Method for solving service conflict of Internet of Things

Abstract

The invention provides a method for solving service conflicts of the Internet of Things, and relates to the technical field of the Internet of Things. Processing the historical preference data of the user by adopting a game theory, and then processing the real-time preference demand of the user by adopting an analytic hierarchy process; and calculating a service preference final value, feeding the final value back to the Internet of Things service in the intelligent space environment, and adjusting own service setting by the service equipment according to the final value. The method has high dynamic adaptability, scene mutation can be dealt with, if a user changes in an intelligent space environment, a body state attribute comparison matrix can be reconstructed, and therefore the weight of each body state attribute can be dynamically adjusted.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and more particularly to a solution for IoT service conflicts. Background Technology

[0002] With the rapid expansion of 5G networks, the smart home sector is experiencing robust growth. Data shows that the penetration rate of smart home devices in Chinese households has exceeded 65%, with new demand from lower-tier cities accounting for 48%, becoming a significant driver of industry growth. Smart home environments rely on a wealth of IoT devices to provide various IoT services. For example, smart lighting can adjust brightness on demand, and smart security can protect home safety in real time. However, conflicts frequently arise when multiple users simultaneously request the same service. For instance, with smart air conditioners, some users expect a comfortable 26°C, while others prefer a cool 22°C. Such conflicts urgently require an efficient service conflict resolution mechanism to ensure the stable operation of smart home systems and provide users with a high-quality experience.

[0003] There are currently four main technologies for resolving IoT service conflicts:

[0004] 1. Conflict resolution based on rule engines. Existing technologies resolve IoT service conflicts through predefined rules (such as priority policies and time-slice rotation). For example, fixed priorities are set for different users, with requests from higher-priority users being executed first. Disadvantages: The rules lack flexibility, making it difficult to adapt to dynamic scenarios (such as temporary permission changes), and manual rule maintenance is costly, and they cannot learn complex conflict patterns.

[0005] 2. Based on negotiation and arbitration mechanisms. Devices interact in multiple rounds through predefined protocols (such as CoAP) to reach a compromise based on utility functions (e.g., choosing the median value for air conditioning temperature); auction bidding models are also used: users bid for service control using virtual points, with the highest bidder gaining control (e.g., intelligent lighting brightness adjustment). Main drawbacks: Significant response latency, with multi-round negotiation averaging 200-500ms, potentially causing system failures in real-time control scenarios (e.g., security alarms); Excessive resource consumption: The complex negotiation process increases device CPU load by 30%-60%, accelerating battery aging; Poor protocol compatibility, with different manufacturers using proprietary negotiation protocols, making cross-brand system integration difficult.

[0006] 3. Static Priority-Driven Solution. This solution determines service allocation order by pre-setting user / device priorities (such as family member roles and device types). Key drawbacks: Lack of flexibility; static priorities cannot adapt to dynamic scenarios (such as temporary visitor needs), leading to a fragmented user experience; fairness concerns; long-term low-priority users may experience service starvation, especially in multi-person households, potentially causing conflicts; high configuration complexity, requiring manual maintenance of multi-level priority rules and frequent adjustments when the home environment changes.

[0007] 4. Edge Computing Optimization Solution. Deploy edge servers on the home gateway to process device requests in real time. Build a conflict resolution rule base based on edge nodes to reduce cloud dependence (e.g., automatically adjusting curtains based on light sensor data) and use a distributed decision tree to resolve conflicts. Disadvantages: High deployment costs, facing computing power bottlenecks, rule base upgrades require professional technical personnel, and are difficult for ordinary users to manage independently.

[0008] In summary, current solutions for resolving IoT service conflicts in smart space environments all suffer from the following problems:

[0009] 1. Existing solutions lack dynamic adaptability and are unable to cope with sudden changes in scenarios.

[0010] 2. High reliance on manual labor, resulting in high maintenance and configuration costs.

[0011] 3. In complex IoT environments with multiple users and multiple devices, none of the above four technologies can balance "fairness in service allocation" and "consistency in user experience".

[0012] 4. All four technologies mentioned above suffer from the problems of "difficult cross-system integration" and "high deployment cost," which restrict their large-scale application. Summary of the Invention

[0013] To address the shortcomings of existing technologies, this invention proposes a conflict resolution method for multiple users simultaneously requesting different service preferences for the same Internet of Things service in a smart space environment.

[0014] To achieve the above objectives, the present invention adopts the following technical solution:

[0015] User historical preference data processing utilizes game theory; real-time user preference demand processing employs the Analytic Hierarchy Process (AHP); and service preference final value calculation is performed, feeding this final value back to the IoT services in the smart space environment, allowing service devices to adjust their service settings accordingly. Through user historical preference analysis, a user historical preference mean (MV) is obtained. gta By processing users' real-time preferences, a real-time preference mean is obtained: MV ahp Finally, the two means are weighted and summed to obtain a compromise preference value: MV. ave In intelligent space environments, conflicting IoT services are determined according to MV. ave This allows you to adjust your service settings to provide fair and reasonable services to multiple users.

[0016] Compared with the prior art, the present invention has the following advantages:

[0017] 1. This invention has great dynamic adaptability and can cope with sudden changes in the scene. If the user changes in the intelligent space environment, the body state attribute comparison matrix can be reconstructed, thereby dynamically adjusting the weight of each body state attribute.

[0018] 2. In complex IoT environments with multiple users and multiple devices, this invention can balance "service allocation fairness" and "user experience consistency" because it fully considers both the historical preference records of all users and analyzes the real-time preference requirements of all users.

[0019] 3. This invention has the advantages of "easy cross-system integration" and "low deployment cost", and can be promoted and applied on a large scale. Attached Figure Description

[0020] Figure 1 This is a top-level architecture diagram of the present invention;

[0021] Figure 2 (a) Comparison of the accuracy of IHS, UF, APA, FP, and OBA under normal distribution in a smart space with three healthy occupants. =0.5); Figure 2 (b) Comparison of the accuracy of IHS, UF, APA, FP, and OBA under uniform distribution in a smart space with three healthy occupants. =0.5); Figure 2 (c) Comparison of the accuracy of IHS, UF, APA, FP, and OBA under a triangular distribution in a smart space with three healthy occupants. =0.5);

[0022] Figure 3 (a) Comparison of the accuracy of IHS, UF, APA, FP, and OBA under a normal distribution for a smart space with four healthy residents. =0.5); Figure 3 (b) Comparison of the accuracy of IHS, UF, APA, FP, and OBA under the average distribution for a smart space with four healthy occupants. =0.5); Figure 3 (c) Comparison of the accuracy of IHS, UF, APA, FP, and OBA under a triangular distribution in a smart space with four healthy residents. =0.5);

[0023] Figure 4 (a) Comparison of the accuracy of IHS, UF, APA, FP, and OBA under a normal distribution when there are 6 intelligent spaces with partial diseases or physical disabilities. =0.2); Figure 4 (b) Comparison of accuracy of IHS, UF, APA, FP, and OBA under uniform distribution when there are 6 intelligent spaces with partial diseases or physical disabilities. =0.2); Figure 4 (c) Comparison of the accuracy of IHS, UF, APA, FP, and OBA under a triangular distribution when there are 6 intelligent spaces with partial diseases or physical disabilities. =0.2);

[0024] Figure 5 (a) Comparison of the accuracy of IHS, UF, APA, FP, and OBA under a normal distribution when there are 6 intelligent spaces with partial diseases or physical disabilities. =0.3); Figure 5 (b) Comparison of the accuracy of IHS, UF, APA, FP, and OBA under the average distribution when there are 6 intelligent spaces with partial diseases or physical disabilities. =0.3); Figure 5 (c) Comparison of the accuracy of IHS, UF, APA, FP, and OBA under a triangular distribution when there are 6 intelligent spaces with partial diseases or physical disabilities. =0.3). Detailed Implementation

[0025] The following are specific embodiments of the present invention, which are described in conjunction with the accompanying drawings. However, the present invention is not limited to these embodiments.

[0026] In a smart space environment, when multiple users simultaneously request the same IoT service, service conflicts occur if each user has different service preferences. This invention designs an IoT service conflict resolution framework that, for multiple users with multiple service preferences, can return a fair, reasonable, and explainable service solution. Figure 1 The flowchart of the top-level architecture of this invention shows that the IoT service conflict resolution framework includes three main steps: processing historical user preference data, using game theory; processing real-time user preference requirements, using the Analytic Hierarchy Process (AHP); and calculating the final value of service preferences, feeding this final value back to the IoT service in the smart space environment, allowing service devices to adjust their service settings accordingly. Figure 1 As shown, through analysis of user historical preferences, a mean of user historical preferences will be obtained: MV gta By processing users' real-time preferences, a real-time preference mean is obtained: MV ahp Finally, the two means are weighted and summed to obtain a compromise preference value: MV. ave In intelligent space environments, conflicting IoT services are determined according to MV. ave This allows you to adjust your service settings to provide fair and reasonable services to multiple users.

[0027] The following details the specific workflow of the IoT service conflict resolution framework designed in this invention, which consists of three main steps: user historical preference data processing; user real-time preference request processing; and service preference final value calculation. Each main step is implemented through a series of smaller steps:

[0028] (1) First major step: Processing user historical preference data

[0029] In the first major step of processing, this invention uses a special case of game theory decision-making models, namely the Weighted Voting Game (WVG), to specifically process historical preference demand data for IoT services from users in a smart space environment. The WVG is used to process this historical data and obtain the average preference value (MV) of all relevant users. gtaSpecifically, five steps are used to process users' historical preference data: preference classification, frequency statistics, majority value calculation, voting, and mean value calculation.

[0030] 1. Preference classification

[0031] An IoT service has many qualitative and quantitative attributes, and users in the same smart space environment may have different preferences for these attributes. This invention addresses a specific IoT service by categorizing user preferences into k classes or k levels based on qualitative or quantitative attributes. For example, based on the temperature attribute of an air conditioner, its temperature levels can be divided into 5 parts (k=5): 16℃-18℃, 19℃-21℃, 22℃-24℃, 25℃-27℃, and 28℃-30℃. The simplest approach is to categorize it based on the air conditioner's fan speed: low, medium, and high (k=3).

[0032] 2. Frequency statistics

[0033] In this small step, the historical preference records of users' IoT service needs in the smart space environment are recorded. From these records, the frequency of each user's preference for each category or attribute level as defined above (step 1) is counted. It indicates that, among them, U i Let i represent the i-th user in the intelligent space environment, and j represent the j-th class or j-th attribute level.

[0034] 3. Majority value calculation

[0035] For user U i The number of preference requests for the j-th category or the j-th attribute level is Its corresponding majority value is , Calculate using formula (1).

[0036] (1)

[0037] Among them, majority_factor j It is a predefined majority factor (which can be dynamically adjusted).

[0038] 4. Voting

[0039] Next, for user U i Multiple values Use formula (2) to determine Does it exceed the majority threshold α (which can be dynamically adjusted)? If If the threshold α is exceeded, then formula (3) is used to assign a value to user U. i Counted as 1 vote.

[0040] (2)

[0041] (3)

[0042] 5. Mean value calculation

[0043] Finally, user U is calculated using formula (4). i If the total number of votes for a certain IoT service is greater than or equal to the set vote threshold β, then user U is... i Add to set U gta In this context, users within the set participate in the final average calculation. In a real-world smart space environment, the conflict-causing properties of IoT services are definite. and These are the left and right boundaries of the attribute value, respectively. The weight of each attribute value of the IoT service is calculated using formula (5) (the attribute value is from...). arrive ),in U gta The user preference attribute value in The number of [items]. Finally, the average preference of all users regarding historical preference records is calculated using formula (6), i.e., MV. gta .

[0044] (4)

[0045] (5)

[0046] (6)

[0047] (2) The second major step: real-time processing of user preferences

[0048] In a smart space environment, each user submits their service preferences on-the-spot when requesting a specific IoT service. Since these preferences differ among users, a robust mechanism is essential to process these real-time service preferences from all users. Below, this invention will detail the specific operational process for handling real-time user preference requests using a concrete example.

[0049] In reality, each user's physical condition is different. For example, some are obese, while others have visual impairments. When handling conflicts in IoT services, these individual differences must be fully considered (for example, if an elderly person has hypertension or other illnesses and is in the same space as a healthy young person, the temperature preference of the elderly person should be taken into account when setting the air conditioning temperature). This invention considers six physical condition attributes: age, illness, visual impairment, hearing loss, height impairment, and obesity. Except for age, the other five physical condition attributes are quantified into values ​​between 1 and 5 according to their severity. Furthermore, the higher the value of these five physical condition attributes, the worse the physical condition. For example, a higher value for the illness attribute means a worse health condition for the user.

[0050] Table 1. Basic scale for element comparison.

[0051] Scale Definition 1 Equally important 3 Medium importance 5 Strong and important 7 Extremely important 9 Extremely important 2, 4, 6, 8 The compromise value between the two adjacent values ​​(1, 3, 5, 7, 9) above reciprocal If the comparison value (a, b) between two preferences a and b is one of the values ​​above (values ​​from 1 to 9), then (b, a) is the reciprocal of that value.

[0052] In the second major step, this invention utilizes the AHP (Analytic Hierarchy Process) method to process users' real-time preference requirements. During the AHP process, the comparison values ​​between each pair of users' physical state attributes within the same intelligent space environment are first determined, and then the weight of each physical state attribute is further determined. The basic scale used for pairwise comparisons of user physical state attributes is shown in Table 1. The second major step specifically includes the following steps:

[0053] 1. Determine the body state attribute comparison matrix

[0054] First, determine the body state attribute comparison matrix. Assume there are three users in a smart space environment: Alice, Tom, and Mary. They all enjoy listening to music and share a single speaker device. In this example, the determined body state attribute comparison matrix is ​​shown in Table 2. In a specific smart space environment, based on the users' actual situation and the characteristics of the specific IoT service, the attribute comparison matrix can be dynamically adjusted. That is, the comparison values ​​between each pair of attributes can be dynamically adjusted, thereby ultimately adjusting the attribute weights.

[0055] Table 2. Comparison matrix of body state attributes.

[0056] age disease Visual impairment Hearing loss Height defects obesity age 1 1 / 7 1 / 5 1 / 9 1 / 3 1 / 3 disease 7 1 5 1 7 3 Visual impairment 5 1 / 5 1 1 / 3 3 3 Hearing loss 9 1 3 1 3 3 Height defects 3 1 / 7 1 / 3 1 / 3 1 1 / 3 obesity 3 1 / 3 1 / 3 1 / 3 3 1

[0057] 2. Standardization (Normalization) of the body state attribute comparison matrix

[0058] Next, the present invention uses formula (7) to normalize the matrix in Table 2, r ij The normalized value is r ij Dividing by the sum of the elements in its column, the normalized matrix in Table 2 corresponds to (8).

[0059] (7)

[0060] (8)

[0061] 3. Determine the weight of each body state attribute.

[0062] Next, the weight of each body state attribute is calculated using formula (9), that is, the weight W of the i-th attribute. i Divide the sum of all values ​​in the i-th row of (8) by m (m=6, i.e., 6 attributes), and the weights of all body state attributes form a weight vector (W=(W1, W2, ..., W6)). The result is shown in Table 3, i.e., W=(0.0321, 0.3530, 0.1521, 0.2927, 0.0646, 0.1055).

[0063] (9)

[0064] Table 3. Weights of physical condition attributes.

[0065] age disease Visual impairment Hearing loss Height defects obesity 0.0321 0.3530 0.1521 0.2927 0.0646 0.1055

[0066] 4. Determine whether the weight vector W determined in step 3 above is reasonable.

[0067] In this step, the consistency ratio (CR) is used to determine whether the previously determined weight vector W is reasonable. Based on this reasoning, if W is unreasonable, the body state attribute comparison matrix R is redefined, i.e., Table 2 is updated, and normalization and weight calculation are performed again. The calculation process of CR is detailed below.

[0068] Based on the findings of other researchers [1-4] W and R have the following relationship: .

[0069] (10)

[0070] in It is a parameter, calculated using formula (10): Specifically, first, add up each column of the comparison matrix R; then, multiply the sum of the first column of R by the first element of the weight vector W, multiply the sum of the second column of R by the second element of W, and so on; finally, summarize these values ​​to obtain the result. Therefore, based on the data provided in the example, we can obtain the value of in this example. =6.551479934.

[0071] Next, the consistency index (CI) is calculated using formula (11). In this example, m=6. Therefore, CI=0.110295987 is obtained.

[0072] (11)

[0073] Finally, the value of CR is calculated using formula (12), where the value of RI (RI is the random consistency index) is directly obtained from typical values ​​determined by other technical works, as shown in Table 4. In the example of this invention, since m=6, that is, 6 body state attributes, the value of RI is 1.24. Thus, the value of CR is calculated to be 0.088948376. In the works of [1][2][3][4], the researchers concluded that if the value of CR is less than 0.1, then the previous attribute comparison matrix (i.e., Table 2) is reasonable. In this example, 0.088948376 is less than 0.1, therefore, the body state attribute comparison matrix Table 2 determined by this invention is reasonable and does not need to be re-determined.

[0074] (12)

[0075] Table 4. Random Consistency Index (RI) values.

[0076] m 1 2 3 4 5 6 7 8 9 10 RI 0 0 .52 .89 1.11 1.24 1.32 1.41 1.45 1.49

[0077] Other technical work results referenced:

[0078] [1]CF Chen, Applying the analytical hierarchy process (ahp)approach to convention site selection, Journal of Travel Research, 45(2)(2006) 167-174.

[0079] [2] E. Caceoglu, HK Yildiz, E. Oguz, N. Huvaj, JM Guerrero, Offshore wind power plant site selection using Analytical Hierarchy Process for Northwest Turkey, Ocean engineering, 252(May 15) (2022) 111178.1-111178.23.

[0080] [3] M. Türkeş, T. Öztaş, E. Tercan, G. Erpul, B. Avcıoğlu, Desertification vulnerability and risk assessment for Turkey via ananalytical hierarchy process model, Land Degradation and Development, 31(2)(2020) 205-214.

[0081] [4]D. Chaki, A. Bouguettaya, Adaptive priority-based conflictresolution of IoT services, in: Proceedings of the 2021 IEEE International Conference on Web Services, 2021.

[0082] 5. Calculate the respective weights of users in the intelligent space environment.

[0083] Next, in this step, the individual weights of each user in the intelligent space environment are calculated. Using the example above, the user weight calculation method is detailed below. Table 5 shows the actual data of the users' physical status information in the example. The weight calculation process for the three users is as follows: First, the data in Table 5 is normalized. Each element is divided by the sum of its column to obtain the normalized value, as shown in Table 6. Then, the normalized value of each attribute for each user in Table 6 is multiplied by the weight corresponding to each attribute in W to obtain the user's weight, as shown in Table 7. For example, for user Alice, her weight is: 0.58333*0.0321+0.60000*0.3530+0.50000*0.1521+0.60000*0.2927+0.50000*0.0646+0.25000*0.1055=0.54086.

[0084] Table 5 shows the actual data of user physical status information in the example.

[0085] user age disease Visual impairment Hearing loss Height defects obesity Alice 42 3 3 3 2 1 Tom 12 1 2 1 1 2 Mary 18 1 1 1 1 1

[0086] Table 6 shows the normalized values ​​of user physical status information data in the example.

[0087] user age disease Visual impairment Hearing loss Height defects obesity Alice 0.58333 0.60000 0.50000 0.60000 0.50000 0.25000 Tom 0.16667 0.20000 0.33333 0.20000 0.25000 0.50000 Mary 0.25000 0.20000 0.16667 0.20000 0.25000 0.25000

[0088] Table 7 User weights in the examples.

[0089] user Weight Ranking Alice 0.54086 1 Tom 0.25409 2 Mary 0.20505 3

[0090] 6. Calculation of average user preferences in real time

[0091] Finally, the average real-time preferences of all users in the same intelligent space environment are calculated. Assume there are n users in the intelligent space environment, and their weights form a vector W. ahp ={ , , ……, Their real-time preferences for the same IoT service in a smart space environment constitute a vector V. pre ={ , ,……, Then, the average value of all users' real-time preferences is calculated using formula (13).

[0092] (13)

[0093] For example, in the case used in the second major step, n=3, following the order Alice-Tom-Mary, W ahp={0.54086, 0.25409, 0.20505} (i.e., the weights in Table 7), if the sound preference vector of the three users for the speaker service is V pre ={45 dB, 65 dB, 55 dB}, then MV ahp =45×0.54086+65×0.25409+55×0.20505≈52dB.

[0094] (3) The third major step: Calculation of the final value of service preferences

[0095] In the first major step, through analysis of users' historical preferences, a mean of users' historical preferences will be obtained: MV. gta In the second major step, by processing users' real-time preferences, a real-time preference mean (MV) is obtained. ahp In the third major step, the two means will be weighted and summed as shown in formula (14), resulting in a compromise preference value: MV ave In intelligent space environments, conflicting IoT services are determined according to MV. ave This allows you to adjust your service settings to provide fair and reasonable services to multiple users.

[0096] (14)

[0097] in, For an adjustment parameter, 0≤ ≤1. If If the value is 0, the conflict resolution system only considers the user's real-time preference requirements. If the value is 1, the conflict resolution system only considers the user's historical preference requirements. In a smart space environment, if all users are healthy, then the setting is... =0.5. However, if there are users with poor health, then The value will be between 0 and 0.5, and the second major step will gain more weight. The conflict resolution system will focus on the user's real-time preference needs.

[0098] Figures 2-5 The figure shows the performance comparison results of the present invention (IHS) and existing technologies on real datasets. UF, APA, and FP are derived from Material [1], and OBA is derived from Material [2] and Material [3].

[0099] [1]D. Chaki, A. Bouguettaya, Adaptive priority-based conflictresolution of IoT services, in: Proceedings of the 2021 IEEE International Conference on Web Services, 2021.

[0100] [2] C. Rui, P. Carreira, I. LynCe, S. Resendes, An ontology-based approach to conflict resolution in home and building automation systems, Expert Systems with Applications, 41(14)(2014) 6161-6173.

[0101] [3]T. Gu, XH Wang, HK Pung, DQ Zhang, An ontology-basedcontext model in intelligent environments. in: Proceedings of the 2004communication networks and distributed systems modeling and simulation, 2004, pp.270-275.

[0102] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1.A method for resolving conflicts of Internet of Things services, characterized in that, It comprises the following steps: when multiple users simultaneously issue service requests to the same Internet of Things service in the same intelligent space environment, if the service preference requirements of each user are different; Step one: first, use game theory to process the historical preference data of the user to obtain a historical preference mean value, and use the analytic hierarchy process to process the real-time preference requirements of the user to obtain a real-time preference mean value; Step two: weighted sum the historical preference mean value and the real-time preference mean value to obtain a compromised preference value, i.e., a service preference final value; Step three: feed back the service preference final value to the Internet of Things service in the intelligent space environment, and the service equipment adjusts its service settings according to the final value to provide fair and reasonable services to multiple users. 2.The method of claim 1, wherein, The method for processing the historical preference data of the user in step one is as follows: Historical preference classification When processing the historical preference data of the user, first, classify the historical preference according to qualitative attributes or quantitative attributes, and divide the user's preference into k classes or k levels; Then the frequency of user history preference is counted, and the history preference demand record of the user in the intelligent space environment is counted, and the number of demand preferences of each user in the qualitative attribute and quantitative attribute is counted, respectively, and is represented by , wherein, U i represents the i-th user in the intelligent space environment, and j represents the j-th type or j-th attribute level. Calculation of multiple values For the user U i , the number of demand preference times for the jth type or jth attribute level is , and the corresponding majority value is , calculated by formula (1). （1）； wherein majority_factor j is a predefined dynamically adjustable majority factor; Voting For user U i Multiple values Use formula (2) to determine Does it exceed the dynamically adjustable majority threshold α? If the threshold α is exceeded, then formula (3) is used to assign a value to user U. i Counted as 1 vote; （2）； （3）； Average value calculation Finally, the user U i is added to the set U i if the total votes for the IoT service is are greater than or equal to the set vote threshold β. The users in the set U gta participate in the final average value calculation; in a real smart space environment, the attributes that cause conflicts for the IoT service is are determined, and are the left and right boundaries of the attribute value, respectively; the weight of each attribute value for the IoT service is is calculated using formula (5), where represents the number of users in U gta whose preferred attribute value is Finally, the average value of all users' preferences for the historical preference record data, i.e., MV gta , is calculated using formula (6). （4）； （5）； （6）。 3.The method of claim 2, wherein, The method for processing the real-time preference requirements of the user in step one is as follows: Consider six body state attributes, i.e., age, disease, visual impairment, hearing loss, height defect, and obesity; except for age, the other five body state attributes are quantified into values between 1 and 5 according to the degree of severity; in addition, the greater the value of the five body state attributes, the worse the physical condition; Use the AHP method to process the real-time preference requirements of the user, first, determine the comparison values between the body state attributes of the user in the same intelligent space environment, and further determine the weight of each body state attribute. 4.The method of claim 3, wherein, The specific method of step two is as follows: First, determine the body state attribute comparison matrix; in a specific intelligent space environment, according to the actual situation of the user and the specific characteristics of the Internet of Things service, the attribute comparison matrix can be dynamically adjusted, i.e., the comparison values between the attributes can be dynamically adjusted, so that the weight values of the attributes can be finally adjusted; Standardization of the body state attribute comparison matrix The body state attribute comparison matrix is normalized using formula (7), r ij The normalized value is r ij Divided by the sum of the elements in the column in which it is located; (7)。 Determination of the weight of each body state attribute Use formula (9) to calculate the weight of each body state attribute, (9)。 Determination of the weight of each body state attribute According to the consistency ratio CR, determine whether the weight vector W determined in the foregoing is reasonable; if W is not reasonable, re-determine the body state attribute comparison matrix R, i.e., update the body state attribute comparison matrix, and re-normalize and calculate the weight; After calculating the respective weight of each user in the intelligent space environment, the average of the real-time preference of all users in the same intelligent space environment is calculated. Assuming that there are n users in the intelligent space environment, the weights of the users form a vector W ahp ={ , , ……, } and the real-time preferences of the users for the same Internet of Things service in the intelligent space environment form a vector V pre ={ , ,……, }, the average of the real-time preference of all users is calculated by using formula (13). (13) 。 5.The method of claim 4, wherein, The specific method of step three is as follows: The formula (14) is used to calculate the user historical preference mean value MV gta The real-time preference mean value MV ahp The weighted sum is obtained, and a compromise preference value MV ave The intelligent space environment under the conflict of Internet of Things service is adjusted according to MV ave , and the service setting information is provided to the multi-user, so as to provide fair and reasonable service; (14)； wherein, is a tuning parameter, 0≤ ≤1; if =0, the conflict resolution system only considers the real-time preference needs of the users; if =1, the conflict resolution system only considers the historical preference needs of the users, and in a smart space environment, if all users are healthy, then =0.5, and if there are users who are not healthy, then will take a value between 0 and 0.5.

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