Marketing electricity larceny prevention data acquisition method based on big data

Abstract

The invention discloses a marketing anti-electricity-stealing data acquisition method based on big data, and relates to the technical field of anti-electricity-stealing, and the method comprises the steps: analyzing the electricity consumption data of all electric devices, generating the electricity consumption data of the devices, reversely analyzing the information of the electric devices according to the electricity consumption information, generating the reverse analysis electricity consumption data, and analyzing the difference data of the two groups of data; the method has the advantages that by collecting the starting time of the electric equipment and calculating the total energy consumption, the electricity utilization rule and the energy consumption condition of each piece of equipment can be mastered in detail, abnormal electricity utilization behaviors can be found easily, and the method is suitable for popularization and application. And the power consumption information at the time point is randomly extracted from the user side energy consumption diagram for reverse analysis, and reverse analysis power consumption data is generated, so that the accuracy and reliability of data analysis are further improved, the information of the power consumption equipment can be verified from multiple angles, and possible abnormal conditions can be quickly locked.

Description

Technical Field

[0001] The present invention relates to the technical field of anti-electricity theft, and in particular to a marketing anti-electricity theft data collection method based on big data. Background Art

[0002] Electricity theft refers to the act of illegally occupying electric energy, not paying or paying less electricity bills, and using illegal means to measure or under-measure electricity usage. Anti-electricity theft is an important measure to maintain power supply order, protect the legitimate rights and interests of power companies and public safety. Electricity theft directly leads to power loss of power supply companies and reduces the company's electricity sales revenue. Through anti-electricity theft work, the phenomenon of electricity theft can be effectively curbed, economic losses can be recovered, and the normal operation and sustainable development of power supply companies can be ensured. The rights and interests of the majority of legal electricity users are maintained and the fairness of electricity use is ensured. If electricity theft is not effectively curbed, then users who pay honestly will need to bear the losses caused by the thieves, which is obviously unfair. Through anti-electricity theft, all users can use electricity under fair rules, creating a fair and orderly electricity use environment;

[0003] The existing marketing anti-electricity theft data collection method cannot accurately collect the startup time of power-consuming equipment when used, which affects the accuracy of the total energy consumption calculation, and then leads to inaccurate equipment power consumption data. It is difficult to understand the power consumption patterns and energy consumption of each device in detail, and it is impossible to quickly lock in possible abnormal situations, affecting the efficiency of subsequent traceability work. To this end, we propose a marketing anti-electricity theft data collection method based on big data. Summary of the invention

[0004] The purpose of the present invention is to provide a marketing anti-electricity theft data collection method based on big data.

[0005] In order to solve the problems raised in the above background technology, the present invention provides the following technical solutions: a marketing anti-electricity theft data collection method based on big data, comprising the following steps:

[0006] S100, establish a user terminal, use the user terminal to collect the user's meter data, obtain the total power consumption data, and obtain the user's newly added power consumption equipment information, and analyze the power consumption data of all power consumption equipment through actual access;

[0007] S200, collecting the startup time of the power-consuming equipment, calculating the total energy consumption of the power-consuming equipment, and generating the power consumption data of the equipment;

[0008] S300, generating a user-side energy consumption graph based on the total power consumption data and the power consumption data of the power-consuming devices, randomly extracting power consumption information of ten groups of time points from the user-side energy consumption graph, reversely analyzing the power-consuming device information based on the power consumption information, and generating reverse analysis power consumption data;

[0009] S400, extracting equipment power consumption data and reversely analyzing power consumption data, and comparing the two sets of data to analyze the difference between the two sets of data;

[0010] S500, analyzing the source of the difference data, matching the difference data with the energy consumption of the electrical equipment, generating traceability information, formulating an abnormal calculation model, and analyzing the rationality of the traceability information;

[0011] S600, establish an authorization unit, then enter the staff's information into the authorization unit, obtain the status of the power control box, when the power control box is opened, capture the portrait, and then match the portrait information with the information in the authorization unit.

[0012] As a further solution of the present invention: in S100, when analyzing the power consumption data of all power-consuming devices, the energy consumption level information of the user equipment is analyzed through the power-consuming equipment information, so as to further determine the energy consumption range of the power-consuming equipment and increase the accuracy of the energy consumption collection of the power-consuming equipment.

[0013] As a further solution of the present invention: In the above S200, when calculating the total energy consumption of different electrical devices, it is necessary to obtain the startup time, energy consumption level and energy consumption information corresponding to different energy consumption levels of different devices. Suppose the startup time of different devices is S Q , assuming that the energy consumption of different devices is N H , assuming the total energy consumption of electrical equipment is Y 总能耗 , let the number of electrical equipment be P;

[0014]

[0015] According to the above formula, the total energy consumption of electrical equipment can be calculated.

[0016] As a further solution of the present invention: In S300, the method for reversely analyzing the information of the electrical equipment is as follows:

[0017] S310, establishing a habit unit, using the habit unit to record the electrical equipment that can be used at different time points, and synchronously extracting the power consumption of different electrical equipment;

[0018] S320, determining the lowest energy consumption electrical equipment among different electrical equipment, generating the lowest power consumption index, and then converting the power consumption of the remaining electrical equipment into index sub-energy consumption based on the lowest power consumption index, and generating an index sub-energy consumption table;

[0019] S330, extracting power consumption information of ten groups of time points, and synchronously converting them into the total energy consumption of the indicator, then analyzing the configurations of different indicator sub-energy consumptions equal to the total energy consumption of the indicator, and then calculating whether the difference between the indicator sub-energy consumption of different configurations and the total energy consumption of the indicator is equal to zero;

[0020] When the difference between the sub-energy consumption of indicators with different configurations and the total energy consumption of indicators is equal to zero, it is judged that there is no abnormality in the electrical equipment;

[0021] When the difference between the sub-energy consumption of indicators with different configurations and the total energy consumption of indicators is not equal to zero, it is determined that there is an abnormality in the electrical equipment.

[0022] As a further solution of the present invention: in the S400, when comparing the equipment power consumption data and the reverse analysis power consumption data, it is necessary to draw the equipment power consumption data and the reverse analysis power consumption data into an equipment line graph and a reverse analysis line graph, and then calculate the difference between the two sets of data at each time point, and set an impact unit at the same time. The impact unit can record the operating time, operating environment and equipment maintenance factors of the equipment, and the user can independently mark the content of the impact unit, so as to more comprehensively affect the impact of the factors in the unit on energy consumption. At this time, the absolute values ​​of all differences are calculated, and the absolute values ​​of all differences are added to generate a total difference, and then the average difference at different time points is calculated based on the total difference and the number of differences. When calculating the difference at each time point, the energy consumption of normal power equipment is analyzed according to the smart meter APP, and the maximum energy consumption is set as the energy consumption abnormality threshold. At the same time, the energy consumption abnormality threshold is controlled by the management personnel, and then the position exceeding the energy consumption abnormality threshold is marked.

[0023] As a further solution of the present invention: In S500, the method for matching the difference data with the energy consumption of the electrical equipment is as follows:

[0024] S510, analyzing the energy consumption difference of the difference data, reversely converting the energy consumption difference according to the minimum power consumption index, and generating the differential power consumption in kilowatt-seconds;

[0025] S520, extracting the energy consumption of electrical equipment and converting it in kilowatt-seconds to obtain the electrical energy required per second for different electrical equipment and generate the electrical power consumption of the equipment;

[0026] S530, then use the difference power consumption as the total value and the equipment power consumption as the sub-value for matching analysis, determine the configuration information of the equipment power consumption, and calculate the difference between the difference power consumption and the equipment power consumption. When the difference is 0, it is determined that the location is in a preliminary elimination state. When the difference is not 0, it is determined that there is abnormal information at the location and it is marked again to facilitate further review by the staff.

[0027] As a further solution of the present invention: the method for analyzing the rationality of the traceability information is as follows:

[0028] S540, extracting the configuration information in S530, analyzing the power consumption time of the power-consuming devices in the configuration information, obtaining the startup modes of all the power-consuming devices, and analyzing the power consumption time under different startup modes;

[0029] S550, after formulating the abnormal calculation module, an abnormal unit is set inside the abnormal calculation module, and then a time abnormal threshold range is set for all the electrical devices in the abnormal unit according to the startup mode and power consumption time of all the electrical devices;

[0030] S560, then calculating whether the power consumption time of all power-consuming devices is within the time anomaly threshold range, and if it is within the time anomaly threshold range, automatically adjusting the power consumption time within the time anomaly threshold range and fixing it, and then generating a layer of data;

[0031] Assume that the time anomaly threshold range is Y F1 -Y F2 , assuming that the power consumption time of the electrical equipment is Y 时间 ;

[0032] When Y F1 ≤Y 时间 ≤Y F2 When the power consumption time of the power-consuming equipment is within the time abnormality threshold range,

[0033] When Y 时间 ≤Y F1 and Y 时间 ≥Y F2 When the power consumption time of the power-consuming equipment is determined to be not within the time abnormality threshold range;

[0034] S570: Transmit the first layer of data to S530, and reanalyze the configuration of the first layer of data.

[0035] As a further solution of the present invention: in S570, after re-analyzing the configuration of one layer of data, the process of S540-S560 is repeated again. When there is still electricity usage time within the time abnormality threshold range, it will be adjusted and fixed again to generate double data, and then the repeated analysis threshold will be set. When the number of repeated analyses exceeds the repeated analysis threshold, the abnormal data will be marked and displayed.

[0036] As a further solution of the present invention: in the S600, when recording the authorized portrait, the authorization unit will extract features of the authorized portrait, obtain initial feature information, and record the extracted features on the corresponding authorized portrait. After capturing the portrait information, the facial features of the portrait information will be extracted first, and then the initial feature information will be retrieved based on the facial features, and the corresponding authorized portrait information will be extracted and compared. The similarity between the authorized portrait information and the captured portrait information will be calculated, and then a similarity threshold will be set to establish an anomaly database, and the similarity and captured portrait information below the similarity threshold will be marked, and the captured portrait information and the capturing time will be recorded in the anomaly database. When abnormal data appears, the information in the anomaly database will be displayed.

[0037] By adopting the above technical solution, compared with the prior art, the beneficial effects of the present invention are:

[0038] 1. The present invention generates equipment power consumption data by collecting the startup time of electrical equipment and calculating the total energy consumption, so as to understand the power consumption rules and energy consumption of each equipment in detail, which is helpful to find abnormal power consumption behavior, randomly extract power consumption information at a time point from the energy consumption diagram of the user end for reverse analysis, and generate reverse analysis power consumption data, which further improves the accuracy and reliability of data analysis, can verify the information of electrical equipment from multiple angles, and can quickly lock possible abnormal situations, providing a clear direction for subsequent tracing work, helping to accurately find the root cause of power theft or other abnormal power consumption, and improving the efficiency and accuracy of anti-power theft work;

[0039] 2. The present invention determines the energy consumption gear information by analyzing the information of electrical equipment, and more meticulously understands the energy consumption characteristics of different equipment, provides a more accurate data basis for subsequent energy consumption analysis and anti-electricity theft work, can accurately reflect the actual electricity consumption, and helps to timely discover abnormal energy consumption fluctuations. By recording the available electrical equipment and energy consumption at different time points, it provides a data source for reverse analysis, more intuitively compares the energy consumption of different equipment, improves the accuracy of reverse analysis, helps to quickly locate the time point where abnormalities may exist, and improves the efficiency and pertinence of anti-electricity theft work;

[0040] 3. The present invention can accurately determine the configuration information of equipment power consumption by reversely converting the energy consumption difference of difference data into differential power consumption, and more accurately find out the equipment or links that may have abnormal power consumption, which is helpful to timely discover power theft or other energy consumption abnormalities. By automatically adjusting the power consumption time within the time abnormality threshold range and generating a layer of data, and repeating the analysis and adjustment process, it can continuously optimize the judgment and processing of abnormal data, ensure comprehensive monitoring and timely processing of abnormal situations, and when power theft is detected, the information in the anomaly database will be retrieved to clarify the direction of further investigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of the method steps in an embodiment of the present invention;

[0042] Figure 2 A schematic diagram of the steps of a method for reversely analyzing information of electrical equipment in an embodiment of the present invention;

[0043] Figure 3 Schematic diagram of the steps of a method for analyzing the rationality of traceability information in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0045] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0046] Embodiment 1:

[0047] Therefore, in order to effectively solve the above problems, this application proposes a marketing anti-electricity theft data collection method based on big data, as shown in the accompanying drawings of the specification. Figure 1-3 As shown, the following steps are included:

[0048] S100, establish a user terminal, use the user terminal to collect the user's meter data, obtain the total power consumption data, and obtain the user's newly added power consumption equipment information, and analyze the power consumption data of all power consumption equipment through actual access;

[0049] Real-time monitoring of the user's power environment parameters, such as temperature and humidity, and by analyzing the correlation between these environmental parameters and the power consumption data of the power-consuming equipment, the energy consumption of the power-consuming equipment can be more accurately determined;

[0050] S200, collecting the startup time of the power-consuming equipment, calculating the total energy consumption of the power-consuming equipment, and generating the power consumption data of the equipment;

[0051] Consider the impact of the equipment's operating status on energy consumption. Factors such as the equipment's load rate and operating efficiency will affect its energy consumption;

[0052] S300, generating a user-side energy consumption graph based on the total power consumption data and the power consumption data of the power-consuming devices, randomly extracting power consumption information of ten groups of time points from the user-side energy consumption graph, reversely analyzing the power-consuming device information based on the power consumption information, and generating reverse analysis power consumption data;

[0053] S400, extracting equipment power consumption data and reversely analyzing power consumption data, and comparing the two sets of data to analyze the difference between the two sets of data;

[0054] S500, analyzing the source of the difference data, matching the difference data with the energy consumption of the electrical equipment, generating traceability information, formulating an abnormal calculation model, and analyzing the rationality of the traceability information;

[0055] When analyzing the source of difference data and matching it with the energy consumption of electrical equipment, consider the impact of external factors on energy consumption, such as seasonal changes, electricity price fluctuations, etc., and establish a correlation model between external factors and energy consumption changes to more accurately analyze the source of difference data;

[0056] S600, establish an authorization unit, then enter the staff's information into the authorization unit, obtain the status of the power control box, when the power control box is opened, capture the portrait, and then match the portrait information with the information in the authorization unit.

[0057] Specific workflow: establish a user terminal, use the user terminal to collect the user's meter data, obtain the user's newly added power-consuming equipment information, analyze the power consumption data of all power-consuming equipment, calculate the total energy consumption of power-consuming equipment, generate equipment power consumption data, generate a user-side energy consumption graph, reversely analyze the power-consuming equipment information based on the power consumption information, generate reverse analysis power consumption data, analyze the difference data between the two sets of data, analyze the source of the difference data, and match the difference data with the energy consumption of the power-consuming equipment, generate traceability information, analyze the rationality of the traceability information, obtain the status of the power control box, and when the power control box is opened, it will capture the portrait, and then match the portrait information with the information in the authorization unit;

[0058] Furthermore, by collecting the startup time of electrical equipment and calculating the total energy consumption to generate equipment power consumption data, we can understand the power consumption patterns and energy consumption of each device in detail, which is helpful to discover abnormal power consumption behavior. The power consumption information of randomly extracted time points in the user-side energy consumption graph is reversely analyzed to generate reverse analysis power consumption data, which further improves the accuracy and reliability of data analysis, verifies power equipment information from multiple angles, and can quickly identify possible abnormal situations, providing a clear direction for subsequent tracing work, helping to accurately find the root cause of power theft or other abnormal power consumption, and improving the efficiency and accuracy of anti-power theft work.

[0059] Embodiment 2:

[0060] Based on the first embodiment, as shown in the accompanying drawings of the specification Figure 1-3 As shown, in S100, when analyzing the power consumption data of all power-consuming devices, the energy consumption level information of the user equipment is analyzed through the power-consuming device information, so as to further determine the energy consumption range of the power-consuming device and increase the accuracy of the energy consumption collection of the power-consuming device;

[0061] In S200, when calculating the total energy consumption of different electrical devices, it is necessary to obtain the startup time, energy consumption level and energy consumption information corresponding to different energy consumption levels of different devices. Suppose the startup time of different devices is S Q , assuming that the energy consumption of different devices is N H , assuming the total energy consumption of electrical equipment is Y 总能耗 , let the number of electrical equipment be P;

[0062]

[0063] According to the above formula, the total energy consumption of electrical equipment can be calculated;

[0064] Install environmental sensors to collect environmental data in real time and incorporate these data into the calculation model of total energy consumption to make the calculation of total energy consumption more accurate;

[0065] In S300, the method for reversely analyzing the information of the electric equipment is as follows:

[0066] S310, establishing a habit unit, using the habit unit to record the electrical equipment that can be used at different time points, and synchronously extracting the power consumption of different electrical equipment;

[0067] S320, determining the lowest energy consumption electrical equipment among different electrical equipment, generating the lowest power consumption index, and then converting the power consumption of the remaining electrical equipment into index sub-energy consumption based on the lowest power consumption index, and generating an index sub-energy consumption table;

[0068] Considering the synergy between different devices, some devices may affect each other's energy consumption when running at the same time. By analyzing this synergy, the energy consumption of the remaining electrical devices can be more accurately converted into index sub-energy consumption;

[0069] S330, extracting power consumption information of ten groups of time points, and synchronously converting them into the total energy consumption of the indicator, then analyzing the configurations of different indicator sub-energy consumptions equal to the total energy consumption of the indicator, and then calculating whether the difference between the indicator sub-energy consumption of different configurations and the total energy consumption of the indicator is equal to zero;

[0070] When the difference between the sub-energy consumption of indicators with different configurations and the total energy consumption of indicators is equal to zero, it is judged that there is no abnormality in the electrical equipment;

[0071] When the difference between the sub-energy consumption of indicators with different configurations and the total energy consumption of indicators is not equal to zero, it is judged that there is an abnormality in the electrical equipment;

[0072] In S400, when comparing the equipment power consumption data and the reverse analysis power consumption data, it is necessary to draw the equipment power consumption data and the reverse analysis power consumption data into an equipment line graph and a reverse analysis line graph, and then calculate the difference between the two sets of data at each time point, and set the impact unit at the same time. The impact unit can record the operating time, operating environment and equipment maintenance factors of the equipment, and the user can mark the content of the impact unit independently, so as to more comprehensively affect the impact of the factors in the unit on energy consumption. At this time, the absolute values ​​of all differences are calculated, and the absolute values ​​of all differences are added to generate a total difference, and then the average difference at different time points is calculated according to the total difference and the number of differences. When calculating the difference at each time point, the energy consumption of normal power consumption equipment is analyzed according to the smart meter APP, and the maximum energy consumption is set as the energy consumption abnormality threshold. At the same time, the energy consumption abnormality threshold is controlled by the management personnel, and then the position exceeding the energy consumption abnormality threshold is marked;

[0073] Specific workflow: Analyze the energy consumption level information of user equipment through the information of power-consuming equipment, so as to further determine the energy consumption range of power-consuming equipment, calculate the total energy consumption of power-consuming equipment, extract the power consumption of different power-consuming equipment, generate the minimum power consumption index, and convert the power consumption of other power-consuming equipment into index sub-energy consumption based on the minimum power consumption index, generate the index sub-energy consumption table, extract the power consumption information of ten groups of time points, and convert it into the total energy consumption of the index synchronously, analyze the configuration of different sub-energy consumption of the index equal to the total energy consumption of the index, draw the equipment line chart and the reverse analysis line chart, set the influencing unit, calculate the absolute value of all differences, generate the total difference, establish the energy consumption abnormality threshold, and then mark the position that exceeds the energy consumption abnormality threshold;

[0074] Furthermore, by analyzing the information of electrical equipment to determine the energy consumption level information, we can understand the energy consumption characteristics of different equipment in more detail, provide a more accurate data basis for subsequent energy consumption analysis and anti-electricity theft work, and accurately reflect the actual electricity consumption situation. It helps to promptly discover abnormal energy consumption fluctuations. By recording the available electrical equipment and energy consumption at different time points, it provides a data source for reverse analysis, more intuitively compares the energy consumption of different equipment, improves the accuracy of reverse analysis, and helps to quickly locate the time points where abnormalities may exist, thereby improving the efficiency and pertinence of anti-electricity theft work.

[0075] Embodiment three:

[0076] Based on the second embodiment, as shown in the accompanying drawings of the specification Figure 1-3 As shown, in S500, the method for matching the difference data with the energy consumption of the electrical equipment is as follows:

[0077] S510, analyzing the energy consumption difference of the difference data, reversely converting the energy consumption difference according to the minimum power consumption index, and generating the differential power consumption in kilowatt-seconds;

[0078] S520, extracting the energy consumption of electrical equipment and converting it in kilowatt-seconds to obtain the electrical energy required per second for different electrical equipment and generate the electrical power consumption of the equipment;

[0079] S530, then use the difference power consumption as the total value and the equipment power consumption as the sub-value to perform matching analysis, determine the configuration information of the equipment power consumption, and calculate the difference between the difference power consumption and the equipment power consumption. When the difference is 0, it is determined that the place is in a preliminary abnormal elimination state. When the difference is not 0, it is determined that there is abnormal information at the place, and it is marked again, so as to facilitate further review by the staff;

[0080] The rationality of the traceability information is analyzed as follows:

[0081] S540, extracting the configuration information in S530, analyzing the power consumption time of the power-consuming devices in the configuration information, obtaining the startup modes of all the power-consuming devices, and analyzing the power consumption time under different startup modes;

[0082] S550, after formulating the abnormal calculation module, an abnormal unit is set inside the abnormal calculation module, and then a time abnormal threshold range is set for all the electrical devices in the abnormal unit according to the startup mode and power consumption time of all the electrical devices;

[0083] When setting the abnormal unit to set the time abnormal threshold range for all electrical equipment, consider the characteristics of electricity consumption in different seasons and time periods;

[0084] S560, then calculating whether the power consumption time of all power-consuming devices is within the time anomaly threshold range, and if it is within the time anomaly threshold range, automatically adjusting the power consumption time within the time anomaly threshold range and fixing it, and then generating a layer of data;

[0085] Assume that the time anomaly threshold range is Y F1 -Y F2 , assuming that the power consumption time of the electrical equipment is Y 时间 ;

[0086] When Y F1 ≤Y 时间 ≤Y F2 When the power consumption time of the power-consuming equipment is within the time abnormality threshold range,

[0087] When Y 时间 ≤Y F1 and Y 时间 ≥Y F2 When the power consumption time of the power-consuming equipment is determined to be not within the time abnormality threshold range;

[0088] S570, transmitting the first layer of data to S530, and reanalyzing the configuration of the first layer of data;

[0089] In S570, after re-analyzing the configuration of the first layer of data, the process of S540-S560 is repeated again. When there is still a power usage time within the time abnormality threshold range, it will be adjusted and fixed again to generate double data, and then the repeated analysis threshold will be set. When the number of repeated analyses exceeds the repeated analysis threshold, the abnormal data will be marked and displayed;

[0090] In S600, when recording the authorized portrait, the authorization unit will extract features of the authorized portrait, obtain initial feature information, and record the extracted features on the corresponding authorized portrait. After capturing the portrait information, the facial features of the portrait information will be extracted first, and then the initial feature information will be retrieved based on the facial features, and the corresponding authorized portrait information will be extracted and compared, and the similarity between the authorized portrait information and the captured portrait information will be calculated. Then, a similarity threshold will be set, and an abnormal image database will be established. The similarity and captured portrait information below the similarity threshold will be marked, and the captured portrait information and the capture time will be recorded in the abnormal image database. When abnormal data appears, the information in the abnormal image database will be displayed;

[0091] Use data analysis tools to identify time periods, locations, or specific devices where abnormalities frequently occur, providing a basis for further strengthening safety management;

[0092] Specific workflow: Analyze the energy consumption differences of the difference data, reversely convert the energy consumption differences according to the minimum power consumption index, and generate differential power consumption in kilowatt-seconds, obtain the electric energy required per second for different power-consuming devices, generate equipment power consumption, determine the configuration information of equipment power consumption, and calculate the difference between the differential power consumption and the equipment power consumption, analyze the power consumption time of the power-consuming devices in the configuration information and the power consumption time under different startup modes, set a time anomaly threshold range for all power-consuming devices, calculate whether the power consumption time of all power-consuming devices is within the time anomaly threshold range, and when it is within the time anomaly threshold range, automatically adjust the power consumption time within the time anomaly threshold range and fix it, and then Then, a layer of data is generated, the layer of data is transmitted to S530, and the configuration of the layer of data is re-analyzed, so that the process of S540-S560 is repeated again. When there is still a power usage time within the time abnormality threshold range, it will be adjusted and fixed again to generate double data. When the number of repeated analyses exceeds the repeated analysis threshold, the abnormal data will be marked and displayed. Feature extraction is performed on the authorized portrait, initial feature information is obtained, facial features of the portrait information are extracted, and then the initial feature information is retrieved according to the facial features. The similarity between the authorized portrait information and the captured portrait information is calculated, and the similarity below the similarity threshold and the captured portrait information are marked;

[0093] Furthermore, by reversely converting the energy consumption difference of the difference data into differential power consumption, it is possible to accurately determine the configuration information of the equipment's power consumption, more accurately identify equipment or links that may have abnormal power consumption, and help to promptly discover power theft or other abnormal energy consumption problems. By automatically adjusting the power consumption time within the time anomaly threshold range and generating a layer of data, and repeating the analysis and adjustment process, it is possible to continuously optimize the judgment and processing of abnormal data, ensuring comprehensive monitoring and timely processing of abnormal situations, and when power theft is detected, the information in the anomaly database will be retrieved to clarify the direction of further investigation.

[0094] Embodiment 4:

[0095] Based on the third embodiment, as shown in the accompanying drawings of the specification Figure 1-3 As shown in the figure, the energy consumption corresponding to different energy consumption gears refers to the different energy consumption information generated by different electrical equipment in different operating modes. For example, the wind speed of hair dryers and electric fans can be adjusted when in use, and the energy consumption required for different wind speeds is different. This can analyze the user's electrical equipment information in more detail. If the agreed energy consumption is set, it is easy to be different from the standard energy consumption, which can avoid the probability of misjudgment.

[0096] The configuration of different indicator sub-energy consumption equals the total indicator energy consumption, because different electrical equipment has specific electrical characteristics when working, these characteristics will be reflected in the power consumption, current waveform, power factor and other aspects. By monitoring and analyzing these electrical parameters, the running electrical equipment can be identified, and there are multiple configurations. Among the multiple configuration schemes, only one reasonable configuration scheme is needed to achieve the required effect. The purpose of multiple configurations is to detect whether the user steals electricity;

[0097] Setting up impact units can more comprehensively consider the impact of other factors on energy consumption. Impact units can record the equipment's operating time, operating environment, equipment maintenance factors, etc., all of which will lead to increased energy consumption. They can be edited by managers and users, and evidence can be attached, and then verified by managers;

[0098] Calculate whether the power consumption time of all power-consuming devices is within the time anomaly threshold range. The power consumption time of different power-consuming devices is different under normal use. Under normal circumstances, the continuous power consumption time of washing machines is mostly less than 60 minutes. If the continuous power consumption time exceeds 60 minutes, it can be preliminarily determined that the power consumption time is within the time anomaly threshold range;

[0099] When analyzing the power consumption data of all power-consuming devices, the energy consumption monitoring and analysis software can be used for analysis. At the same time, the power consumption data of all power-consuming devices needs to be generated in combination with the above-mentioned access information;

[0100] Embodiment five:

[0101] 1. The startup time of different devices is 0.5h, 1h and 2h, the corresponding energy consumption of different devices is 0.1kWh, 0.5kWh and 1kWh, and the number of power-consuming devices is 3;

[0102] Y 总能耗 =0.5h×0.1kWh+1h×0.5kWh+2h×1kWh=2.55kW

[0103] At this point, the total energy consumption of electrical equipment can be calculated;

[0104] Assume the sub-energy consumption of the indicator is 1kWh, 2kWh and 3kWh, and the total energy consumption of the indicator is 12kWh;

[0105] 2. Then the configurations where the energy consumption of different indicators is equal to the total energy consumption of the indicator are:

[0106] Two 1kWh energy consumption indicators, two 2kWh energy consumption indicators, and two 3kWh energy consumption indicators;

[0107] 4 1kWh energy consumption indicators, 1 2kWh energy consumption indicator and 2 3kWh energy consumption indicators;

[0108] 6 1kWh indicators, 3 2kWh indicators, and 0 3kWh indicators, etc.

[0109] 3. The energy consumption of the electrical equipment is 2kWh. The running time of the equipment recorded in the impact unit will increase the energy consumption by 10%. After the equipment is repaired, the energy consumption can be reduced by 15%;

[0110] 2+2×20%=2.4kWh

[0111] 2.4-2.4×15%=2.04kWh

[0112] At this time, the impact of the influencing unit on the energy consumption of the electrical equipment can be calculated, thereby calculating the actual energy consumption of the electrical equipment;

[0113] 4. The abnormal power usage threshold range of the equipment is set at 60min-80min. When the analyzed power usage time of the equipment is 66min, 66min is between 60min-80min, so it is judged that the power usage time of the equipment is within the time abnormality threshold range. When the analyzed power usage time of the equipment is 50min, 50min is outside 60min-80min, so it can be judged that the power usage time of the equipment is not within the time abnormality threshold range.

[0114] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A marketing anti-electricity theft data collection method based on big data, characterized in that: The following steps are involved: S100, establishing a user terminal, using the user terminal to collect the user's meter data, obtain the total electricity consumption data, and then obtain the user's newly added electrical equipment information through user surveys, self-reports and electrical equipment identification technology; S200, collecting the startup time of the power-consuming equipment, calculating the total energy consumption of the power-consuming equipment, and generating the power consumption data of the equipment; S300, generating a user-side energy consumption graph based on the total power consumption data and the power consumption data of the power-consuming devices, randomly extracting power consumption information of ten groups of time points from the user-side energy consumption graph, reversely analyzing the power-consuming device information based on the power consumption information, and generating reverse analysis power consumption data; S400, extracting equipment power consumption data and reversely analyzing power consumption data, and comparing the two sets of data to analyze the difference between the two sets of data; S500, analyzing the source of the difference data, matching the difference data with the energy consumption of the electrical equipment, generating traceability information, formulating an abnormal calculation model, and analyzing the rationality of the traceability information; S600, establish an authorization unit, then enter the staff's information into the authorization unit, obtain the status of the power control box, when the power control box is opened, capture the portrait, and then match the portrait information with the information in the authorization unit.

2. According to the big data-based marketing anti-electricity theft data collection method of claim 1, it is characterized by: In the above S100, when analyzing the power consumption data of all power-consuming devices, the energy consumption level information of the user equipment is analyzed through the power-consuming device information, so as to further determine the energy consumption range of the power-consuming devices and increase the accuracy of the energy consumption collection of the power-consuming devices.

3. According to the big data-based marketing anti-electricity theft data collection method of claim 2, it is characterized by: In S200, when calculating the total energy consumption of different electrical devices, it is necessary to obtain the startup time, energy consumption level and energy consumption information corresponding to different energy consumption levels of different devices. Suppose the startup time of different devices is S Q , assuming that the energy consumption of different devices is N H , assuming the total energy consumption of electrical equipment is Y 总能耗 , let the number of electrical equipment be P; According to the above formula, the total energy consumption of electrical equipment can be calculated.

4. The method for collecting marketing anti-electricity theft data based on big data according to claim 1 is characterized by: In S300, the method for reversely analyzing the information of the electric equipment is as follows: S310, establishing a habit unit, using the habit unit to record the electrical equipment that can be used at different time points, and synchronously extracting the power consumption of different electrical equipment; S320, determining the lowest energy consumption electrical equipment among different electrical equipment, generating the lowest power consumption index, and then converting the power consumption of the remaining electrical equipment into index sub-energy consumption based on the lowest power consumption index, and generating an index sub-energy consumption table; S330, extracting power consumption information of ten groups of time points, and synchronously converting them into the total energy consumption of the indicator, then analyzing the configurations of different indicator sub-energy consumptions equal to the total energy consumption of the indicator, and then calculating whether the difference between the indicator sub-energy consumption of different configurations and the total energy consumption of the indicator is equal to zero; When the difference between the sub-energy consumption of indicators with different configurations and the total energy consumption of indicators is equal to zero, it is judged that there is no abnormality in the electrical equipment; When the difference between the indicator sub-energy consumption of different configurations and the indicator total energy consumption is not equal to zero, it is determined that there is an abnormality in the electrical equipment.

5. The method for collecting marketing anti-electricity theft data based on big data according to claim 1 is characterized by: In the S400, when comparing the equipment power consumption data and the reverse analysis power consumption data, it is necessary to draw the equipment power consumption data and the reverse analysis power consumption data into an equipment line graph and a reverse analysis line graph, and then calculate the difference between the two sets of data at each time point, and set an impact unit at the same time. The impact unit can record the operating time, operating environment and equipment maintenance factors of the equipment, and the user can independently mark the content of the impact unit, so as to more comprehensively affect the impact of the factors in the unit on energy consumption. At this time, the absolute values ​​of all differences are calculated, and the absolute values ​​of all differences are added to generate a total difference. Then, the average difference at different time points is calculated based on the total difference and the number of differences. When calculating the difference at each time point, the energy consumption of normal power equipment is analyzed according to the smart meter APP, and the maximum energy consumption is set as the energy consumption abnormality threshold. At the same time, the energy consumption abnormality threshold is controlled by the management personnel, and then the position exceeding the energy consumption abnormality threshold is marked.

6. The method for collecting marketing anti-electricity theft data based on big data according to claim 1 is characterized by: In S500, the method for matching the difference data with the energy consumption of the electrical equipment is as follows: S510, analyzing the energy consumption difference of the difference data, reversely converting the energy consumption difference according to the minimum power consumption index, and generating the differential power consumption in kilowatt-seconds; S520, extracting the energy consumption of electrical equipment and converting it in kilowatt-seconds to obtain the electrical energy required per second for different electrical equipment and generate the electrical power consumption of the equipment; S530, then use the difference power consumption as the total value and the equipment power consumption as the sub-value for matching analysis, determine the configuration information of the equipment power consumption, and calculate the difference between the difference power consumption and the equipment power consumption. When the difference is 0, it is determined that the location is in a preliminary elimination state. When the difference is not 0, it is determined that there is abnormal information at the location and it is marked again to facilitate further review by the staff.

7. The method for collecting marketing anti-electricity theft data based on big data according to claim 6 is characterized in that: The rationality method of analyzing the traceability information is as follows: S540, extracting the configuration information in S530, analyzing the power consumption time of the power-consuming devices in the configuration information, obtaining the startup modes of all the power-consuming devices, and analyzing the power consumption time under different startup modes; S550, after formulating the abnormal calculation module, an abnormal unit is set inside the abnormal calculation module, and then a time abnormal threshold range is set for all electrical devices in the abnormal unit according to the startup mode and power consumption time of all electrical devices; S560, then calculating whether the power consumption time of all power-consuming devices is within the time anomaly threshold range, and if it is within the time anomaly threshold range, automatically adjusting the power consumption time within the time anomaly threshold range and fixing it, and then generating a layer of data; Assume that the time anomaly threshold range is Y F1 -Y F2 , assuming that the power consumption time of the electrical equipment is Y 时间 ; When Y F1 ≤Y 时间 ≤Y F2 When the power consumption time of the power-consuming equipment is within the time abnormality threshold range, When Y 时间 ≤Y F1 and Y 时间 ≥Y F2 When the power consumption time of the power-consuming equipment is determined to be not within the time abnormality threshold range; S570: Transmit the first layer of data to S530, and reanalyze the configuration of the first layer of data.

8. The method for collecting marketing anti-electricity theft data based on big data according to claim 7 is characterized by: In S570, after re-analyzing the configuration of one layer of data, the process of S540-S560 is repeated again. When there is still electricity usage time within the time abnormality threshold range, it will be adjusted and fixed again to generate double data, and then the repeated analysis threshold will be set. When the number of repeated analyses exceeds the repeated analysis threshold, the abnormal data will be marked and displayed.

9. The method for collecting marketing anti-electricity theft data based on big data according to claim 1 is characterized by: In the S600, when recording the authorized portrait, the authorization unit will extract features of the authorized portrait, obtain initial feature information, and record the extracted features on the corresponding authorized portrait. After capturing the portrait information, the facial features of the portrait information will be extracted first, and then the initial feature information will be retrieved based on the facial features, and the corresponding authorized portrait information will be extracted and compared. The similarity between the authorized portrait information and the captured portrait information will be calculated, and then a similarity threshold will be set to establish an anomaly database, and the similarity and captured portrait information below the similarity threshold will be marked, and the captured portrait information and the capture time will be recorded in the anomaly database. When abnormal data appears, the information in the anomaly database will be displayed.

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