Equipment grouping management method based on Internet of Things card

Abstract

The invention discloses a device grouping management method based on an Internet of Things card, and the method comprises the steps: obtaining V historical groups of U devices of which operation parameters are monitored through the Internet of Things card, and monitoring and obtaining the Internet of Things flow data and aging information of the U devices; according to the U pieces of aging information, aging influence compensation is carried out on the U pieces of Internet of Things traffic data, and U pieces of compensated Internet of Things traffic data are obtained; the method comprises the following steps: configuring a traffic weight, randomly regrouping U devices, obtaining V first groups, obtaining compensation Internet of Things traffic data and aging information of the devices in the V first groups, calculating to obtain first group fitness, adjusting and optimizing the groups, obtaining V optimized groups, and performing device group management. The technical problem that equipment maintenance efficiency is low due to the fact that equipment grouping management cannot adapt to equipment dynamic operation characteristic changes in the prior art is solved, and the technical effect that equipment grouping management is carried out based on the equipment dynamic operation characteristic changes, so that the equipment maintenance efficiency is improved is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of the Internet of Things, and specifically to a device grouping management method based on Internet of Things cards. Background Art

[0002] With the rapid development of Internet of Things technology, more and more devices are connected to the network and transmit data through Internet of Things cards. In large enterprises, data centers or industrial scenarios, managing a large number of connected devices has become an important task. Device grouping management, as a commonly used management method, can group devices with similar characteristics into the same group for centralized maintenance and management.

[0003] Currently, device grouping management is mainly carried out based on static parameters or simple operating indicators of devices, such as device models, installation locations, service life, etc. However, this grouping method ignores the dynamic characteristic changes of devices during actual operation. In fact, even devices of the same model and service life may have significant differences in their operating characteristics due to different work tasks. At the same time, devices will age to varying degrees over time, which further leads to changes in their operating characteristics. Therefore, the existing device grouping management method cannot adapt to the changes in the dynamic operating characteristics of devices, resulting in a mismatch between the grouping results and the actual operating status of the devices. When maintenance personnel maintain the devices within a group, they may face devices with different operating characteristics and need to adopt different maintenance strategies for each device, significantly reducing the maintenance efficiency.

[0004] Therefore, how to perform effective grouping management based on the dynamic operating characteristics of devices to improve device maintenance efficiency is a technical problem that needs to be solved urgently at present. Summary of the Invention

[0005] The purpose of the present invention is to provide a device grouping management method based on Internet of Things cards to solve the problem in the prior art that device grouping management cannot adapt to the changes in the dynamic operating characteristics of devices, resulting in low device maintenance efficiency as mentioned in the above background art.

[0006] To achieve the above object, the present application provides a device grouping management method based on Internet of Things (IoT) cards. The method includes: obtaining V historical groupings of U devices whose operating parameters are monitored through IoT cards, monitoring and obtaining the IoT traffic data of the U devices within a preset time in the past, and collecting the aging information of the U devices, where U and V are positive integers; compensating for the aging impact on the U IoT traffic data according to the U aging information to obtain U compensated IoT traffic data; configuring traffic weights according to the change ranges of the U compensated IoT traffic data, randomly re-grouping the U devices to obtain V first groupings, obtaining the compensated IoT traffic data and aging information of the devices within the V first groupings, and calculating to obtain the first grouping fitness; adjusting and optimizing the V first groupings according to the first grouping fitness to obtain V optimized groupings, and performing device grouping management.

[0007] Optionally, obtaining V historical groupings of U devices whose operating parameters are monitored through IoT cards, monitoring and obtaining the IoT traffic data of the U devices within a preset time in the past, and collecting the aging information of the U devices includes: obtaining V historical groupings of U devices whose operating parameters are monitored through IoT cards; monitoring and obtaining the total IoT traffic of the U devices within a preset time range as the U IoT traffic data; obtaining the current cumulative usage time of the U devices, calculating the ratio to the expected usage time, and obtaining the U aging information.

[0008] Optionally, compensating for the aging impact on the U IoT traffic data according to the U aging information to obtain U compensated IoT traffic data includes: classifying according to the U aging information to obtain multiple IoT traffic impact coefficients; performing aging impact compensation calculation on the U IoT traffic data according to the multiple IoT traffic impact coefficients to obtain U compensated IoT traffic data, as shown in the following formula: ; where is the compensated IoT traffic data, G is the IoT traffic data, is the IoT traffic impact coefficient.

[0009] Optionally, classifying according to the U aging information to obtain multiple IoT traffic impact coefficients includes: collecting a sample aging information set according to the IoT traffic monitoring data of the same type of devices within the historical time, and collecting the change ranges of the traffic under the same usage parameters for different sample aging information, and labeling them as a sample IoT traffic impact coefficient set; constructing a mapping table between the sample aging information set and the sample IoT traffic impact coefficient set to obtain an aging traffic impact mapping table; respectively inputting the U aging information into the aging traffic impact mapping table to map and obtain U IoT traffic impact coefficients.

[0010] Optionally, configure the traffic weight according to the variation range of the U compensated IoT traffic data, including: obtaining the U historical IoT traffic data calculated and obtained by the U devices during the previous grouping; respectively calculating the variation ranges between the U compensated IoT traffic data and the U historical IoT traffic data to obtain U traffic variation ranges; multiplying the ratio of the mean of the U traffic variation ranges to the historical average traffic variation range by a preset traffic weight to obtain the traffic weight; and calculating and obtaining the aging weight according to the traffic weight.

[0011] Optionally, randomly regroup the U devices to obtain V first groups, and obtain the compensated IoT traffic data and aging information of the devices in the V first groups, and calculate and obtain the first group fitness, including: randomly regrouping the U devices to obtain V first groups, where the number of devices in each group is the same; obtaining the compensated IoT traffic data and aging information of the devices in the V first groups, and combining the historical IoT traffic data and historical aging information of the devices in the V historical groups to calculate and obtain the first group fitness, as shown in the following formula: ; where is the grouping fitness, is the traffic weight, is the aging weight, V is the number of groups, is the variance of multiple compensated IoT traffic data in the i-th first group, is the variance of multiple aging information in the i-th first group, is the mean of multiple compensated IoT traffic data in the i-th first group, is the mean of multiple historical IoT traffic data in the i-th historical group, is the mean of multiple aging information in the i-th first group, is the mean of multiple historical aging information in the i-th historical group.

[0012] Optionally, according to the first group fitness, adjust and optimize the V first groups to obtain V optimized groups for device grouping management, including: continuing to randomly regroup the U devices to obtain V second groups; obtaining the compensated IoT traffic data and aging information of the devices in the V second groups, and combining the historical IoT traffic data and historical aging information of the devices in the V historical groups to calculate and obtain the second group fitness; continuing the grouping iteration optimization of the U devices until convergence, and outputting the V groups with the largest grouping fitness as the V optimized groups.

[0013] By adopting the above technical solution, the present application introduces device aging information to compensate the Internet of Things traffic data, and performs optimized grouping based on the compensated data, so that the grouping result can adapt to the changes in the dynamic operation characteristics of the devices. By grouping devices with similar operation characteristics and aging degrees into the same group, it is convenient for maintenance personnel to carry out targeted and efficient management. At the same time, the continuous management of devices with similar characteristics by maintenance personnel is maintained, the adaptation cost of maintenance personnel is reduced, and the overall maintenance efficiency is improved.

[0014] Compared with the prior art, the beneficial effects that the present application can achieve are as follows: Obtain V historical groups of U devices whose operating parameters are monitored through Internet of Things cards, monitor and obtain the Internet of Things traffic data of U devices in the past preset time, and collect the aging information of U devices to provide basic data support for subsequent grouping optimization; according to the U aging information, compensate the U Internet of Things traffic data for the aging impact to obtain U compensated Internet of Things traffic data, and compensate and correct the traffic data through the aging information to make the traffic data better reflect the real operating characteristics of the devices; according to the change range of the U compensated Internet of Things traffic data, configure traffic weights, randomly regroup the U devices to obtain V first groups, obtain the compensated Internet of Things traffic data and aging information of the devices in the V first groups, calculate the first group fitness, perform preliminary grouping based on the corrected traffic data, and evaluate the grouping effect through fitness calculation to provide a basis for subsequent optimization; according to the first group fitness, adjust and optimize the V first groups to obtain V optimized groups, and perform device grouping management. Optimize and adjust the preliminary grouping according to the fitness evaluation result, so that the final grouping result can conform to the changes in the dynamic operation characteristics of the devices, so that devices with similar Internet of Things transmission traffic and aging states are grouped into the same group, which is convenient for maintenance personnel to carry out efficient management and improve the maintenance efficiency of the devices. Brief Description of the Drawings

[0015] Figure 1 It is a schematic flow chart of a device grouping management method based on Internet of Things cards provided by the present invention; Figure 2 It is a schematic flow chart of configuring traffic weights in a device grouping management method based on Internet of Things cards provided by the present invention.

[0016] The realization, functional features and advantages of the purpose of the present application will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] An embodiment of the present application provides a method for device grouping management based on Internet of Things (IoT) cards. As Figure 1 shown, the method includes: S100: Obtain V historical groupings of U devices whose operating parameters are monitored through IoT cards, monitor and obtain the IoT traffic data of the U devices within a preset past time period, and collect the aging information of the U devices, where U and V are positive integers.

[0019] Specifically, when performing grouping management on devices, first, obtain V historical groupings of U devices whose operating parameters are monitored through IoT cards, monitor and obtain the IoT traffic data of the U devices within a preset past time period, and collect the aging information of the U devices, where U and V are positive integers. These parameters reflect the working characteristics and operating states of the devices.

[0020] Among them, the V historical groupings of U devices whose operating parameters are monitored through IoT cards are the current (last grouping) grouping situations, reflecting the existing management methods of the devices; the IoT traffic data of the U devices within a preset past time period is an indication of the working content and characteristics of the devices; the aging information of the U devices is an indicator reflecting the usage time and state of the devices.

[0021] By obtaining these data, a necessary data basis is provided for subsequent optimization of device grouping based on traffic characteristics and aging states. Since devices with similar IoT transmission traffic are likely to have similar working contents, and devices with similar aging degrees have relatively consistent maintenance strategies, these data are the basis for realizing efficient device grouping management. By dividing devices with similar traffic and similar aging degrees into the same group, the same maintenance personnel can centrally maintain and manage these devices with similar characteristics, thereby improving management efficiency.

[0022] S200: According to the U aging information, perform aging impact compensation on the U IoT traffic data to obtain U compensated IoT traffic data.

[0023] Specifically, by performing aging impact compensation on the U IoT traffic data according to the U aging information, U compensated IoT traffic data is obtained, and based on the U aging information, aging impact compensation processing of the IoT traffic data is realized.

[0024] During the use of the device, different degrees of aging will occur. This aging will lead to a decline in the performance of device components, thereby affecting the Internet of Things (IoT) communication characteristics of the device. For example, for the same data transmission task, more traffic resources may be consumed on a device with a higher degree of aging. This change in IoT traffic caused by aging will interfere with the traffic-based device grouping judgment and reduce the accuracy and effectiveness of grouping.

[0025] By introducing an aging impact compensation mechanism, the interference of aging factors on IoT traffic data is eliminated or reduced, so that the compensated traffic data can more accurately reflect the actual working content and characteristics of the device, rather than the differences in its physical state. This compensation process enables subsequent grouping optimization to be based on the working characteristics of the device without being interfered by different degrees of device aging, thereby making an equivalent comparison of devices in different aging stages, making the grouping results more objective and accurate, and laying a more reliable data foundation for subsequent grouping optimization.

[0026] S300: According to the change range of the U compensated IoT traffic data, configure traffic weights, randomly regroup the U devices to obtain V first groups, obtain the compensated IoT traffic data and aging information of the devices within the V first groups, and calculate the first group fitness.

[0027] Specifically, after completing the aging compensation of the IoT traffic data, first, set traffic weights according to the change range of the compensated IoT traffic data to reflect the importance of traffic changes in grouping evaluation. Through weight configuration, the sensitivity of grouping to traffic factors can be dynamically adjusted to meet the grouping requirements under different traffic change conditions. Subsequently, randomly regroup the U devices to generate V first groups as the initial scheme for grouping optimization. By random grouping rather than based on historical grouping, it is possible to avoid the grouping optimization falling into local optimal solutions and enhance the ability to find the global optimal grouping scheme. For the obtained V first groups, further obtain the compensated IoT traffic data and aging information of the devices within each group, and calculate the first group fitness in combination with these data to comprehensively evaluate the quality of the grouping scheme and provide a basis and direction for subsequent grouping optimization.

[0028] S400: According to the first group fitness, adjust and optimize the V first groups to obtain V optimized groups for device grouping management.

[0029] Specifically, after obtaining the preliminary grouping scheme (V first groupings) and its fitness evaluation (first grouping fitness), the grouping result is further improved through iterative optimization to obtain a device grouping scheme with higher fitness. First, continue to randomly regroup the U devices to obtain V second groupings as new candidate solutions. Then, in the same way as the first grouping, obtain the compensated IoT traffic data and aging information of the devices within the second grouping, and calculate the second grouping fitness in combination with the historical grouping information. After that, compare the fitness of the V first groupings and the V second groupings, retain the grouping scheme with higher fitness, and continue the random grouping iteration on this basis. This iterative optimization process continues until the preset convergence condition is reached, such as the upper limit of the number of iterations or the fitness improvement amplitude is lower than the threshold, etc. Finally, output the V groupings with the maximum grouping fitness as the final V optimized groupings.

[0030] Through iterative optimization, the optimal solution can be searched among a large number of possible grouping schemes, effectively improving the grouping quality. The finally obtained V optimized groupings not only ensure the consistency of the device characteristics within the group, but also maintain a reasonable continuity with the historical groupings, thus realizing efficient device grouping management. Through the optimized grouping management method, the same maintenance personnel can manage devices with similar characteristics, significantly improving the maintenance efficiency and management quality, and reducing resource waste and management costs.

[0031] Furthermore, obtain V historical groupings of U devices whose operating parameters are monitored through IoT cards, monitor and obtain the IoT traffic data of the U devices within a preset past time period, and collect the aging information of the U devices, including: S110: Obtain V historical groupings of U devices whose operating parameters are monitored through IoT cards; S120: Monitor and obtain the total IoT traffic of the U devices within a preset past time range as U IoT traffic data; S130: Obtain the current cumulative usage time of the U devices, calculate the ratio with the expected usage time, and obtain U aging information.

[0032] In a feasible implementation, there are U devices for monitoring the operating parameters of the IoT cards. First, obtain the current grouping situation of these U devices, that is, V historical groupings. The V historical groupings of the U devices record the grouping numbers to which each device belongs currently, reflecting the device management structure and classification method of the current U devices, and providing a reference basis for the optimization process. Then, monitor and count the total IoT traffic generated by each of the U devices within a preset time range (such as the recent month or the recent quarter) through the IoT card interface, and use these statistical values as the U IoT traffic data. These data quantify the communication behaviors and working intensities of the devices, can directly reflect the data transmission loads and working characteristics of the devices, and are important bases for identifying the similarity of the working contents of the devices. Subsequently, obtain the current cumulative usage time of the U devices, and form U normalized aging information by calculating the ratio to the expected usage time. This ratio-form aging index makes the aging degrees of devices of different types and different design lifetimes comparable. The closer the aging information is to 1, the closer the device is to the end of its expected service life, and the higher the aging degree. For example, when the ratio of the cumulative usage time of a certain device to its expected usage time is close to 1, it indicates that the device is close to its design life and has a higher aging degree.

[0033] By obtaining the basic data required for device grouping management, it provides comprehensive and accurate data support for subsequent IoT traffic compensation and grouping optimization.

[0034] Furthermore, according to the U aging information, perform aging impact compensation on the U IoT traffic data to obtain U compensated IoT traffic data, including: S210: Classify and obtain multiple IoT traffic impact coefficients according to the U aging information; S220: Perform aging impact compensation calculation on the U IoT traffic data according to the multiple IoT traffic impact coefficients to obtain U compensated IoT traffic data, as shown in the following formula: ; where is the compensated IoT traffic data, G is the IoT traffic data, is the IoT traffic impact coefficient.

[0035] In a preferred embodiment, during the process of compensating for the aging effect on the IoT device traffic data, first, based on the U pieces of aging information obtained, multiple IoT traffic impact coefficients are classified. Specifically, the U pieces of aging information are divided into several intervals according to the numerical range, and each interval corresponds to an IoT traffic impact coefficient. For example, the aging information is divided into a low aging interval (0 - 0.3), a medium aging interval (0.3 - 0.6), and a high aging interval (0.6 - 1.0), and different IoT traffic impact coefficients correspond to each interval respectively. As the aging degree increases, the corresponding IoT traffic impact coefficient also increases accordingly, reflecting the positive correlation between the severity of aging and the increase in traffic. This classification method enables devices with similar aging degrees to adopt the same compensation coefficient, simplifying the calculation while ensuring the rationality of compensation.

[0036] Secondly, based on the multiple IoT traffic impact coefficients, aging effect compensation calculations are performed on the U pieces of IoT traffic data to obtain U pieces of compensated IoT traffic data. The compensation calculation uses the following mathematical relationship: . Wherein, is the compensated IoT traffic data, G is the original IoT traffic data, is the IoT traffic impact coefficient. The principle of this compensation mechanism is based on the relationship model between device aging and traffic increase. As the device ages, the performance of its internal components deteriorates and the processing efficiency decreases, often resulting in the consumption of more communication resources for the same work task, manifested as an increase in IoT traffic. The term in this formula represents the traffic increase factor caused by aging. The larger the value, the more significant the traffic increase caused by aging. By dividing the original traffic by this factor, the actual traffic of the aging device can be restored to the equivalent traffic value in a theoretically non-aging state, obtaining U pieces of compensated IoT traffic data. For example, when the value of a device with a high aging degree is 0.2, it means that about 20% more traffic is generated for the same work content compared to a new device. Through the formula = / (1 + 0.2), its actual traffic is converted into compensated traffic , enabling it to be compared with the traffic data of new devices on the same basis, thereby excluding the interference of aging factors and more accurately reflecting the actual work content characteristics of the device, providing a more objective and accurate data basis for subsequent grouping optimization based on work characteristics.

[0037] Furthermore, based on the U pieces of aging information, multiple IoT traffic impact coefficients are classified, including: S211: Collect a set of sample aging information based on the IoT traffic monitoring data of similar devices within a historical time period, and collect the traffic change amplitudes of the same usage parameters under different sample aging information, which are labeled as a set of sample IoT traffic impact coefficients. S212: Construct a mapping table between the set of sample aging information and the set of sample IoT traffic impact coefficients to obtain an aging traffic impact mapping table. S213: Input the U pieces of aging information into the aging traffic impact mapping table respectively, and map to obtain U IoT traffic impact coefficients.

[0038] In a preferred implementation manner, when classifying to obtain multiple IoT traffic impact coefficients according to the U pieces of aging information, first, collect a set of sample aging information based on the IoT traffic monitoring data of similar devices within a historical time period, and collect the traffic change amplitudes of the same usage parameters under different sample aging information, which are labeled as a set of sample IoT traffic impact coefficients. Specifically, collect the IoT traffic data of similar devices at different aging stages within a historical time period to form a set of sample aging information; at the same time, measure and record the traffic change amplitudes of these devices under the condition of the same usage parameters, which are labeled as a set of sample IoT traffic impact coefficients. This sampling method based on measured data ensures the objectivity and applicability of the traffic impact coefficients. Then, by constructing a mapping table between the set of sample aging information and the set of sample IoT traffic impact coefficients, establish a mapping relationship between the aging information and the IoT traffic impact coefficients to obtain an aging traffic impact mapping table. This mapping table can be in the form of a lookup table, systematically recording the corresponding traffic impact coefficient values under different aging degrees, providing support for subsequent parameter queries. Subsequently, input the aging information of the U devices into the aging traffic impact mapping table respectively, and map to obtain the IoT traffic impact coefficients corresponding to the U devices. Through the lookup table operation, assign an IoT traffic impact coefficient matching the aging degree of each specific device, providing accurate parameter input for subsequent traffic compensation calculations.

[0039] By the method based on historical monitoring data and mapping relationship, compared with the simple interval division method, it can more accurately reflect the actual influence law of different aging degrees on IoT traffic, thereby improving the accuracy and effectiveness of traffic compensation and laying a more reliable data foundation for subsequent device grouping optimization.

[0040] Further, as Figure 2 shown, configure traffic weights according to the change amplitudes of the U compensated IoT traffic data, including: S310: Obtain the U historical IoT traffic data calculated when the U devices were grouped last time. S320: Calculate the change amplitudes between the U compensated IoT traffic data and the U historical IoT traffic data respectively to obtain U traffic change amplitudes. S330: Obtain a traffic weight by multiplying the ratio of the mean of the U traffic change amplitudes to the historical average traffic change amplitude by a preset traffic weight. S340: Calculate and obtain an aging weight based on the traffic weight.

[0041] In a preferred embodiment, when configuring the traffic weight according to the change amplitudes of the U compensated IoT traffic data, first, obtain the U historical IoT traffic data calculated during the previous grouping of the U devices. These data serve as a comparison benchmark and reflect the previous communication traffic characteristics of the devices. Subsequently, calculate the differences between the U compensated IoT traffic data and the U historical IoT traffic data respectively to form U traffic change amplitude values, quantifying the specific changes in the IoT traffic of each device. Then, based on these traffic change amplitude data, further calculate the ratio of the mean of the U traffic change amplitudes to the historical average traffic change amplitude. This ratio reflects the relative intensity of the current traffic change relative to the historical traffic change. When this ratio is large, it indicates that the IoT traffic characteristics of the current device have fluctuated greatly, and the traffic factor should account for a greater weight in the grouping; otherwise, reduce the traffic weight. Multiply this ratio by a preset traffic weight (such as 0.5) to achieve the adaptive adjustment of the traffic weight. After that, calculate and obtain the aging weight according to the calculated traffic weight. Since the traffic factor and the aging factor need to be balanced with each other in the grouping evaluation and there is a correlation between their weights, by setting an appropriate calculation formula, the corresponding aging weight can be determined according to the traffic weight to ensure the comprehensive balance of the two factors in the grouping optimization.

[0042] Through the adaptive weight configuration method based on the traffic change amplitude, the grouping evaluation process can adjust the evaluation criteria according to the actual changes in the IoT traffic, enhancing the adaptability of the grouping algorithm to traffic fluctuations and providing a reasonable weight reference for the subsequent optimization of device grouping.

[0043] Furthermore, randomly regroup the U devices to obtain V first groupings, obtain the compensated IoT traffic data and aging information of the devices within the V first groupings, and calculate and obtain the first grouping fitness, including: S350: Randomly regroup the U devices to obtain V first groupings, where the number of devices in each group is the same. S360: Obtain the compensated IoT traffic data and aging information of the devices within the V first groupings, and calculate and obtain the first grouping fitness in combination with the historical IoT traffic data and historical aging information of the devices within the V historical groupings, as shown in the following formula: ; where, is the grouping fitness, is the traffic weight, is the aging weight, V is the number of groups, is the variance of multiple compensated IoT traffic data within the i-th first group, is the variance of multiple aging information within the i-th first group, is the mean of multiple compensated IoT traffic data within the i-th first group, is the mean of multiple historical IoT traffic data within the i-th historical group, is the mean of multiple aging information within the i-th first group, is the mean of multiple historical aging information within the i-th historical group.

[0044] In a preferred embodiment, first, randomly re-group the U devices to obtain V first groups, with the same number of devices in each group, ensuring the balance of resource allocation. The random grouping method can break the original grouping restrictions, provide more extensive grouping possibilities, and increase the chance of finding the global optimal solution. Then, after completing the random grouping, obtain the compensated IoT traffic data and aging information of the devices within the V first groups, and at the same time combine the historical IoT traffic data and historical aging information of the devices within the V historical groups to calculate the first group fitness. Specifically, the formula for calculating the fitness is: ; where, is the group fitness, is the traffic weight, is the aging weight, V is the number of groups, is the variance of multiple compensated IoT traffic data within the i-th first group, is the variance of multiple aging information within the i-th first group, is the mean of multiple compensated IoT traffic data within the i-th first group, is the mean of multiple historical IoT traffic data within the i-th historical group, is the mean of multiple aging information within the i-th first group, is the mean of multiple historical aging information within the i-th historical group.

[0045] This formula can be divided into two main parts, which are respectively evaluated for the two dimensions of intra-group balance and management continuity. The first part focuses on the balance of device characteristics within the group. By calculating the variances of the IoT traffic and aging information of the devices within each group and taking the reciprocal weighted sum, a quantitative evaluation of the intra-group consistency is achieved. The smaller the variance, the closer the device characteristics within the group are, and the larger its reciprocal is, the higher the fitness contribution. This part ensures that the devices within the same group have similar IoT traffic and aging characteristics, enabling maintenance personnel to adopt a unified maintenance strategy and significantly improving the intra-group management efficiency. The second part Then focus on the continuity of management. The V historical groups and the V first groups respectively correspond to V management personnel. By calculating the relative deviation of the average traffic and average aging information between the new and old groups corresponding to the same management personnel, and then subtracting this deviation from 1, the similarity index of the new and old groups is obtained. The smaller the deviation, the higher the similarity, indicating that the new group faced by the management personnel is closer to the equipment group they managed before in terms of characteristics, which helps to maintain the continuity of management experience, reduce the management adaptation cost after group switching, and improve the overall management effect. Through the weighted combination of these two parts of indicators, this formula realizes a comprehensive evaluation of the quality of the grouping scheme. While ensuring the consistency of the equipment characteristics within the group, it tries to reduce the difficulty for management personnel to adapt to the new equipment combination, so as to achieve the best balance between improving the management efficiency of a single group and maintaining the continuity of management, and provide a scientific grouping basis for equipment maintenance management.

[0046] Furthermore, according to the fitness of the first group, the V first groups are adjusted and optimized to obtain V optimized groups for equipment grouping management, including: S410: Continue to randomly re-group the U devices to obtain V second groups; S420: Obtain the compensated IoT traffic data and aging information of the devices in the V second groups, and combine the historical IoT traffic data and historical aging information of the devices in the V historical groups to calculate and obtain the second group fitness; S430: Continue the grouping iteration optimization of the U devices until convergence, and output the V groups with the largest group fitness as the V optimized groups.

[0047] In a preferred embodiment, the preliminary grouping scheme (V first groups) is iteratively optimized by repeatedly trying different grouping combinations to find the grouping scheme with the highest fitness.

[0048] First, after obtaining V first groups and their corresponding first group fitness values, continue to randomly re-group the U devices to obtain V second groups. The random grouping mechanism can break through the limitation of the local optimal solution, explore a broader solution space, and increase the possibility of finding the global optimal solution. Each random grouping provides a new candidate solution for the system as a possible optimization direction. Secondly, obtain the compensated IoT traffic data and aging information of the devices within the V second groups, and combine the historical IoT traffic data and historical aging information of the devices within the V historical groups. Use the same method as calculating the first group fitness to calculate the second group fitness, ensuring that the newly generated grouping solution (V second groups) can be evaluated according to a unified standard, making the fitness values between different grouping solutions comparable. After that, according to the calculated fitness values, retain the grouping solution with higher fitness, and continue the iterative process of random grouping and fitness calculation on this basis. This iterative optimization continues until the convergence condition is met, such as reaching the upper limit of the preset number of iterations, the fitness improvement is not obvious for several consecutive iterations, or the fitness reaches the preset threshold, etc. After the iteration ends, output the V groups with the maximum fitness obtained during the entire optimization process as the final V optimized groups for actual device grouping management.

[0049] Through the method based on random search and iterative optimization, continuously try different grouping combinations and retain the optimal results to obtain a grouping solution close to the global optimum, providing support for the efficient management of devices in the IoT environment.

[0050] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present application.

Claims

1. A device grouping management method based on Internet of Things cards, characterized in that, The method includes: Obtaining V historical groups of U devices that monitor operating parameters through IoT cards, monitoring and obtaining the IoT traffic data of the U devices in the past preset time, and collecting the aging information of the U devices, where U and V are positive integers; According to the U aging information, compensating the U IoT traffic data for the aging impact to obtain U compensated IoT traffic data; According to the change range of the U compensated IoT traffic data, configuring traffic weights, randomly regrouping the U devices to obtain V first groups, obtaining the compensated IoT traffic data and aging information of the devices in the V first groups, and calculating the first group fitness; According to the first group fitness, adjusting and optimizing the V first groups to obtain V optimized groups for device group management.

2. The device grouping management method based on the Internet of Things card according to claim 1, characterized in that, Obtaining V historical groups of U devices that monitor operating parameters through IoT cards, monitoring and obtaining the IoT traffic data of the U devices in the past preset time, and collecting the aging information of the U devices, including: Obtaining V historical groups of U devices that monitor operating parameters through IoT cards; Monitoring and obtaining the total IoT traffic of the U devices in the past preset time range as the U IoT traffic data; Obtaining the current cumulative usage time of the U devices, calculating the ratio with the expected usage time to obtain U aging information.

3. The method for device group management based on IoT cards according to claim 1, wherein, According to the U aging information, compensating the U IoT traffic data for the aging impact to obtain U compensated IoT traffic data, including: Classifying according to the U aging information to obtain multiple IoT traffic impact coefficients; According to the multiple IoT traffic impact coefficients, performing aging impact compensation calculation on the U IoT traffic data to obtain U compensated IoT traffic data, as shown in the following formula: ； Among them, To compensate for the IoT traffic data, G is the IoT traffic data, which is the IoT traffic impact coefficient.

4. The method for device group management based on IoT cards according to claim 3, wherein Classifying according to the U aging information to obtain multiple IoT traffic impact coefficients, including: Collecting a sample aging information set based on the IoT traffic monitoring data of the same type of devices in the historical time, and collecting the traffic change range under the same usage parameters for different sample aging information, and labeling it as a sample IoT traffic impact coefficient set; Constructing a mapping table between the sample aging information set and the sample IoT traffic impact coefficient set to obtain an aging traffic impact mapping table; Respectively inputting the U aging information into the aging traffic impact mapping table to map and obtain U IoT traffic impact coefficients.

5. The method for device group management based on IoT cards according to claim 1, wherein Configuring traffic weights according to the change range of the U compensated IoT traffic data, including: Obtaining the U historical IoT traffic data calculated when the U devices were grouped last time; Respectively calculating the change ranges of the U compensated IoT traffic data and the U historical IoT traffic data to obtain U traffic change ranges; Multiplying the ratio of the mean of the U traffic change ranges to the historical average traffic change range by a preset traffic weight to obtain the traffic weight; Calculating the aging weight according to the traffic weight.

6. The method for device group management based on IoT cards according to claim 1, wherein Randomly regrouping the U devices to obtain V first groups, obtaining the compensated IoT traffic data and aging information of the devices in the V first groups, and calculating the first group fitness, including: Randomly regroup the U devices to obtain V first groups, where the number of devices in each group is the same; Obtain the compensated IoT traffic data and aging information of the devices in the V first groups, and combine the historical IoT traffic data and historical aging information of the devices in the V historical groups to calculate the first group fitness as follows: ； Among them, is the group fitness, is the traffic weight, is the aging weight, V is the number of packets, is the variance of multiple compensated IoT traffic data within the i-th first packet, is the variance of multiple aging information within the i-th first packet, is the mean of multiple compensated IoT traffic data within the i-th first packet, is the mean of multiple historical IoT traffic data within the i-th historical packet, is the mean of multiple aging information within the i-th first packet, is the mean of multiple historical aging information within the i-th historical packet.

7. The method for device grouping management based on IoT cards according to claim 1, wherein According to the first group fitness, adjust and optimize the V first groups to obtain V optimized groups for device group management, including: Continue to randomly regroup the U devices to obtain V second groups; Obtain the compensated IoT traffic data and aging information of the devices in the V second groups, and combine the historical IoT traffic data and historical aging information of the devices in the V historical groups to calculate the second group fitness; Continue the grouping iterative optimization of the U devices until convergence, and output the V groups with the maximum group fitness as the V optimized groups.

Images