Intelligent socket and control method based on industrial gateway and power consumption linkage protection

Abstract

The application provides a kind of intelligent socket based on industrial gateway and power linkage protection and control method, belongs to artificial intelligence technical field, specifically includes: input analysis module: data acquisition module, data transmission module, alarm and control module, wherein data acquisition module is responsible for including the collection and detection of power data, data transmission module is responsible for transmitting the collected power data and alarm information to the cloud platform through the gateway module, alarm and control module is responsible for leakage alarm, over-temperature alarm and on-off control, and uses the control instruction of cloud or local control instruction to carry out on-off control, improves the power safety.

Description

Technical Field

[0001] The present invention belongs to the technical field of gateways, and particularly relates to a smart socket based on an industrial gateway and power consumption linkage protection, and a control method thereof. Background Art

[0002] According to statistics, fires caused by electrical reasons in China account for more than 30% of the total number of fires every year, among which line overload, short circuit, heating of old lines, long-time operation of high-power equipment and the like are main inducing factors. In scenarios such as homes of the elderly, rental houses, old residential areas, and industrial enterprises, power consumption safety problems are particularly prominent. However, traditional power consumption protection only relies on air switches and residual current operated protectors in distribution boxes. The air switch adopts a fixed overcurrent protection threshold (such as 16A, 20A), and cannot perform fine setting for different electrical appliances — a 1000W microwave oven has a current of about 4.5A during normal operation, while a 2000W electric heater has a current of about 9A, and the two have the same overload threshold, which cannot distinguish "normal high power" from "abnormal overload". In addition, traditional protection devices only cut off the faulty circuit, cannot realize linkage protection of associated devices (for example, automatically turning off high-power devices such as air conditioners when the electric heater overheats), and cannot provide early warning information to users.

[0003] To solve the above technical problems, in the invention patent application CN202410348356.8 entitled "A Smart Home Gateway Capable of Managing Power of Electrical Appliances and Management Method Thereof", the smart socket is provided with an API interface controlled by a switch, so that a cloud platform can control the on and off of a relay of the smart socket through the interface, and further a user can remotely control the switch of the electrical appliance through a mobile terminal. The smart gateway is built-in with an electrical appliance power monitoring plug-in, which brings more convenience and intelligent experience to family life, but the following technical defects still exist: Differences in power consumption devices in smart sockets on the same line will lead to differences in the risk of over-temperature occurring in the line. Therefore, how to perform identification and update processing on power consumption device groups that are prone to cause over-temperature risks according to usage data of the smart sockets of power users, and determine operation control methods for gateway devices of different power users according to the result of the identification and update processing on the power consumption device groups that are prone to cause over-temperature risks, so as to ensure the operation reliability of the gateway devices of the power users with over-temperature risks has become an urgent technical problem to be solved.

[0004] Therefore, there is an urgent need for a smart socket based on an industrial gateway and power consumption linkage protection and a control method thereof. Contents of the Invention

[0005] To achieve the above purpose of the present invention, the present invention adopts the following technical solutions: Specifically, the present application provides a control method, which specifically includes: S1 uses the usage data of smart sockets in different power users to determine the usage time period of the power user in different smart sockets, and combines the smart socket data of different power users to determine the users whose electricity consumption characteristics are identified according to a set period, that is, the identification method of power users to be monitored. S2 performs the update identification process for the power users of interest based on the identification method, and determines the degree of correlation between the power consumption characteristics of the power devices in the smart sockets of different power users of interest based on the identification results of the power consumption characteristics of the power devices in different power users of interest, and determines the update control strategy for the risk power device group based on the degree of correlation. S3 uses the update control strategy to update the risky electrical equipment group, treats the electrical users other than the power users under concern as other users, and determines the monitoring and management method of the power consumption characteristics of other users based on the update processing results of the risky electrical equipment group and the composition data of the electrical equipment in different risky electrical equipment groups. S4 determines the monitoring smart sockets of other users based on the monitoring and management method, and determines the monitoring and control method of the gateways of the other users based on the monitoring smart socket data of other users and the data of the power users of interest.

[0006] The beneficial effects of this invention are as follows: Based on smart socket data and usage data from different power users, the required level of reliability for gateway devices is determined. Specifically, the more smart sockets a power user has and the longer they are used, the higher the required level of reliability for gateway devices. This required level of reliability is then used to identify power users of interest, specifically those whose electricity consumption characteristics are analyzed at set intervals. This lays the foundation for determining the electricity consumption risks of different power users based on the identified characteristics, and ultimately for controlling the operation of gateway devices.

[0007] Based on data from monitored electricity users, the efficiency of updating and processing risky electrical equipment groups under the current monitored electricity users is determined. Furthermore, by combining data from other users' smart sockets, the reliability of monitoring and processing of other users' gateway devices is determined. Based on the efficiency of updating and processing risky electrical equipment groups under the current monitored electricity users and the reliability of monitoring and processing of other users' gateway devices, the monitoring and control methods for other users' gateways are determined. Specifically, under what circumstances should periodic monitoring and analysis of the gateway's operational status be performed? This not only ensures the reliability of monitoring and processing of gateway devices for all electricity users as a whole, but also guarantees the reliability of monitoring and processing of gateway devices for electricity users with overheating risks, as well as the efficiency of updating and processing risky electrical equipment groups.

[0008] Furthermore, the usage time of the power user in different smart sockets is determined based on the monitoring data in the power user's smart sockets.

[0009] Furthermore, the smart socket data among the power users includes the number of smart sockets among the power users.

[0010] Furthermore, the method for determining the identification method of the electricity users of interest is as follows: Based on the smart socket data of the electricity users, determine the number of smart sockets in the electricity users; Based on the usage time of the power user in different smart sockets, determine the smart sockets whose average daily usage time of the power user is greater than a preset time threshold, and use them as the matching sockets; A method for identifying interested electricity users is used to determine the number of matching sockets and smart sockets among different electricity users.

[0011] Furthermore, the method for determining the monitoring and management method of the electricity consumption characteristics of the electricity user is as follows: Based on the update processing results of the risky electrical equipment groups, the number of risky electrical equipment groups is determined; The number of electrical devices in each risky electrical device group is determined based on the composition data of the electrical devices in different risky electrical device groups. A monitoring and management method for the electricity consumption characteristics of the power user is determined based on the number of risky electrical equipment groups and the number of electrical equipment in different risky electrical equipment groups.

[0012] Secondly, this application provides a smart socket based on an industrial gateway and power consumption linkage protection, employing the aforementioned control method, specifically including: Data acquisition module, data transmission module, alarm and control module; The data acquisition module is responsible for collecting and detecting electricity consumption data, identifying the electricity consumption characteristics of electrical equipment based on the detection results, and identifying the load characteristics of the smart socket based on the identification results of the electricity consumption characteristics of the electrical equipment. The data transmission module is responsible for transmitting the collected power consumption data and alarm information to the cloud platform through the gateway module; The alarm and control module is responsible for leakage alarm, over-temperature alarm and on / off control, and uses cloud control commands or local control commands to perform on / off control.

[0013] Furthermore, the power consumption data includes voltage, current, leakage current, terminal temperature, power, power consumption, and load identification data.

[0014] Furthermore, the gateway module and the data transmission module are constructed and communicate using the Dianhong system.

[0015] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0018] Figure 1 It is a flowchart of a control method; Figure 2 This is a flowchart illustrating the method for determining the identification of users of interest in electricity among electricity users; Figure 3 This is a flowchart illustrating the method for determining the updated control strategy for groups of high-risk electrical equipment. Figure 4 This is a framework diagram of a smart socket based on an industrial gateway and power-related protection. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0020] Example 1 like Figure 1 As shown, this application provides a control method, specifically including: S1 uses the usage data of smart sockets in different power users to determine the usage time period of the power user in different smart sockets, and combines the smart socket data of different power users to determine the users whose electricity consumption characteristics are identified according to a set period, that is, the identification method of power users to be monitored. S2 performs the update identification process for the power users of interest based on the identification method, and determines the degree of correlation between the power consumption characteristics of the power devices in the smart sockets of different power users of interest based on the identification results of the power consumption characteristics of the power devices in different power users of interest, and determines the update control strategy for the risk power device group based on the degree of correlation. S3 uses the update control strategy to update the risky electrical equipment group, treats the electrical users other than the power users under concern as other users, and determines the monitoring and management method of the power consumption characteristics of other users based on the update processing results of the risky electrical equipment group and the composition data of the electrical equipment in different risky electrical equipment groups. S4 determines the monitoring smart sockets of other users based on the monitoring and management method, and determines the monitoring and control method of the gateways of the other users based on the monitoring smart socket data of other users and the data of the power users of interest.

[0021] Furthermore, the usage time of the power user in different smart sockets is determined based on the monitoring data in the power user's smart sockets.

[0022] Specifically, the smart socket data for the electricity user includes the number of smart sockets in the electricity user.

[0023] Specifically, such as Figure 2 As shown, the method for determining the identification method of the electricity users of interest is as follows: In this embodiment, based on the smart socket data and usage data of different power users, the required level of reliable operation of the gateway device is determined for each power user. That is, the more smart sockets a power user has and the longer the usage time of the smart sockets, the higher the required level of reliable operation of the gateway device. Using the required level of reliable operation of the gateway device, the power users of interest are identified, that is, the users whose electricity consumption characteristics are identified according to a set period are identified. This lays the foundation for determining the electricity consumption risk of different power users based on the identification results of electricity consumption characteristics, and then for the operation control processing of the gateway device.

[0024] S11 determines the number of smart sockets in the power user based on the smart socket data in the power user; The number of smart sockets refers to the number of smart sockets that have been deployed and are in operation for a single electricity user.

[0025] Suppose a household of an electricity user has multiple smart sockets installed, used for monitoring the power consumption of different appliances such as air conditioners, refrigerators, and water heaters. The total number of these smart sockets is the number of smart sockets installed by the electricity user.

[0026] This step involves conducting a basic count of the number of smart sockets for electricity users. The significance of this step is to use the scale information of smart sockets as the first-level screening criterion—electricity users with too few smart sockets have limited richness of electricity consumption characteristic data and a lower degree of identification needs.

[0027] The above steps include the following: Based on the number of smart sockets in different power users, power users whose number of smart sockets is greater than a preset socket number threshold are selected as power users. It is determined whether the number of selected power users is greater than the preset screened user number threshold. If yes, proceed to step S12. If no, the number of smart sockets in the power users is small. Therefore, by utilizing the characteristics of power equipment in power users that are prone to line overheating, the gateway monitoring and management strategy is determined, thereby ensuring that the reliability of abnormal event monitoring is low. Therefore, power users whose number of smart sockets in the history of alarms is greater than the preset alarm number threshold are selected as power users of concern. The selected electricity users refer to those whose number of smart sockets exceeds a preset threshold. These users have a large number of smart sockets and possess rich electricity consumption characteristic data.

[0028] The preset alarm frequency threshold refers to the critical value used to determine whether the alarm of the smart socket is frequent; the preset alarm socket quantity threshold refers to the minimum number of alarm sockets used to determine the overall serious alarm of the power user.

[0029] Assuming a preset threshold for the number of smart sockets is set, power users with more smart sockets than this threshold are marked as selected power users. If the total number of selected power users is not greater than the preset threshold for the number of selected users, it indicates that the overall deployment scale of smart sockets is relatively small. In this case, a stricter dual alarm condition is adopted—requiring both that the number of alarm sockets reach the threshold and that the number of alarms in these sockets exceeds a certain threshold, in order to determine the power users of interest.

[0030] This step is the first-level branch judgment of S1. Its significance lies in using stricter multiple alarm conditions to screen power users when the overall deployment scale of smart sockets is small.

[0031] S12 determines the smart sockets whose average daily usage time of the power user is greater than a preset time threshold based on the usage time of the power user in different smart sockets, and uses them as matching sockets for use. The matched sockets refer to smart sockets whose average daily usage time exceeds a preset time threshold. Because of their frequent use, these sockets have higher monitoring value in terms of load status and temperature rise characteristics.

[0032] Suppose that a smart socket of a certain electricity user is mainly used for air conditioning, and its daily usage time exceeds a preset time threshold, then the socket is marked as a matching socket, indicating that the socket has enough usage data for feature extraction.

[0033] This step introduces the usage duration dimension for further refinement. Its significance lies in adding a usage frequency filter to the existing quantity filter. The more sockets that are matched, the higher the actual electricity consumption activity of the electricity user, and the greater the need for attention.

[0034] The above steps include the following: Based on the proportion of matching sockets among the screened power users, determine the matching weight coefficient of the screened power users, and determine whether the average value of the matching weight coefficients of different screened power users is greater than the preset matching weight coefficient threshold. If so, determine that the method for identifying power users of interest among the power users is to identify power users whose number of smart sockets that have alarmed in history is greater than the preset alarm socket number threshold as power users of interest. If not, proceed to step S13. The matching usage weighting coefficient refers to the ratio of the number of matching sockets among the screened power users to the total number of smart sockets among the screened power users, and is used to measure the proportion of frequently used sockets among the screened power users.

[0035] Suppose a screened electricity user has A smart sockets, of which B are matched for use. The matching weight coefficient for this user is B ÷ A. The average matching weight coefficient for all screened electricity users is calculated and compared with a preset matching weight coefficient threshold to make a judgment.

[0036] This step is the second-level branch judgment of S1. Its significance is that when the proportion of high-frequency socket use among the screened power users is relatively high, it indicates that there is sufficient power consumption characteristic data, and a relatively lenient single alarm condition can be used to identify power users of concern; if the proportion is not high enough, it is necessary to enter S13 for a deeper comprehensive evaluation.

[0037] S13 determines the identification method of the power users of interest by using the number of matching sockets and smart sockets among different power users.

[0038] Taking into account the proportion of selected electricity users and the compliance rate of matching weight coefficients, the regulatory management requirement value is calculated, and the identification conditions are determined based on the requirement value.

[0039] This step is the final decision-making step of S1. Its significance lies in making a precise selection between the two identification conditions through comprehensive calculation of the regulatory management demand value when the method cannot be directly determined in the first two levels of judgment.

[0040] Understandably, based on the matching weight coefficients of different screened power users, the proportion of screened power users whose matching weight coefficients are greater than a preset matching weight coefficient threshold is determined. Combined with the proportion of screened power users among all power users, the regulatory management requirement value of the smart socket is determined. It is then determined whether the regulatory management requirement value of the smart socket is greater than a preset requirement threshold. If so, the method for identifying power users of interest is to define power users whose number of smart sockets that have historically triggered alarms exceeds a preset alarm socket number threshold as users of interest. If not, the method for identifying power users of interest is to define power users whose number of smart sockets that have historically triggered alarms exceeds a preset alarm socket number threshold, and whose smart sockets have alarm counts exceeding a preset alarm count threshold, as users of interest.

[0041] The regulatory management demand value refers to the comprehensive demand index value calculated after comprehensively screening the scale information of electricity users and matching the compliance status of weight coefficients. It is used to judge the degree of regulatory management demand of the current electricity user group for smart sockets.

[0042] Let R1 be the proportion of screened power users whose matching weight coefficient is greater than the preset matching weight coefficient threshold, and R2 be the proportion of screened power users in the total number of power users. Then, the regulatory management demand value can be determined by combining R1 and R2 (such as calculating their average).

[0043] This step calculates the regulatory management demand value by merging two dimensions. Its significance lies in unifying the scale dimension and the activity dimension into a single decision variable. When the regulatory demand is high, a more lenient single alarm condition is used to expand the monitoring coverage, while a more stringent dual alarm condition is used when the regulatory demand is low to ensure the accuracy of identification.

[0044] This embodiment achieves adaptive determination of the power user identification method through S1, and its core value is reflected in three aspects: First, it assesses the richness of power user's electricity consumption characteristics through the dual dimensions of the number of smart sockets and usage time, reflecting a demand-driven screening logic; second, it achieves progressive optimization of identification accuracy from coarse to fine through a three-layer progressive judgment (screening the number of power users → matching the average value of usage weight coefficients → regulatory management requirement value); and third, it achieves a dynamic balance between monitoring coverage and identification accuracy by outputting identification conditions of different strictness at different levels.

[0045] Furthermore, the identification result of the electrical consumption characteristics of the electrical device is determined based on the current characteristics of the electrical device during the power consumption process read from the smart socket.

[0046] It is understood that the degree of correlation of the electricity consumption characteristics of the electrical equipment among the electricity users concerned is determined based on the degree of overlap of the electrical equipment that meets the requirements of the similarity of the electricity consumption characteristics among the electricity users concerned.

[0047] Specifically, such as Figure 3 As shown, the method for determining the update control strategy for the group of high-risk electrical equipment is as follows: In this application, based on the correlation between the electrical consumption characteristics of electrical equipment among different power users of interest, it is determined under which groups of electrical equipment are prone to overheating accidents. Based on the number of groups of equipment prone to overheating accidents, the severity of the groups of equipment that are prone to overheating risks is determined. Furthermore, combined with the number of monitored risk socket groups, the reliability of identifying the current groups of equipment prone to overheating accidents is determined. Based on the severity and reliability of the identification of groups of equipment that are prone to overheating risks, the update control strategy for risky electrical equipment groups of smart sockets is determined. This lays the foundation for determining the acquisition and processing strategy of different power users' electrical consumption characteristics based on risky electrical equipment groups and the control processing method of gateway devices.

[0048] S21 divides the smart sockets of the power users in the same line into the same socket group, and determines the historical alarm data of the smart sockets in the socket group based on the alarm data of the smart sockets in the socket group caused by overheating. The socket group refers to a group of smart sockets belonging to the same line among the same power users. Smart sockets on the same line share the current path, and their overheating risks are related.

[0049] Suppose that a power user U1 has 3 lines L1, L2 and L3. There are 3 smart sockets on L1, 2 smart sockets on L2 and 3 smart sockets on L3. Then, three socket groups are formed, namely G1(L1), G2(L2) and G3(L3).

[0050] This step involves grouping sockets according to their circuit affiliation. The significance of this step is that since smart sockets on the same circuit share the same current path, overheating at any point could affect the safety of the entire circuit. Therefore, grouping sockets by circuit is reasonable and necessary.

[0051] S22 determines the monitored risk socket group in the socket group based on the historical alarm data, and determines the associated electrical equipment group of the monitored risk socket group based on the identification results of the electricity consumption characteristics in the monitored risk socket group; The monitored risk socket group is a socket group with historical alarm data, that is, at least one smart socket in the group has previously generated an alarm due to overheating.

[0052] The associated electrical equipment group refers to the set of electrical equipment formed by classifying the electrical equipment connected to each smart socket in the monitored risk socket group according to the type information of the electrical equipment and the identification results of the electrical characteristics.

[0053] Suppose a monitoring risk socket group contains multiple smart sockets, which are connected to electrical appliances such as air conditioners, electric water heaters, and induction cookers. By identifying the current characteristics read from each socket, the types of these electrical appliances can be determined and they can be classified into an associated electrical appliance group.

[0054] This step maps risk groups from the socket level to the electrical equipment level. Its significance lies in the fact that the root cause of overheating risk is usually the high load characteristics of the electrical equipment itself. By establishing associated electrical equipment groups, it is possible to reveal "which combinations of electrical equipment are prone to overheating".

[0055] Specifically, the monitored risk socket group is a socket group with historical alarm data.

[0056] Suppose that among five socket groups of a certain electricity user, three groups have smart sockets that have generated over-temperature alarms, then these three groups are the socket groups under monitoring risk.

[0057] S23 determines the similarity of associated electrical equipment groups among monitoring risk socket groups by assessing the degree of correlation between the electrical consumption characteristics of electrical equipment among different power users with different concerns, and determines the update control strategy for the risk electrical equipment groups based on the similarity and the monitoring risk socket group data.

[0058] The similarity refers to the degree of similarity in the composition of electrical equipment among related groups of electrical equipment of different electricity users, including the number and proportion of the same electrical equipment.

[0059] Suppose that the associated electrical equipment group of power user U1 contains equipment {A, B, C}, and the associated electrical equipment group of U6 contains equipment {A, B, D}, and the two have two identical equipment (A and B), then there is a certain similarity between the two.

[0060] This step is the core decision-making step of S2. Its significance lies in discovering common high-risk electrical equipment combination patterns through cross-user equipment group similarity analysis, thereby formulating more targeted risk electrical equipment group update control strategies.

[0061] It is understood that the associated device group is a group of electrical devices obtained based on the identification results of the electrical consumption characteristics.

[0062] Suppose that the electrical devices on a certain line are identified by the current characteristics of a smart socket, including a certain brand and model of air conditioner, an induction cooker of a certain power, etc., and these devices are grouped into a group of related electrical devices.

[0063] Specifically, based on the aforementioned similar situations and the data from the monitored risk socket group, an update control strategy for the risky electrical equipment group is determined, including: Based on the similarity situation, S231 determines the similarity coefficient between the associated electrical equipment groups, and classifies the associated electrical equipment groups whose similarity coefficients are greater than a preset similarity coefficient threshold into the same equipment group. It then determines whether the number of the equipment groups is greater than a preset equipment group number threshold. If so, it determines that the update control strategy for the risky electrical equipment group is to classify the associated electrical equipment groups in the equipment group with multiple associated electrical equipment groups as risky electrical equipment groups. If not, it proceeds to step S232. The similarity coefficient refers to the degree of similarity in the composition of electrical equipment between two related groups of electrical equipment, and is determined based on the proportion of the number of identical electrical equipment in the two groups to the total number of electrical equipment in the smaller group.

[0064] Suppose that the associated electrical equipment group X has 3 devices {A, B, C} and group Y has 4 devices {A, B, D, E}. The two groups have 2 devices in common {A, B}. Then the similarity coefficient = 2 ÷ 3 = 0.667.

[0065] This step uses similarity coefficient clustering and the number of equipment groups to determine the strategy for traffic diversion. The significance of this step is that when a large number of equipment groups are discovered, it indicates that there are a large number of equipment groups with overheating risk. Therefore, the associated equipment groups among the equipment groups with multiple associated electrical equipment groups are identified as risky electrical equipment groups. When the number of equipment groups is small, it is necessary to further combine the identification and matching ratio of the monitored risk socket groups for judgment.

[0066] It should be noted that the similarity coefficient between the associated electrical equipment groups is determined based on the proportion of the minimum number of electrical equipment in the associated electrical equipment group compared to the number of electrical equipment in other associated electrical equipment groups.

[0067] If group P of related electrical equipment has 5 devices and group Q has 3 devices, and the number of identical devices in both groups is 2, with a minimum value of 3, then the similarity coefficient = 2 ÷ 3 = 0.667.

[0068] S232 determines the identification matching ratio based on the ratio of the number of device groups to the number of monitored risk socket groups, and determines whether the identification matching ratio is greater than a preset ratio threshold. If yes, the update control strategy for the risk electrical equipment group is determined to be to take the associated electrical equipment groups in the device group with multiple associated electrical equipment groups as risk electrical equipment groups. If no, the update control strategy for the risk electrical equipment group is determined to take the associated electrical equipment groups in the device group with a target number of associated electrical equipment groups, or the device group with multiple associated electrical equipment groups and a similarity coefficient between them that is greater than a preset similarity coefficient threshold, as risk electrical equipment groups.

[0069] The identification matching ratio refers to the ratio of the number of device groups to the number of monitored risk socket groups. It is used to measure the degree of matching between the device group identification results obtained based on similarity coefficient analysis and the original monitored risk groups. The higher the degree of matching, the less similar the distribution of associated electrical equipment groups of the monitored risk socket groups are. Therefore, a lenient approach is used to determine the risk electrical equipment groups. Conversely, if the distribution of associated electrical equipment groups of the monitored risk socket groups is similar, a strict approach is used to determine the risk electrical equipment groups.

[0070] Assuming that K device groups are obtained after clustering using similarity coefficients, and the number of risk-prone socket groups is M, then the matching ratio = K ÷ M. A higher ratio indicates a higher degree of matching between the clustering results and the original risk data.

[0071] Suppose that five electricity users of interest are identified: U1, U6, U10, U12, and U15.

[0072] In S21, the smart sockets for each user with electricity concerns are grouped according to their respective lines. Taking U1 as an example, assume that U1 has 8 smart sockets, located on 3 lines: 3 on line L1-1, 2 on line L1-2, and 3 on line L1-3, forming 3 socket groups. Including all 5 users with electricity concerns, a total of 18 socket groups are formed.

[0073] Check the historical alarm data of each socket group and assume that the smart sockets in 10 socket groups have previously triggered over-temperature alarms (i.e., there is historical alarm data).

[0074] In S22, the 10 socket groups with historical alarm data are the monitored risk socket groups. The types of electrical devices connected to each smart socket in each monitored risk socket group are identified, forming their respective associated electrical device groups. For example, in monitored risk socket group G1(U1, L1-1), if the electrical devices connected to the smart socket are identified as {Air Conditioner Type A, Electric Water Heater Type B, Induction Cooker Type C}, then this associated electrical device group contains 3 types of devices.

[0075] In S231 of S23, it is assumed that 10 monitored risk socket groups correspond to 10 associated electrical equipment groups (denoted as D1 to D10 respectively). The similarity coefficient is calculated pairwise (number of identical electrical equipment ÷ number of equipment in the smaller group), and the preset similarity coefficient threshold is 0.600.

[0076] Assuming the calculation results are as follows: D1 and D3 have a similarity coefficient of 0.750, D1 and D5 have a similarity coefficient of 0.800, and D3 and D5 have a similarity coefficient of 0.667. Since all three are >0.600, they are grouped into the same equipment group, P1. D2 and D7 have a similarity coefficient of 0.833 and are grouped into P2. D4 and D8 have a similarity coefficient of 0.667 and are grouped into P3. D6 and D9 have a similarity coefficient of 0.750 and are grouped into P4. D10 has a similarity coefficient ≤0.600 with all other groups and forms a separate group, P5.

[0077] There are a total of 5 equipment groups: P1 includes D1 / D3 / D5 (3 units), P2 includes D2 / D7 (2 units), P3 includes D4 / D8 (2 units), P4 includes D6 / D9 (2 units), and P5 includes D10 (1 unit).

[0078] The preset threshold for the number of equipment groups is 4, and 5 > 4. Therefore, the update control strategy for determining risky equipment groups is as follows: Related equipment groups among equipment groups with multiple related equipment groups are considered risky equipment groups. That is, P1 contains 3, P2 contains 2, P3 contains 2, and P4 contains 2, all of which fall under the category of "multiple related equipment groups". Therefore, all 9 related equipment groups in P1 / P2 / P3 / P4 are risky equipment groups.

[0079] Specifically, the method for determining the monitoring and management method for the electricity consumption characteristics of the electricity users is as follows: In this embodiment, based on the update processing results of the risky electrical equipment group and the composition data of the electrical equipment in the risky electrical equipment group, the matching probability of the risky electrical equipment group that is prone to over-temperature alarm signals is determined. Based on the matching probability of the risky electrical equipment group that is prone to over-temperature alarm signals, the monitoring and management method of the power user's electricity consumption characteristics is determined, thereby laying the foundation for ensuring the operational reliability of the gateway equipment of power users with over-temperature risk based on the similarity between the electricity consumption characteristics and the risky electrical equipment group.

[0080] S31 determines the number of risky electrical equipment groups based on the update processing results of the risky electrical equipment groups; The number of risky electrical equipment groups refers to the total number of risky electrical equipment groups finally determined after the update process of S2.

[0081] If S2 ultimately identifies 9 groups of high-risk electrical equipment, then the number is 9.

[0082] This step involves counting the total number of risky electrical equipment groups. The significance of this step is to use quantity as the first-level judgment criterion—a large quantity indicates a richer risk pattern, allowing for simpler monitoring and management methods; a small quantity requires further analysis.

[0083] The above steps include the following: If the number of risky electrical equipment groups is less than a preset threshold for the number of risky electrical equipment groups, proceed to step S32. If not, the number of risky electrical equipment groups is too large. Therefore, in order to determine whether there are risky electrical equipment groups for power users, the monitoring and management method for determining the electricity consumption characteristics of power users is to extract the electricity consumption characteristics of the smart sockets in the line when the number of smart sockets in the power user's line is greater than a preset value. For example, assuming there are 9 risky electrical equipment groups and a preset threshold of 6 risky electrical equipment groups, and 9 is not less than 6, then the most lenient monitoring and management method is directly adopted: as long as the number of smart sockets in a certain line of other users is greater than the preset value of the number of sockets, the power consumption characteristics are extracted for that line.

[0084] This step involves triaging based on the number of risky electrical equipment groups. The significance of this step is that when there are a large number of risky electrical equipment groups, it indicates that the types of high-risk electrical equipment combinations are diverse, and the monitoring coverage of other users should be sufficiently broad. The lowest threshold triggering conditions should be used to quickly identify potential risks.

[0085] S32 determines the number of electrical devices in the risky electrical device group based on the composition data of the electrical devices in different risky electrical device groups; The composition data of the electrical equipment refers to the type and quantity information of the electrical equipment included in each risk electrical equipment group.

[0086] If a certain risky electrical equipment group includes equipment {air conditioner type A, electric water heater type B, induction cooker type C}, then the number of electrical equipment in this group is 3.

[0087] This step moves from the group level to the device level. The significance of this step is that small groups with fewer electrical devices are more likely to find matches among other users, and the analysis of these small groups can more accurately assess the matching probability.

[0088] The above steps include the following: Scenario 1: If, based on the number of electrical devices in the risky electrical device group, it is determined that there is no risky electrical device group with a number of electrical devices less than a preset threshold, then the probability of overlapping with the risky electrical device group is small. Therefore, the monitoring and management method for the electricity user's electricity consumption characteristics is as follows: if the number of smart sockets in the electricity user's line is greater than a preset value for the number of sockets, and there are multiple matching sockets among the smart sockets in the line, then the electricity consumption characteristics are extracted from the smart sockets in the line. Assuming the preset device quantity threshold is 3, if the number of devices in all 9 risky electrical device groups is ≥3, meaning there are no groups with fewer than the threshold, it indicates that the number of devices in the risky groups is generally large, and the probability of overlap with other users is small. Therefore, a stricter triggering condition is adopted - in addition to meeting the standard number of sockets, there must also be multiple matching sockets.

[0089] This step uses "no small groups" as the criterion. The significance of this is that when the number of devices in all risk groups is large, the matching is more difficult. It is necessary to add the condition of matching the socket to improve the accuracy of feature extraction and avoid ineffective extraction and waste of resources.

[0090] Case 2: When it is determined that there is a risky group of electrical equipment with a number of electrical devices less than the preset threshold, the risky group of electrical equipment with a number of electrical devices less than the preset threshold is taken as an available matching group. It is determined whether the number of available matching groups is above the preset threshold. If so, the monitoring and management method of the electricity user's electricity consumption characteristics is determined to be that as long as the number of smart sockets in the electricity user's line is greater than the preset value of the number of sockets, the electricity consumption characteristics are extracted from the smart sockets in the line. If not, proceed to step S33. The available matching group refers to a risky electrical equipment group whose number of electrical devices is less than a preset threshold. This type of group has fewer devices and is easier to find a match with other users.

[0091] If three out of nine risky electrical equipment groups have fewer than three electrical equipment, then these three are considered available matching groups. If the number of available matching groups is above a preset threshold (e.g., two), it indicates that the number of matching groups is sufficient, and the most lenient method is adopted.

[0092] This step introduces "number of available matching groups" as a criterion. The significance of this is that when there are enough small risk groups, these groups are more likely to find matches among other users. Therefore, the most lenient triggering conditions can be adopted to ensure that no potential risks are overlooked.

[0093] S33 determines the monitoring and management method for the electricity user's electricity consumption characteristics based on the number of risky electrical equipment groups and the number of electrical equipment in different risky electrical equipment groups.

[0094] Taking into account the total number of all risky electrical equipment groups and the number of available matching groups, the total number of groups index is calculated, and the final monitoring and management method is determined accordingly.

[0095] This step is the final decision-making step in S3. Its significance lies in determining whether to adopt a lenient or strict monitoring and management method when the number of available matching groups is insufficient, through a comprehensive assessment of the total number of groups.

[0096] Specifically, in the above steps, the total number of groups is determined based on the sum of the number of available matching groups and the number of risky electrical equipment groups. It is then determined whether the total number of groups is greater than a preset value for the number of groups. If so, the monitoring and management method for the electricity user's electricity consumption characteristics is determined to be that if the number of smart sockets in the electricity user's line is greater than a preset value for the number of sockets, then the electricity consumption characteristics of the smart sockets in the line are extracted. If not, the monitoring and management method for the electricity user's electricity consumption characteristics is determined to be that if the number of smart sockets in the electricity user's line is greater than a preset value for the number of sockets, and there are multiple matching sockets among the smart sockets in the line, then the electricity consumption characteristics of the smart sockets in the line are extracted.

[0097] The total number of groups refers to the sum of the number of available matching groups and the number of risky electrical equipment groups, which is used to comprehensively reflect the overall size and matching potential of risky groups.

[0098] Let A be the number of available matching groups and B be the number of risky electrical equipment groups. Then the total number of groups = A + B.

[0099] This step makes the final judgment based on the total number of groups. Its significance lies in unifying the matching potential dimension and the overall size dimension into a single indicator. When the total number is large, a lenient method is used to expand coverage, and when the total number is small, a strict method is used to improve efficiency.

[0100] This embodiment achieves multi-level adaptive determination of other user electricity consumption characteristic monitoring and management methods through S3. Its core value is reflected in three aspects: First, by judging the number of risky electrical equipment groups in the early stage, it realizes rapid decision-making in scenarios with sufficient scale; second, by identifying and evaluating the number of available matching groups, it cleverly utilizes the high matching probability of small groups for strategy optimization; and third, by comprehensively evaluating the total number of groups, it achieves a balance between matching potential and overall scale.

[0101] Following the specific implementation of S2, S2 ultimately identified 9 groups of high-risk electrical equipment. There are 15 other users (U2, U3, U4, U5, U7, U8, U9, U11, U13, U14, U16, U17, U18, U19, U20).

[0102] In S31, the number of risky electrical equipment groups is 9, and the preset threshold for the number of risky electrical equipment groups is set to 6. Since 9 is not less than 6, the monitoring and management method is directly determined as follows: as long as the number of smart sockets in the line of the power user (i.e. other users) is greater than the preset value of the number of sockets (set to 3), the power consumption characteristics of the smart sockets in that line will be extracted.

[0103] Furthermore, the method for determining the monitoring and control method for the gateways of other users is as follows: In this embodiment, based on data from monitored power users, the efficiency of updating and processing risky electrical equipment groups under the current monitored power users is determined. Furthermore, by combining data from other users' monitored smart sockets, the reliability of monitoring and processing of other users' gateway devices is determined. Based on the efficiency of updating and processing risky electrical equipment groups under the current monitored power users and the reliability of monitoring and processing of other users' gateway devices, the monitoring and control method for other users' gateways is determined. This determines under what circumstances the periodic monitoring and analysis of the gateway's operating status will be performed. This not only ensures the reliability of monitoring and processing of gateway devices for all power users overall, but also guarantees the reliability of monitoring and processing of gateway devices for power users with overheating risks, as well as the efficiency of updating and processing risky electrical equipment groups.

[0104] S41 determines the number of the electricity users of interest based on the electricity user data; The number of electricity users of interest refers to the total number of electricity users of interest identified in S1.

[0105] For example, if S1 identifies 5 users who are interested in electricity, then the number is 5.

[0106] This step involves statistically analyzing the scale of electricity users under surveillance. Its significance lies in using the proportion of such users as the first-level judgment criterion—a high proportion indicates that the update and processing efficiency of risky electrical equipment groups is relatively high, and gateway monitoring and control can adopt a shorter cycle.

[0107] Specifically, in the above steps, it is determined whether the proportion of the power users of concern among the power users is greater than the preset user proportion threshold. If so, the efficiency of updating the risky power equipment group using the power users of concern is relatively high. Therefore, the monitoring and control method for the other users is to perform monitoring and control of the gateway of the other users when there is a risky power equipment group in the monitoring smart socket of any line of the other users. The communication status between the gateway and the smart socket is determined according to the preset period. If not, proceed to step S42. The number of electricity users under monitoring is 5, out of a total of 20, representing a percentage of 5 ÷ 20 = 0.250. The preset threshold for the percentage of users is 0.300. If 0.250 is not greater than 0.300, proceed to step S42.

[0108] This step uses the proportion of power users under surveillance as the diversion condition. Its significance is that when the proportion of power users under surveillance is high, there is sufficient material for updating and processing risky electrical equipment groups, and the most direct triggering condition can be adopted - as long as a risky electrical equipment group is detected in the smart socket, the gateway monitoring and control will be triggered.

[0109] S42 identifies other users who monitor smart sockets based on monitoring data from other users, and identifies them as monitoring users. The monitoring user refers to a user among other users who has at least one monitoring smart socket. These users have the value of gateway monitoring and control because they have the ability to monitor smart sockets.

[0110] Suppose that among the 15 other users, 9 users' lines include smart sockets for monitoring due to the extraction of electricity usage characteristics; then these 9 users are the monitored users.

[0111] This step filters out other users with actual monitoring capabilities. Its significance lies in excluding users who do not monitor smart sockets at all, and focusing monitoring resources on users who have actually deployed them.

[0112] The above steps include the following: The number of monitored users is obtained, and it is determined whether the proportion of the monitored users among all other users is greater than a preset monitoring user proportion threshold. If yes, proceed to step S43; otherwise, it is determined that the number of monitored users is small. In order to ensure the reliability of the upload processing when a warning signal is issued, the monitoring and control method for other users is determined to be that when the similarity coefficient between the monitored smart socket in any line of other users and the risky electrical equipment group is above a preset coefficient threshold, the monitoring and control of the gateway of the other users is carried out, and the communication status between the gateway and the smart socket is determined according to a preset period. The number of monitored users is 9, and the total number of other users is 15, with a ratio of 9 ÷ 15 = 0.600. The preset threshold for the proportion of monitored users is 0.500. If 0.600 > 0.500, proceed to S43. If the proportion is not greater than 0.500, the similarity coefficient trigger condition is used.

[0113] This step distributes traffic based on the proportion of monitored users. Its significance lies in the fact that when the number of monitored users is small, in order to avoid the uploading and processing of early warning signals being missed due to insufficient monitoring coverage, a more sensitive similarity coefficient trigger condition is used to compensate for insufficient coverage.

[0114] Specifically, the similarity coefficient between the monitoring smart sockets in the line and the risky electrical equipment group is determined based on the proportion of the number of electrical equipment in the risky electrical equipment group that overlaps with the number of electrical equipment in the monitoring smart sockets in the line.

[0115] Suppose a group of high-risk electrical equipment contains N electrical devices. If a user's smart socket monitoring system has M electrical devices that are identical to those in the same group, then the similarity coefficient = M ÷ N.

[0116] S43 determines the monitoring and control method for the gateway of the other users based on the composition data of the monitoring smart sockets among different monitoring users and the number of power users of interest.

[0117] The monitoring matching coefficient is calculated by combining the proportion of monitored users and the proportion of users paying attention to electricity, and the selection is made between the two triggering conditions accordingly.

[0118] This step is the core integrated decision-making step of S4. Its significance lies in integrating the scale dimension of power users and the coverage dimension of monitoring users through the monitoring matching coefficient, so as to achieve a more refined selection of gateway monitoring and control methods.

[0119] It is understood that the above steps include the following: S431 Based on the average proportion of the number of the power users of concern among the power users and the proportion of the number of the monitored users among all other users, determine the monitoring matching coefficient, and determine whether the monitoring matching coefficient is greater than the preset monitoring matching coefficient threshold. If so, determine that the monitoring control method for the other users is to perform monitoring control of the gateway of the other users when there is a group of risky electrical equipment in the monitoring smart socket of any line of the other users. Determine whether the communication status between the gateway and the smart socket is stable according to the preset period. If not, proceed to step S432. The monitoring matching coefficient refers to the comprehensive matching index obtained by averaging the proportion of power users under attention and the proportion of monitored users, and is used to measure the overall maturity of monitoring and management.

[0120] Let A be the proportion of users who are concerned about electricity and B be the proportion of users who are monitored. Then the monitoring matching coefficient = (A + B) ÷ 2.

[0121] This step uses the average of two percentages to make a judgment. Its significance lies in using the most lenient triggering method when the overall monitoring and management maturity is high, so as to make full use of the existing monitoring system.

[0122] S432 determines whether the proportion of monitored smart sockets among the smart sockets of the power users under concern is greater than the monitoring socket proportion threshold. If so, the monitoring and control method for other users is determined to be that when there is a group of risky electrical equipment in the monitored smart sockets of any line of other users, the monitoring and control of the gateway of the other users is carried out, and the communication status between the gateway and the smart socket is determined to be stable according to a preset period. If not, it is determined that the number of monitored users is small at this time. In order to ensure the reliability of the upload processing when a warning signal occurs, the monitoring and control method for other users is determined to be that when the similarity coefficient between the monitored smart sockets of any line of other users and the group of risky electrical equipment is above the second preset coefficient threshold (greater than the preset coefficient threshold), the monitoring and control of the gateway of the other users is carried out, and the communication status between the gateway and the smart socket is determined to be stable according to a preset period.

[0123] The percentage of monitored smart sockets refers to the proportion of all smart sockets belonging to monitored smart sockets among all smart sockets for electricity users.

[0124] Assuming that 5 out of the 8 smart sockets for power user U1 are identified as monitoring smart sockets, the percentage is 5 ÷ 8 = 0.625.

[0125] This step uses the proportion of monitoring sockets within the power user's premises as the final judgment criterion. Its significance lies in the fact that when the monitoring coverage of the power user itself is already high, the monitoring and control of other users can adopt the most direct triggering conditions; when the monitoring coverage is insufficient, a higher similarity coefficient threshold (second preset coefficient threshold) is used to trigger the monitoring, and stricter conditions are used to make up for the lack of monitoring coverage.

[0126] This embodiment achieves multi-level adaptive determination of other user gateway monitoring and control methods through S4. Its core value is reflected in three aspects: First, by focusing on the proportion of power users, it reflects the efficiency level of risk equipment group update processing; second, by monitoring the proportion of users, it reflects the monitoring coverage of other users; and third, by progressively judging the monitoring matching coefficient and the proportion of monitoring sockets, it achieves a dynamic balance between the maturity of monitoring management and the strictness of triggering conditions, ensuring that gateway monitoring and control is both timely and effective while avoiding unnecessary resource consumption.

[0127] Example 2 Secondly, such as Figure 4 As shown, this application provides a smart socket based on an industrial gateway and power consumption linkage protection, employing the aforementioned control method, specifically including: Data acquisition module, data transmission module, alarm and control module; The data acquisition module is responsible for collecting and detecting electricity consumption data, identifying the electricity consumption characteristics of electrical equipment based on the detection results, and identifying the load characteristics of the smart socket based on the identification results of the electricity consumption characteristics of the electrical equipment. The data transmission module is responsible for transmitting the collected power consumption data and alarm information to the cloud platform through the gateway module; The alarm and control module is responsible for leakage alarm, over-temperature alarm and on / off control, and uses cloud control commands or local control commands to perform on / off control.

[0128] Furthermore, the power consumption data includes voltage, current, leakage current, terminal temperature, power, power consumption, and load identification data.

[0129] Furthermore, the data transmission module adopts a dual-mode communication module, that is, it is constructed and communicates using the DL / T645-2007 protocol and the power HarmonyOS protocol.

[0130] The Power Harmony OS refers to an IoT operating system specifically designed for the power industry. It provides standardized communication protocols and data interaction mechanisms for data transmission between gateway modules and smart sockets, meets the DL / T645-2007 protocol, and is compatible with the Power Harmony OS ecosystem.

[0131] Based on the intelligent sockets or intelligent circuit breakers of the Power Harmony platform, abnormal operating conditions such as leakage current, high temperature, overload, overvoltage, undervoltage, and unbalanced operation of home appliances or electrical equipment can be monitored. Simultaneously, the electrical load of home appliances or electrical equipment can be identified and processed. The intelligent socket incorporates a judgment program, and the IoT platform sets thresholds. It employs a dual-mode communication module. When an anomaly occurs, the intelligent socket sends the anomaly information to the gateway module via a SPLC that meets the DL / T645-2007 protocol and the Power Harmony protocol, or it uploads the information to the gateway module using a wireless communication module that meets the Power Harmony protocol. The gateway module then uploads the anomaly information to the cloud platform via 4G. The measurement data upload link is: indoor intelligent socket --> gateway module --> cloud platform, where SPLC is an ultra-high-speed low-voltage power line broadband carrier (Super PLC / next-generation PLC). Both the gateway module and the smart socket's data transmission module are built on the Dianhong system. The two interact with each other through the communication protocol defined by the Dianhong system. The smart socket encapsulates the collected electricity consumption data according to the Dianhong system's data format and sends it to the gateway module. The gateway module then parses the data and forwards it to the cloud platform.

[0132] The gateway module and data transmission module are constructed and communicate using the Elec-Tech system. The significance of this is that it enables interconnection and interoperability between devices through a standardized IoT operating system, avoiding the data silo problem caused by incompatibility of communication protocols between devices from different manufacturers. At the same time, the security mechanism provided by the Elec-Tech system also ensures information security during data transmission.

[0133] The data acquisition module, data transmission module, alarm and control module, and gateway module are sequentially connected and work collaboratively with the cloud platform. Specifically, the data acquisition module collects the power consumption data of each smart socket in real time and transmits it to the data transmission module. The data transmission module sends the data to the gateway module through the Dianhong system. The gateway module uploads the data to the cloud platform for centralized storage and analysis. The cloud platform uses the control method described in Embodiment 1 to determine the power users of interest in S1, determine the updated control strategy for risky power consumption equipment groups in S2, determine the power consumption characteristic monitoring and management method for other users in S3, and determine the gateway monitoring and control method for other users in S4. The alarm and control module executes safety protection operations such as leakage current alarm, over-temperature alarm, and on / off control according to the control commands issued by the cloud platform or the locally preset alarm rules.

[0134] The data acquisition module and data transmission module employ a dual-mode communication module, built using the DL / T645-2007 protocol and the Power HarmonyOS protocol. Data transmission is achieved via an internal communication bus or wireless method. The gateway module communicates with the cloud platform via a wired or wireless wide area network. The cloud platform communicates with the alarm and control module through the gateway module, forwarding control commands. The collaborative operation of these modules forms a complete closed-loop system encompassing data acquisition, analysis, processing, and security control.

[0135] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0136] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0137] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A control method, characterized in that, Specifically, it includes: Using the usage data of smart sockets in different power users, the usage time periods of the power users in different smart sockets are determined. Combined with the smart socket data of different power users, users whose electricity consumption characteristics are identified according to a set period are identified. This is the identification method for power users to be monitored. Based on the identification method, the update identification process for the power users of interest is performed. The degree of correlation between the power consumption characteristics of the electrical devices in the smart sockets of different power users of interest is determined by the identification results of the power consumption characteristics of the electrical devices in different power users of interest. Based on the degree of correlation, the update control strategy for the risk electrical device group is determined. The update control strategy is used to update the risky electrical equipment groups, and the electrical users other than the power users under concern are treated as other users. Based on the update processing results of the risky electrical equipment groups and the composition data of the electrical equipment in different risky electrical equipment groups, a monitoring and management method for the electricity consumption characteristics of other users is determined. Based on the aforementioned monitoring and management method, the monitoring smart sockets of other users are identified. Based on the monitoring smart socket data of other users and the data of the power users of interest, the monitoring and control method for the gateways of the other users is determined.

2. The control method as described in claim 1, characterized in that, The usage time of the power user in different smart sockets is determined based on the monitoring data of the power user's smart socket.

3. The control method as described in claim 1, characterized in that, The data on smart sockets in the power user includes the number of smart sockets in the power user.

4. The control method as described in claim 1, characterized in that, The method for determining the identification method of the electricity users of interest is as follows: Based on the smart socket data of the electricity users, determine the number of smart sockets in the electricity users; Based on the usage time of the power user in different smart sockets, determine the smart sockets whose average daily usage time of the power user is greater than a preset time threshold, and use them as the matching sockets; A method for identifying interested electricity users is used to determine the number of matching sockets and smart sockets among different electricity users.

5. The control method as described in claim 1, characterized in that, The identification result of the power consumption characteristics of the electrical device is determined based on the current characteristics of the electrical device during the power consumption process read from the smart socket.

6. The control method as described in claim 1, characterized in that, The method for determining the update control strategy for the group of high-risk electrical equipment is as follows: The smart sockets of the power users in question are grouped into the same socket group on the same line. Based on the alarm data of the smart sockets in the socket group caused by overheating, the historical alarm data of the smart sockets in the socket group is determined. Based on the historical alarm data, the monitored risk socket groups in the socket groups are determined, and based on the identification results of the power consumption characteristics in the monitored risk socket groups, the associated power equipment groups of the monitored risk socket groups are determined; By determining the degree of correlation between the electricity consumption characteristics of electrical equipment among different power users, the similarity of associated electrical equipment groups among monitored risk socket groups among different power users is determined. Based on the similarity and the monitored risk socket group data, an update control strategy for the risk electrical equipment group is determined.

7. The control method as described in claim 1, characterized in that, The method for determining the monitoring and control method for the gateways of the other users is as follows: Based on the data on the electricity users of interest, determine the number of the electricity users of interest; Based on the monitoring data of other users' smart sockets, identify other users who are monitoring smart sockets and designate them as monitoring users. Based on the composition data of the monitoring smart sockets among different monitoring users and the number of power users of interest, the monitoring and control method of the gateway for the other users is determined.

8. The control method as described in claim 7, characterized in that, Based on the average proportion of the power users of interest among all power users and the proportion of the monitored users among all other users, a monitoring matching coefficient is determined. When the monitoring matching coefficient is greater than a preset monitoring matching coefficient threshold, the monitoring control method for the other users is determined to be that when there is a group of risky electrical equipment in the monitoring smart socket of any line of the other users, the gateway of the other users is monitored and controlled, and the communication status between the gateway and the smart socket is determined to be stable according to a preset period.

9. A smart socket based on an industrial gateway and power consumption linkage protection, employing the control method described in any one of claims 1-8, characterized in that, Specifically, it includes: Data acquisition module, data transmission module, alarm and control module; The data acquisition module is responsible for collecting and detecting electricity consumption data, identifying the electricity consumption characteristics of electrical equipment based on the detection results, and identifying the load characteristics of the smart socket based on the identification results of the electricity consumption characteristics of the electrical equipment. The data transmission module is responsible for transmitting the collected power consumption data and alarm information to the cloud platform through the gateway module; The alarm and control module is responsible for leakage alarm, over-temperature alarm and on / off control, and uses cloud control commands or local control commands to perform on / off control.

10. A smart socket based on an industrial gateway and power supply linkage protection as described in claim 9, characterized in that, The data transmission module adopts a dual-mode communication module, which is constructed and communicates using the DL / T645-2007 protocol and the power HarmonyOS protocol.

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