Hotel self-service equipment collaborative management system and method based on Internet of Things

Abstract

The invention relates to the technical field of equipment management, and discloses a hotel self-service equipment collaborative management system and method based on the Internet of Things, and the method comprises the steps: collecting the communication characteristic data and energy consumption characteristic data of each piece of equipment, constructing an equipment communication-energy consumption characteristic mapping model, calculating an equipment entropy conversion efficiency index; constructing a network-energy coupling state diagram, and determining a key resource list through threshold screening; and generating a multi-dimensional resource allocation scheme set through a distributed entropy balance algorithm and a parameter iteration updating method. According to the method, the optimization contradiction between network communication and energy consumption is solved, resource allocation optimization is achieved, the system has self-organization and self-adaption characteristics, the energy efficiency ratio, the communication quality, the resource utilization rate, the response speed and the fault recovery capacity are remarkably improved, and an efficient and stable network environment is provided for hotel self-service.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment management, and more specifically, to a hotel self-service equipment collaborative management system and method based on the Internet of Things. Background Art

[0002] In the field of hotel equipment management driven by IoT technology, existing technologies face the problem of coordinated optimization of network communication and energy consumption. With the increase in the types and number of hotel self-service equipment (including check-in terminals, guest room control systems, food delivery robots, smart door locks, self-service laundry equipment, etc.), traditional management systems have exposed the following technical defects: The contradiction between communication and energy consumption is irreconcilable: In existing systems, there is a general negative correlation between network communication quality and energy consumption. High-throughput communication will inevitably lead to a surge in equipment energy consumption, while energy-saving mode will significantly reduce data transmission reliability, forming a "performance-energy consumption" deadlock; Serious imbalance in energy distribution: The topological structure and business characteristics of the device network lead to uneven distribution of energy consumption. Key nodes are in a state of overload for a long time (energy consumption deviation can reach 40%-60%), while the resource utilization rate of edge nodes is less than 20%, resulting in low overall energy efficiency. Lack of dynamic adaptability: Traditional threshold control methods perform poorly in dealing with sudden load fluctuations. During network congestion, the communication quality drops by more than 50%, while the energy waste rate increases by 30%-45%, forming a vicious cycle of "high consumption and low efficiency"; Centralized architecture is rigid: The control mode based on the central server has a response delay (usually more than 500ms), and cannot achieve real-time resource allocation in a complex network environment, causing the system adjustment to lag behind environmental changes.

[0003] The existing technical solutions have theoretical framework defects: communication optimization problems (such as routing selection and QoS guarantee) and energy management problems (such as power control and load balancing) are separated and optimized using independent objective functions. This separate processing method causes the system to fall into a local optimal trap and cannot achieve the optimal global resource configuration. Therefore, it is urgent to establish a new theoretical framework and technical system to fundamentally solve the problem of coordinated optimization of network communication and energy consumption. Summary of the invention

[0004] The present invention provides a hotel self-service equipment collaborative management system and method based on the Internet of Things, which solves the technical problems in the above-mentioned related technologies.

[0005] The present invention provides a hotel self-service equipment collaborative management method based on the Internet of Things, comprising the following steps: Collect the communication characteristic data and energy consumption characteristic data of each device in the hotel self-service equipment network, build a device communication-energy consumption characteristic mapping model, and calculate the device entropy conversion efficiency index; Based on the equipment communication-energy consumption characteristic mapping model and entropy conversion efficiency index, a network-energy coupling state diagram is constructed, and a key resource list is determined through threshold screening; The system's global entropy conversion efficiency optimization objective function is established, and a set of multi-dimensional resource allocation solutions is generated through a distributed entropy balance algorithm and parameter iterative update method.

[0006] Furthermore, the device communication-energy consumption characteristic mapping model is implemented using a bidirectional neural network structure, which includes two parts: an encoder and a decoder. The encoder maps communication characteristics to energy consumption characteristics, and the decoder maps energy consumption characteristics to communication characteristics.

[0007] Furthermore, the entropy conversion efficiency index is calculated by the following formula: ; in: It represents the entropy conversion efficiency index of device i; represents the change in information entropy of device i; represents the energy entropy change of device i; The information entropy of a device is calculated using the following formula: ; in: Represents the information entropy of device i represents the probability of communication state j of device i occurring; It means summing the index j, where the range of j includes all communication states of device i; represents the logarithmic function, where the logarithm with base 2 is used; The energy entropy of a device is calculated using the following formula: ; in: represents the energy entropy of device i; is the Boltzmann constant; is the number of microscopic states of the system; is internal energy; is the equivalent temperature; Represents the natural logarithm.

[0008] Furthermore, in the network-energy coupling state diagram, the node entropy coupling degree is calculated by the following formula: ; in: represents the node entropy coupling degree of node i; represents the partial derivative of the information entropy of device i with respect to the energy entropy; represents the link weight coefficient; Represents all links connected to node i Sum, where E represents the set of all links in the network; The link entropy coupling is calculated by the following formula: ; in: Indicates link Link entropy coupling degree; represents the partial derivative of link information entropy with respect to energy entropy; Represents the link weight coefficient.

[0009] Furthermore, the key resource list includes a key node set and a key link set: ; in: Represents a set of key nodes; represents the node entropy coupling threshold; ; in: represents a set of critical links; Indicates the link entropy coupling threshold.

[0010] Furthermore, the system global entropy conversion efficiency optimization objective function is: ; in: Represents a set of system configuration parameters; Represents a set of parameters Find the maximum value; ; ; in: and is the node weight coefficient; It represents the sum of all nodes i, and V represents the set of device nodes.

[0011] Furthermore, the distributed entropy balance algorithm transforms the global optimization problem into a local optimization problem, and the calculation formula is as follows: ; in: Represents the set of neighbor nodes of node i, that is, all nodes directly connected to node i; Represents the impact factor of the information entropy change of node j on node i; represents the impact factor of the energy entropy change of node j on node i; Represents a subset of parameters Find the maximum value; It represents the sum of all neighbor nodes j of node i.

[0012] Furthermore, the parameter iterative updating method includes introducing an entropy damping factor to adjust the parameter updating process: ; in: is the step size parameter; is the entropy damping factor; is the gradient vector of the local entropy conversion efficiency with respect to the configuration parameters; represents the configuration parameters of node i at time t+1; Represents the configuration parameters of node i at time t.

[0013] Furthermore, the multi-dimensional resource allocation scheme set includes a routing allocation scheme, a power allocation scheme and a load allocation scheme, wherein: the optimal communication path determined by the routing allocation scheme satisfies: ; in: represents the optimal communication path; Indicates that in all path sets Find the path that maximizes the following expression; represents the set of all paths from node i to node j; Indicates the path The amount of change in information entropy brought about; Indicates the path The amount of energy entropy change brought about; The optimal transmission power determined by the power allocation scheme satisfies: ; in: represents the optimal transmission power; Indicates the power range Find the power value that maximizes the following expression; represents the transmission power of node i; Indicates the minimum power value; Indicates the maximum power value; Indicates power The amount of change in information entropy brought about; Indicates power The amount of energy entropy change brought about; The calculated load ratio determined by the load distribution scheme satisfies: ; in: Indicates load ratio; and Indicates the entropy conversion efficiency of the node; and Indicates the entropy coupling degree of the node; represents the sum of all nodes j, and V represents the set of all device nodes in the network.

[0014] A hotel self-service equipment collaborative management system based on the Internet of Things, which is used to execute the above-mentioned hotel self-service equipment collaborative management method based on the Internet of Things, comprising: The equipment characteristic modeling module is used to collect the communication characteristic data and energy consumption characteristic data of each device in the hotel self-service equipment network, build the equipment communication-energy consumption characteristic mapping model, and calculate the equipment entropy conversion efficiency index; A network analysis module, for constructing a network-energy coupling state diagram based on the device communication-energy consumption characteristic mapping model and the entropy conversion efficiency index, and determining a key resource list through threshold screening; The resource optimization module is used to establish the system's global entropy conversion efficiency optimization objective function, and generate a multi-dimensional resource allocation solution set including communication path, power control and load balancing through a distributed entropy balancing algorithm and parameter iterative update method.

[0015] The beneficial effects of the present invention are: The information-energy entropy balance theory proposed in the present invention unifies information entropy and physical entropy into the same mathematical framework, establishes a quantitative mapping relationship between information processing and energy consumption, breaks through the limitation of traditional methods that treat communication and energy as independent problems, and fundamentally solves the contradiction between the mutual constraints of the optimization objectives of the two.

[0016] The present invention proposes the concept of "entropy conversion efficiency" in network systems, transforms the contradiction between network performance optimization and energy consumption minimization into a system entropy balance problem, and achieves the optimization of resource allocation by maximizing the information entropy gain corresponding to the unit energy entropy generation.

[0017] The present invention establishes a two-way constraint adaptive control framework between network performance and energy consumption, so that the two are no longer a one-way constraint relationship, but a synergistic relationship of mutual promotion and common optimization. The system can dynamically adjust the resource allocation strategy according to the business importance and network status.

[0018] The distributed entropy balance mechanism designed in the present invention breaks through the traditional centralized control mode, enabling the system to exhibit "self-organization" and "adaptive" characteristics similar to those of organisms. It can achieve global optimization without a central controller, significantly improving the system's response speed and adaptability.

[0019] The energy-information channel self-organization formation algorithm proposed in the present invention realizes the joint optimization of energy efficiency and communication paths in the network, enables the system to automatically identify and form efficient energy-information transmission channels, maintain optimal performance in a dynamic environment, and significantly improves resource utilization efficiency.

[0020] The present invention realizes the spontaneous response and adaptation of the system to environmental changes based on the principle of entropy dynamics, no longer relying on predefined rules or simple threshold triggering mechanisms, and greatly improves the system's fault recovery capability and service availability.

[0021] The present invention significantly reduces energy consumption while maintaining communication quality through information-energy collaborative optimization, or improves network performance at the same energy consumption level, bringing obvious economic benefits and improved user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of a hotel self-service equipment collaborative management method based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0023] The subject matter described herein will now be discussed with reference to example implementations. It should be understood that the discussion of these implementations is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the present specification. Various examples may omit, replace, or add various processes or components as needed. In addition, the features described in some examples may also be combined in other examples.

[0024] Implementation method 1, a collaborative management method of hotel self-service equipment based on the Internet of Things, such as Figure 1 As shown, the following steps are included: Step 101: receiving hotel self-service equipment network operation monitoring data, generating equipment communication-energy consumption characteristic mapping model and information-energy entropy conversion efficiency index; Step 101 includes the following sub-steps: Step 101-1, collect dual-domain data of each node device i in the hotel self-service equipment network, including: communication characteristic data: data transmission rate , communication delay , Packet loss rate , signal strength etc., where t represents the time variable; Energy consumption characteristic data: power consumption , voltage / current fluctuations , Energy efficiency ratio wait; Step 101-2, preprocessing the collected raw data to obtain a standardized feature vector ,in: represents the communication feature subvector; represents the energy consumption characteristic sub-vector; Step 101-3: Build a bidirectional feature mapping model , which describes the quantitative relationship between device communication characteristics and energy consumption characteristics: ; The mapping model is implemented using a bidirectional neural network structure, including: ; ; in: is the encoder function, which maps the communication features into energy consumption features; is the decoder function, which maps the energy consumption characteristics to communication characteristics; and is the corresponding model parameter set; Step 101-4, based on the bidirectional characteristic mapping model, calculating the entropy attribute index of the device, including: Information Entropy , characterizing the uncertainty of the device communication state: ; in: represents the information entropy of device i; represents the probability of communication state j of device i occurring; represents the sum of index j, where the range of j includes all possible communication states of device i; represents the logarithmic function, where the logarithm with base 2 is used; since represents the probability of device i's communication state j occurring, so The value is (0,1), so, If is a negative value, is a positive value, that is Is a positive value.

[0025] Energy Entropy , characterizing the chaos of the device energy state: ; in: represents the energy entropy of device i; is the Boltzmann constant; is the number of microscopic states of the system; is internal energy; is the equivalent temperature; represents the natural logarithm; Step 101-5, calculating the entropy conversion efficiency index of the device , which quantifies the information entropy gain that can be brought about by a unit change in energy entropy: ; in: It represents the entropy conversion efficiency index; Indicates the change in information entropy of device i; represents the change in energy entropy of device i; Step 102: Analyze the network topology and energy status data to generate a network-energy coupling state diagram and a key resource list; Receive the device characteristic mapping model set generated in step 101 Entropy conversion efficiency index set , analyze the network topology and energy status data, and generate a network-energy coupling status diagram and a list of key resources.

[0026] Step 102 includes the following sub-steps: Step 102-1: Build a basic network topology diagram ,in: Represents a collection of device nodes; Represents a set of network connection relationships; for any two nodes , a connection relationship exists if and only if there is a communication link between them. Established; Step 102-2, calculate the dual-domain characteristic parameters of the network link, for each link ,Sure: Communication state feature vector ; Energy consumption state feature vector ; in: Indicates link bandwidth; Indicates communication delay; Indicates the packet loss rate; Indicates power consumption; It represents energy efficiency ratio; Step 102-3, calculating the entropy coupling value of the network unit by an entropy coupling analyzer, includes: Node entropy coupling degree: ; in: represents the node entropy coupling degree of node i; represents the partial derivative of information entropy with respect to energy entropy; represents the link weight coefficient; Represents all links connected to node i Sum, where E represents the set of all links in the network; Link entropy coupling degree: ; in: Indicates link Link entropy coupling degree; represents the partial derivative of link information entropy with respect to energy entropy; represents the link weight coefficient; Step 102-4, generate a network-energy coupling state diagram ,in: Represents the set of node entropy coupling values; Represents a set of link entropy coupling values; Represents the network-energy coupling state diagram; This figure intuitively shows the coupling relationship between network communication structure and energy distribution, which serves as the basis for resource optimization; Step 102-5, extracting a list of key resources from the coupling state diagram through a threshold filter, including: Key node collection: ; in: Represents a set of key nodes; represents the node entropy coupling threshold; Key link set: ; in: represents a set of critical links; Indicates the link entropy coupling threshold; These thresholds are automatically adjusted by analyzing historical network status data to ensure that the number of critical resources is within a reasonable range; Step 103: Apply an entropy conversion efficiency optimization algorithm to generate a multi-dimensional resource allocation solution set; Receiving the network-energy coupling state diagram generated in step 102 and a list of key resources ,The entropy conversion efficiency optimization algorithm is applied to generate a set of multi-dimensional resource allocation schemes including communication path, power control and load balancing.

[0027] Step 103 includes the following sub-steps: Step 103-1, establish the system global optimization objective function, which is based on the principle of maximizing entropy conversion efficiency:

[0028] in: Represents a set of system configuration parameters, including transmission power, routing table, load ratio and other adjustment quantities; Indicates the change in the overall information entropy of the system; It represents the change of the overall energy entropy of the system; Represents a set of parameters Find the maximum value; The change in system entropy is calculated by weighted summation: ; ; in: and is the node weight coefficient; represents the sum of all nodes i, and V represents the set of device nodes; Step 103-2, execute the distributed entropy balance algorithm to transform the global optimization problem into a set of local sub-problems. For each node i, its local optimization goal is:

[0029] in: Represents the set of neighbor nodes of node i, that is, all nodes directly connected to node i; Represents the impact factor of the information entropy change of node j on node i; represents the impact factor of the energy entropy change of node j on node i; represents the subset of configuration parameters that can be controlled by node i; Represents a subset of parameters Find the maximum value; It means summing all neighbor nodes j of node i; Step 103-3, using the parameter iteration update method to solve the local optimization problem, the update rule for each time step is:

[0030] in: is the step size parameter, which controls the update speed; is the gradient vector of the local entropy conversion efficiency with respect to the configuration parameters; represents the configuration parameters of node i at time t+1; represents the configuration parameters of node i at time t; Step 103-4, introduce the adaptive stabilizer, through the entropy damping factor Adjustment parameter update process:

[0031] in: is the step size parameter; is the entropy damping factor; is the gradient vector of the local entropy conversion efficiency with respect to the configuration parameters; represents the configuration parameters of node i at time t+1; represents the configuration parameters of node i at time t; The entropy damping factor is automatically calculated based on the node status: ; in: represents the entropy damping factor of node i; is the node entropy coupling degree; It is a node status fluctuation indicator, reflecting the stability of the node in the recent time window; Step 103-5: Generate three types of resource allocation plans based on the optimization results: Routing Allocation Scheme: Determining the Optimal Set of Communication Paths , where each path satisfies: ; in: represents the optimal communication path; Indicates that in the set of all possible paths Find the path that maximizes the following expression; represents the set of all possible paths from node i to node j; Indicates the path The amount of change in information entropy brought about; Indicates the path The amount of energy entropy change brought about; Power allocation scheme: Determine the optimal transmission power value set for each node , where each power value satisfies: ; in: represents the optimal transmission power; Indicates the power range Find the power value that maximizes the following expression; represents the transmission power of node i; Indicates the minimum power value; Indicates the maximum power value; Indicates power The amount of change in information entropy brought about; Indicates power The amount of energy entropy change brought about; Load distribution plan: determine the set of computing load proportions for each node , where each ratio value satisfies:

[0032] in: Indicates load ratio; and Indicates the entropy conversion efficiency of the node; and Indicates the entropy coupling degree of the node; represents the sum of all nodes j, and V represents the set of all device nodes in the network; Step 104: Apply entropy gravitational field theory and adaptive maintenance algorithm to construct an energy-information transmission channel network with self-organization and self-repair capabilities.

[0033] Receive the multi-dimensional resource allocation solution set generated in step 103 , applying entropy gravitational field theory and adaptive maintenance algorithm, constructing an energy-information transmission channel network with self-organization and self-repair capabilities.

[0034] Step 104 includes the following sub-steps: Step 104-1, define the dual-domain transmission channel data structure, each channel To log the complete set of transport properties: Information domain attributes: including bandwidth capacity , transmission delay , Link reliability And other parameters; Energy domain attributes: including energy consumption levels , Energy efficiency ratio , temperature gradient And other parameters; Step 104-2, construct an entropy gravitational field model, which uses the difference in entropy conversion efficiency as the source of attraction and provides a basis for priority path selection for data traffic: The calculation formula of entropy gravity between nodes is:

[0035] in: is the system gravitational constant, controlling the overall gravitational strength; is the network topological distance between nodes; is the node function similarity coefficient, with a value range of [0,1]; and They represent the entropy conversion efficiency index of node i and node j respectively; Step 104-3, generate the system entropy potential field distribution, which describes the tendency of data flow in the network space:

[0036] in: Represents the location coordinates in the network topology space; Represents the distance function from the position to node i; represents the sum of all nodes i; Represents the location in the network topology space The entropy potential field strength at ; Step 104-4, using the gradient tracking algorithm to form an optimized transmission channel in the entropy potential field, each channel path satisfy:

[0037] in: The preset gradient threshold controls the accuracy of channel formation; means or; Step 104-5, establish a channel health monitoring and adjustment mechanism, including three core components: health assessor, channel reconstruction trigger and backup channel manager: Health Evaluator: Calculates channel health status indicators :

[0038] in: is the health evaluation weight coefficient, satisfying , dynamically adjusted according to business needs; Indicates bandwidth capacity; Indicates the maximum bandwidth; Indicates transmission delay; Indicates the maximum delay; represents the energy efficiency ratio; Indicates the maximum energy efficiency ratio; Channel reconstruction trigger: When the channel health is lower than the threshold, the reconstruction process is started: Then execute the channel Refactoring; in: The health threshold is automatically adjusted by the system based on historical operation data, which is consistent with the node threshold in step 102-5. and link threshold different; Backup Channel Manager: Maintains backup paths for critical channels , fast switching when the main channel fails: if it is detected Activate if fault occurs Alternative.

[0039] Step 104-6, implement the load balancing algorithm between channels and calculate the optimal traffic distribution ratio according to the channel performance parameters:

[0040] in: represents the channel entropy conversion efficiency; Indicates channel health; It represents the sum of all transmission channels in the system; The system allocates data traffic accordingly:

[0041] in: Indicates channel data flow; Indicates the channel flow distribution ratio; Indicates the total data traffic of the system.

[0042] The self-maintaining energy-information transmission channel network has the capabilities of self-organization, adaptive adjustment and fault self-recovery, and can maintain the optimal balance between network communication quality and energy efficiency in a dynamic environment.

[0043] The technical effects achieved by this implementation are as follows: This implementation is based on the theoretical framework of information-energy entropy balance, and effectively solves the optimization contradiction between communication quality and energy consumption in the hotel self-service equipment network, the problem of unbalanced network resource allocation, the problem of slow system response, and the problem of insufficient fault recovery capability. Through core technologies such as entropy conversion efficiency modeling, network-energy coupling state analysis, multi-dimensional resource dynamic allocation, and self-maintaining energy-information transmission channels, this implementation significantly improves the energy efficiency ratio, communication quality, resource utilization, adaptability, and robustness of the hotel self-service equipment network, and provides an efficient, stable, and energy-saving network operating environment for the hotel self-service system, which is particularly suitable for hotel intelligent scenarios with complex communication requirements and limited energy resources.

[0044] An application example of implementation mode 1 is as follows: This system was deployed in a hotel with 178 rooms. The hotel fully deployed a network-energy symbiosis system driven by entropy dynamics, connecting to the following 57 IoT self-service device nodes.

[0045] The main challenges facing the hotel included: The surge in passenger traffic during the peak business season (October to January of the following year) causes network congestion and equipment response delays; The network coverage on different floors and areas is uneven, and some dead spots have weak signals, resulting in reduced service quality; Equipment energy consumption is unbalanced, with some equipment (such as food delivery robots and self-service laundry equipment) maintaining high energy consumption during low-usage periods; Traditional centralized network management solutions have delayed responses during peak periods and are unable to adjust resource allocation in real time; Equipment failure handling relies on manual intervention, and the average repair time is too long (about 23.7 minutes); The hotel management hopes to solve the above problems through the implementation of this system, especially to reduce energy consumption while improving network service quality, reduce manual intervention, and improve customer satisfaction.

[0046] Implementation process example: Step 101: receiving hotel self-service equipment network operation monitoring data, generating equipment communication-energy consumption characteristic mapping model and information-energy entropy conversion efficiency index implementation case; During the deployment process of the hotel, the system first collected 15 days of equipment operation benchmark data, sampling every 5 minutes to form a device communication-energy consumption characteristic mapping model. The actual data samples of some typical equipment are shown in Table 1: Table 1: Communication-energy consumption characteristics and entropy conversion efficiency of typical devices

[0047] The system uses a customized bidirectional neural network to build a mapping model. The network consists of a 5-layer structure (input layer - 3 hidden layers - output layer), where: Input layer: receives 6 communication characteristic parameters or 4 energy consumption characteristic parameters - Hidden layer: uses a fully connected neural network with an attention mechanism, with the number of nodes being 12-8-12 respectively; Output layer: outputs corresponding energy consumption prediction or communication characteristic prediction; By training on a large amount of data from different time periods (peak / off-peak periods) and different types of equipment, the system generated a device characteristic mapping model, achieving a prediction error rate of ±6.4% on the validation set, meeting the requirements of actual applications.

[0048] It is particularly noteworthy that the system shows significant differences in entropy conversion efficiency for different types of equipment. Smart door locks and smart front desk terminals show higher entropy conversion efficiency, while food delivery robots have relatively low entropy conversion efficiency. This difference becomes an important basis for subsequent resource optimization allocation.

[0049] Step 102: Analyze the network topology and energy status data, and generate a network-energy coupling state diagram and an implementation case of a key resource list; Based on the above equipment characteristic mapping model, the system constructs the hotel global network-energy coupling state diagram. The distribution of node entropy coupling degree is shown in Table 2: Table 2: Node entropy coupling distribution

[0050] Based on the set entropy coupling threshold (node ​​threshold , link threshold ), the key resource list automatically generated by the system is shown in Table 3: Table 3: List of key resources

[0051] Through in-depth analysis of the network topology and energy status, the system identified several key problem areas in the hotel network: the network link between the lobby and the kitchen became a communication bottleneck, with an entropy coupling degree of up to 2.8, making it the area that most needs to be optimized; The smart door lock device cluster in the guest room area on floors 8-12 forms an "island" with extremely low energy utilization efficiency in the weak signal area, and the entropy coupling degree is less than 0.5; Although the number of self-service laundry equipment is small, their sudden high power consumption characteristics cause significant interference to the surrounding communication network, and the entropy coupling degree fluctuates widely (0.4-2.3); Step 103: Applying an entropy conversion efficiency optimization algorithm to generate implementation cases of a multi-dimensional resource allocation solution set; Based on the network-energy coupling state diagram and the key resource list, the system executed the entropy conversion efficiency optimization algorithm and generated a multi-dimensional resource allocation plan for the hotel equipment network. The following is the actual situation of the algorithm execution: Initial settings: Global optimization goal: maximize the system entropy conversion efficiency; node weight: =0.3+0.7·( - ) / ( - ); step size parameter: γ=0.15; initial value of entropy damping factor: ξ=0.8; maximum number of iterations: 200; According to the network-energy coupling state diagram and the key resource list, the system executes the entropy conversion efficiency optimization algorithm and generates a multi-dimensional resource allocation plan for the hotel equipment network. The iteration of the algorithm execution is shown in Table 4: Table 4: Iteration of entropy conversion efficiency optimization algorithm

[0052] After 186 iterations, the algorithm converged to the local optimal solution (the system entropy conversion efficiency increased from 1.28 to 1.98), and the generated multi-dimensional resource allocation solution included: Routing allocation scheme: The network communication path table calculated based on the principle of maximizing entropy conversion efficiency covers 382 main communication links in the hotel. The optimization effects of some key links are shown in Table 5: Table 5: Routing allocation scheme optimization effect

[0053] Power allocation scheme: The optimal configuration value of the transmission power of network nodes is calculated based on the entropy conversion efficiency. This scheme pays special attention to key nodes and realizes dynamic power adjustment. The details are shown in Table 6: Table 6: Power allocation scheme

[0054] Load distribution scheme: Determine the computing load ratio of each node and optimize the data processing and computing task distribution in the network. The optimization results are shown in Table 7: Table 7: Load distribution scheme optimization results

[0055] Through iterative convergence, the system gradually slows down the gain of entropy conversion efficiency until it reaches a stable value, indicating that resource allocation is close to the global optimum.

[0056] Step 104: Apply entropy gravitational field theory and adaptive maintenance algorithm to build a case study of energy-information transmission channel network with self-organization and self-repair capabilities; After completing the resource allocation plan generation, the system builds an energy-information transmission channel network with self-organization and self-repair capabilities. The following is the actual implementation data of this step: Entropy gravitational field model: The system constructs a model using the difference in entropy conversion efficiency as the source of gravity. The gravitational constant G is set to 1.5×10^-3. The actual constructed field intensity distribution is shown in Table 8: Table 8: Field strength distribution of entropy gravitational field model

[0057] Channel health monitoring and adjustment: The system calculates the channel health status based on the communication quality and energy efficiency ratio, sets the health threshold θH=0.65, and monitors and maintains the channel health status in real time. The details are shown in Table 9: Table 9: Channel health monitoring and adjustment

[0058] Load balancing distribution: The system dynamically distributes data traffic based on channel health and entropy conversion efficiency. The improvement compared with the traditional load balancing algorithm is shown in Table 10: Table 10: Load balancing distribution improvement effect

[0059] Technical effect verification: Co-optimization effect of communication and energy consumption: The first core technical effect of this system is to achieve the coordinated optimization of network communication quality and energy consumption, breaking the relationship of one increasing while the other decreasing in traditional technology. The comparison data before and after implementation is shown in Table 11: Table 11: Comparison of communication and energy consumption coordinated optimization effects

[0060] To verify the stability of the technology under different load conditions, the technical team paid special attention to the system performance during peak and trough periods. The communication-energy consumption synergy index at different times is shown in Table 12: Table 12: Communication-energy consumption synergy index at different time periods

[0061] Among them, the communication-energy consumption synergy index is defined as the ratio of the comprehensive score of system communication quality to the unit energy consumption, normalized to the interval [0,1]. The closer to 1, the better the synergy effect.

[0062] During a peak holiday season, the system had an occupancy rate of >95%. Even when the system load was 37% higher than the baseline period, the overall network energy consumption remained at 72% of the baseline period, and communication quality indicators exceeded the baseline period by more than 25%.

[0063] System adaptability and self-recovery capabilities: The second core technical effect of this system is that it demonstrates excellent adaptive and self-recovery capabilities, which greatly improves the robustness and availability of the system. The technical team verified this effect in two ways: Adaptive Capability Measurements in Daily Use: The adaptive capabilities of the system in daily use are shown in Table 13: Table 13: System adaptability measurement

[0064] Fault handling and recovery capability testing: The technical team conducted 20 simulated fault tests on the system, including scenarios such as node hardware failure, link interruption, and power supply fluctuation. The test results are shown in Table 14: Table 14: Fault handling and recovery capability test results

[0065] The application results show that this system significantly reduces energy consumption while improving service quality, providing an efficient, stable and sustainable technical solution for the collaborative management of hotel self-service equipment. The entropy dynamic characteristics of the system enable it to not only adapt to peak operating pressures, but also demonstrate excellent resilience and self-healing capabilities in the event of a fault, which is of great value in improving the hotel's intelligence level and customer satisfaction.

[0066] Implementation 2: This implementation is applied to the collaborative management scenario of hotel self-service equipment based on the Internet of Things. Based on implementation 1, it focuses on solving the following technical problems: Solve the decision delay problem in large-scale device networks. The centralized decision architecture of implementation mode 1 forms computing bottlenecks and communication delays when the number of devices increases; Solve the problem that different types of equipment have different requirements for decision timeliness. Implementation method 1 uses a unified decision cycle, which cannot meet the differentiated requirements of equipment such as check-in terminals and food delivery robots; Solve the problem of underutilization of computing resources in edge devices. The calculation of entropy conversion efficiency in implementation mode 1 is mainly completed in the central node; Solve the problem of insufficient system robustness in the event of network partition or communication interruption.

[0067] This implementation introduces an edge-enhanced hierarchical entropy balancing framework, which enables each device node in the network to autonomously optimize at different decision-making levels and time scales through a hierarchical collaborative scheduling mechanism of asynchronous decision-making, while maintaining the consistency of the goal of global entropy balance.

[0068] Implementation method 2 is optimized and expanded on the basis of implementation method 1. Implementation method 2 is a hotel unmanned check-in method based on face recognition, comprising the following steps: Step 201: Collect equipment characteristic data and perform timeliness classification; Receive hotel self-service equipment feature set , where each device characteristic Contains device identifier, communication frequency data , Business Priority Indicators , User interaction frequency , Energy state time series And computing resource parameters ,The equipment is classified through the timeliness grading analysis algorithm, and the equipment timeliness grading table and hierarchical decision cycle configuration are generated.

[0069] Step 201 includes the following sub-steps: Step 201-1, calculating the service response sensitivity of each device :

[0070] in: Indicates the service response sensitivity of device i; is the weight coefficient and satisfies ; represents the communication frequency data of device i; Indicates the service priority index of device i; represents the user interaction frequency of device i; Step 201-2, calculate the energy change rate of each device :

[0071] in: represents the energy change rate of device i; Indicates the length of the sampling period; Indicates 1 to Sum all time points t; represents the energy state of device i at time t; represents the energy state of device i at time t-1; Step 201-3, calculate the computing capability index of each device :

[0072] in: Indicates the computing capability index of device i; is the weight coefficient and satisfies ; Indicates the CPU resource parameters of device i; Represents the memory resource parameters of device i; Represents the storage resource parameters of device i; Step 201-4, combining the above three indicators to calculate the comprehensive timeliness index :

[0073] in: represents the comprehensive timeliness index of device i; is the weight coefficient and satisfies ; Indicates the service response sensitivity of device i; represents the energy change rate of device i; Indicates the computing capability index of device i; Step 201-5, use K-means clustering algorithm to cluster the devices according to Values ​​are divided into three levels of timeliness, and a corresponding decision cycle is assigned to each level: High real-time performance (level 1): decision cycle =10-100 ms; Medium real-time (level 2): ​​Decision cycle = 1-5 seconds; Low real-time performance (level 3): decision cycle =30-120 seconds; Step 202: using a hierarchical entropy calculation topology generation algorithm to organize the device network into a hierarchical entropy calculation topology structure; Receive the equipment timeliness classification table generated in step 201 , hierarchical decision cycle configuration Physical connection diagram of the device ,A hierarchical entropy calculation topology generation algorithm is used to organize the device network into a hierarchical entropy calculation topology structure.

[0074] Step 202 includes the following sub-steps: Step 202-1, perform entropy calculation responsibility division, according to the timeliness level and computing power of the equipment, for each node Assign responsibility for entropy calculations to: ; in: represents the entropy calculation responsibility scope of node i; Represents any node j in the device node set V; represents the physical distance between node i and node j; represents the responsibility radius of node i; represents the timeliness level of node j; represents the timeliness level of node i; The node responsibility radius is calculated by the following formula: ; in: represents the responsibility radius of node i; is the adjustable scale parameter of the system; represents the computing power index of node i; represents the timeliness level of node i; Step 202-2: Generate computing dependency edges, and construct a logical computing dependency edge set based on the entropy computing responsibility relationship between nodes. : For each pair of nodes , if the node At the node If the entropy calculation responsibility is within the scope of arrive Directed edge arrive middle; Step 202-3, determine the hierarchical boundary nodes, and identify the boundary node sets connecting different timeliness level layers:

[0075] in: Represents the set of nodes that connect timeliness level i and level j; Represents any node v in the device node set V; Indicates the timeliness level of node v; Indicates the entropy calculation responsibility scope of node v; represents the timeliness level of node u; Step 202-4, topology redundancy optimization is performed. To improve system robustness, redundant entropy calculation paths are added to key nodes: For each node , calculate the number of its upstream nodes, if this number is less than the preset redundancy threshold , then find the nearest node with sufficient computing power ,Will Add to Scope of liability middle; Step 203: using a multi-time scale asynchronous entropy balancing algorithm, decomposing the global synchronous optimization into asynchronous optimization processes executed in parallel on multiple time scales, and realizing distributed asynchronous entropy balancing optimization; Receiving the hierarchical entropy calculation topology structure generated in step 202 , hierarchical decision cycle configuration and real-time device status data stream ,A multi-time-scale asynchronous entropy balancing algorithm is adopted to decompose the global synchronous optimization into asynchronous optimization processes that are executed in parallel on multiple time scales.

[0076] Step 203 includes the following sub-steps: Step 203-1, constructing a hierarchical local entropy model, and constructing a local entropy model suitable for each node according to the timeliness level of the device node: Information entropy calculation formula: ; in: Represents the information entropy of device node i with timeliness level k; represents the influence weight of node j on node i in level k; represents the communication probability between node i and node j; represents the logarithmic function; represents the sum of all nodes j within the responsibility range of node i; Energy entropy calculation formula: ; in: represents the energy entropy of device node i with timeliness level k; represents the influence weight of node j on node i in level k; represents the energy consumption required for node i to communicate with node j; represents the decision cycle of the k-th level device; represents the communication energy efficiency ratio; represents the sum of all nodes j within the responsibility range of node i; Step 203-2, perform asynchronous optimization scheduling, and perform three levels of optimization calculations according to the timeliness level of the device: High real-time device (level 1) optimization objective function:

[0077] in: Indicates the routing configuration and power configuration These two parameters seek the values ​​that minimize the objective function; represents the rate of change of information entropy of device i in the high real-time layer over time; represents the rate of change of energy entropy of device i in the high real-time layer over time; represents the entropy conversion efficiency coefficient of device i at the high real-time layer; Indicates routing configuration; Indicates power configuration; Medium real-time equipment (level 2) optimization objective function:

[0078] in: Indicates the routing configuration and power configuration These two parameters seek the values ​​that minimize the objective function; represents the rate of change of the information entropy of device i in the real-time layer over time; represents the rate of change of energy entropy of device i in the real-time layer over time; represents the entropy conversion efficiency coefficient of device i in the middle real-time layer; Represents the set of all high-real-time devices within the responsibility scope of node i; represents the influence coefficient; represents the rate of change of information entropy of device j in the high real-time layer over time; represents the sum of all high real-time devices j within the responsibility range of node i; Optimization objective function for low real-time devices (level 3):

[0079] in: Indicates the routing configuration , Power Configuration and load sharing These three parameters seek the values ​​that minimize the objective function; represents the global influence coefficient of node j; represents the rate of change of information entropy of device j in the low real-time layer over time; represents the rate of change of energy entropy of device j in the low real-time layer over time; represents the entropy conversion efficiency coefficient of device j at the low real-time layer; Indicates the sum of all device nodes j; Step 203-3, applying state prediction and smoothing update, to coordinate the consistency of decisions at different time scales, introduce state prediction calculation:

[0080] in: represents the predicted state value of device i at time t+Δt in the future; Represents the state value of device i at the current time t; Indicates that the device is Historical status value at a certain moment; represents the prediction weight; Indicates the state sampling interval; Indicates the number of steps back in the historical state; represents the prediction time interval; It means summing all k values ​​from 1 to m; Decision smoothing update formula: ; in: represents the decision value after smooth update; Indicates the timeliness level is The decision value of the device at the current moment; Indicates the decision value of the previous timeliness level; Indicates the timeliness level of the equipment The associated smoothing coefficient; represents the weight coefficient of local decision; Step 203-4, execute asynchronous message transmission, establish three types of message transmission channels, and support state synchronization between decision-making units of different time scales: Uplink messages: transmitted from low-timeliness layers to high-timeliness layers, including current status summaries and forecast trends; Downlink messages: transmitted from high-timeliness layers to low-timeliness layers, including optimization strategies and constraints; Emergency messages: triggered cross-layer emergency synchronization when rapid status changes exceeding preset thresholds are detected; Step 204: adopting an edge autonomous entropy balancing algorithm to enable edge devices to maintain autonomous decision-making capabilities under network abnormalities and establish an edge autonomous entropy balancing mechanism; Receive the hierarchical asynchronously optimized resource allocation solution set generated in step 203 , Device local status , global state snapshot and communication status indicator ,The edge autonomous entropy balancing algorithm is adopted to enable edge devices to maintain autonomous decision-making capabilities in the event of network anomalies.

[0081] Step 204 includes the following sub-steps: Step 204-1, perform communication status detection, each device Monitor the connection status of its computing dependent nodes through the heartbeat detection method: Generates a communication status indicator ,The communication status detection cycle is dynamically adjusted according to the timeliness level of the device, and the detection frequency of high real-time devices is higher; Step 204-2, create a global state snapshot, during normal communication ( ), each device Periodically store the latest global state snapshot: ; in: represents the global state snapshot at time t; represents the state value of device j at time t; Represents any node j in the device node set V; Construct a simplified entropy model: ; in: represents the simplified entropy model of device i; represents the entropy conversion efficiency of node j; represents the communication energy efficiency ratio between node j and node k; represents the communication probability; It means that both node j and node k are within the responsibility range of device i; Step 204-3, generating edge autonomous decision, when communication interruption is detected ( ), calculate the autonomous adjustment boundary: ; in: It means autonomous adjustment of boundaries; Indicates the initial border size; represents the time attenuation coefficient; Indicates the current time; Indicates the time of the most recent global state snapshot; represents an exponential decay function; Perform a local entropy optimization calculation:

[0082] in: represents the edge autonomous entropy equilibrium decision set; Indicates the routing configuration and power configuration Find the decision combination that minimizes the objective function; represents the rate of change of the local information entropy of device i over time; represents the rate of change of the local energy entropy of device i over time; represents the entropy conversion efficiency coefficient of device i; The optimization calculation is subject to the following constraints: ; in: Indicates the currently calculated routing configuration; Indicates the routing configuration obtained from the last global optimization; It means autonomous adjustment of boundaries; Adjust the autonomous boundary parameters:

[0083] in: Indicates the autonomous adjustment boundary of the next period; Indicates the autonomous adjustment boundary of the current period; Indicates the boundary adjustment step size; Indicates the key performance indicators at the current moment, including communication quality and energy efficiency; Step 204-4, perform state consistency recovery, when communication is restored ( From 0 to 1), calculate the decision deviation: ; in: Indicates decision bias; represents the edge autonomous decision set; Indicates the latest global decision corresponding to the timeliness level of the equipment; represents the norm distance between two decision sets; According to the deviation With preset threshold and The comparison of the two strategies respectively adopts the gradual synchronization, fast synchronization or emergency synchronization strategy to ensure the smooth transition of the system state to the globally consistent state; The technical results of this step are the edge autonomous entropy balancing decision set and autonomous adjustment boundary conditions, which are used to guide devices to make local optimization decisions within a limited range in the event of communication interruption, ensuring that the system can still operate normally during network partitioning.

[0084] Step 205: adopt a multi-level adaptive transmission channel construction algorithm to organize the transmission channel into a hierarchical structure to form a multi-level adaptive entropy-information transmission channel network.

[0085] Receive the hierarchical asynchronously optimized resource allocation solution set generated in step 203 , the edge autonomous entropy equilibrium decision set generated in step 204 and the hierarchical entropy calculation topology constructed in step 202 ,A multi-level adaptive transmission channel construction algorithm is adopted to organize the transmission channel into a hierarchical structure to meet different timeliness requirements.

[0086] Step 205 includes the following sub-steps: Step 205-1, define a multi-level channel data structure, and create a dedicated transmission channel data structure for each timeliness level: ; in: Indicates A collection of transmission channels at the timeliness level; Represents a collection of device nodes Any two nodes in ; and Respectively represent nodes and nodes The timeliness level of Indicates the timeliness level index (1-high timeliness level, 2-medium timeliness level, 3-low timeliness level); Represents a transmission channel descriptor; The transport channel descriptor contains the following three types of attributes: Information domain attributes: , represent bandwidth capacity, transmission delay and link reliability respectively; Energy Domain Properties: , respectively represent energy consumption level, energy efficiency ratio and temperature gradient; Timeliness attributes: , representing refresh frequency, priority, and availability respectively; Step 205-2: construct an intra-layer transmission channel. Based on the hierarchical asynchronous optimized resource allocation scheme, construct an internal transmission channel for each timeliness level: High real-time layer transmission channel ( ): Prioritize low latency and high reliability, and avoid congestion through reserved resource strategies; Real-time layer transmission channel ( ): Balance communication quality and energy consumption and adopt dynamic resource allocation strategy; Low real-time layer transmission channel ( ): Prioritize energy efficiency and reduce the number of transmissions through batch processing and data aggregation; Step 205-3: Establish a cross-level transmission channel. To support data exchange between different timeliness levels, a cross-level transmission channel is constructed: ; in: Represents a set of cross-level transmission channels; Represents a collection of device nodes Any two nodes in ; Representation Node and nodes The timeliness level is different; Indicates that there is a logical computational dependency between nodes i and j; Represents a cross-layer transmission channel descriptor; Cross-level channels are divided into two categories: Uplink channel: transmits data from the low-timeliness layer to the high-timeliness layer, using data summary and event-triggered transmission methods; Downlink channel: transmits data from the high-timeliness layer to the low-timeliness layer, using policy broadcast and constraint distribution transmission methods; Step 205-4, calculate the hierarchical entropy potential field and construct an independent entropy potential field model for each timeliness level:

[0087] in: Represents the entropy potential field value of the temporal level k at position (x, y); represents the set of all devices with timeliness level k; represents the information entropy of device i at level k; represents the energy entropy of device i at level k; represents the entropy conversion efficiency of device i at level k; Represents the physical location coordinates of device i; represents the Euclidean distance; represents the sum of all devices i with timeliness level k; Each layer of entropy potential field has different update cycles and transmission characteristics: High real-time layer entropy potential field: the update cycle is short (millisecond level), mainly driving local fast path adjustment; Entropy potential field in the middle real-time layer: obvious regional characteristics, driving the coordinated optimization of equipment within the range; Low real-time layer entropy potential field: global smooth distribution, driving the execution of long-term optimization decisions; Step 205-5, implement the multi-time scale channel maintenance function, and adopt corresponding channel maintenance methods according to the timeliness requirements of different levels: High real-time layer channel maintenance: local prediction and fast reconstruction methods are used, and the maintenance cycle is ; Medium real-time layer channel maintenance: using collaborative detection and regional recovery methods, the maintenance cycle is ; Low real-time layer channel maintenance: comprehensive inspection and resource rebalancing methods are adopted, and the maintenance cycle is ; Cross-level channel maintenance: implement channel status consistency check function, the maintenance cycle is ; The technical effects of this implementation are as follows: While maintaining the theoretical basis of information-energy entropy balance in implementation method 1, this framework effectively solves the decision delay problem in large-scale device networks, the differentiated demand for decision timeliness of different types of devices, the underutilization of edge device computing resources, and the problem of insufficient system robustness when the network is partitioned or the communication is interrupted. Through core technologies such as hierarchical timeliness management, asynchronous optimization scheduling, edge autonomous entropy balance, and multi-level transmission channels, this implementation method significantly improves the response speed, service quality, resource utilization efficiency, and system robustness of the hotel self-service equipment network, and is particularly suitable for complex hotel environments with high requirements for response speed and system robustness.

[0088] The above describes an embodiment of the present invention, but this embodiment is not limited to the above-mentioned specific implementation mode. The above-mentioned specific implementation mode is merely illustrative and not restrictive. Under the guidance of this embodiment, ordinary technicians in this field can also make more forms of equivalent embodiments, all of which are within the protection of this embodiment.

Claims

1. A hotel self-service equipment collaborative management method based on the Internet of Things, characterized in that: The following steps are involved: Collect the communication characteristic data and energy consumption characteristic data of each device in the hotel self-service equipment network, build a device communication-energy consumption characteristic mapping model, and calculate the device entropy conversion efficiency index; Based on the equipment communication-energy consumption characteristic mapping model and entropy conversion efficiency index, a network-energy coupling state diagram is constructed, and a key resource list is determined through threshold screening; The system's global entropy conversion efficiency optimization objective function is established, and a set of multi-dimensional resource allocation solutions is generated through a distributed entropy balance algorithm and parameter iterative update method.

2. A hotel self-service equipment collaborative management method based on the Internet of Things according to claim 1, characterized in that: The device communication-energy consumption characteristic mapping model is implemented by a bidirectional neural network structure, and includes two parts, an encoder and a decoder. The encoder maps communication characteristics to energy consumption characteristics, and the decoder maps energy consumption characteristics to communication characteristics.

3. A hotel self-service equipment collaborative management method based on the Internet of Things according to claim 2, characterized in that: The entropy conversion efficiency index is calculated by the following formula: ； in: It represents the entropy conversion efficiency index of device i; represents the change in information entropy of device i; represents the energy entropy change of device i; The information entropy of a device is calculated using the following formula: ； in: represents the information entropy of device i; represents the probability of communication state j of device i occurring; It means summing the index j, where the range of j includes all communication states of device i; represents the logarithmic function, where the logarithm with base 2 is used; The energy entropy of a device is calculated using the following formula: ； in: represents the energy entropy of device i; is the Boltzmann constant; is the number of microscopic states of the system; is internal energy; is the equivalent temperature; Represents the natural logarithm.

4. A hotel self-service equipment collaborative management method based on the Internet of Things according to claim 3, characterized in that: In the network-energy coupling state diagram, the node entropy coupling degree is calculated by the following formula: ； in: represents the node entropy coupling degree of node i; represents the partial derivative of the information entropy of device i with respect to the energy entropy; represents the link weight coefficient; Represents all links connected to node i Sum, where E represents the set of all links in the network; The link entropy coupling is calculated by the following formula: ； in: Indicates link Link entropy coupling degree; represents the partial derivative of link information entropy with respect to energy entropy; Represents the link weight coefficient.

5. A hotel self-service equipment collaborative management method based on the Internet of Things according to claim 4, characterized in that: The key resource list includes a key node set and a key link set: ； in: Represents a set of key nodes; Indicates the node entropy coupling threshold; ； in: represents a set of critical links; Indicates the link entropy coupling threshold.

6. A hotel self-service equipment collaborative management method based on the Internet of Things according to claim 5, characterized in that: The system global entropy conversion efficiency optimization objective function is: ； in: Represents a set of system configuration parameters; Represents a set of parameters Find the maximum value; ； ； in: and is the node weight coefficient; It represents the sum of all nodes i, and V represents the set of device nodes.

7. A hotel self-service equipment collaborative management method based on the Internet of Things according to claim 6, characterized in that: The distributed entropy balance algorithm transforms the global optimization problem into a local optimization problem, and the calculation formula is as follows: ； in: Represents the set of neighbor nodes of node i, that is, all nodes directly connected to node i; Represents the impact factor of the information entropy change of node j on node i; represents the impact factor of the energy entropy change of node j on node i; Represents a subset of parameters Find the maximum value; It represents the sum of all neighbor nodes j of node i.

8. A hotel self-service equipment collaborative management method based on the Internet of Things according to claim 7, characterized in that: The parameter iterative updating method includes introducing an entropy damping factor to adjust the parameter updating process: ； in: is the step size parameter; is the entropy damping factor; is the gradient vector of the local entropy conversion efficiency with respect to the configuration parameters; represents the configuration parameters of node i at time t+1; Represents the configuration parameters of node i at time t.

9. A hotel self-service equipment collaborative management method based on the Internet of Things according to claim 8, characterized in that: The multi-dimensional resource allocation scheme set includes a routing allocation scheme, a power allocation scheme and a load allocation scheme, wherein: the optimal communication path determined by the routing allocation scheme satisfies: ； in: represents the optimal communication path; Indicates that in all path sets Find the path that maximizes the following expression; represents the set of all paths from node i to node j; Indicates the path The amount of change in information entropy brought about; Indicates the path The amount of energy entropy change brought about; The optimal transmission power determined by the power allocation scheme satisfies: ； in: represents the optimal transmission power; Indicates the power range Find the power value that maximizes the following expression; represents the transmission power of node i; Indicates the minimum power value; Indicates the maximum power value; Indicates power The amount of change in information entropy brought about; Indicates power The amount of energy entropy change brought about; The calculated load ratio determined by the load distribution scheme satisfies: ； in: Indicates load ratio; and Indicates the entropy conversion efficiency of the node; and Indicates the entropy coupling degree of the node; represents the sum of all nodes j, and V represents the set of all device nodes in the network.

10. A hotel self-service equipment collaborative management system based on the Internet of Things, characterized in that: It is used to execute a hotel self-service equipment collaborative management method based on the Internet of Things as described in any one of claims 1 to 9, comprising: The equipment characteristic modeling module is used to collect the communication characteristic data and energy consumption characteristic data of each device in the hotel self-service equipment network, build the equipment communication-energy consumption characteristic mapping model, and calculate the equipment entropy conversion efficiency index; A network analysis module, for constructing a network-energy coupling state diagram based on the device communication-energy consumption characteristic mapping model and the entropy conversion efficiency index, and determining a key resource list through threshold screening; The resource optimization module is used to establish the system's global entropy conversion efficiency optimization objective function, and generate a multi-dimensional resource allocation solution set including communication path, power control and load balancing through a distributed entropy balancing algorithm and parameter iterative update method.

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