An intelligent electricity meter with monitoring and traceability

Abstract

The present invention relates to the field of blockchain technology, and specifically relates to an intelligent electric meter with monitoring and traceability, including the following steps: The single-chip microcomputer receives external data through the sensor interface, and after preliminary processing of the received data, the single-chip microcomputer transmits it to the traceability unit through the network communication module. The traceability unit includes a data memory and a processor connected to each other. The processor in the traceability unit is also connected to the cloud server through a communication chip. The traceability unit uploads the types of external data information collected to the cloud server. The user logs in to the cloud server to set the data transfer route of the external data. The cloud server calculates the amount of each external data information according to the information type and data transfer route of the external data, and determines the storage frequency and storage space of the electric meter data and external data according to the amount of information with collection information of all devices in the current same group. When the user queries, the traceability unit performs the query and output.

Description

Technical Field

[0001] The present invention relates to the field of data tracing, and in particular to a smart electric meter with monitoring and tracing function. Background Art

[0002] With the rapid development of my country's economy, the demand for electricity in all walks of life is increasing. The accurate collection and measurement of electricity consumption data involves the direct interests of power supply companies and power users, and is an important link in ensuring the healthy operation of the power supply market. Furthermore, in enterprises, it is not only necessary to monitor the power data. In the era of the Internet of Everything, the use of power information is obviously not limited to billing, but should have a wider application. For example, the energy meter is combined with other sensors in the equipment to better understand the operating status of equipment and instruments, and all data is stored for traceability and comparison. This is an application direction of smart meters in the era of the Internet of Everything. However, in contrast, the problem of information storage and selection in traceability is that blindly seeking the accumulation of large amounts of data is not suitable in terms of cost. In addition, too much repeated data will lead to an increase in junk data, reducing the content of effective data.

[0003] Chinese patent application number: CN202010995699.5, published on January 8, 2021, discloses a data caching method, electronic device and computer-readable medium: first, obtain the first data size of the data to be cached and the second data size of the data set to which the data to be cached belongs; secondly, determine the cache threshold corresponding to the first data size and the second data size; then determine the cache mode corresponding to the cache threshold, so as to cache the data set including the data to be cached through the corresponding cache mode. Since the cache mode is determined based on the first data size of the data to be cached and the second data size of the data set to which the data to be cached belongs, it is possible to determine a cache mode suitable for different data sets, realize intelligent cache allocation of data sets according to the attributes of the data sets themselves, and set a variety of different cache modes to adapt to different cache situations, so that the data sets can be reasonably allocated to caches at different levels, thereby improving the overall search performance.

[0004] This technical content only takes into account the storage method of existing data, but fails to take into account the correlation between data. Therefore, there is still room for improvement in this field. Summary of the invention

[0005] The technical problem to be solved by the present invention is that only the existing data storage method is considered, but the correlation between the data is not considered. Therefore, there is still room for improvement in this field. A smart meter with monitoring and tracing is proposed.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a smart meter with monitoring and tracing, wherein a plurality of electric energy meters and a tracing unit are configured for the same group of equipment, wherein the electric energy meters all include a perception layer for metering, a transmission layer for internal data transmission, an application layer responsible for monitoring, managing and analyzing the real-time data of the system obtained by the perception layer, and a terminal layer for real-time acquisition of operation data and fault alarm information, wherein the perception layer is connected to the terminal layer through the transmission layer and the application layer; wherein the tracing unit is configured on one electric energy meter, and the remaining electric energy meters are interconnected with the tracing unit through a network, wherein the electric energy meter is configured with a connection module, wherein the connection module is electrically connected to a plurality of sensor interfaces for equipment safety monitoring and includes a single-chip microcomputer, a network communication module, and the single-chip microcomputer The single-chip microcomputer is electrically connected to the metering module of the electric energy meter, receives external data through the sensor interface, and transmits the received data to the tracing unit through the network communication module after preliminary processing by the single-chip microcomputer. The tracing unit includes a data storage device and a processor connected to each other. The processor in the tracing unit is also connected to the cloud server through the communication chip. The tracing unit uploads the information type of the collected external data to the cloud server. The user logs in to the cloud server to set the data transmission route of the external data. The cloud server calculates the information volume of each external data according to the information type and data transmission route of the external data, and determines the storage frequency and storage space of the electric energy meter data and the external data according to the information volume of all the collected information of the same group of equipment. When the user inquires, the tracing unit inquires and outputs. The present invention arranges data reasonably according to the correlation between data and the electric energy meter, samples and retains more data with more correlation to the electric energy meter, and samples and retains less data with less correlation to the electric energy meter. Through the above method, the data correlation is retained, which can be approximated to continuous recording compared with the electric energy meter data. The sampling and recording intervals of external data, especially some sensor data, are large and cannot be equivalent to the records of the electric energy meter. Therefore, the data correlation method is adopted and then proportionally collected and saved, which will not generate too much repeated data. At a low cost, the data with the highest correlation can be retained in a relatively limited storage space for effective tracing. The traced data can be used by users for reference on the current energy-saving and emission-reduction measures of electric energy, and can also be used to monitor the operating status of the equipment.

[0007] Preferably, the user logs in to the cloud server and manually arranges all external data in a hierarchical structure. The data transmission relationship between the external data and the corresponding electric energy meter is: if the external data is directly related to the electric energy meter, it is determined to be the second data layer. If the external data needs to be related to the electric energy meter through the second data layer, it is the third data layer. All external data are traversed, and so on. In the present invention, if the electric energy meter is in the same device and the relevant parameters are detected, it is determined to be the second data layer. If it is an adjacent upstream and downstream device of the device, the external data is determined to be the third data layer. In this way, a hierarchical structure is established, and the sum of the information volume of each layer of data is less than or equal to the information volume of the previous layer of data.

[0008] Preferably, the correlation between the external data in a data layer and the corresponding electric energy meter is set. If the data of the electric energy meter is only related to one external data in a data layer, this external data is set to 1, and the sum of the correlations between all data layers and the corresponding electric energy meters is less than or equal to 1.

[0009] Preferably, the correlation between the external data in the current data layer and the corresponding external data in the previous data layer is set; if the external data in the previous data layer is only related to the corresponding external data in the current data layer, the correlation of the current external data is set to be the same as the correlation of the corresponding external data in the previous data layer; in the current data layer, the sum of the correlations of the external data for the same external data in the previous data layer is less than or equal to the correlation of the external data in the previous data layer.

[0010] Preferably, the information type of the current external data includes several dimensions including the collection object of the external data, the source device of the external data, and the device monitored by the external data. The cloud server stores several correlation values ​​of the external data, and each correlation value of the external data corresponds to several dimensions including the collection object, the source device of the external data, and the device monitored by the external data. The Euclidean distance between the current external data and the correlation values ​​of the several external data stored in the cloud server is calculated, and the correlation value of the external data with the closest Euclidean distance is selected as the recommended basic value of the current external data. The number of external data in the current data layer is found as the recommended weighted value, and the correlation value of the current external data is obtained by combining the recommended basic value and the recommended weighted value.

[0011] Preferably, if the external data in the next data layer targets the same external data in the previous data layer, they are the same group of external data, and the sum of the correlation values ​​of the same group of external data is less than or equal to the correlation value of the external data in the previous data layer it targets.

[0012] Preferably, the amount of information of each external data is calculated by the following formula:

[0013]

[0014] In the above formula, P(n) is the correlation value of the external data numbered n, and c is the number of external data in the same group of external data as the external data numbered n.

[0015] Preferably, the storage space allocated to each electric energy meter is determined manually, and the storage frequency of the external data numbered n is:

[0016]

[0017] In the above formula, f(n) is the storage frequency of the external data numbered n, H(max) is the amount of information of the external data with the largest amount of information among all the external data in the electric energy meter corresponding to the external data numbered n, and f(c) is the data storage frequency of the electric energy meter corresponding to the external data numbered n.

[0018] Preferably, the data stored in the external data with the maximum information volume corresponding to the same electric energy meter has its collection time completely corresponding to the collection time of the electric energy meter data, and the storage time of other external data also corresponds to the collection time of the electric energy meter data according to the storage frequency.

[0019] As a preferred method, the recommended storage space allocated to each electric energy meter is calculated by the following formula:

[0020] R i =(H i / H A )×R

[0021] In the above formula, R i is the storage space occupied by all the data for tracing corresponding to the electric energy meter numbered i, H i is the sum of the information of the external data corresponding to the electric energy meter numbered i, H A The number is the sum of the information volume of the external data corresponding to all electric energy meters for the same group of equipment, and R is a basic storage unit.

[0022] The substantial effect of the present invention is that the present invention arranges the data reasonably according to the correlation between the data and the electric energy meter, samples and retains more data with more correlation to the electric energy meter, and samples and retains less data with less correlation to the electric energy meter. Through the above method, the data correlation is retained, which can be approximated to continuous recording compared with the electric energy meter data. The sampling and recording intervals of external data, especially some sensor data, are larger and cannot be equivalent to the records of the electric energy meter. Therefore, the data correlation method is adopted and then collected and saved in proportion, which will not generate too much repeated data. The data with the highest correlation can be retained in a relatively limited storage space at a low cost for effective tracing. The traced data can be used by users for reference on the current energy-saving and emission-reduction measures of electric energy, and can also be used to monitor the operating status of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a schematic diagram of the overall layout of the smart electric energy meter in Example 1.

[0024] Figure 2 This is a diagram showing the layout of the hierarchical data structure in Example 1.

[0025] Figure 3 This is a schematic diagram of the overall process of Example 1. DETAILED DESCRIPTION

[0026] The specific implementation of the present invention will be further described below through specific examples in conjunction with the accompanying drawings.

[0027] Embodiment 1:

[0028] A smart meter with monitoring and tracing function (see Appendix Figure 1 and attached Figure 3), a plurality of electric energy meters and a tracing unit are configured for the same group of equipment, the electric energy meters all include a perception layer for metering, a transmission layer for internal data transmission, an application layer responsible for monitoring, managing and analyzing the real-time data of the system obtained by the perception layer, and a terminal layer for real-time acquisition of operation data and fault alarm information, the perception layer is connected to the terminal layer through the transmission layer and the application layer; the tracing unit is configured on one electric energy meter, and the remaining electric energy meters are interconnected with the tracing unit through a network, the electric energy meter is configured with a connection module, the connection module is electrically connected to a single-chip microcomputer, a network communication module, and a plurality of sensor interfaces for equipment safety monitoring, the single-chip microcomputer is electrically connected to the metering module of the electric energy meter, the single-chip microcomputer is connected through The sensor interface receives external data, and the single-chip microcomputer performs preliminary processing on the received data and transmits it to the tracing unit through the network communication module. The tracing unit includes a data storage device and a processor that are interconnected. The processor in the tracing unit is also connected to the cloud server through a communication chip. The tracing unit uploads the collected external data information type to the cloud server. The user logs in to the cloud server to set the data transmission route of the external data. The cloud server calculates the information volume of each external data according to the information type and data transmission route of the external data, and determines the storage frequency and storage space of the electric energy meter data and the external data according to the information volume of all the collected information of the same group of equipment. When the user inquires, the tracing unit inquires and outputs.

[0029] The user logs in to the cloud server and manually arranges all external data in a hierarchical structure (see Appendix Figure 2 ), according to the relationship between the equipment corresponding to the external sensor and the data directly detected by the electric energy meter, the data are layered and arranged in a tree-like structure, wherein the data directly detected by the electric energy meter is in the first data layer, and the data detected by the sensor are all external data. However, if this external data is directly related to the electric energy meter, or the current sensor belongs to the monitoring of the process quantity in the equipment monitored by the current electric energy meter, then these data belong to the second data layer; if the external data belongs to the parameters of the adjacent upstream and downstream equipment, then it belongs to the third data layer. Furthermore, the data to be traced can be manually set, and if it needs to be subdivided, the directly monitored parameters can be set as the second data layer. For example, the directly monitored parameters, such as the number of processed products in the equipment, are set in the second data layer, and the related direct safety monitoring parameters are the third data layer, and the indirectly monitored parameters are set as the third data layer, and so on.

[0030] For example, according to the data transmission relationship between the external data and the corresponding electric energy meter: if the external data is directly related to the electric energy meter, it is determined to be the second data layer; if the external data needs to be related to the electric energy meter through the second data layer, it is the third data layer, and so on. All external data are traversed, and so on. In addition, the correlation between the external data in a data layer and the corresponding electric energy meter is set. If the data of the electric energy meter is only related to one external data in a data layer, this external data is set to 1, and the sum of the correlations between all data layers and the corresponding electric energy meter is less than or equal to 1. It is also necessary to further set the correlation between the external data in the current data layer and the corresponding external data in the previous data layer. If the external data in the previous data layer is only related to the corresponding external data in the current data layer, the correlation of the current external data is set to be the same as the correlation of the corresponding external data in the previous data layer. In the current data layer, the sum of the correlations of the external data for the same external data in the previous data layer is less than or equal to the correlation of the external data in the previous data layer. That is, as a constraint, if several data in the next data layer are related to a certain data in the previous data layer, then the sum of the correlations of several data in the next data layer should not be greater than the correlation of a certain data in the previous data layer; and the sum of the correlations of all data in the next data layer should not be greater than the sum of the correlations of all data in the previous data layer. That is, if the external data in the next data layer is for the same external data in the previous data layer, it is the same group of external data, and the sum of the correlation values ​​of the same group of external data is less than or equal to the correlation value of the external data in the previous data layer it is for.

[0031] The relevance of data needs to be set manually, but the manually set relevance is difficult to determine. Therefore, in this embodiment, a recommended basic value and a recommended weighted value are set, wherein the recommended basic value can be pre-set by big data or expert setting, and then: the information type of the current external data includes several dimensions including the collection object of the external data, the source device of the external data, and the device monitored by the external data. The cloud server stores several relevance values ​​of the external data, each relevance value of the external data corresponds to several dimensions including the collection object, the source device of the external data, and the device monitored by the external data. The Euclidean distance between the current external data and the relevance values ​​of the several external data stored in the cloud server is calculated, and the information type of the current external data includes the external data. The cloud server stores several correlation values ​​of external data, which can be pre-set by big data or expert setting. Each correlation value of external data corresponds to several dimensions including the collection object, the source device of external data, and the device monitored by external data. The Euclidean distance between the current external data and the correlation values ​​of several external data stored in the cloud server is calculated, and the correlation value of the external data with the closest Euclidean distance is selected as the recommended basic value of the current external data. The number of external data in the current data layer is found as the recommended weighted value, and the correlation value of the current external data is obtained by combining the recommended basic value and the recommended weighted value. In this embodiment, the preset of the correlation includes the correlation value and the corresponding dimensions that express the value. The more dimensions there are, the closer the application scenario expressed by the value is to the target. For example, in the preset, there is a correlation value of the temperature sensor of the heating furnace, which is similar to the dimension of the temperature sensor of the target heating furnace. Then, the correlation value of the temperature sensor of the preset heating furnace can be used as the recommended basic value. Then, according to the data layer of the temperature sensor in the current target and the number of internal and external data in the current data layer, a corresponding recommended weighted value can be given. The recommended weighted value is combined with the recommended basic value to give a recommended value, and then the recommended value is checked for constraints. If it meets the constraint test, it is set as the default value. At the same time, this default value is not completely certain and can be modified manually.

[0032] The amount of information of each external data is calculated by the following formula:

[0033]

[0034] In the above formula, P(n) is the correlation value of the external data numbered n, and c is the number of external data in the same group of external data as the external data numbered n.

[0035] The storage space allocated to each electric energy meter is determined manually, and the storage frequency of the external data numbered n is:

[0036]

[0037] In the above formula, f(n) is the storage frequency of the external data numbered n, H(max) is the amount of information of the external data with the largest amount of information among all the external data in the electric energy meter corresponding to the external data numbered n, and f(c) is the data storage frequency of the electric energy meter corresponding to the external data numbered n.

[0038] The data stored in the external data with the maximum information volume corresponding to the same electric energy meter has a collection time that completely corresponds to the collection time of the electric energy meter data. The storage time of other external data also corresponds to the collection time of the electric energy meter data according to the storage frequency.

[0039] The recommended storage space allocated to each energy meter is calculated by the following formula:

[0040] R i =(H i / H A )×R

[0041] In the above formula, R i is the storage space occupied by all the data for tracing corresponding to the electric energy meter numbered i, H i is the sum of the information of the external data corresponding to the electric energy meter numbered i, H A The number is the sum of the information volume of the external data corresponding to all electric energy meters for the same group of equipment, and R is a basic storage unit.

[0042] This embodiment uses the above method to reasonably allocate the space for data storage and store it at a reasonable frequency. The main logic is that if it is the main core parameter of the same device, then the relationship with the electric energy meter parameters is extremely close. Therefore, it is necessary to strengthen the frequency of collection and storage. If it is the parameter of the adjacent device, for example, in a spraying machine, the temperature control parameter of the upstream incoming equipment is not so important for the spraying machine itself, or it is a parameter that plays an auxiliary role. Taking the spraying machine as an example, the dust monitoring parameters and other parameters involving environmental detection are not particularly important for the spraying machine itself. These parameters can be saved at a lower frequency and will not affect the overall traceability of the electric energy efficiency data of the spraying machine. Then, through the arrangement of a large number of electric energy meters and related sensors, the energy efficiency of the entire production line and the entire factory area can be better monitored, and the data source can be streamlined under relatively limited costs, so as to better trace the overall operation of the factory area.

[0043] In summary, this embodiment arranges the data reasonably according to the correlation with the electric energy meter, samples and retains more data with more correlation with the electric energy meter, and samples and retains less data with less correlation with the electric energy meter. Through the above method, the data correlation is retained, which can be approximated to continuous recording compared with the electric energy meter data. The sampling and recording intervals of external data, especially some sensor data, are large and cannot be equivalent to the records of the electric energy meter. Therefore, the data correlation method is adopted and then collected and saved in proportion, which will not generate too much repeated data. At a low cost, the data with the highest correlation can be retained in a relatively limited storage space for effective tracing. The traced data can be used by users for reference on the current energy-saving and emission reduction measures of electric energy, and can also be used to monitor the operating status of the equipment.

[0044] The above-described embodiment is only a preferred solution of the present invention and does not limit the present invention in any form. There are other variations and modifications without exceeding the technical solution described in the claims.

Claims

1. An intelligent electric meter with monitoring and traceability, where several electric energy meters and a traceability unit are configured for the same group of devices. Each of the electric energy meters includes a perception layer for metering, a transmission layer for internal data transmission, an application layer responsible for monitoring, managing, and analyzing the system real-time data obtained by the perception layer, and a terminal layer for real-time obtaining operation data and fault alarm information. The perception layer is connected to the terminal layer through the transmission layer and the application layer; It is characterized in that, The traceability unit is configured on one electric energy meter, and the remaining electric energy meters are interconnected with the traceability unit through a network. A connection module is configured on the electric energy meter. The connection module includes a single-chip microcomputer and a network communication module, and is electrically connected with several sensor interfaces for device safety monitoring. The single-chip microcomputer is electrically connected with the metering module of the electric energy meter. The single-chip microcomputer receives external data through the sensor interfaces, and after preliminarily processing the received data, transmits it to the traceability unit through the network communication module. The traceability unit includes a data memory and a processor connected to each other. The processor in the traceability unit is also connected to the cloud server through a communication chip. The traceability unit uploads the types of external data information collected to the cloud server. The user logs in to the cloud server to set the data transfer route of the external data. The cloud server calculates the amount of each external data information according to the information type and data transfer route of the external data, and determines the storage frequency and storage space of the electric energy meter data and external data according to the amount of information with collection information of all devices in the current same group. When the user queries, the traceability unit performs the query and output; The user arranges all external data in a hierarchical structure by logging in to the cloud server manually; Set the correlation between the external data in a data layer and the corresponding electric energy meter; The calculation process of the correlation is as follows: The information type of the current external data includes several dimensions such as the collection object of the external data, the source device of the external data, and the device monitored by the external data. The cloud server stores the correlation values of several external data. The correlation value of each external data corresponds to several dimensions including the collection object, the source device of the external data, and the device monitored by the external data. Calculate the Euclidean distance between the current external data and the correlation values of several external data stored in the cloud server, and select the correlation value of an external data with the closest Euclidean distance as the recommended basic value of the current external data. Find the number of external data in the current data layer as the recommended weighting value, and combine the recommended basic value and the recommended weighting value to obtain the correlation value of the current external data.

2. An intelligent electric meter with monitoring and traceability according to claim 1, It is characterized in that, The data transfer relationship between the external data and the corresponding electric energy meter is: if the external data is directly related to the electric energy meter, it is determined as the second data layer; if the external data needs to be related to the electric energy meter through the second data layer, it is the third data layer. Traverse all external data and so on.

3. An intelligent electric meter with monitoring and traceability according to claim 2, It is characterized in that, If the data of the electricity meter is only related to one external data in a data layer, then set this external data to 1, and the sum of the correlations between all data in the first data layer and the corresponding electricity meter is less than or equal to 1.

4. An intelligent electricity meter with monitoring and traceability according to claim 3, wherein, Set the correlation between the external data in the current data layer and the corresponding external data in the previous data layer. If the external data in the previous data layer is only related to the corresponding external data in the current data layer, then set the correlation of the current external data to be the same as the correlation of the corresponding external data in the previous data layer. In the current data layer, the sum of the correlations of the external data for the same external data in the previous data layer is less than or equal to the correlation of the external data in the previous data layer.

5. An intelligent electricity meter with monitoring and traceability according to claim 4, wherein, If the external data in the next data layer is for the same external data in the previous data layer, it is the same group of external data, and the sum of the correlation values of the same group of external data is less than or equal to the correlation value of the external data in the previous data layer it is directed to.

6. An intelligent electricity meter with monitoring and traceability according to claim 1, wherein, The storage space allocated to each electricity meter is determined manually. The storage frequency of the external data numbered n is: In the above formula, f(n) is the storage frequency of the external data numbered n, H(n) is the information volume of the external data numbered n, H(max) is the information volume of the external data with the largest information volume among all the external data corresponding to the electricity meter corresponding to the external data numbered n, and f(c) is the data storage frequency of the electricity meter corresponding to the external data numbered n.

7. An intelligent electricity meter with monitoring and traceability according to claim 1, wherein, For the data stored by the external data with the largest information volume corresponding to the same electricity meter, its acquisition time corresponds exactly to the acquisition time of the electricity meter data, and the storage time of other external data also corresponds to the acquisition time of the electricity meter data according to the storage frequency.

8. An intelligent electricity meter with monitoring and traceability according to claim 1, wherein, The recommended storage space allocated to each electricity meter is calculated by the following formula: R i = (H i / H A ) × R In the above formula, R i is the storage space occupied by all the traceable data corresponding to the electricity meter numbered i, H i is the sum of the amounts of external data corresponding to the electricity meter numbered i, H A is the sum of the amounts of external data corresponding to all the electricity meters for the same group of devices, and R is a basic storage unit.

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