology has a large bandwidth and a high transmission rate, which can meet the higher requirements of low-voltage power line carrier communication.

[0003] Since the HPLC technology can achieve more efficient data transmission, stable guarantees can be obtained in terms of data integrity and real-time performance. Thus, for the acquisition and storage of load curve data by intelligent electric energy meters, it is no longer necessary to focus on factors such as the integrity and timeliness of transmitted data as before, and the local data storage capacity has also been greatly simplified. However, currently, there is no complete processing method that fully matches the characteristics of the HPLC technology for forming new load curve data acquisition and storage.

[0004] Therefore, designing an electric energy meter HPLC module with load curve acquisition and storage and an acquisition and storage method, which can fully combine the characteristics of the HPLC technology to acquire and store load curve data, so as to optimize the entire load curve data processing system and improve the resource utilization status of the system, is an urgent problem to be solved at present. Summary of the Invention

[0005] The object of the present invention is to provide a collection and storage method. By obtaining the real-time segmented index data provided by the upper system unit, the interval of the segmented collection period based on the change of the local load curve data is determined, so as to provide a reasonable segmented interval adapted to the local load change for the subsequent reasonable segmented fitting of the load curve over the entire collection period, and reduce the complexity of curve fitting. At the same time, considering that curve fitting is based on the load data obtained at evenly spaced time collection points, the interval is further subdivided according to the number of collection points within the interval, further reducing the complexity of curve fitting. Such interval division is to obtain a load curve function that can include the load data obtained at all time collection points, and only use the load curve function as the basic data for sending the load curve data. Compared with the method of sending the load data of time collection points as a whole, the data transmission volume is greatly simplified. In addition, the obtained load curve function is uploaded in real time using HPLC after formation, and the temporarily stored data after transmission is deleted based on the feedback of the transmission signal. Due to the stable and efficient transmission characteristics of HPLC, the local data storage volume can be greatly reduced, and the resource usage of local devices in data storage can be reduced. The entire collection and storage method matches the high efficiency and stability demonstrated by HPLC technology in ensuring data integrity and real-time performance, and optimizes the form of load curve data collection and storage.

[0006] The object of the present invention is also to provide an electric energy meter HPLC module with load curve collection and storage. A simple and efficient load curve collection and storage system is formed by a data collection unit, an upload / download unit, a curve fitting unit, and a cache unit, and fully combines the characteristics of HPLC technology to reduce the load curve data for transmission in real time after collection, and at the same time complete the short-term caching of local data, reducing the resource usage of the system in data collection and storage.

[0007] In a first aspect, the present invention provides a collection and storage method, including obtaining real-time segmented index data of a collection period, and updating the time interval of segmented collection according to the real-time segmented index data to form initial segmented collection period data; according to the load point data in the segmented collection period interval in the initial segmented collection period data, and performing segmented adjustment analysis based on the aggregation amount of collection points to form adjusted segmented collection period data; according to the adjusted segmented collection period data, performing fitting processing on the load curve for each adjusted time interval to form interval load curve information; and performing a suspension storage process on the interval load curve information in sequence based on information transmission confirmation.

[0008] In the present invention, the method obtains real-time segmented index data provided by the upper system unit to perform the interval of the segmented acquisition period based on the change of the local load curve data, thereby providing a reasonable segmented interval adapted to the local load change for subsequent reasonable segmented fitting of the load curve over the entire acquisition period, and reducing the complexity of curve fitting. At the same time, considering that curve fitting is performed based on the load data obtained at evenly spaced time acquisition points, the interval is further subdivided according to the number of acquisition points within the interval, further reducing the complexity of curve fitting. Such interval division is to obtain a load curve function that can include the load data obtained at all time acquisition points, and only use the load curve function as the basic data for sending the load curve data. Compared with the method of sending the load data of the time acquisition points as a whole, the data transmission volume is greatly simplified. In addition, the obtained load curve function is uploaded in real time using HPLC after being formed, and the data temporarily stored after transmission is deleted based on the feedback of the transmission signal. Due to the stable and efficient transmission characteristics of HPLC, the local data storage volume can be greatly reduced, and the resource usage of the local device in data storage can be reduced. The entire acquisition and storage method matches the high efficiency and stability demonstrated by HPLC technology in ensuring data integrity and real-time performance, optimizing the form of load curve data acquisition and storage.

[0009] As a possible implementation, real-time segmented index data of the acquisition period is obtained, and the time interval of segmented acquisition is updated according to the real-time segmented index data to form initial segmented acquisition period data, including: obtaining the real-time segmented index data of the acquisition period, and determining the real-time acquisition time interval of each real-time segment; in the order of acquisition time, adjusting the interval boundary of each real-time acquisition time interval based on the acquisition time point to form the initial segmented acquisition period data.

[0010] In the present invention, the purpose of segmenting the intervals within the acquisition period is to reasonably segment and fit the load curve using the load data obtained at subsequent time acquisition points. It can be understood that the electricity load fluctuates in different time periods of the load curve, and this fluctuation is related to the electricity consumption environment, user behavior, etc. In different time intervals of the fluctuation, due to the large variation in the load data, it will increase the complexity of the fitting process during subsequent curve fitting. Therefore, in order to reduce this complexity and obtain more accurate fitting data in real time based on the variation of the load data, the upper-level data processing center can analyze the load data for different load electricity consumption objects and then adjust the segmented intervals divided locally in a timely manner according to the variation of the load data volatility, so as to improve the accuracy of subsequent data fitting. Of course, the adjustment of the segmented intervals is essentially the grouping and division of the data acquisition time points for curve fitting. Therefore, after segmenting based on the segmentation information sent by the upper-level processing center, it is also necessary to appropriately adjust the area included in the segmented interval in combination with the position of the acquisition time point.

[0011] As a possible implementation method, in the order of the acquisition time, the boundaries of each real-time acquisition time interval are adjusted based on the acquisition time points to form the initial segmented acquisition period data, including: sequentially obtaining each real-time acquisition time interval in the order of the acquisition time for the following judgment and adjustment: if the boundary of the real-time acquisition time interval is located at the load data acquisition time point in the order of the acquisition time, then the boundary of the real-time acquisition time interval is not adjusted; if the boundary at the front in the time order of the real-time acquisition time interval is in the time period between the load data acquisition time points in the order of the acquisition time, then the boundary of the real-time acquisition time interval is adjusted to the load data acquisition time point after the boundary in the order of the acquisition time; if the boundary at the back in the time order of the real-time acquisition time interval is in the time period between the load data acquisition time points in the order of the acquisition time, then the boundary of the real-time acquisition time interval is adjusted to the load data acquisition time point before the boundary in the order of the acquisition time; combining all the adjusted real-time acquisition time intervals to form the initial segmented acquisition period data.

[0012] In the present invention, after completing the division of the segmented intervals, it is necessary to determine the position of the acquisition time points in the intervals, especially considering the position relationship between the interval boundaries and the acquisition time points. After all, when the interval boundary is not located at the acquisition time point, the part from the adjacent acquisition time point to the interval boundary cannot be reasonably expressed by the fitting curve. Therefore, the adjustment of the segmented intervals in the present invention is mainly to adjust the interval boundary to the adjacent acquisition time point for correspondence, which can make the boundary position of the segmented interval clearer and ensure that the data expressed by the fitting curve is reasonable throughout the interval.

[0013] As a possible implementation, load point data in the segmented acquisition time period interval of the initial segmented acquisition time period data is collected, and segmented adjustment analysis based on the aggregation amount of acquisition points is performed to form adjusted segmented acquisition time period data, including: determining the acquisition time points of all load data acquisition points in the acquisition time sequence; according to the segmented acquisition time period interval of the initial segmented acquisition time period data, determining the number N of acquisition time points in each interval i , where i represents the number of the segmented acquisition time period interval in the initial segmented acquisition time period data; setting a quantity limit threshold α, performing segmented adjustment analysis based on the aggregation amount of acquisition points on the number of acquisition time points in each segmented acquisition time period interval to form adjusted segmented acquisition time period data.

[0014] In the present invention, segmenting the acquisition period to form multiple fitting curves can greatly simplify the complexity of fitting. Similarly, the number of acquisition time points in each segmented interval also directly affects the complexity of fitting. After all, in order to ensure that the fitting curve includes the load data obtained at the acquisition time points in all intervals, it is necessary to establish a multivariate equation of the corresponding power for solution according to the number of load data. Therefore, in order to further simplify the complexity of curve fitting, the complexity of the multivariate equation required for fitting can be reduced by limiting the number of acquisition time points in the segmented interval. Here, the quantity limit threshold can be determined according to actual needs.

[0015] As a possible implementation, setting a quantity limit threshold α, performing segmented adjustment analysis based on the aggregation amount of acquisition points on the number of acquisition time points in each segmented acquisition time period interval to form adjusted segmented acquisition time period data, including: if α ≥ N i , then no adjustment is made to the corresponding segmented acquisition time period interval; if α < N i ≤ 2α, then the acquisition time points in the corresponding segmented acquisition time period interval are evenly divided according to the quantity to form two new segmented acquisition time period intervals, and the boundaries of the new segmented acquisition time period intervals are adjusted to the nearest acquisition time points; if N i > 2α, the corresponding segmented acquisition time period interval is determined as the target interval, then the number of acquisition time points in the segmented acquisition time period interval adjacent to the target interval is obtained: when the number of acquisition time points in the adjacent segmented acquisition time period interval is N i < α, and after incorporating the N i - 2α number of time acquisition points in the target interval into the adjacent segmented acquisition time period interval, the number of acquisition time points in the adjacent segmented acquisition time period interval does not exceed the quantity limit threshold α, then the N i- The time acquisition points of the quantity of -2α are incorporated into the adjacent segmented acquisition time period intervals, and the remaining time acquisition points in the target interval are evenly divided to form new segmented acquisition time period intervals. Then, the boundaries of the new segmented acquisition time period intervals and the boundaries of the adjacent segmented acquisition time period intervals incorporating the time acquisition points are adjusted to the nearest acquisition time points. When the number of acquisition time points in the adjacent segmented acquisition time period intervals is N i <α, and N i - After the time acquisition points of the quantity of -2α are incorporated into the adjacent segmented acquisition time period intervals and the number of acquisition time points in the adjacent segmented acquisition time period intervals still exceeds the quantity limit threshold α, directly perform an even division of the target interval to form new segmented acquisition time period intervals, and adjust the boundaries of the new segmented acquisition time period intervals to the nearest acquisition time points, where represents rounding ; When the number of acquisition time points in the adjacent segmented acquisition time period intervals does not have N i <α, directly perform an even division of the target interval to form new segmented acquisition time period intervals, and adjust the boundaries of the new segmented acquisition time period intervals to the nearest acquisition time points, where represents rounding ; Combining all the adjusted segmented acquisition time period intervals forms adjusted segmented acquisition time period data.

[0016] In the present invention, under the judgment based on the quantity limit threshold, a method for re-segmenting and adjusting the segmented interval is provided. That is, when the quantity is at twice the quantity limit threshold, an even division is performed to form two new segmented intervals. Of course, for the case where there are an odd number of acquisition time points, the extra quantity can be randomly allocated to the newly divided segmented intervals as needed. When the number of acquisition time points exceeds twice the quantity limit threshold, consider whether the part exceeding twice can be incorporated into the adjacent segmented interval and then continue to use the method of even division to determine the new segmented intervals. If it cannot be done, then consider directly dividing into the corresponding number of segmented intervals based on the multiple of the quantity limit threshold. Similarly, for the case where the quantity cannot be made uniform in each newly divided interval, consider randomly allocating the extra quantity to these newly divided segmented intervals.

[0017] As a possible implementation method, according to the adjusted segmented acquisition time period data, perform a fitting process on the load curve for each adjusted time period interval to form interval load curve information, including: obtaining the real-time load value at each acquisition time point in each adjusted segmented acquisition interval in the adjusted segmented acquisition time period data based on the time dimension sequence; performing curve fitting based on the real-time load value for each adjusted segmented acquisition interval to form the corresponding load fitting curve Fk (t: [T k , T k+1 ), where k is the sequence number of the adjusted segmented acquisition interval in the data of the adjusted segmented acquisition period, and [T k , T k+1 represents the acquisition time interval corresponding to the load fitting curve.

[0018] In the present invention, when performing curve fitting, it is necessary to fully ensure that the fitted curve covers the load data obtained at all corresponding acquisition time points. In this way, after the upper data center obtains the simple fitted curve function, it can determine the load data at each acquisition time point according to the acquisition time points with a constant defined acquisition interval and the corresponding acquisition cycle length, and then can perform more accurate data processing again by itself, or can use these segmented fitted curves for further analysis and processing.

[0019] As a possible implementation manner, curve fitting is performed on each adjusted segmented acquisition interval based on the real-time load value to form a corresponding load fitting curve F k (t: [T k , T k+1 ), including: determining the number m of acquisition time points on the adjusted segmented acquisition interval, and establishing a curve equation of the corresponding power: F k = a1t m-1 + a2t m-2 + … + a m-1 t 1 + a m , where a m is the parameter corresponding to each power in the curve equation; taking the real-time load value corresponding to the acquisition time point and the time point as the solution of the curve equation, establishing a system of equations to determine the parameters of the curve equation, and forming a load fitting curve F k (t: [T k , T k+1 ).

[0020] In the present invention, in order to make the fitted curve function include the load data at all corresponding acquisition time points, a power equation corresponding to the number of acquisition time points is used to establish the fitted curve function. Since the simplification of the number of acquisition time points in each interval segment has been performed on the previous interval segments, the solution complexity of the power equation is also greatly reduced, and an accurate fitted curve function can be obtained quickly and efficiently.

[0021] As a possible implementation manner, the interval load curve information is sequentially processed for suspended storage based on information transmission confirmation, including: the load fitting curve F k (t: [T k , T k+1) As the uploaded data, it is uploaded in the order of the time dimension; all the load fitting curves F corresponding to the adjusted segmented acquisition period data are k (t: [T k , T k+1 ) are temporarily stored based on the determined information downloaded.

[0022] In the present invention, the HPLC technology has a large data transmission volume, is efficient and timely, and can fully ensure the integrity and real-time nature of data transmission. Therefore, when transmitting load data, on the one hand, only the fitting curve function is used for transmission, which can broaden the amount of transmitted data, and more user load data can be accommodated on a parallel transmission path. On the other hand, the data after uploading is only simply temporarily stored and can be transmitted immediately after receiving the transmission confirmation signal. The temporary storage time is very short, which can fully reduce the local storage space and reduce the resource consumption of the storage unit.

[0023] As a possible implementation method, all the load fitting curves F corresponding to the adjusted segmented acquisition period data are k (t: [T k , T k+1 ) are temporarily stored based on the determined information downloaded, including: all the load fitting curves F corresponding to the adjusted segmented acquisition period data are k (t: [T k , T k+1 ) are locally temporarily stored, and the following situations are used to judge the processing of the temporary storage: if the downloaded confirmation reception information is obtained, the adjusted segmented acquisition period data corresponding to the temporary storage is deleted; if the temporarily stored data exceeds the storage capacity of the storage space, the adjusted segmented acquisition period data stored temporarily is automatically uploaded and deleted once in the order from long to short of the temporary storage time.

[0024] In the present invention, the processing of the temporary storage is usually to delete it after obtaining the confirmation information, so that the upper processing center has obtained the corresponding data and no data loss will occur. For the information for which the upload confirmation has not been obtained, it is limited by the size of the local storage space. When the storage space is full, the earliest stored data is deleted after being sent again. The upper processing center can process it by establishing a storage station for these data sent again. Of course, since the real-time nature of HPLC transmission is basically not affected by the situation of not receiving the upload confirmation information very rarely, and the deleted data starts from the earliest and occurs when the storage space is insufficient, this can further ensure the integrity of the data.

[0025] In a second aspect, the present invention provides an HPLC module for an electricity meter with load curve acquisition and storage, which is applied to the acquisition and storage method described in the first aspect. The module includes a data acquisition unit for acquiring real-time load data; an upload / download unit for obtaining real-time segmented index data and downloading and confirming received information for uploading load fitting curve data; a curve fitting unit for performing processing on the load fitting curve for the real-time load data acquired by the data acquisition unit, and for obtaining the real-time segmented index data downloaded by the upload / download unit to adjust the acquisition time period interval to form adjusted segmented acquisition time period data; and a cache unit for temporarily storing the load fitting curve data formed by the curve fitting unit and for obtaining the downloaded and confirmed received information of the upload / download unit to delete the temporarily stored data, and for sending the load fitting curve data to the upload / download unit and deleting it when the cache space is insufficient.

[0026] In the present invention, the module forms a simple and efficient load curve acquisition and storage system through the data acquisition unit, the upload / download unit, the curve fitting unit, and the cache unit, and fully combines the characteristics of the HPLC technology to reduce the load curve data to be transmitted in real time after acquisition, and at the same time complete the short-term caching of local data, reducing the resource usage of the system in data acquisition and storage.

[0027] The beneficial effects of the HPLC module for an electricity meter with load curve acquisition and storage and the acquisition and storage method provided by the present invention are as follows:

[0028] The method obtains the interval of the segmented acquisition time period based on the change of the local load curve data by acquiring the real-time segmented index data provided by the upper system unit, and then provides a reasonable segmented interval adapted to the local load change for the subsequent reasonable segmented fitting of the load curve over the entire acquisition cycle, reducing the complexity of curve fitting. At the same time, considering that curve fitting is performed based on the load data obtained at evenly spaced time acquisition points, the interval is further subdivided according to the number of acquisition points within the interval, further reducing the complexity of curve fitting. Such interval division is to obtain a load curve function that can include the load data obtained at all time acquisition points, and only use the load curve function as the basic data for sending the load curve data. Compared with the method of sending the load data of the time acquisition points as a whole, the data transmission volume is greatly simplified. In addition, the obtained load curve function is uploaded in real time using HPLC after formation, and the temporarily stored data after transmission is deleted based on the feedback of the transmission signal. Due to the stable and efficient transmission characteristics of HPLC, the local data storage volume can be greatly reduced, and the resource usage of the local device in data storage can be reduced. The entire acquisition and storage method matches the high efficiency and stability demonstrated by the HPLC technology in ensuring data integrity and real-time performance, optimizing the form of load curve data acquisition and storage.

[0029] This module forms a simple and efficient load curve acquisition and storage system through a data acquisition unit, an upload / download unit, a curve fitting unit, and a cache unit. It fully combines the characteristics of HPLC technology to reduce the load curve data for transmission in real time after acquisition, and at the same time completes the short-term caching of local data, reducing the resource usage of the system in data acquisition and storage. Brief Description of the Drawings

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required to be used in the embodiments of the present invention. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as a limitation of the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0031] Figure 1 It is a step diagram of the acquisition and storage method provided for the embodiments of the present invention. Detailed Embodiments

[0032] The following will describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention.

[0033] HPLC is high-speed power line carrier, also known as broadband power line carrier, which is a broadband power line carrier technology for data transmission on low-voltage power lines. The broadband power line carrier communication network uses the power line as the communication medium to realize the communication network for the aggregation, transmission, and interaction of the power consumption information of low-voltage power users. Compared with the traditional low-speed narrowband power line carrier technology, HPLC technology has a large bandwidth and a high transmission rate, which can meet the higher requirements of low-voltage power line carrier communication.

[0034] Since HPLC technology can achieve more efficient data transmission, stable guarantees can be obtained in terms of data integrity and real-time performance. In this way, for the acquisition and storage of the load curve data by smart electricity meters, there is no need to focus on factors such as the integrity and timeliness of the transmitted data as before, and the local data storage volume has also been greatly simplified. However, at present, there is no complete processing method that fully matches the characteristics of HPLC technology for forming new load curve data acquisition and storage.

[0035] Reference Figure 1, an embodiment of the present invention provides a collection and storage method. This method obtains the real-time segmented index data provided by the upper system unit to determine the interval of the segmented collection period based on the change of the local load curve data, thereby providing a reasonable segmented interval adapted to the local load change for the subsequent reasonable segmented fitting of the load curve over the entire collection period, and reducing the complexity of curve fitting. At the same time, considering that curve fitting is performed based on the load data obtained at evenly spaced time collection points, the interval is further subdivided according to the number of collection points within the interval to further reduce the complexity of curve fitting. Such interval division is to obtain a load curve function that can include the load data obtained at all time collection points, and only use the load curve function as the basic data for sending the load curve data. Compared with the method of sending the load data of the time collection points as a whole, the data transmission volume is greatly simplified. In addition, the obtained load curve function is uploaded in real time using HPLC after it is formed, and the data temporarily stored after transmission is deleted based on the feedback of the transmission signal. Due to the stable and efficient transmission characteristics of HPLC, the local data storage volume can be greatly reduced, and the resource usage of the local device in data storage can be reduced. The entire collection and storage method matches the high efficiency and stability demonstrated by the HPLC technology in ensuring the integrity and real-time nature of the data, optimizing the form of load curve data collection and storage.

[0036] The collection and storage method specifically includes the following steps:

[0037] S1: Obtain the real-time segmented index data of the collection period, and update the time interval of segmented collection according to the real-time segmented index data to form the initial segmented collection period data.

[0038] Obtain the real-time segmented index data of the collection period, and update the time interval of segmented collection according to the real-time segmented index data to form the initial segmented collection period data, including: obtaining the real-time segmented index data of the collection period, and determining the real-time collection time interval of each real-time segment; in the order of collection time, adjust the interval boundary of each real-time collection time interval based on the collection time point to form the initial segmented collection period data.

[0039] For the interval segmentation during the collection period, the purpose is to reasonably segment and fit the load curve using the load data obtained at subsequent time collection points. It can be understood that the electricity load shows volatility in different time periods of the load curve, and this volatility is related to the electricity usage environment, user behavior, etc. In different time intervals of the volatility, due to the large change in load data, it will increase the complexity of the fitting process during subsequent curve fitting. Therefore, in order to reduce this complexity and obtain relatively accurate fitting data based on the change of load data in real time, the upper data processing center can analyze the load data for different load electricity consumption objects and then adjust the segmented intervals divided locally in a timely manner according to the change of load data volatility to improve the accuracy of subsequent data fitting. Of course, the adjustment of the segmented interval is essentially the grouping and division of the data collection time points for curve fitting. Therefore, after segmenting based on the segmentation information sent by the upper processing center, it is also necessary to appropriately adjust the area included in the segmented interval in combination with the position of the collection time point.

[0040] In the order of collection time, adjust the boundaries of each real-time collection time interval based on the collection time points to form the initial segmented collection period data, including: sequentially obtaining each real-time collection time interval in the order of collection time and making the following judgment and adjustment: If the boundary of the real-time collection time interval is located at the load data collection time point in the order of collection time, do not adjust the boundary of the real-time collection time interval; If the boundary at the front in the time order of the real-time collection time interval is in the time period between the load data collection time points in the order of collection time, adjust the boundary of the real-time collection time interval to the load data collection time point after the boundary in the order of collection time; If the boundary at the back in the time order of the real-time collection time interval is in the time period between the load data collection time points in the order of collection time, adjust the boundary of the real-time collection time interval to the load data collection time point before the boundary in the order of collection time; Combine all the adjusted real-time collection time intervals to form the initial segmented collection period data.

[0041] After completing the division of the segmented interval, it is necessary to determine the position of the collection time points in the interval, especially considering the position relationship between the interval boundary and the collection time point. After all, when the interval boundary is not located at the collection time point, the part from the adjacent collection time point to the interval boundary cannot be reasonably expressed by the fitting curve. Therefore, the adjustment of the segmented interval in the present invention is mainly to adjust the interval boundary to the adjacent collection time point for correspondence, which can make the boundary position of the segmented interval clearer and ensure that the data expressed by the fitting curve is reasonable throughout the interval.

[0042] S2: Collect the load point data in the segmented acquisition time period range of the initial segmented acquisition time period data, and perform segmented adjustment analysis based on the aggregation amount of acquisition points to form adjusted segmented acquisition time period data.

[0043] Collect the load point data in the segmented acquisition time period range of the initial segmented acquisition time period data, and perform segmented adjustment analysis based on the aggregation amount of acquisition points to form adjusted segmented acquisition time period data, including: determining the acquisition time points of all load data acquisition points in the acquisition time sequence; according to the segmented acquisition time period range of the initial segmented acquisition time period data, determining the number N of acquisition time points in each range i , where i represents the number of the segmented acquisition time period range in the initial segmented acquisition time period data; setting a quantity limit threshold α, and performing segmented adjustment analysis based on the aggregation amount of acquisition points on the number of acquisition time points in each segmented acquisition time period range to form adjusted segmented acquisition time period data.

[0044] Segmenting the acquisition cycle to form multiple fitting curves can greatly simplify the complexity of fitting. Similarly, the number of acquisition time points in each segmented range also directly affects the complexity of fitting. After all, in order to ensure that the fitting curve includes the load data obtained at the acquisition time points in all ranges, it is necessary to establish a multivariate equation of the corresponding power for solution according to the quantity of load data. Therefore, in order to further simplify the complexity of curve fitting, the complexity of the multivariate equation required for fitting can be reduced by limiting the number of acquisition time points in the segmented range. Here, the quantity limit threshold can be determined according to actual needs.

[0045] Setting a quantity limit threshold α, and performing segmented adjustment analysis based on the aggregation amount of acquisition points on the number of acquisition time points in each segmented acquisition time period range to form adjusted segmented acquisition time period data, including: if α ≥ N i , then no adjustment is made to the corresponding segmented acquisition time period range; if α < N i ≤ 2α, then divide the acquisition time points in the corresponding segmented acquisition time period range evenly according to the quantity to form two new segmented acquisition time period ranges, and adjust the boundaries of the new segmented acquisition time period ranges to the nearest acquisition time points; if N i > 2α, determine the corresponding segmented acquisition time period range as the target range, then obtain the number of acquisition time points in the segmented acquisition time period range adjacent to the target range: when the number of acquisition time points in the adjacent segmented acquisition time period range is N i < α, and the number of acquisition time points in the target range after incorporating N i -2α time acquisition points into the adjacent segmented acquisition time period range does not exceed the quantity limit threshold α, then N in the target range i- The time acquisition points with a quantity of -2α are incorporated into the adjacent segmented acquisition time period intervals, and the remaining time acquisition points in the target interval are evenly divided to form new segmented acquisition time period intervals. Then, the boundaries of the new segmented acquisition time period intervals and the boundaries of the adjacent segmented acquisition time period intervals incorporating the time acquisition points are adjusted to the nearest acquisition time points; when the number of acquisition time points in the adjacent segmented acquisition time period intervals is N i <α, and after incorporating the time acquisition points with a quantity of -2α in the target interval into the adjacent segmented acquisition time period intervals, the number of acquisition time points in the adjacent segmented acquisition time period intervals still exceeds the quantity limit threshold α, then directly perform an i even division on the target interval to form new segmented acquisition time period intervals, and adjust the boundaries of the new segmented acquisition time period intervals to the nearest acquisition time points, where represents rounding of ; when the number of acquisition time points in the adjacent segmented acquisition time period intervals does not have N i <α, then directly perform an even division on the target interval to form new segmented acquisition time period intervals, and adjust the boundaries of the new segmented acquisition time period intervals to the nearest acquisition time points, where represents rounding of

[0046] ; Combining all the adjusted segmented acquisition time period intervals, adjusted segmented acquisition time period data is formed.

[0047] S3: According to the adjusted segmented acquisition time period data, perform a fitting process on the load curve for each adjusted time period interval to form interval load curve information.

[0048] Adjust and segment the data collection period, perform curve fitting on the load curve for each adjusted period interval to form interval load curve information, including: obtaining the real-time load value at each collection time point within each adjusted segmented collection interval in the adjusted segmented collection period data based on the time dimension order; performing curve fitting on each adjusted segmented collection interval based on the real-time load value to form the corresponding load fitting curve F k (t: [T k , T k+1 ), where k is the sequence number of the adjusted segmented collection interval in the adjusted segmented collection period data, and [T k , T k+1 represents the collection time interval corresponding to the load fitting curve.

[0049] During curve fitting, it is necessary to fully ensure that the fitted curve covers the load data obtained at all corresponding collection time points. In this way, after the upper data center obtains the simple fitting curve function, it can determine the load data at each collection time point according to the collection time points with a constant defined collection interval and the corresponding collection cycle length, and then can perform more accurate data processing again by itself, or can use these segmented fitting curves for further analysis and processing.

[0050] Perform curve fitting on each adjusted segmented collection interval based on the real-time load value to form the corresponding load fitting curve F k (t: [T k , T k+1 ), including: determining the number m of collection time points on the adjusted segmented collection interval and establishing a curve equation of the corresponding power: F k = a1t m-1 + a2t m-2 +…+ a m-1 t 1 + a m , where a m is the parameter corresponding to each power in the curve equation; taking the real-time load value corresponding to the collection time point and the time point as the solution of the curve equation, establishing a system of equations to determine the parameters of the curve equation, and forming the load fitting curve F k (t: [T k , T k+1 ).

[0051] In order to make the fitted curve function include the load data at all corresponding collection time points, a power equation corresponding to the number of collection time points is used to establish the fitting curve function. Since the simplification of the number of collection time points in each interval segment has been carried out in the previous interval segmentation, the solution complexity of the power equation is also greatly reduced, and an accurate fitting curve function can be obtained quickly and efficiently.

[0052] S4: Perform deferred storage processing on the interval load curve information in sequence based on information transmission confirmation.

[0053] Perform deferred storage processing on the interval load curve information in sequence based on information transmission confirmation, including: taking the load fitting curve F k (t: [T k , T k+1 ) as the uploaded data and uploading it in the order of the time dimension; temporarily storing all the load fitting curves F k (t: [T k , T k+1 ) corresponding to the adjusted segmented acquisition period data based on the downloaded confirmation information.

[0054] The HPLC technology has a large data transmission volume, is efficient and timely, and can fully ensure the integrity and real-time nature of data transmission. Therefore, when transmitting load data, on the one hand, only the fitting curve function is used for transmission, which can broaden the amount of transmitted data and accommodate more user load data on a parallel transmission path. On the other hand, the data after uploading is only simply temporarily stored and can be transmitted when the transmission confirmation signal is obtained. The temporary storage time is very short, which can fully reduce the local storage space and reduce the resource consumption of the storage unit.

[0055] Temporarily store all the load fitting curves F k (t: [T k , T k+1 ) corresponding to the adjusted segmented acquisition period data based on the downloaded confirmation information, including: temporarily storing all the load fitting curves F k (t: [T k , T k+1 ) corresponding to the adjusted segmented acquisition period data locally, and making a judgment on the temporary storage processing according to the following situations: if the downloaded confirmation reception information is obtained, delete the corresponding temporarily stored adjusted segmented acquisition period data; if the temporarily stored data exceeds the storage capacity of the storage space, automatically perform an upload and deletion of the temporarily stored adjusted segmented acquisition period data in the order from long to short temporary storage time.

[0056] The processing of the temporary storage is usually to delete it after obtaining the confirmation information. In this way, the upper-level processing center has obtained the corresponding data, and data loss will not occur. For the information for which the upload confirmation has not been obtained, it is limited by the size of the local storage space. When the storage space is full, the earliest stored data will be deleted after being sent again. The upper-level processing center can process it by establishing a storage station for these data sent again. Of course, due to the real-time nature of HPLC transmission, the situation of basically not receiving the upload confirmation information is extremely rare, and the deleted data starts from the earliest and occurs when the storage space is insufficient, which can further ensure the integrity of the data.

[0057] The present invention also provides an HPLC module for an electric energy meter with load curve acquisition and storage. This module adopts the acquisition and storage method provided by the present invention, including a data acquisition unit for acquiring real-time load data; an upload / download unit for obtaining real-time segmented index data and downloading confirmation received information, and for uploading load fitting curve data; a curve fitting unit for processing the real-time load data acquired by the data acquisition unit to perform load fitting curve processing, for obtaining the real-time segmented index data downloaded by the upload / download unit, and for adjusting the acquisition time period interval to form adjusted segmented acquisition time period data; a cache unit for temporarily storing the load fitting curve data formed by the curve fitting unit and for obtaining the downloading confirmation received information of the upload / download unit to delete the temporarily stored data, and for sending the load fitting curve data to the upload / download unit and deleting it when the cache space is insufficient.

[0058] This module forms a simple and efficient load curve acquisition and storage system through the data acquisition unit, upload / download unit, curve fitting unit, and cache unit, and fully combines the characteristics of HPLC technology to reduce the load curve data for transmission after acquisition in real time, and at the same time complete the short-term caching of local data, reducing the resource usage of the system in data acquisition and storage.

[0059] In summary, the beneficial effects of the cloud service computing power evaluation device and method provided by the embodiments of the present invention are as follows:

[0060] This method obtains the real-time segmented index data provided by the upper-level system unit to determine the interval of the segmented acquisition period based on the changes in the local load curve data, thereby providing a reasonable segmented interval adapted to the local load changes for the subsequent reasonable segmented fitting of the load curve over the entire acquisition period, reducing the complexity of curve fitting. At the same time, considering that curve fitting is performed based on the load data obtained at evenly spaced time acquisition points, the interval is further subdivided according to the number of acquisition points within the interval, further reducing the complexity of curve fitting. Such interval division is all aimed at obtaining a load curve function that can include the load data obtained at all time acquisition points, and only using the load curve function as the basic data for sending the load curve data. Compared with the method of sending the load data of time acquisition points as a whole, the data transmission volume is greatly simplified. In addition, the obtained load curve function is uploaded in real time using HPLC after formation, and the temporarily stored data after transmission is deleted based on the feedback of the transmission signal. Due to the stable and efficient transmission characteristics of HPLC, the local data storage volume can be greatly reduced, and the resource usage of local devices for data storage can be reduced. The entire acquisition and storage method matches the high efficiency and stability demonstrated by HPLC technology in ensuring the integrity and real-time nature of data, optimizing the form of load curve data acquisition and storage.

[0061] This module forms a simple and efficient load curve acquisition and storage system through a data acquisition unit, an upload / download unit, a curve fitting unit, and a cache unit, and fully combines the characteristics of HPLC technology to reduce the load curve data to be transmitted in real time after acquisition, and at the same time complete the short-term caching of local data, reducing the resource usage of the system in data acquisition and storage.

[0062] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or its similar expressions refer to any combination of these items, including any combination of single item(s) or plural item(s). For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, c can be single or multiple.

[0063] It should be understood that in various embodiments of the present invention, the magnitudes of the sequence numbers of the above processes do not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0064] Those of ordinary skill in the art can realize that the units and method steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0065] Those skilled in the art can clearly understand that for the sake of convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0066] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0067] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0068] In addition, the functional units in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0069] When the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0070] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0071] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent should be subject to the appended claims.

Claims

1. A method for acquisition and storage, characterized in that, Including: Obtain real-time segmented index data of the acquisition period, and update the time period range of segmented acquisition according to the real-time segmented index data to form initial segmented acquisition time period data; Based on the load point data in the segmented acquisition time period range of the initial segmented acquisition time period data, perform segmented adjustment analysis based on the aggregation amount of acquisition points to form adjusted segmented acquisition time period data; According to the adjusted segmented acquisition time period data, perform fitting processing on the load curve for each adjusted time period range to form interval load curve information; Perform a deferred storage process based on information transmission confirmation on the interval load curve information in sequence; Among them, based on the load point data in the segmented acquisition time period range of the initial segmented acquisition time period data, perform segmented adjustment analysis based on the aggregation amount of acquisition points to form adjusted segmented acquisition time period data, including: Determine the acquisition time points of all load data acquisition points in the acquisition time sequence; Determine the number of acquisition time points N within each interval according to the segmented acquisition time period interval for collecting the initial segmented acquisition time period data i , where i represents the number of the segmented acquisition time period interval in the initial segmented acquisition time period data; Set a quantity limit threshold α, and perform segmented adjustment analysis based on the aggregation amount of acquisition points on the number of acquisition time points in each segmented acquisition time period range to form adjusted segmented acquisition time period data: If α ≥ N i , then no adjustment is made to the corresponding segmented acquisition time period interval; If α < N i ≤ 2α, then the collection time points in the corresponding segmented collection time period interval are evenly divided according to the quantity to form two new segmented collection time period intervals, and the boundaries of the new segmented collection time period intervals are adjusted to the nearest collection time points; If N i > 2α, determine the corresponding segmented acquisition time period interval as the target interval, and then obtain the number of acquisition time points in the segmented acquisition time period interval adjacent to the target interval: When the number of acquisition time points in the adjacent segmented acquisition time period intervals is N i <α, and N i -2α number of time acquisition points in the target interval satisfy that the number of acquisition time points in the adjacent segmented acquisition time period intervals does not exceed the number limit threshold α after being incorporated into the adjacent segmented acquisition time period intervals, then incorporate the N i -2α number of time acquisition points in the target interval into the adjacent segmented acquisition time period intervals, and evenly divide the remaining time acquisition points in the target interval to form new segmented acquisition time period intervals, and adjust the boundaries of the new segmented acquisition time period intervals and the boundaries of the adjacent segmented acquisition time period intervals where the time acquisition points are incorporated to the nearest acquisition time points; When the number of acquisition time points in the adjacent segmented acquisition time period intervals is N i <α, and N i -2α number of time acquisition points in the target interval are incorporated into the adjacent segmented acquisition time period interval, and the number of acquisition time points in the adjacent segmented acquisition time period interval still exceeds the quantity limit threshold α, then directly perform average division of the target interval to form new segmented acquisition time period intervals, and adjust the boundaries of the new segmented acquisition time period intervals to the nearest acquisition time points, where represents rounding up; When the number of acquisition time points in the adjacent segmented acquisition time period intervals does not reach N i < α, directly perform an average division of the target interval to form new segmented acquisition time period intervals, and adjust the boundaries of the new segmented acquisition time period intervals to the nearest acquisition time points, where represents rounding up. Combine all the adjusted segmented acquisition time period ranges to form the adjusted segmented acquisition time period data.

2. The acquisition and storage method according to claim 1, wherein The obtaining of the real-time segmented index data of the acquisition period, and updating the time period range of segmented acquisition according to the real-time segmented index data to form initial segmented acquisition time period data, includes: Obtain the real-time segmented index data of the acquisition period, and determine the real-time acquisition time range of each real-time segment; In the order of acquisition time, adjust the interval boundaries of each real-time acquisition time range based on the acquisition time points to form the initial segmented acquisition time period data.

3. The acquisition and storage method according to claim 2, wherein The adjusting of the interval boundaries of each real-time acquisition time range based on the acquisition time points in the order of acquisition time to form the initial segmented acquisition time period data includes: In the order of acquisition time, sequentially obtain each real-time acquisition time range for the following judgment and adjustment: If the boundary of the real-time acquisition time range is located at the load data acquisition time point in the acquisition time sequence, do not adjust the boundary of the real-time acquisition time range; If the boundary at the front in the time sequence of the real-time acquisition time range is in the time period between the load data acquisition time points in the acquisition time sequence, adjust the boundary of the real-time acquisition time range to the load data acquisition time point after the boundary in the acquisition time sequence; If the boundary at the back in the time sequence of the real-time acquisition time range is in the time period between the load data acquisition time points in the acquisition time sequence, adjust the boundary of the real-time acquisition time range to the load data acquisition time point before the boundary in the acquisition time sequence; Combine all the adjusted real-time acquisition time ranges to form the initial segmented acquisition time period data.

4. The acquisition and storage method according to claim 3, wherein The performing of fitting processing on the load curve for each adjusted time period range according to the adjusted segmented acquisition time period data to form interval load curve information includes: Based on the time dimension order, obtain the real-time load value at each collection time point within each adjusted segmented collection interval in the data of the adjusted segmented collection period; For each of the adjusted segmented acquisition intervals, perform curve fitting based on the real-time load value to form a corresponding load fitting curve F k (t: [T k , T k+1 ), where k is the sequence number of the adjusted segmented acquisition interval in the data of the adjusted segmented acquisition period, and [T k , T k+1 represents the acquisition time interval corresponding to the load fitting curve.

5. The acquisition and storage method according to claim 4, characterized in that Performing curve fitting on each of the adjusted segmented acquisition intervals based on the real-time load value to form a corresponding load fitting curve F k (t: [T k , T k+1 ), including: Determine the number m of acquisition time points in the adjusted segmented acquisition interval, and establish a curve equation of the corresponding power: F k = a1t m-1 + a2t m-2 + … + a m-1 t 1 + a m , where a m is the parameter corresponding to each power in the curve equation; Taking the real-time load value and time point corresponding to the collection time point as the solutions of the curve equation, establishing a system of equations to determine the parameters of the curve equation, and forming the load fitting curve F k (t: [T k , T k+1 ).

6. The acquisition and storage method according to claim 5, characterized in that The delaying storage process of the interval load curve information based on information transmission confirmation in sequence includes: Fit the load fitting curve F k (t: [T k , T k+1 ) as the uploaded data and upload it in the order of the time dimension; Temporarily store all the load fitting curves F corresponding to the adjusted segmented acquisition time period data k (t: [T k , T k+1 ) based on the determined information downloaded.

7. The acquisition and storage method according to claim 6, wherein All of the load fitting curves F corresponding to the adjusted segmented acquisition time period data k (t: [T k , T k+1 ) are temporarily stored based on the downloaded determination information, including: All the load fitting curves F corresponding to the adjusted segmented acquisition time period data k (t: [T k , T k+1 ) are locally stored, and the following situations are used to make a judgment on the storage processing: If the downlink confirmation reception information is obtained, delete the temporarily stored data of the adjusted segmented collection period corresponding thereto; If the temporarily stored data exceeds the storage capacity of the storage space, automatically upload and then delete the temporarily stored data of the adjusted segmented collection period in the order from long to short in terms of the temporary storage time.

8. An HPLC module of an electric energy meter with load curve acquisition and storage, adopting the acquisition and storage method described in any one of claims 1-7, characterized in that, It includes: A data collection unit for collecting real-time load data; An upload / download unit for obtaining real-time segmented index data and downlink confirmation reception information, and for uploading load fitting curve data; A curve fitting unit for performing load fitting curve processing on the collected real-time load data collected by the data collection unit, and for obtaining the real-time segmented index data downloaded by the upload / download unit to adjust the collection period interval to form adjusted segmented collection period data; A cache unit for temporarily storing the load fitting curve data formed by the curve fitting unit and for obtaining the downlink confirmation reception information of the upload / download unit to delete the temporarily stored data, and for sending the load fitting curve data to the upload / download unit and deleting it when the cache space is insufficient; Among them, the adjusted segmented collection period data is formed in the following manner: Among them, according to the load point data in the segmented collection period interval of the initial segmented collection period data, and performing segmented adjustment analysis based on the collection point aggregation amount to form adjusted segmented collection period data, including: Determine the collection time points of all load data collection points in the collection time sequence; Determine the number of acquisition time points N within each interval according to the segmented acquisition time period interval for collecting the initial segmented acquisition time period data i , where i represents the number of the segmented acquisition time period interval in the initial segmented acquisition time period data Set a quantity limit threshold α, and perform segmented adjustment analysis based on the collection point aggregation amount on the number of collection time points in each segmented collection period interval to form adjusted segmented collection period data: If α ≥ N i , then no adjustment is made to the corresponding segmented acquisition time period interval; If α < N i ≤ 2α, then the collection time points in the corresponding segmented collection time period interval are evenly divided according to the quantity to form two new segmented collection time period intervals, and the boundaries of the new segmented collection time period intervals are adjusted to the nearest collection time point; If N i > 2α, determine the corresponding segmented acquisition time period interval as the target interval, and then obtain the number of acquisition time points in the segmented acquisition time period interval adjacent to the target interval: When the number of acquisition time points in the adjacent segmented acquisition time period intervals is N i <α, and N i -2α number of time acquisition points in the target interval satisfy that the number of acquisition time points in the adjacent segmented acquisition time period intervals does not exceed the number limit threshold α after being incorporated into the adjacent segmented acquisition time period intervals, then incorporate the N i -2α number of time acquisition points in the target interval into the adjacent segmented acquisition time period intervals, and evenly divide the remaining time acquisition points in the target interval to form new segmented acquisition time period intervals, and adjust the boundaries of the new segmented acquisition time period intervals and the boundaries of the adjacent segmented acquisition time period intervals where the time acquisition points are incorporated to the nearest acquisition time points; When the number of acquisition time points in the adjacent segmented acquisition time period intervals is N i <α, and the number of N i - 2α time acquisition points in the target interval are incorporated into the adjacent segmented acquisition time period interval, and the number of acquisition time points in the adjacent segmented acquisition time period interval still exceeds the quantity limit threshold α, then directly perform average division of the target interval to form new segmented acquisition time period intervals, and adjust the boundaries of the new segmented acquisition time period intervals to the nearest acquisition time points, where represents rounding to the nearest integer; When the number of acquisition time points in the adjacent segmented acquisition time period intervals does not exist N i <α, then directly perform on the target interval average division to form new segmented acquisition time period intervals, and adjust the boundaries of the new segmented acquisition time period intervals to the nearest acquisition time point, where represents rounding up for Combine all the adjusted segmented collection period intervals to form the adjusted segmented collection period data.

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