A method for data mining and traceability using smart meters

Abstract

The present invention specifically relates to a method for data mining and traceability using an intelligent electricity meter. The intelligent electricity meter performs the following steps: The intelligent electricity meter marks all the collected data according to the source of collection and uploads it to the server. According to the composition method of the external data, the data extraction method of the intelligent electricity meter is issued; The intelligent electricity meter unifies the formats of all the external data according to the data extraction method; The server assigns correlation values according to the result of data stratification; The selected external data is associated according to the collection time to form an association parameter; The association parameter is saved in the form of a data chain; The user submits a requirement for data mining or traceability to the server. The server searches for a calculation formula, and the server uses or issues the calculation formula to the intelligent electricity meter; The intelligent electricity meter displays the calculation result of the calculation formula.

Description

Technical Field

[0001] The present invention relates to the field of data traceability, and specifically relates to a method for data mining and traceability using smart meters. Background Art

[0002] With the rapid development of China's economy, the demand for electricity in all walks of life is increasing. The accurate collection and measurement of electricity consumption data involve the direct interests of power supply enterprises and electricity users, and are an important link to ensure the healthy operation of the electricity supply market. At present, there are already a large number of smart meters for storing data and traceable smart meters on the market. On this basis, how to better apply smart meters has become the next topic.

[0003] Chinese Patent Application No.: CN201720369691.1, with a publication date of November 24, 2017, discloses a traceable smart meter system. It solves the problems of the existing smart meters without traceability function and single function. The system includes a meter terminal, an operation terminal, and a server terminal. The meter terminal includes a meter housing, and a collection unit, a control unit, a communication unit, an electronic tag, and a reading and writing unit are arranged inside the housing. The electronic tag is inductively connected to the reading and writing unit, and the reading and writing unit, the collection unit, and the communication unit are respectively connected to the control unit. There is a reading unit on the operation terminal, and the reading unit is connected to the electronic tag in the induction area. The communication unit is connected to the server terminal through the network. The utility model enables the meter to have a traceability function. The use of electronic tags enables operators to read the information of multiple electronic tags within the induction range without reading the information one by one separately, which is convenient and fast. Operators can also conveniently and quickly collect the meter index information. It can be seen that traceable smart meters already exist in the market and have a certain living space. Then, on this basis, whether the application of smart meters can be further improved and the application of smart meters in actual production can be further strengthened is the next topic. Further, in an enterprise, not only the monitoring of electricity data is required. In the era of Internet of Everything, the utilization of electric energy information is obviously not limited to billing, but should have a broader application. Cooperating the electricity meter with other sensors in the equipment to obtain a better operating state of the equipment and instruments, and storing all the data for traceability and comparison is an application direction of smart meters in the era of Internet of Everything. Summary of the Invention

[0004] The technical problem to be solved by the present invention is that only the storage of existing data is considered, and smart meters are not fully utilized, resulting in a waste of resources of a large number of smart meters. The present invention proposes a method for data mining and traceability using smart meters.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is as follows: A method for data mining and traceability using smart meters, including a number of smart meters connected to each other for communication. All smart meters are configured with a number of connection interfaces. The smart meters are connected to external sensors through the connection interfaces. The data input from the external sensors to the smart meters is external data. The smart meters are connected to a computing server. The smart meters perform time calibration with external detection devices. After the determination of the delay time, the smart meters perform the following steps:

[0006] Step 1, the smart meters mark all the collected data according to the collection sources and upload them to the server.

[0007] Step 2, the server analyzes all the external data. First, it performs data stratification according to the sources of the external data and their relevance to the smart meters. Then, according to the composition method of the external data, it issues the data extraction method for the smart meters.

[0008] Step 2, the smart meters unify the formats of all the external data according to the data extraction method.

[0009] Step 3, the server assigns relevance values according to the results of the data stratification and calculates the information volume of the external data in the way that the higher the relevance, the greater the influence degree.

[0010] Step 4, calculate the information volume of all the external data, determine the selected external data according to the proportion of the information volume, and associate the selected external data according to the collection time to form an association parameter.

[0011] Step 5, save the association parameter in the form of a data chain.

[0012] Step 6, the user submits the requirements for data mining or traceability to the server. The server searches for calculation formulas according to the requirements for data mining or traceability and the content of the association parameter. The server uses or issues the calculation formulas to the smart meters.

[0013] Step 7, the smart meters display the calculation results of the calculation formulas.

[0014] The present invention adopts the method of connecting smart meters with other sensors, makes the smart meters into a small recording center with storage and traceability functions, and at the same time introduces a server. After the smart meters upload data, the real-time electric energy and other detection parameters are combined for in-depth data mining, and more favorable energy efficiency data, carbon emission data, etc. can be obtained, thereby improving the utilization rate of smart meters and also improving the utilization rate of the electric energy data obtained from the smart meters.

[0015] Preferably, in the server, the levels of each data are set, where the higher the importance, the higher the level, and the lower the importance, the lower the level. Further, the associated relationships between the upper-level data can also be set, and the sum of the correlations of the external data in the lower level must be less than the value of the correlation of the corresponding external data in the upper data level.

[0016] Preferably, the amount of information of each external data is calculated by the following formula:

[0017] H(n) = -∑ n p(n)log c [p(n)]

[0018] In the above formula, P(n) is the correlation value of the external data numbered n, and the correlation value of the external data is determined by the level of the data layer where this data is located and the number of external data in the same level. c is the number of external data in the same level where the external data numbered n is located. The storage space allocated to each electric energy meter is determined manually. The storage frequency of the external data numbered n is:

[0019]

[0020] 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 corresponding to 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.

[0021] Preferably, the sums of the correlation values of the external data are arranged in sequence according to the levels of the data layers, with the sum of the correlation values of the external data in the first data layer being the largest and the sum of the correlation values of the external data in the last data layer being the smallest, and the sum of the correlation values of the external data in the first data layer being less than or equal to 1.

[0022] Preferably, in step six, the data mining formula is a carbon emission data conversion formula. For each carbon emission data conversion formula in the server, a number of domain dimensions and corresponding power data ranges are set. The domain dimensions at least include equipment types, equipment categories, and equipment rated parameter ranges. The server searches for the closest carbon emission data conversion formula according to the domain dimensions, power data, and external data ranges corresponding to the associated parameters.

[0023] Preferably, when the server searches for the corresponding carbon emission data conversion formula, it uses the method of dimension distance measurement to search for the closest carbon emission data conversion formula:

[0024] Sub-step 1: Assign values to the dimensions corresponding to each carbon emission data conversion formula. All carbon emission data conversion formulas correspond to a multi-dimensional data position value.

[0025] Sub-step 2: Calculate the multi-dimensional data position value corresponding to the smart meter.

[0026] Sub-step 3: Select the carbon emission data conversion formula with the closest Euclidean distance to the multi-dimensional data position value of the smart meter.

[0027] Sub-step 4: Compare the proportional relationship between the rated parameter range of the external detection device and the rated parameter range corresponding to the carbon emission data conversion formula, and perform proportional correction on the carbon emission data conversion formula.

[0028] Preferably, when all external detection devices are initially connected to the server, immediately assign values to the corresponding dimensions, perform a preliminary screening of the selection range of the carbon emission data conversion formula, and select the carbon emission data conversion formula with a distance less than the threshold for each dimension to enter the preliminary screening alternative library. When using the method of measuring the dimension distance to find the closest carbon emission data conversion formula, only select within the preliminary screening alternative library.

[0029] The substantial effect of the present invention is that the present invention adopts the method of connecting the smart meter with other sensors, makes the smart meter into a small recording center with storage and traceability functions, and at the same time introduces a server. After the smart meter uploads data, it combines real-time electric energy and other detection parameters for in-depth data mining, and can obtain more favorable energy efficiency data, carbon emission data, etc., thereby improving the utilization rate of the smart meter and also improving the utilization rate of the electric energy data obtained from the smart meter. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the overall process of the smart electric energy meter in Embodiment 1.

[0031] Figure 2 It is a schematic diagram of a selection process when the mining formula is a carbon emission conversion formula in Embodiment 1. DETAILED DESCRIPTION OF THE INVENTION

[0032] The following will further specifically describe the specific implementation manners of the present invention through specific embodiments and in combination with the drawings.

[0033] Embodiment 1:

[0034] A method for data mining and traceability using a smart meter (see the attached Figure 1) It includes several smart meters that communicate with each other. All smart meters are configured with several connection interfaces. The smart meters are connected to external sensors through the connection interfaces. The data input from the external sensors to the smart meters is external data. The smart meters are connected to a computing server. The smart meters perform time calibration with external detection devices. After the determination of the delay time, the smart meters execute the following steps:

[0035] Step 1: The smart meters mark all the collected data according to the source of collection and upload it to the server.

[0036] Step 2: The server analyzes all the external data. First, it performs data stratification based on the source of the external data and its relevance to the smart meters. Then, according to the composition method of the external data, it issues the data extraction method for the smart meters.

[0037] Step 2: The smart meters unify the formats of all the external data according to the data extraction method.

[0038] Step 3: The server assigns relevance values according to the results of data stratification and calculates the information volume of the external data in the way that the higher the relevance, the greater the impact.

[0039] Step 4: Calculate the information volume of all the external data, determine the selected external data according to the proportion of the information volume, and associate the selected external data according to the collection time to form an association parameter.

[0040] Step 5: Save the association parameter in the form of a data chain.

[0041] Step 6: The user submits the need for data mining or traceability to the server. The server searches for a calculation formula according to the need for data mining or traceability and the content of the association parameter. The server uses or issues the calculation formula to the smart meters.

[0042] Step 7: The smart meters display the calculation results of the calculation formula.

[0043] In the server, the levels of each data are set. The higher the importance, the higher the level; the lower the importance, the lower the level.

[0044] The information volume of each external data is calculated by the following formula.

[0045] H(n)=-∑ n p(n)log c [p(n)]

[0046] In the above formula, P(n) is the correlation value of the external data numbered n. The correlation value of the external data is determined by the level of the data layer where this data is located and the number of external data in the same level. c is the number of external data in the same level where the external data numbered n is located. The storage space allocated to each electric energy meter is determined manually. The storage frequency of the external data numbered n is:

[0047]

[0048] In the above formula, f(n) is the storage frequency 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 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.

[0049] The sums of the correlation values of the external data are arranged in sequence according to the levels of the data layers. The sum of the correlation values of the external data in the first data layer is the largest, the sum of the correlation values of the external data in the last data layer is the smallest, and the sum of the correlation values of the external data in the first data layer is less than or equal to 1.

[0050] The user logs in to the cloud server and manually arranges all the external data in the way of the hierarchical structure (see Appendix Figure 2 ). It is stratified according to the relationship between the device corresponding to the external sensor and the data directly detected by the electric energy meter, and is arranged in a structure similar to a tree. Among them, the data directly detected by the electric energy meter forms the first data layer. The data detected by the sensor all belong to the 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 of the device monitored by the current electric energy meter, then these data all belong to the second data layer. If the external data belongs to the parameters of adjacent upstream and downstream devices, then it belongs to the third data layer. Further, the data that needs to be traced can be set manually. Among them, for those that need to be subdivided, the directly monitored parameters can be set as the second data layer. For example, the number of processed products in the device, etc., are set as the second data layer, and the relevant direct safety monitoring parameters are the third data layer, and the indirectly monitored parameters are set as the third data layer, and so on.

[0051] For example, the data transfer relationship between the external data and the corresponding electricity meter is as follows: if the external data is directly related to the electricity meter, it is determined as a two - data layer; if the external data needs to be related to the electricity meter through the two - data layer, it is a three - data layer. Traverse all the external data and so on. Moreover, set the relevance between the external data in a certain data layer and the corresponding electricity meter. If the data of the electricity meter is only related to one external data in a certain data layer, then set this external data as 1, and the sum of the relevance between all the external data in this data layer and the corresponding electricity meter is less than or equal to 1. It is also necessary to further set the relevance 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 relevance of the current external data to be the same as the relevance of the corresponding external data in the previous data layer. In the current data layer, the sum of the relevance of the external data corresponding to the same external data in the previous data layer is less than or equal to the relevance of the external data in the previous data layer. That is, as a constraint condition, if several data in the next data layer are related to a certain data in the previous data layer, then the sum of the relevance of these several data in the next data layer should not be greater than the relevance of a certain data in the previous data layer; and the sum of the relevance of all the data in the next data layer should not be greater than the sum of the relevance of all the 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 external data, and the sum of the relevance values of the same - group external data is less than or equal to the relevance value of the external data in the previous data layer that it is directed to.

[0052] The relevance of data needs to be set manually, but there are problems in determining the relevance set manually. Therefore, in this embodiment, a recommended base value and a recommended weighting value are set. Among them, the recommended base value can be preset through big data or the way set by experts, and then: that is, the information type of the current external data includes several dimensions such as the acquisition object of the external data, the source device of the external data, and the device monitored by the external data. There are several relevance values of external data stored in the cloud server. Each relevance value of external data corresponds to several dimensions including the acquisition 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 several relevance values of external data stored in the cloud server. Select the information type of the current external data including several dimensions such as the acquisition object of the external data, the source device of the external data, and the device monitored by the external data. There are several relevance values of external data stored in the cloud server. The several relevance values of external data stored here can be preset through big data or the way set by experts. Each relevance value of external data corresponds to several dimensions including the acquisition 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 several relevance values of external data stored in the cloud server. Select the relevance value of an external data with the closest Euclidean distance as the recommended base 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 base value and the recommended weighting value to obtain the relevance value of the current external data. In this embodiment, the preset of relevance includes the relevance value and several dimensions corresponding to expressing this value. The more dimensions there are, the closer the application scenario expressed by this value is to the target. For example, in the preset, there is a relevance 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 this preset relevance value of the temperature sensor of the heating furnace can be used as the recommended base value. Then, according to the data layer of the temperature sensor in the current target and the number of external data in the current data layer, the corresponding recommended weighting value can be given. Combine the recommended weighting value with the recommended base value to give a recommended value, and then check the constraint conditions for the recommended value. If it meets the check of the constraint conditions, it is set as the default value. At the same time, this default value is not completely determined and can be corrected manually.

[0053] Since power consumption data and external data often involve fields such as carbon emission monitoring, the company's own data chain can be used to save data in a tamper-proof manner, thus achieving better data security. The smart meter integrates associated parameters in the first cycle and writes the associated parameters into the specified buffer area; a data preparation layer is constructed. The data preparation layer retrieves the metering readings from the buffer area in the first cycle to establish saved data, and the saved data includes associated parameters, timestamps, smart meter numbers, and specified storage addresses; a storage proxy layer is established. The storage proxy layer creates several storage areas on the storage medium, generates storage blocks that match the size of the saved data on the storage areas, addresses the storage blocks, and the storage proxy layer establishes an association table. The association table records the storage block addressing and the associated random codes, and periodically provides a preset number of random codes to the data preparation layer for use as the specified storage address; the storage proxy layer receives the saved data, queries the association table according to the specified storage address therein. If the specified storage address exists in the association table, the saved data is stored on the storage block addressing corresponding to the specified storage address, and the random code is deleted from the association table. If the specified storage address does not exist in the association table, the saved data is discarded; a certification and fixation layer is constructed. In the second cycle, the saved data newly stored in the storage area is incorporated into the set to be fixed, the timestamps of all the saved data in the set to be fixed are extracted as the time set, the hash values of each structure in the set to be fixed are extracted as the hash set, the hash value of the time set and the hash set together is extracted as the fixed hash value, and the fixed hash value is uploaded to the blockchain for storage to obtain the corresponding block height and block hash value. The block height, block hash value, fixed hash value, time set, and hash set are associated and stored as a certification data packet; in the third cycle, the metering readings and timestamps included in the newly stored saved data are uploaded to the server.

[0054] The saved data further includes exhaustive numbers and matching numbers. The exhaustive numbers, matching numbers, metering readings, and timestamps meet the proof-of-work conditions. The proof-of-work conditions are: extracting the hash values of the metering readings and timestamps, the last N digits of the matching numbers take the same values as the hash values, the first M digits of the hash value extracted from the exhaustive numbers and the matching numbers together take the value of 0. The matching numbers come from a matching number set, and the matching number set is generated and issued by the server and is periodically replaced.

[0055] The smart meter uploads the stored saved data and certification data packets to the server in the fourth cycle;

[0056] The server verifies the integrity of the saved data, looks up the time set where the timestamp is located according to the timestamp in the saved data, and finds the certification data packet corresponding to the certification of the saved data according to the time set;

[0057] Obtain the evidence storage hash value, block hash value, and block height in the evidence storage data packet, access the blockchain for verification. If the evidence storage hash value does not exist in the corresponding block, it is determined that the smart meter is abnormal and manual inspection or replacement is required;

[0058] If the evidence storage hash value exists in the corresponding block, extract the hash value of the saved data, search for the hash value in the hash set. If it exists, the verification passes and the verification of the next smart meter is carried out. If it does not exist, it is determined that the smart meter is abnormal and manual inspection or replacement is required.

[0059] In this embodiment, the common data mining formula is the carbon emission data conversion formula. The detection of carbon emissions is generally carried out in the form of a conversion formula. Especially in terms of electricity consumption and fossil energy consumption, the conversion formula is used for calculation. For each carbon emission data conversion formula in the server, several domain dimensions and corresponding power data ranges are set. The domain dimensions at least include equipment types, equipment categories, and equipment rated parameter ranges. The server searches for the closest carbon emission data conversion formula according to the domain dimensions corresponding to the associated parameters, power data, and external data ranges. However, general carbon emission data conversion can only calculate the overall carbon emissions, and there is a lack of corresponding supervision on whether the active power, reactive power, and carbon emissions are saved and reasonable. Therefore, in the present invention, it is proposed to combine other external detection data, such as gas flow rate, with the active power data and reactive power data in the electricity meter data, so as to achieve the phased display of carbon emission data according to various application parameters. In the future, through higher calculation frequencies, more intensive data monitoring can be realized, forming a carbon emission data monitoring that approximates real-time monitoring and real-time display. Further, this embodiment can also combine the active power part in the smart meter with the gas flow rate data to monitor whether there are points worthy of improvement in the actual efficiency and carbon emissions of the equipment. Whether it is possible to reduce the gas usage when the reactive power is high, thereby improving the overall carbon emission efficiency of the equipment.

[0060] In this embodiment, according to different parameters and uses of the equipment, several carbon emission data conversion formulas can be preset manually. A relatively rough carbon emission data conversion formula is as follows: According to expert statistics, for every 1 kWh of electricity saved, 0.4 kg of standard coal is correspondingly saved, and at the same time, 0.272 kg of carbon dust, 0.997 kg of carbon dioxide, 0.03 kg of sulfur dioxide, 0.015 kg of nitrogen oxides, and so on are reduced. However, the detection of electricity use is relatively simple, while the detection of coal use is relatively complex. For example, there is a certain amount of coal in the combustion chamber, and there is also the case of electric heating assistance. Then, during the combustion process, the number of coals consumed in real time and the carbon emission data cannot be directly detected. At this time, temperature detection, electricity detection, and electric energy parameter detection are required, and phased data can be obtained according to the corresponding conversion formula. Therefore, finding a suitable carbon emission data conversion formula is a key point in this embodiment.

[0061] When the server searches for the corresponding carbon emission data conversion formula (see Appendix Figure 2 ), the method of dimension distance measurement is used to find the closest carbon emission data conversion formula:

[0062] Sub-step one, assign values to the dimensions corresponding to each carbon emission data conversion formula. All carbon emission data conversion formulas correspond to a multi-dimensional data position value. For example, the first dimension of formula E1 is the use of the equipment assigned as a1, the second dimension is the type of energy consumption of the equipment assigned as b1, the third dimension is the applicable parameter range of the equipment assigned as c1, the fourth dimension is the corresponding industrial type assigned as d1, and so on. And the first dimension of formula E2 is assigned as a2, the second dimension is assigned as b2, the third dimension is assigned as c2, the fourth dimension is assigned as d2, and so on. Each carbon emission data conversion formula is assigned according to different dimensions in turn.

[0063] Sub-step two, calculate the multi-dimensional data position value corresponding to the smart meter. For example, the dimensions of smart meter C1 are a4, b4, c3, d2 respectively, and this is the multi-dimensional data position value of smart meter C1;

[0064] Sub-step three, select the carbon emission data conversion formula with the closest Euclidean distance to the multi-dimensional data position value of the smart meter; calculate the dimension distance between C1 and E1 as:

[0065] C1 - E1 = ((a4 - a1) 2 + (b4 - b1) 2 + (c3 - c1) 2 + (d2 - d1) 2 ) 1 / 2

[0066] Correspondingly,

[0067] C1 - E2 = ((a4 - a2) 2 +(b4 - b2) 2 +(c3 - c2) 2 +(d2 - d2) 2 ) 1 / 2

[0068] Assume that there are only the above two carbon emission data conversion formulas. Then, compare the differences between the above two formulas, and select the carbon emission data conversion formula with the smallest difference.

[0069] Sub-step 4: Compare the proportional relationship between the rated parameter range of the external detection device and the rated parameter range corresponding to the carbon emission data conversion formula, and perform proportional correction on the carbon emission data conversion formula. This situation corresponds to that the rated parameter of device A is 1A - 2A, while the rated maximum parameter of the device corresponding to the smart meter is 10A - 20A. Then, it may be necessary to perform proportional correction on the carbon emission data conversion formula among them.

[0070] When all external detection devices are initially connected to the server, immediately assign values to the corresponding dimensions, perform a preliminary screening of the selection range of the carbon emission data conversion formula, and select the carbon emission data conversion formula with the distance of each dimension less than the threshold into the preliminary screening alternative library. When using the method of dimension distance measurement to find the closest carbon emission data conversion formula, only select within the preliminary screening alternative library. Through the alternative library method, the difficulty of screening can be effectively reduced, and the working efficiency of the server can be further improved.

[0071] Each time the server receives the associated parameters, compare the power data and external data parsed out with the average power data and the average external data in the previously received associated parameters. If both the power data and the external data are within the threshold range, calculate according to the previously selected carbon emission data conversion formula. If the power data or the external data is outside the threshold range, weight the dimension distance of the power data or the external data outside the threshold range. Then, the server searches for the corresponding carbon emission data conversion formula in the preliminary screening alternative library again and recalculates the carbon emission data. If both the power data and the external data are outside the threshold range, the server searches for the corresponding carbon emission data conversion formula again and recalculates the carbon emission data.

[0072] In the above embodiments, through the data interaction between the smart meter and other devices, the overall display of data is carried out, and through the application of power data and other fossil energy or other energy data, the carbon emission data is calculated using the carbon emission data conversion formula, so as to realize the on-site real-time display of carbon emission data. Through the real-time monitoring method, it provides guidance for the carbon emission reduction and energy conservation of enterprises.

[0073] The embodiments described above are only a preferred solution of the present invention, and do not impose any form of limitation on the present invention. There are other variations and modifications without exceeding the technical solutions described in the claims.

Claims

1. A method for data mining and traceability using a smart meter, characterized in that, It includes several smart meters that communicate with each other. All smart meters are configured with several connection interfaces. The smart meters are connected to external sensors through the connection interfaces. The data input from the external sensors to the smart meters is external data. The smart meters are connected to a computing server. The smart meters perform time calibration with external detection devices. After the delay time measurement is completed, the smart meters perform the following steps: Step 1: The smart meters mark all the collected data according to the collection sources and upload them to the server; Step 2: The server analyzes all the external data. First, it stratifies the data according to the sources of the external data and their relevance to the smart meters, and then, according to the composition method of the external data, issues the data extraction methods for the smart meters; Step 2: The smart meters unify the formats of all the external data according to the data extraction methods; Step 3: The server assigns relevance values according to the results of data stratification and calculates the information volume of the external data in the way that the higher the relevance, the greater the impact; Step 4: Calculate the information volume of all the external data, determine the selected external data according to the proportion of the information volume, and associate the selected external data according to the collection time to form an association parameter; Step 5: Save the association parameter in the form of a data chain; Step 6: The user submits the requirements for data mining or traceability to the server. The server searches for calculation formulas according to the requirements for data mining or traceability and the content of the association parameter. The server uses or issues the calculation formulas to the smart meters; A data mining formula is used for calculation. The data mining formula is a carbon emission data conversion formula. For each carbon emission data conversion formula in the server, several domain dimensions and corresponding power data ranges are set. The domain dimensions at least include equipment types, equipment categories, and equipment rated parameter ranges. The server searches for the closest carbon emission data conversion formula according to the domain dimensions, power data, and external data ranges corresponding to the association parameter; Step 7: The smart meters display the calculation results of the calculation formulas.

2. The method for data mining and traceability using a smart meter according to claim 1, characterized in that, In the server, the levels of each data are set. The higher the importance, the higher the level; the lower the importance, the lower the level.

3. The method for data mining and traceability using a smart meter according to claim 1, characterized in that The sums of the relevance values of the external data are arranged in sequence according to the levels of the data layers. The sum of the relevance values of the external data in the first data layer is the largest, the sum of the relevance values of the external data in the last data layer is the smallest, and the sum of the relevance values of the external data in the first data layer is less than or equal to 1.

4. The method for data mining and traceability using a smart meter according to claim 1, characterized in that, When the server searches for the corresponding carbon emission data conversion formula, it uses the method of dimension distance measurement to find the closest carbon emission data conversion formula: Sub-step 1: Assign values to the dimensions corresponding to each carbon emission data conversion formula. All carbon emission data conversion formulas correspond to a multi-dimensional data position value; Sub-step 2: Calculate the multi-dimensional data position value corresponding to the smart meter; Sub-step 3: Select the carbon emission data conversion formula with the closest Euclidean distance to the multi-dimensional data position value of the smart meter; Sub-step 4: Compare the proportional relationship between the rated parameter range of the external detection device and the rated parameter range corresponding to the carbon emission data conversion formula, and perform proportional correction on the carbon emission data conversion formula.

5. The method for data mining and traceability using a smart meter according to claim 4, characterized in that, When all external detection devices are initially connected to the server, immediately assign values to the corresponding dimensions, conduct a preliminary screening of the selection range of the carbon emission data conversion formula, and select the carbon emission data conversion formula with a distance less than the threshold for each dimension to enter the preliminary screening alternative library. When using the method of dimension distance measurement to find the closest carbon emission data conversion formula, only select from the preliminary screening alternative library.

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