An internet-of-things-based intelligent campus internet-of-things management system

Abstract

The application relates to the technical field of Internet of Things and intelligent scheduling, in particular to a smart campus Internet of Things management system based on the Internet of Things; the system comprises a data acquisition module, an antagonistic strength calculation module, a digital countermeasure index evolution module, a dynamic exchange rate calculation module and a behavior induction closed-loop execution module; the system obtains multi-dimensional behavior data, and calculates global antagonistic strength; the core is to combine statistical anomalies and space-time logic, calculate a cumulative digital countermeasure index, and determine an efficiency sacrifice ratio according to a safety threshold; then, an induced reward value is calculated, a guide instruction is generated to complete traffic distribution; the application realizes accurate quantification of irrational competitive intentions, and effectively solves the problems of group antagonistic perception and intelligent dredging in a resource-limited environment.

Description

Technical Field

[0001] This invention relates to the fields of Internet of Things (IoT) and intelligent scheduling technology, specifically to a smart campus IoT management system based on IoT. Background Technology

[0002] With the deepening application of IoT technology in smart campus scenarios, the scale of access to terminal devices across the entire domain and the frequency of service requests have increased significantly, and the supply and demand relationship of resources within the campus exhibits a high degree of dynamism and complexity at specific times.

[0003] Existing campus IoT management systems mainly rely on static rule matching or basic physical status monitoring, lacking the ability to deeply perceive group confrontational behavior in resource-constrained environments. Traditional scheduling strategies are unable to effectively isolate environmental noise to accurately identify users' irrational competitive intentions, let alone quantify the accumulated psychological resistance of user groups based on the principles of non-equilibrium thermodynamics. When faced with high-concurrency traffic surges, this management model often fails to establish a dynamic exchange mechanism between efficiency sacrifice and order maintenance, resulting in system lag in responding to sudden congestion, leading to deterioration of service experience and imbalance in resource scheduling.

[0004] Therefore, how to construct a management mechanism that can achieve flexible traffic diversion and dynamic steady-state scheduling of the system by quantifying the intensity of environmental adversarial forces and the psychological evolution of users is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a smart campus IoT management system based on the Internet of Things (IoT). Specifically, the technical solution of this invention includes:

[0006] The data acquisition module is used to acquire multi-dimensional behavioral data of terminals across the entire campus. The multi-dimensional behavioral data includes: original terminal logs, geographic location coordinates, request frequency, historical request frequency average, and historical request frequency standard deviation.

[0007] The adversarial strength calculation module is used to calculate the global adversarial strength index at the current moment by calling the request frequency, the historical average request frequency, the historical standard deviation of the request frequency, and the geographical coordinates.

[0008] The Digital Resistance Index Evolution Module is used to call the global resistance intensity index, the preset service experience threshold, and the real-time monitored actual service experience value to calculate the cumulative digital resistance index.

[0009] The dynamic exchange rate calculation module is used to call the cumulative digital resistance index and the preset safety threshold inflection point to determine the current allowable efficiency sacrifice ratio of the system.

[0010] The behavior-inducing closed-loop execution module is used to call the efficiency sacrifice ratio, the coordinates of the diversion target area calculated by the system, and the user's current real-time coordinates to calculate the inducement reward value to be pushed to the user, and generate guidance instructions in response to the inducement reward value to complete the traffic diversion.

[0011] Preferably, the adversarial strength calculation module calculates the global adversarial strength index at the current moment, including:

[0012] Calculate the statistical outlier of the request frequency based on the call request frequency, the mean of historical request frequencies, and the standard deviation of historical request frequencies;

[0013] The sampling time interval generated by the geographic location coordinates and the system clock is used to calculate the spatiotemporal logic violation behavior index;

[0014] Call the preset frequency anomaly weight coefficient and the preset logic anomaly weight coefficient;

[0015] A weighted average of statistical outlier degree and spatiotemporal logic violation behavior indicators is used to generate a global adversarial intensity index.

[0016] Preferably, the indicators for calculating spatiotemporal logic violation behaviors include:

[0017] The distance the computing terminal has moved between the current time and the previous sampling time;

[0018] The instantaneous speed of the terminal is calculated based on the travel distance and the sampling time interval;

[0019] Calls the maximum movement speed threshold set by the physical world;

[0020] Compare the instantaneous speed with the maximum movement speed threshold;

[0021] If the instantaneous speed exceeds the maximum movement speed threshold, the spatiotemporal logic violation behavior indicator will be set as a logical anomaly value.

[0022] If the instantaneous speed is less than or equal to the maximum moving speed threshold, the spatiotemporal logic violation behavior indicator will be set to the logical normal value.

[0023] Preferably, the digital resistance index evolution module calculates the cumulative digital resistance index, including:

[0024] The residual resistance index from the previous moment, the preset natural dissipation coefficient of emotion, and the time length of the sliding window are used to calculate the value.

[0025] Based on the natural dissipation coefficient of emotion, the residual resistance index is naturally decayed to generate the decayed residual resistance index.

[0026] Determine the average rate of experience impairment relative to the service experience threshold within the calculation sliding window;

[0027] The average experience impairment rate is amplified by using the global adversarial intensity index to generate the environmental catalytic impairment rate;

[0028] The environmental catalytic damage rate is superimposed on the residual resistance index after decay to generate the cumulative digital resistance index at the current moment.

[0029] Preferably, determining the average experience impairment rate relative to the service experience threshold within the calculation sliding window includes:

[0030] Calculate the difference between the service experience threshold and the actual service experience value;

[0031] If the actual service experience value is less than the service experience threshold, the difference is discretely accumulated, summed, and averaged to generate the average experience impairment rate.

[0032] If the actual service experience value is greater than or equal to the service experience threshold, stop accumulating and output a zero value as the average experience impairment rate.

[0033] Preferably, the dynamic exchange rate calculation module determines the current allowable efficiency sacrifice ratio of the system, including:

[0034] Invoke the maximum physical resource sacrifice limit allowed by the system and the preset policy response sensitivity coefficient;

[0035] Calculate the difference between the cumulative digital resistance index and the inflection point of the safety threshold;

[0036] Based on the S-shaped response function model, the efficiency sacrifice ratio is calculated using the difference, the strategy response sensitivity coefficient, and the maximum physical resource sacrifice limit.

[0037] Preferably, the behavior-inducing closed-loop execution module calculates the inducing reward value pushed to the user, including:

[0038] Invoke the basic incentive unit, the preset cost conversion coefficient, and the maximum topological diameter constant of the campus;

[0039] Based on the efficiency sacrifice ratio and cost conversion coefficient, a dynamic incentive multiplier is generated;

[0040] Calculate the logical distance between the coordinates of the target area for evacuation and the user's current real-time coordinates;

[0041] Calculate the distance weighting coefficient based on the path logical distance and the maximum topological diameter constant of the campus;

[0042] The basic incentive unit, dynamic incentive multiplier, and distance weight coefficient are multiplied together to generate the induced reward value.

[0043] Preferably, the calculation of distance weighting coefficients includes:

[0044] Obtain the actual travel distance between two points through the navigation map application programming interface;

[0045] Define the actual travel distance as the logical path distance;

[0046] Divide the logical path distance by the maximum topological diameter constant of the campus to generate a normalized distance value;

[0047] The normalized distance value is defined as the distance weight coefficient.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. This system, through its adversarial intensity calculation module, employs a combination of statistical anomaly detection and physical kinematic constraints to effectively address the problem of existing technologies' difficulty in accurately identifying irrational competitive intentions. The system can jointly analyze the statistical outlier of request frequency and the spatiotemporal logical violation of geographical location, accurately removing environmental noise, thereby quantifying the global adversarial intensity index that characterizes the intensity of irrational competition in the current environment, achieving a deep perception of group adversarial behavior in resource-constrained environments.

[0050] 2. This system introduces a digital resistance index evolution module based on the principle of non-equilibrium thermodynamics, filling the gap in the quantitative assessment of the cumulative psychological effects of user groups in existing technologies. This module constructs a psychological state model that evolves dynamically over time by simulating the natural decay of emotions over time and the catalytic amplification effect of environmental resistance intensity on the rate of experience impairment. By accumulating the integral of the service experience difference, the cumulative digital resistance index can be accurately calculated, thereby more realistically reflecting the psychological evolution of user groups in congested environments.

[0051] 3. This system establishes a dynamic exchange mechanism between efficiency and order, enabling dynamic steady-state scheduling of the system under high-concurrency traffic impact. The dynamic exchange rate calculation module utilizes an S-shaped response function model to intelligently determine the current allowable efficiency sacrifice ratio of the system based on the difference between the cumulative digital resistance index and the inflection point of the safety threshold. This nonlinear response mechanism ensures that when the resistance index approaches the critical point, the system can rapidly increase the degree of resource sacrifice to alleviate the resistance pressure and avoid system collapse caused by response lag.

[0052] 4. This system implements flexible traffic diversion based on game theory principles. Through a behavior-guided closed-loop execution module, the dimensionless efficiency sacrifice ratio is transformed into a specific incentive reward value. The system comprehensively considers the congestion level of the target area and the logical path distance between the user's real-time coordinates and the target area to generate differentiated incentive instructions. This mechanism not only provides reasonable compensation for users at long distances but also actively guides user behavior by dynamically adjusting the incentive multiplier, thus completing the transformation from passively responding to congestion to actively diverting traffic. Attached Figure Description

[0053] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0054] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0056] Example 1:

[0057] Please see Figure 1 A smart campus IoT management system based on the Internet of Things includes:

[0058] The data acquisition module is used to acquire multi-dimensional behavioral data of terminals across the entire campus. The multi-dimensional behavioral data includes: original terminal logs, geographic location coordinates, request frequency, historical request frequency average, and historical request frequency standard deviation.

[0059] The adversarial strength calculation module is used to calculate the global adversarial strength index at the current moment by calling the request frequency, the historical average request frequency, the historical standard deviation of the request frequency, and the geographical coordinates.

[0060] The Digital Resistance Index Evolution Module is used to call the global resistance intensity index, the preset service experience threshold, and the real-time monitored actual service experience value to calculate the cumulative digital resistance index.

[0061] The dynamic exchange rate calculation module is used to call the cumulative digital resistance index and the preset safety threshold inflection point to determine the current allowable efficiency sacrifice ratio of the system.

[0062] The behavior-inducing closed-loop execution module is used to call the efficiency sacrifice ratio, the coordinates of the diversion target area calculated by the system, and the user's current real-time coordinates to calculate the inducement reward value to be pushed to the user, and generate guidance instructions in response to the inducement reward value to complete the traffic diversion.

[0063] This embodiment proposes an IoT-based smart campus IoT management system, aiming to achieve dynamic steady-state scheduling of the system under resource-constrained and group-based adversarial environments. The system includes multiple collaborative core processing modules. The data acquisition module acquires multi-dimensional behavioral data from all terminals, including raw terminal logs, geographic coordinates provided by a wireless positioning system, request frequency per unit time, and the average and standard deviation of historical request frequencies retrieved from a historical behavior database. The adversarial intensity calculation module performs noise stripping and intent recognition logic, calculating a global adversarial intensity index characterizing the intensity of irrational competition in the current environment by jointly analyzing request frequency, its statistical features, and geographic coordinates. The Digital Resistance Index Evolution Module, based on the principle of non-equilibrium thermodynamics, calls upon the global antagonism intensity index, preset service experience thresholds, and real-time monitored actual service experience values ​​to calculate the cumulative digital resistance index, simulating the cumulative psychological effect of a user group. The dynamic exchange rate calculation module, as the core of the system's decision-making, determines the current allowable efficiency sacrifice ratio based on the comparison between the cumulative digital resistance index and the preset safety threshold inflection point. This establishes a dynamic exchange mechanism between efficiency and order; the behavior-inducing closed-loop execution module calculates the incentive reward value to be pushed to the user based on the efficiency sacrifice ratio, the coordinates of the target area calculated by the system, and the user's current real-time coordinates. Based on this, a guiding instruction is generated to achieve flexible traffic diversion.

[0064] Example 2:

[0065] The adversarial strength calculation module calculates the global adversarial strength index at the current moment, including:

[0066] Calculate the statistical outlier of the request frequency based on the call request frequency, the mean of historical request frequencies, and the standard deviation of historical request frequencies;

[0067] The sampling time interval generated by the geographic location coordinates and the system clock is used to calculate the spatiotemporal logic violation behavior index;

[0068] Call the preset frequency anomaly weight coefficient and the preset logic anomaly weight coefficient;

[0069] A weighted average of statistical outlier degree and spatiotemporal logic violation behavior indicators is used to generate a global adversarial intensity index.

[0070] Indicators of spatiotemporal logic violation include:

[0071] The distance the computing terminal has moved between the current time and the previous sampling time;

[0072] The instantaneous speed of the terminal is calculated based on the travel distance and the sampling time interval;

[0073] Calls the maximum movement speed threshold set by the physical world;

[0074] Compare the instantaneous speed with the maximum movement speed threshold;

[0075] If the instantaneous speed exceeds the maximum movement speed threshold, the spatiotemporal logic violation behavior indicator will be set as a logical anomaly value.

[0076] If the instantaneous speed is less than or equal to the maximum moving speed threshold, the spatiotemporal logic violation behavior indicator will be set to the logical normal value.

[0077] Based on preset frequency anomaly weighting coefficients and preset logical anomaly weighting coefficients, the statistical outlier degree and spatiotemporal logical violation behavior indicators of each access terminal are weighted and calculated to generate a single adversarial strength index; the single adversarial strength indexes of all access terminals are arithmetically averaged to generate a global adversarial strength index.

[0078] The adversarial strength calculation module employs a combination of statistical anomaly detection and physical kinematic constraints to quantify adversarial noise in the environment. This module calculates the global adversarial strength index at the current moment based on the following formula. :

[0079] ;

[0080] in, The total number of active terminals currently connected to the system, obtained by the gateway's real-time counting. For the terminal The frequency of resource requests within the current time window; and These are terminals derived from statistics based on historical behavior databases. The historical request frequency mean and historical request frequency standard deviation; function Used to standardize frequency deviation to Intervals are used to address the issue of weight ineffectiveness caused by inconsistent dimensions; This is a preset minimum variance constant used to prevent calculation overflow caused by the denominator approaching zero due to insufficient historical data for new users; and These are the preset frequency anomaly weight coefficient and logical anomaly weight coefficient, respectively, and their sum is 1; and Terminals At the present moment Compared with the previous sampling time Geographical coordinates; The sampling time interval generated by the system clock; A maximum movement speed threshold is set for the physical world, based on the limit speed of non-motorized vehicles within the campus environment; This is a logical violation detection function; it is defined as: when Output 1 if the condition is met, otherwise output 0.

[0081] The execution logic of this module is as follows: Utilize request frequency Average frequency of historical requests and the standard deviation of historical request frequency Calculate the statistical outlier of request frequency to identify high-frequency anomalous behavior; calculate the distance the terminal has moved between the current time and the previous sampling time. Combined with sampling time interval Export terminal instantaneous speed ; Compare instantaneous speed with the maximum movement speed threshold If a comparison is made, If it is determined to be a violation of physical location logic, the spatiotemporal logic violation behavior index is set to a logic anomaly value of 1; if If so, set it to the logical normal value of 0; use the weighting coefficient. and The individual adversarial strength of the terminal is generated by weighting statistical outlier and spatiotemporal logic violation indicators; all endpoints within the system are then traversed. For each active terminal, calculate the arithmetic mean of the individual adversarial strength of all terminals to generate a global adversarial strength index. .

[0082] Example 3:

[0083] The Digital Resistance Index Evolution Module calculates the cumulative digital resistance index, including:

[0084] The residual resistance index from the previous moment, the preset natural dissipation coefficient of emotion, and the time length of the sliding window are used to calculate the value.

[0085] Based on the natural dissipation coefficient of emotion, the residual resistance index is naturally decayed to generate the decayed residual resistance index.

[0086] Determine the average rate of experience impairment relative to the service experience threshold within the calculation sliding window;

[0087] The average experience impairment rate is amplified by using the global adversarial intensity index to generate the environmental catalytic impairment rate;

[0088] The environmental catalytic damage rate is superimposed on the residual resistance index after decay to generate the cumulative digital resistance index at the current moment.

[0089] Determine the average rate of experience impairment relative to the service experience threshold within the calculation sliding window, including:

[0090] Calculate the difference between the service experience threshold and the actual service experience value;

[0091] If the actual service experience value is less than the service experience threshold, the difference is discretely accumulated, summed, and averaged to generate the average experience impairment rate.

[0092] If the actual service experience value is greater than or equal to the service experience threshold, stop accumulating and output a zero value as the average experience impairment rate.

[0093] The Digital Resistance Index Evolution Module constructs a psychological state model that evolves dynamically over time, and calculates the cumulative Digital Resistance Index using the following formula. :

[0094] ;

[0095] in, This is the naturally decaying component. As an environmental catalytic component, To calculate the sliding window The total number of sampling points contained therein, i.e. Discrete average value calculation is used here instead of continuous integral to adapt to the sampling characteristics of digital systems. The remaining resistance index from the previous moment; The natural dissipation coefficient of emotion is obtained by fitting historical emotion decay cycle data and has the reciprocal of time as its dimension. To calculate the time length of the sliding window; This is a dimensionless gain coefficient for emotional sensitivity. This is the global adversarial intensity index; The service experience threshold set according to the service level agreement; This represents the actual service experience value at the corresponding historical sampling point. It is a unit step function; the summation term in the formula is multiplied by Discrete averaging is performed to ensure that the summation result is converted into a dimensionless average experience impairment rate, thereby ensuring the dimensionality consistency of the formula as a whole.

[0096] This evolutionary model corresponds to the entropy change process in non-equilibrium thermodynamics: where the exponential decay term... The simulation term simulates the entropy reduction process of the system under no external disturbance, while the environmental catalytic term simulates the entropy increase process of the system caused by external counteracting work.

[0097] During the calculation process, the module uses the natural dissipation coefficient of emotion. Residual resistance index of the previous moment Exponential decay calculations are performed to simulate the natural calming of user emotions over time; recent data is extracted from the raw terminal logs obtained by the data acquisition module. Response time of a network request Using the inverse proportional mapping function Calculate the actual service experience value at the current moment, where, For time variables Real-time monitoring of actual service experience values. As the reference response time constant, The current sampling time Instantaneous response time; calculating service experience thresholds Compared with actual service experience value The difference is calculated as follows: when the actual service experience value is less than the service experience threshold, the difference is discretely accumulated and summed, and the arithmetic mean is taken to generate the average experience impairment rate; when the actual service experience value is greater than or equal to the service experience threshold, the step function outputs zero; the global adversarial strength index is used. The average experience impairment rate is amplified to generate an environmental catalytic impairment rate, which is then added to the decayed residual resistance index to generate the cumulative digital resistance index at the current moment. .

[0098] Example 4:

[0099] The dynamic exchange rate calculation module determines the system's current allowable efficiency sacrifice ratio, including:

[0100] Invoke the maximum physical resource sacrifice limit allowed by the system and the preset policy response sensitivity coefficient;

[0101] Calculate the difference between the cumulative digital resistance index and the inflection point of the safety threshold;

[0102] Based on the S-shaped response function model, the efficiency sacrifice ratio is calculated using the difference, the strategy response sensitivity coefficient, and the maximum physical resource sacrifice limit.

[0103] The dynamic exchange rate calculation module determines the degree of efficiency sacrifice of system resources by establishing a nonlinear response mechanism, and calculates the proportion of efficiency sacrifice. The formula is as follows:

[0104] ;

[0105] in, This is the maximum allowable physical resource sacrifice limit for the system, and this value is set based on the system's operating cost red line. This is a preset strategy response sensitivity coefficient used to adjust the slope of the response curve; To accumulate a numerical resistance index; The safety threshold inflection point of the resistance index is determined by back-calculation based on data from historical system collapse critical points;

[0106] This module calculates the cumulative numerical resistance index. With the safety threshold inflection point The difference, based on the S-shaped response function model, is used to determine the strategy response sensitivity coefficient. and maximum physical resource sacrifice limit Solve for the efficiency sacrifice ratio This mechanism ensures that when the resistance index approaches the safety threshold, the system can quickly increase the proportion of resources sacrificed to alleviate the pressure of resistance.

[0107] Example 5:

[0108] The behavior-inducing closed-loop execution module calculates the incentive reward value pushed to the user, including:

[0109] Invoke the basic incentive unit, the preset cost conversion coefficient, and the maximum topological diameter constant of the campus;

[0110] Based on the efficiency sacrifice ratio and cost conversion coefficient, a dynamic incentive multiplier is generated;

[0111] Calculate the logical distance between the coordinates of the target area for evacuation and the user's current real-time coordinates;

[0112] Calculate the distance weighting coefficient based on the path logical distance and the maximum topological diameter constant of the campus;

[0113] The basic incentive unit, dynamic incentive multiplier, and distance weight coefficient are multiplied together to generate the induced reward value.

[0114] Calculate the distance weighting coefficients, including:

[0115] Obtain the actual travel distance between two points through the navigation map application programming interface;

[0116] Define the actual travel distance as the logical path distance;

[0117] Divide the logical path distance by the maximum topological diameter constant of the campus to generate a normalized distance value;

[0118] The normalized distance value is defined as the distance weight coefficient.

[0119] The behavior-inducing closed-loop execution module calculates differentiated traffic diversion incentives based on game theory principles, and calculates the inducing reward value pushed to users. The formula is as follows:

[0120] ;

[0121] in, The basic incentive unit set for the system budget; This is a preset cost conversion coefficient used to convert the dimensionless efficiency sacrifice ratio into an economic incentive multiple. Sacrificing proportion for efficiency; The coordinates of the target area for diversion calculated by the system; The user's current real-time coordinates; This is a function for calculating the logical distance of a path. The maximum topological diameter constant of the campus is derived from campus map surveying data;

[0122] Call the preset campus area distribution table, which contains The center coordinates of the candidate diversion areas and the corresponding current area congestion level Based on the greedy algorithm principle, the optimal coordinates of the dredging target area are selected using the following formula. :

[0123] Ptarget= , where the index value The objective function is solved as follows:

[0124] ;

[0125] In the formula, For the first The center coordinates of the candidate diversion areas; For the first Current congestion levels in the alternative diversion areas The maximum topological diameter constant of the campus, as defined above, is used to normalize physical distances to the [0,1] interval, ensuring that distance weights are consistent with congestion levels. They are at the same dimensional level; As a balancing factor between distance and crowding; the objective function is achieved through... Adjusting the weight between distance cost and destination congestion; when When the migration is large, the system prioritizes areas closer to the user to reduce migration costs; when... When the flow of people is relatively small, the system prioritizes areas with sparse pedestrian traffic to ensure effective crowd control; through The system can perform calculations to identify the candidate region index that minimizes the overall cost. During calculation, the module is based on the efficiency sacrifice ratio. Cost conversion factor Generate dynamic incentive multipliers; obtain the coordinates of the target area for evacuation via the navigation map application programming interface. Real-time coordinates of users The actual travel distance between them is taken as the logical path distance; the logical path distance is divided by the maximum topological diameter constant of the campus. Obtain the normalized distance weight coefficient; multiply the basic incentive unit, dynamic incentive multiplier, and distance weight coefficient together to generate the final induced reward value. This enables high compensation for long-distance users and strong regulation of highly competitive environments.

[0126] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A smart campus IoT management system based on the Internet of Things, characterized in that, include: The data acquisition module is used to acquire multi-dimensional behavioral data of terminals across the entire campus. The multi-dimensional behavioral data includes: original terminal logs, geographic location coordinates, request frequency, historical request frequency average, and historical request frequency standard deviation. The adversarial strength calculation module is used to calculate the global adversarial strength index at the current moment by calling the request frequency, the historical average request frequency, the historical standard deviation of the request frequency, and the geographical coordinates. The Digital Resistance Index Evolution Module is used to call the global resistance intensity index, the preset service experience threshold, and the real-time monitored actual service experience value to calculate the cumulative digital resistance index. The dynamic exchange rate calculation module is used to call the cumulative digital resistance index and the preset safety threshold inflection point to determine the current allowable efficiency sacrifice ratio of the system. The behavior-inducing closed-loop execution module is used to call the efficiency sacrifice ratio, the coordinates of the diversion target area calculated by the system, and the user's current real-time coordinates to calculate the inducement reward value to be pushed to the user, and generate guidance instructions in response to the inducement reward value to complete the traffic diversion. The adversarial strength calculation module calculates the global adversarial strength index at the current moment, including: Calculate the statistical outlier of the request frequency based on the call request frequency, the mean of historical request frequencies, and the standard deviation of historical request frequencies; The sampling time interval generated by the geographic location coordinates and the system clock is used to calculate the spatiotemporal logic violation behavior index; Call the preset frequency anomaly weight coefficient and the preset logic anomaly weight coefficient; A weighted average of statistical outlier and spatiotemporal logic violation indicators is calculated to generate a global adversarial intensity index; Indicators of spatiotemporal logic violation include: The distance the computing terminal has moved between the current time and the previous sampling time; The instantaneous speed of the terminal is calculated based on the travel distance and the sampling time interval; Calls the maximum movement speed threshold set by the physical world; Compare the instantaneous speed with the maximum movement speed threshold; If the instantaneous speed exceeds the maximum movement speed threshold, the spatiotemporal logic violation behavior indicator will be set as a logical anomaly value. If the instantaneous speed is less than or equal to the maximum moving speed threshold, the spatiotemporal logic violation behavior index will be set to the logical normal value. The Digital Resistance Index Evolution Module calculates the cumulative digital resistance index, including: The residual resistance index from the previous moment, the preset natural dissipation coefficient of emotion, and the time length of the sliding window are used to calculate the value. Based on the natural dissipation coefficient of emotion, the residual resistance index is naturally decayed to generate the decayed residual resistance index. Determine the average rate of experience impairment relative to the service experience threshold within the calculation sliding window; The average experience impairment rate is amplified by using the global adversarial intensity index to generate the environmental catalytic impairment rate; The environmental catalytic damage rate is superimposed on the residual resistance index after decay to generate the cumulative digital resistance index at the current moment.

2. The smart campus IoT management system based on the Internet of Things according to claim 1, characterized in that, Determine the average rate of experience impairment relative to the service experience threshold within the calculation sliding window, including: Calculate the difference between the service experience threshold and the actual service experience value; If the actual service experience value is less than the service experience threshold, the difference is discretely accumulated, summed, and averaged to generate the average experience impairment rate. If the actual service experience value is greater than or equal to the service experience threshold, stop accumulating and output a zero value as the average experience impairment rate.

3. The smart campus IoT management system based on the Internet of Things according to claim 1, characterized in that, The dynamic exchange rate calculation module determines the system's current allowable efficiency sacrifice ratio, including: Invoke the maximum physical resource sacrifice limit allowed by the system and the preset policy response sensitivity coefficient; Calculate the difference between the cumulative digital resistance index and the inflection point of the safety threshold; Based on the S-shaped response function model, the efficiency sacrifice ratio is calculated using the difference, the strategy response sensitivity coefficient, and the maximum physical resource sacrifice limit.

4. The smart campus IoT management system based on the Internet of Things according to claim 1, characterized in that, The behavior-inducing closed-loop execution module calculates the incentive reward value pushed to the user, including: Invoke the basic incentive unit, the preset cost conversion coefficient, and the maximum topological diameter constant of the campus; Based on the efficiency sacrifice ratio and cost conversion coefficient, a dynamic incentive multiplier is generated; Calculate the logical distance between the coordinates of the target area for evacuation and the user's current real-time coordinates; Calculate the distance weighting coefficient based on the path logical distance and the maximum topological diameter constant of the campus; The basic incentive unit, dynamic incentive multiplier, and distance weight coefficient are multiplied together to generate the induced reward value.

5. A smart campus IoT management system based on the Internet of Things according to claim 4, characterized in that, Calculate the distance weighting coefficients, including: Obtain the actual travel distance between two points through the navigation map application programming interface; Define the actual travel distance as the logical path distance; Divide the logical path distance by the maximum topological diameter constant of the campus to generate a normalized distance value; The normalized distance value is defined as the distance weight coefficient.

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