A smart electronic information management system based on smart cities

Abstract

This invention discloses an intelligent electronic information management system based on smart cities, relating to the field of data transmission technology. It includes a search particle determination unit, a particle update unit, an interception coefficient analysis unit, a relay node response unit, an access node connection unit, and a manhole cover status analysis unit. The invention constructs multiple groups containing different initial center point coordinates after the search particle determination unit statistically analyzes the position coordinates of all manhole cover sensor nodes around the access node. The particle update unit then adjusts the search particles corresponding to each group, thereby optimizing the initial center point coordinates within each group. This design aims to quickly determine the initial center point coordinates in the optimal group. Relay nodes are then deployed according to these initial center point coordinates, enabling more efficient data transmission from each sensor node and achieving load balancing of the relay nodes.

Description

Technical Field

[0001] This invention relates to the field of data transmission technology, specifically to an intelligent electronic information management system based on smart cities. Background Technology

[0002] The intelligent electronic information management system based on smart cities integrates diverse data such as urban infrastructure and transportation to achieve intelligent management across the entire city. This system performs real-time monitoring, dynamic analysis, and intelligent decision-making on hardware facilities such as manhole covers, optimizing resource allocation and thereby improving public service efficiency and emergency response capabilities. Patent application number 202310729077.1 discloses "an intelligent electronic information management system based on smart cities, including smart city management. The smart city management is divided into four layers based on urban resources: the urban system layer, the urban user layer, the urban application layer, and the urban data layer. The core capabilities of the smart city management are set as the Internet of Things, cloud computing, and big data. The smart city management consists of the urban user layer, the urban system layer, the urban application layer, and the urban data layer. The urban user layer is for the general public, relevant leaders, and various professional units, while the urban application layer is for all users of the smart city, covering fields such as communities, healthcare, education, logistics, and manufacturing. The core capabilities of smart city management rely on the Internet of Things, cloud computing, and big data. Cloud computing is a key technology for realizing smart cities, providing powerful computing and storage capabilities for smart city operations and applications."

[0003] The aforementioned existing technologies have solved problems such as the limited scope of business applications and the lack of resource integration. However, during system operation, the lack of reasonable grouping of sensor nodes may lead to unscientific deployment of relay nodes, resulting in some nodes having excessively long communication distances or unbalanced loads, which affects data transmission efficiency. Furthermore, the system lacks a dynamic interception coefficient and cannot flexibly adjust the number of node access requests, which may lead to data conflicts and retransmissions. At the same time, the manhole cover status analysis relies solely on raw data reporting, which cannot quickly identify anomalies, thus increasing operation and maintenance costs to some extent. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent electronic information management system based on smart cities to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent electronic information management system based on smart cities, comprising an interception coefficient analysis unit, a relay node response unit, an access node connection unit, and a manhole cover status analysis unit;

[0006] The search particle determination unit, after statistically analyzing the position coordinates of all manhole cover sensor nodes around the access node, constructs multiple groups containing different initial center point position coordinates. For each group, search particles and observation particles are set, and the number of exploration particles and the vector value of the search particles are determined. Based on the vector values ​​of the search particles in different groups, the corresponding new vector values ​​are calculated. If the survival rate of the new vector value is greater than that of the original vector value, the new vector value of the search particle is used to replace the original vector value; otherwise, the selection count of the group is incremented by one. The correlation coefficient of the group is analyzed based on the survival rate of each search particle.

[0007] The particle update unit sets the correlation coefficient for each observed particle. After determining the group corresponding to each observed particle according to the correlation coefficient between each group and the observed particle, it uses the observed particles to analyze the selection count of the group and the search particle vector value. If the selection count of the group is greater than the threshold and the survival rate is not the maximum value, the current group is deleted and a new group is generated. The vector value of the exploration particle is determined according to the position coordinates of the initial center point in the new group. The new search particle and selection count of the corresponding group are analyzed using the exploration particles. The iteration count is incremented by one. If the iteration count is greater than the preset maximum iteration count, the position coordinates of each initial center point in the group with the highest current survival rate are output.

[0008] Preferably, the search particle determination unit includes a grouping construction module, a search particle setting module, a vector value calculation module, a particle marking module, and a correlation coefficient analysis module. The grouping construction module sets the total number of leading sequences for each access node, counts the position coordinates of all manhole cover sensor nodes around the access node, constructs multiple groups, generates multiple initial center points according to the sensor node position coordinates, the total number of leading sequences, and the number of groups, and transmits the position coordinates of the initial center points of each group to different groups. The search particle setting module sets corresponding search particles and observation particles for each group, determines the number of exploration particles, the maximum number of iterations, and the upper and lower limits of each particle, calculates the vector dimension of the search particle using the data dimension corresponding to the position coordinates and the number of initial center points within the group, and analyzes the current vector values ​​of each dimension of the search particle based on the vector dimension and the position coordinates of each initial center point in the group. The vector value calculation module counts the number of search particles O in the xth unmarked group. x The vector value o in the k-th dimension xk And the search particle O within the y-th group y The vector value o in the k-th dimension yk Then, according to O x The vector value o in the k-th dimension xk and O y The vector value o in the k-th dimension yk Calculate Ox The new vector value o′ in the k-th dimension xk Where x, y, k represent the ordinal numbers, and o′ xk =o xk +rand(-1,1)(o xk -o yk The particle labeling module uses a survival value analysis algorithm to label o xk and o′ xk Calculations are performed to obtain o xk The corresponding search particle survival rate and o′ xk The corresponding search particle survival rate, if o′ xk Survivability greater than o xk Survival rate is determined by o′ xk Replace the original o xk As the current search particle O x The vector value o in the k-th dimension xk And search for particle O x Mark the selection; otherwise, increment the selection count and apply the same to the search particle O. x The labeling process is repeated by the correlation coefficient analysis module until all search particles are labeled. After calculating the survival rate of search particles in different groups, all survival rates are summed to obtain the total survival rate. The ratio between the survival rate of search particles and the total survival rate is used as the correlation coefficient for the corresponding group. The survival rate analysis algorithm is as follows:

[0009]

[0010] Where, f(a) α ) represents the survival rate of the α-th group, a αβ Let a represent the value of the β-th dimension vector in the α-th group. ρβ ε represents the value of the β-th dimension vector in the ρ-th group. max This represents the maximum adjustment coefficient. The maximum and minimum adjustment coefficients represent the difference, L represents the maximum number of iterations, T represents the current number of iterations, and α, β, and ρ represent the parameters. The coefficient of fitness is represented by v, the number of dimensions is represented by a. α This represents the α-th group.

[0011] Preferably, the particle update unit includes an observation particle analysis module, a survival calculation module, an optimal group selection module, and a position coordinate output module. The observation particle analysis module calculates the correlation coefficient for each group, divides the correlation range of each group according to the magnitude of the correlation coefficient, and generates corresponding correlation coefficients for each observation particle using a random function. If the current observation particle is within the correlation range of a group, the vector values ​​of each dimension of the current observation particle are determined using the search particle vector values ​​of the group. The survival calculation module calculates the vector values ​​of the current observation particle, replaces the vector values ​​of the current observation particle with those of other search particles in the same dimension, and compares the survival rates of the observation particles before and after the replacement. If the survival rate after replacement is greater than the survival rate before replacement, a new search particle for the current group is determined based on the replaced observation particle; otherwise, the selection count is incremented. The optimal group selection module calculates the survival rate and selection count of each group, and selects the group with the highest survival rate as the optimal group. If no group is selected more than a threshold, no operation is performed. Otherwise, it is determined whether the group selected more than the threshold is the best group. If the group is the best group, it is retained. If the group is not the best group, it is deleted. A new group is generated according to the position coordinates of the sensor nodes, the total number of preceding sequences, and the number of groups. The vector values ​​of each dimension of the exploration particle are determined according to the position coordinates of the initial center point in the new group. The vector values ​​of the current exploration particle and other search particles in the same dimension are replaced. The survival rate of the exploration particle before and after the replacement is compared. If the survival rate after the replacement is greater than the survival rate before the replacement, the new search particle of the current group is determined according to the replacement exploration particle. Otherwise, the selection count is incremented by one. The position coordinate output module increments the current iteration count by one. If the iteration count is greater than the maximum iteration count, the position coordinates of each initial center point in the current best group are output. Otherwise, the vector values ​​of the search particles are updated.

[0012] Preferably, the interception coefficient analysis unit includes a relay node generation module, a sensor analysis module, and an interception coefficient calculation module. The relay node generation module determines the position information of the corresponding relay node based on the position coordinates of each initial center point, calculates the distance values ​​between each manhole cover sensor node and each relay node, determines the optimal relay node corresponding to each manhole cover sensor node according to the distance values, and reads the configuration information of the optimal relay node. The sensor node that needs to send the initial access request waits to receive the interception coefficient broadcast by the optimal relay node. The sensor analysis module determines the current time interval as t. i And the correction coefficient ω, and the statistical analysis of the time interval t of the first three times. i-3 ,t i-2 ,t i-1 The number of sensor nodes n that were not intercepted i-3 ,n i-2 ,n i-1and the corresponding interception coefficient θ i-3 ,θ i-2 ,θ i-1 Then, according to n i-3 ,n i-2 ,n i-1 and θ i-3 ,θ i-2 ,θ i-1 The current time interval is determined to be t. i The number N of all sensor nodes that initiate the initial access request i ,in i represents a parameter, and the interception coefficient calculation module obtains the current time interval as t. i The total number of preamble sequences M, and the number of sensor nodes N that need to initiate the initial access request, based on the total number of preamble sequences M and the number of sensor nodes N that need to initiate the initial access request. i Calculate t i Interception coefficient θ i ,in

[0013] Preferably, the relay node response unit includes a random number comparison module, a preamble sequence allocation module, and a data transmission module. The random number comparison module, after all sensor nodes that need to initiate an initial access request receive the interception coefficient corresponding to the current time slot, generates a random number and compares it with the interception coefficient. If the random number is greater than or equal to the interception coefficient, the current sensor node is not allowed to issue an initial access request, and its priority is incremented by one. If the random number is less than the interception coefficient, the current sensor node is allowed to issue an initial access request. The preamble sequence allocation module extracts all allowed sensor nodes within the current time slot and prioritizes... The sensor node with the highest priority selects the preamble sequence first. After setting a corresponding preamble sequence for each sensor node according to priority, the preamble sequences of each sensor node are traversed. If there are identical preamble sequences between different sensor nodes, the duplicate preamble sequence is assigned to the sensor node with the higher priority. The priority of the remaining sensor nodes is increased by one, and they are not allowed to issue an initial access request in the current time interval. After the data transmission module receives the preamble sequence and the initial access request from the best relay node, it sends an adjustment command to the sensor node. After the sensor node returns the response information, it transmits the collected data to the best relay node.

[0014] Preferably, the access node connection unit includes a configuration information reading module, a node interception module, a resource reservation module, an access response module, a connection request sending module, and a connection confirmation module. The configuration information reading module calculates the distance between each relay node and each access node, determines the optimal access node for each relay node based on the distance value, and after reading the configuration information of the optimal relay node, the relay node that needs to send the initial resource reservation request waits to receive the interception coefficient broadcast by the optimal relay node. The node interception module determines whether each relay node is allowed within the current time interval based on the interception coefficient. If the relay node is allowed, a preamble sequence is selected for the relay node according to its priority, and the preamble sequence and resource reservation request are sent to the optimal access node. If not allowed, the priority of the relay node is adjusted, and it continues to wait for the interception coefficient. The resource reservation module receives the preamble sequence from the relay node. After the sequence number and resource reservation request are completed, a temporary access ID and temporary access channel are set for the relay node. The access response module sends a random access request to the optimal access node based on the temporary access ID and temporary access channel. After receiving the random access request, the optimal access node returns random access response information to the relay node. After receiving the random access response information, the connection request sending module determines the connection ID and configuration information of the current relay node based on the random access response information and sends a connection request to the optimal access node. After receiving the connection request, the optimal access node returns setting response information to the relay node. After receiving the setting response information, the sensor node constructs a control channel and a user channel based on the setting response information and sends connection completion information to the optimal access node. After the optimal access node returns connection confirmation information, the data in the relay node is transmitted to the optimal access node.

[0015] Preferably, the manhole cover status analysis unit includes a type classification module, a time series data generation module, and a sliding window setting module. The type classification module classifies the manhole cover status into four types: normal state, tilted state, water accumulation state, and empty state. The time series data generation module calculates the location coordinates, sensor data, and recording time of different manhole covers based on the node data received by the access node, and combines the sensor data and recording time of each manhole cover to generate corresponding time series data. The sliding window setting module sets the size and movement step of the sliding window, and then uses the sliding window to traverse the time series data of each manhole cover in turn. Before each movement, the window range of the current sliding window is determined, the mean and standard deviation of the time series data within the window are calculated, and stored in the sliding set. The sliding window is moved according to the movement step until all time series data have been traversed.

[0016] Preferably, the manhole cover state analysis unit further includes a synthetic vector calculation module, a state probability analysis module, and a state output module. The synthetic vector calculation module calculates the tilt angle synthetic vector based on the tilt angle data of each manhole cover contained in the sensor data, and analyzes the vibration energy value using the manhole cover position offset data and vibration frequency data contained in the sensor data. The state probability analysis module combines the time series data, sliding set, tilt angle synthetic vector, and vibration energy value of each manhole cover to obtain the time series feature vector of each manhole cover. The time series feature vector of the manhole cover is transmitted to the LSTM model for analysis. Corresponding weight coefficients are set for different state types, and a weight vector is constructed using all the weight coefficients. The state prediction algorithm is used to analyze the weight vector and the time series feature vector to obtain the probability value of the manhole cover corresponding to different states. The state output module takes the state type corresponding to the maximum probability value as the actual state type of the current manhole cover and transmits it to the visualization interface.

[0017] Compared with the prior art, the beneficial effects of the present invention are:

[0018] 1. This invention constructs multiple groups containing different initial center point coordinates by a search particle determination unit after statistically analyzing the position coordinates of all manhole cover sensor nodes around the access node. The particle update unit then adjusts the search particles corresponding to the groups, thereby optimizing the initial center point coordinates within each group. This design aims to quickly determine the initial center point coordinates in the optimal group. By deploying relay nodes according to these initial center point coordinates, the data transmission process of each sensor node becomes more efficient, and load balancing of the relay nodes is achieved.

[0019] 2. This invention sets an interception coefficient in the interception coefficient analysis unit. This is mainly to reduce the probability of data transmission conflicts. The interception coefficient can control the number of sensor nodes sending access requests in the same time interval to a certain extent, so that the data can be transmitted in an orderly manner, reducing the hardware requirements of the equipment. Since the interception coefficient in different time intervals is related to the interception situation in the previous three time intervals, the interception coefficient in each time interval may change, thereby ensuring that the number of nodes allowed to send requests is more scientific and reasonable, further ensuring the effective transmission of data. At the same time, the relay node response unit and the access node connection unit allocate a preamble sequence to each node according to priority, so that nodes that have been waiting for a long time can receive priority response, reducing the occurrence of nodes failing to transmit data in a timely manner. The manhole cover status analysis unit analyzes the real-time data collected by each manhole cover sensor node to determine the actual status of each manhole cover in the city, so that monitoring personnel can detect abnormalities in a timely manner and ensure the safety of residents when passing through roads. Attached Figure Description

[0020] Figure 1 A schematic diagram of the overall system flow is provided for embodiments of the present invention;

[0021] Figure 2 This is an internal module block diagram of the search particle determination unit provided in an embodiment of the present invention;

[0022] Figure 3 This is an internal module block diagram of the particle update unit provided in an embodiment of the present invention;

[0023] Figure 4 This is an internal module block diagram of the interception coefficient analysis unit provided in an embodiment of the present invention;

[0024] Figure 5 This is an internal module block diagram of the access node connection unit provided in an embodiment of the present invention;

[0025] Figure 6 This is a block diagram of the internal modules of the manhole cover status analysis unit provided in an embodiment of the present invention.

[0026] In the diagram: 1. Search particle determination unit; 101. Grouping construction module; 102. Search particle setting module; 103. Vector value calculation module; 104. Particle marking module; 105. Correlation coefficient analysis module; 2. Particle update unit; 201. Observation particle analysis module; 202. Survival calculation module; 203. Optimal group selection module; 204. Position coordinate output module; 3. Interception coefficient analysis unit; 301. Relay node generation module; 302. Sensor analysis module; 303. Interception coefficient calculation module; 4. Relay node response unit; 401. 402. Random number comparison module; 403. Preamble sequence allocation module; 404. Data transmission module; 5. Access node connection unit; 505. Configuration information reading module; 506. Node interception module; 507. Resource reservation module; 508. Access response module; 509. Connection request sending module; 5000. Connection confirmation module; 6. Manhole cover status analysis unit; 600. Type classification module; 601. Time series data generation module; 602. Sliding window setting module; 603. Composite vector calculation module; 604. State probability analysis module; 605. State output module. Detailed Implementation

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0028] Please see Figures 1-6 The present invention provides a technical solution: an intelligent electronic information management system based on smart cities, including an interception coefficient analysis unit 3, a transit node response unit 4, an access node connection unit 5, and a manhole cover status analysis unit 6;

[0029] Search particle determination unit 1: After statistically analyzing the position coordinates of all manhole cover sensor nodes around the access node, search particle determination unit 1 constructs multiple groups containing different initial center point position coordinates. For each group, search particles and observation particles are set, and the number of exploration particles and the vector value of the search particles are determined. Based on the vector values ​​of the search particles in different groups, the corresponding new vector values ​​are calculated. If the survival rate of the new vector value is greater than the survival rate of the original vector value, the new vector value of the search particle is used to replace the original vector value. Otherwise, the selection count of the group is incremented by one. The correlation coefficient of the group is analyzed based on the survival rate of each search particle.

[0030] Particle Update Unit 2 sets the correlation coefficient for each observed particle. After determining the group corresponding to each observed particle according to the correlation coefficient between each group and the observed particle, it uses the observed particles to analyze the selection count of the group and the search particle vector value. If the selection count of the group is greater than the threshold and the survival rate is not the maximum value, the current group is deleted and a new group is generated. The vector value of the exploration particle is determined according to the position coordinates of the initial center point in the new group. The new search particle and selection count of the corresponding group are analyzed using the exploration particles. The iteration count is incremented by one. If the iteration count is greater than the preset maximum iteration count, the position coordinates of each initial center point in the group with the highest current survival rate are output.

[0031] The search particle determination unit 1 includes a grouping construction module 101, a search particle setting module 102, a vector value calculation module 103, a particle marking module 104, and a correlation coefficient analysis module 105. The grouping construction module 101 sets the total number of leading sequences for each access node, counts the position coordinates of all manhole cover sensor nodes around the access node, and constructs multiple groups. It generates multiple initial center points according to the sensor node position coordinates, the total number of leading sequences, and the number of groups, and transmits the position coordinates of the initial center points of each group to different groups. The search particle setting module 102 sets corresponding search particles and observation particles for each group, and determines the number of exploration particles, the maximum number of iterations, and the upper and lower limits of each particle. It calculates the vector dimension of the search particles using the data dimension corresponding to the position coordinates and the number of initial center points within the group. Based on the vector dimension and the position coordinates of each initial center point in the group, it analyzes the current vector values ​​of each dimension of the search particles. The vector value calculation module 103 counts the number of search particles O in the xth unmarked group. x The vector value o in the k-th dimension xkAnd the search particle O within the y-th group y The vector value o in the k-th dimension yk Then, according to O x The vector value o in the k-th dimension xk and O y The vector value o in the k-th dimension yk Calculate O x The new vector value o′ in the k-th dimension xk Where x, y, k represent the ordinal numbers, and o′ xk =o xk +rand(-1,1)(o xk -o yk The particle labeling module 104 uses a survival value analysis algorithm to label o xk and o′ xk Calculations are performed to obtain o xk The corresponding search particle survival rate and o′ xk The corresponding search particle survival rate, if o′ xk Survivability greater than o xk Survival rate is determined by o′ xk Replace the original o xk As the current search particle O x The vector value o in the k-th dimension xk And search for particle O x Mark the selection; otherwise, increment the selection count and apply the same to the search particle O. x The labeling process is repeated in the correlation coefficient analysis module 105 until all search particles are labeled. After calculating the survival rate of search particles in different groups, all survival rates are summed to obtain the total survival rate. The ratio between the survival rate of search particles and the total survival rate is used as the correlation coefficient for the corresponding group. The specific survival value analysis algorithm is as follows:

[0032]

[0033] Where, f(a) α ) represents the survival rate of the α-th group, a αβ Let a represent the value of the β-th dimension vector in the α-th group. ρβ ε represents the value of the β-th dimension vector in the ρ-th group. max This represents the maximum adjustment coefficient. The maximum and minimum adjustment coefficients represent the difference, L represents the maximum number of iterations, T represents the current number of iterations, and α, β, and ρ represent the parameters. The coefficient of fitness is represented by v, the number of dimensions is represented by a. α Indicates the α-th group;

[0034] Particle Update Unit 2 includes an observation particle analysis module 201, a survival calculation module 202, an optimal group selection module 203, and a position coordinate output module 204. The observation particle analysis module 201 calculates the correlation coefficient for each group, divides the correlation range of each group according to the magnitude of the correlation coefficient, and generates corresponding correlation coefficients for each observation particle using a random function. If the current observation particle is within the correlation range of a group, the vector values ​​of each dimension of the current observation particle are determined using the search particle vector values ​​of the group. The survival calculation module 202 calculates the vector values ​​of the current observation particle, replaces the vector values ​​of the current observation particle with those of other search particles in the same dimension, and compares the survival rates of the observation particles before and after the replacement. If the survival rate after replacement is greater than the survival rate before replacement, a new search particle for the current group is determined based on the replaced observation particle; otherwise, the selection count is incremented. The optimal group selection module 203 calculates the survival rate and selection count of each group, and selects the group with the highest survival rate. If no group is selected more than the threshold, no operation is performed. Otherwise, it is determined whether the group selected more than the threshold is the best group. If the group is the best group, it is retained. If the group is not the best group, it is deleted. A new group is generated according to the position coordinates of the sensor node, the total number of preceding sequences, and the number of groups. The vector values ​​of each dimension of the exploration particle are determined according to the position coordinates of the initial center point in the new group. The vector values ​​of the current exploration particle and other search particles in the same dimension are replaced. The survival rate of the exploration particle before and after the replacement is compared. If the survival rate after the replacement is greater than the survival rate before the replacement, the new search particle of the current group is determined according to the replacement exploration particle. Otherwise, the selection count is incremented by one. The position coordinate output module 204 increments the current iteration count by one. If the iteration count is greater than the maximum iteration count, the position coordinates of each initial center point in the current best group are output. Otherwise, the vector values ​​of the search particles are updated.

[0035] The interception coefficient analysis unit 3 includes a relay node generation module 301, a sensor analysis module 302, and an interception coefficient calculation module 303. The relay node generation module 301 determines the location information of the corresponding relay node based on the position coordinates of each initial center point, calculates the distance between each manhole cover sensor node and each relay node, determines the optimal relay node for each manhole cover sensor node based on the distance values, and reads the configuration information of the optimal relay node. Sensor nodes that need to send an initial access request wait to receive the interception coefficient broadcast by the optimal relay node. The sensor analysis module 302 determines the current time interval as t. i And the correction coefficient ω, and the statistical analysis of the time interval t of the first three times. i-3 ,t i-2 ,t i-1 The number of sensor nodes n that were not intercepted i-3 ,n i-2 ,ni-1 and the corresponding interception coefficient θ i-3 ,θ i-2 ,θ i-1 Then, according to n i-3 ,n i-2 ,n i-1 and θ i-3 ,θ i-2 ,θ i-1 The current time interval is determined to be t. i The number N of all sensor nodes that initiate the initial access request i ,in i represents a parameter, and the interception coefficient calculation module 303 obtains the current time interval as t. i The total number of preamble sequences M, and the number of sensor nodes N that need to initiate the initial access request, based on the total number of preamble sequences M and the number of sensor nodes N that need to initiate the initial access request. i Calculate t i Interception coefficient θ i ,in

[0036] The relay node response unit 4 includes a random number comparison module 401, a preamble sequence allocation module 402, and a data transmission module 403. The random number comparison module 401 generates a random number after all sensor nodes that need to initiate an initial access request receive the interception coefficient corresponding to the current time slot. It compares the random number with the interception coefficient. If the random number is greater than or equal to the interception coefficient, the current sensor node is not allowed to issue an initial access request, and its priority is incremented. If the random number is less than the interception coefficient, the current sensor node is allowed to issue an initial access request. The preamble sequence allocation module 402 extracts all allowed sensor nodes within the current time slot. Then, the sensor node with the highest priority selects the preamble sequence first. After setting the corresponding preamble sequence for each sensor node according to priority, the preamble sequences of each sensor node are traversed. If there are identical preamble sequences between different sensor nodes, the duplicate preamble sequence is assigned to the sensor node with the higher priority. The priority of the remaining sensor nodes is increased by one and they are not allowed to issue the initial access request in the current time interval. After the data transmission module 403 receives the preamble sequence and the initial access request from the best relay node, it sends an adjustment command to the sensor node. After the sensor node returns the response information, it transmits the collected data to the best relay node.

[0037] The access node connection unit 5 includes a configuration information reading module 501, a node interception module 502, a resource reservation module 503, an access response module 504, a connection request sending module 505, and a connection confirmation module 506. The configuration information reading module 501 calculates the distance between each relay node and each access node, determines the optimal access node for each relay node based on the distance, and reads the configuration information of the optimal relay node. Relay nodes that need to send an initial resource reservation request wait to receive the interception coefficient broadcast by the optimal relay node. The node interception module 502 determines whether each relay node is allowed within the current time interval based on the interception coefficient. If a relay node is allowed, a preamble sequence is selected for the relay node according to its priority, and the preamble sequence and resource reservation request are sent to the optimal access node. If not allowed, the priority of the relay node is adjusted, and it continues to wait for the interception coefficient. The resource reservation module 503 receives the request from the optimal access node. After the relay node completes its preamble sequence and resource reservation request, a temporary access ID and temporary access channel are set for the relay node. The access response module 504 sends a random access request to the optimal access node based on the temporary access ID and temporary access channel. Upon receiving the random access request, the optimal access node returns random access response information to the relay node. The connection request sending module 505, upon receiving the random access response information, determines the current relay node's connection ID and configuration information based on the random access response information and sends a connection request to the optimal access node. The connection confirmation module 506, upon receiving the connection request, returns setting response information to the relay node. Upon receiving the setting response information, the sensor node constructs a control channel and a user channel based on the setting response information and sends connection completion information to the optimal access node. After the optimal access node returns connection confirmation information, the data in the relay node is transmitted to the optimal access node.

[0038] The manhole cover status analysis unit 6 includes a type classification module 601, a time series data generation module 602, and a sliding window setting module 603. The type classification module 601 classifies the manhole cover status into four types: normal status, tilted status, water accumulation status, and empty status. The time series data generation module 602 calculates the location coordinates, sensor data, and recording time of different manhole covers based on the node data received by the access node. It then combines the sensor data and recording time of each manhole cover to generate the corresponding time series data. The sliding window setting module 603 sets the size and movement step of the sliding window, and then uses the sliding window to traverse the time series data of each manhole cover in turn. Before each movement, it determines the window range of the current sliding window, calculates the mean and standard deviation of the time series data within the window, stores them in the sliding set, and moves the sliding window according to the movement step until all time series data have been traversed.

[0039] The manhole cover state analysis unit 6 also includes a synthetic vector calculation module 604, a state probability analysis module 605, and a state output module 606. The synthetic vector calculation module 604 calculates the tilt angle synthetic vector based on the tilt angle data contained in the sensor data of each manhole cover, and analyzes the vibration energy value using the manhole cover position offset data and vibration frequency data contained in the sensor data. The state probability analysis module 605 combines the time-series data, sliding set, tilt angle synthetic vector, and vibration energy value of each manhole cover to obtain the time-series feature vector of each manhole cover. The time-series feature vector of the manhole cover is then transmitted to an LSTM model for analysis. Corresponding weight coefficients are set for different state types, and a weight vector is constructed using all weight coefficients. A state prediction algorithm is used to analyze the weight vector and time-series feature vector to obtain the probability value of the manhole cover corresponding to different states. The state output module 606 takes the state type corresponding to the maximum probability value as the actual state type of the current manhole cover and transmits it to the visualization interface. The state prediction algorithm is as follows:

[0040]

[0041] Wherein, P(s=U N ) represents the probability value corresponding to the Nth state type, s represents the state type, and U N This represents the Nth state type label, e represents the natural constant, and γ N The weight vector γ represents the Nth state type. M Let R represent the weight vector for the Mth state type, and let Q represent the encoded temporal feature vector. N Q represents the adjustment coefficient for the Nth type. M Let M represent the adjustment coefficient for the Mth type, M and N represent the number of numbers, and K represent the total number of types.

[0042] Working Principle: This invention generates multiple groups containing different initial center point coordinates through the grouping construction module 101 in the search particle determination unit 1. The search particle setting module 102 analyzes the current vector values ​​of each dimension of the search particles. The vector value calculation module 103 determines the new vector values ​​of the search particles within each group. The particle marking module 104 calculates the survival rate and selection count of the search particles within each group. The correlation coefficient analysis module 105 calculates the correlation coefficient of the corresponding group based on the survival rate of the search particles. The observation particle analysis module 201 in the particle update unit 2 determines the vector values ​​of each dimension of the observed particles. The survival rate calculation module 202 determines the new search particles for the current group based on the observed particles. The group selection module 203 determines the vector value of the exploration particle by using the position coordinates of the initial center point in the new group, analyzes the new search particle in the current group using the exploration particle, and determines the position coordinates of each initial center point in the current best group through the position coordinate output module 204. The relay node generation module 301 in the interception coefficient analysis unit 3 analyzes the position information of the corresponding relay node according to the position coordinates of the initial center point. The sensor analysis module 302 estimates the number of all sensor nodes that initiated the initial access request within the current time interval. The interception coefficient calculation module 303 calculates the interception coefficient for the current time interval. The random number comparison module 401 in the relay node response unit 4 generates a corresponding random number for each sensor node. If the random number is less than the interception coefficient, the current sensor node is allowed to issue an initial access request. After the preamble sequence allocation module 402 allocates the preamble sequence to the sensor node, the data transmission module 403 transmits the data collected by the sensor node to the optimal relay node. The configuration information reading module 501 in the access node connection unit 5 determines the optimal access node corresponding to each relay node according to the distance value. The node interception module 502 determines the priority of the relay node and sets a temporary access ID and temporary access channel for the relay node through the resource reservation module 503. After the relay node sends a random access request to the optimal access node in the access response module 504, the optimal access node returns random access response information. The connection request sending module 505 sends a connection request to the optimal access node through the relay node. After the connection confirmation module 506 waits for the optimal access node to be determined, it transmits the data in the relay node to the optimal access node. The manhole cover state analysis unit 6 classifies the state of the manhole cover through the type classification module 601. The time series data generation module 602 determines the time series data corresponding to the sensor. The sliding window setting module 603 uses the sliding window to traverse the time series data of each manhole cover in turn. After the composite vector calculation module 604 analyzes the tilt angle composite vector and vibration energy value, the state probability analysis module 605 obtains the probability value of the manhole cover corresponding to different states. The state output module 606 outputs the actual state type of the manhole cover.

[0043] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0044] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart electronic information management system based on smart cities, comprising a relay node response unit (4), an access node connection unit (5), and a manhole cover status analysis unit (6), characterized in that: The search particle determination unit (1) counts the position coordinates of all manhole cover sensor nodes around the access node, constructs multiple groups containing different initial center point position coordinates, sets search particles and observation particles for each group, and determines the number of exploration particles and the vector value of the search particles. Then, it calculates the corresponding new vector value based on the vector value of the search particles in different groups. If the survival rate of the new vector value is greater than the survival rate of the original vector value, the new vector value of the search particle is used to replace the original vector value. Otherwise, the number of selections of the group is incremented by one. The correlation coefficient of the group is analyzed based on the survival rate of each search particle. Particle update unit (2), the particle update unit (2) sets the correlation coefficient of each observed particle, determines the group corresponding to each observed particle according to the correlation coefficient of each group and the observed particle, and uses the observed particles to analyze the number of selections of the group and the vector value of the search particle. If the number of selections of the group is greater than the threshold and the survival rate is not the maximum value, the current group is deleted and a new group is generated. The vector value of the exploration particle is determined according to the position coordinates of the initial center point in the new group. The new search particle and the number of selections of the corresponding group are analyzed using the exploration particles. The iteration count is incremented by one. If the iteration count is greater than the preset maximum iteration count, the position coordinates of each initial center point in the group with the highest current survival rate are output. The interception coefficient analysis unit (3) includes a relay node generation module (301), a sensor analysis module (302), and an interception coefficient calculation module (303). The relay node generation module (301) determines the position information of the corresponding relay node based on the position coordinates of each initial center point, calculates the distance between each manhole cover sensor node and each relay node, determines the optimal relay node corresponding to each manhole cover sensor node according to the distance value, and reads the configuration information of the optimal relay node. The sensor node that needs to send the initial access request waits to receive the interception coefficient broadcast by the optimal relay node. The sensor analysis module (302) determines the current time interval as t. i And the correction coefficient ω, and the statistical analysis of the time interval t of the first three times. i-3 ,t i-2 ,t i-1 The number of sensor nodes n that were not intercepted i-3 ,n i-2 ,n i-1 and the corresponding interception coefficient θ i-3 ,θ i-2 ,θ i-1 Then, according to n i-3 ,n i-2 ,n i-1 and θ i-3 ,θ i-2 ,θ i-1 The current time interval is determined to be t. i The number N of all sensor nodes that initiate the initial access request i ,in i represents a parameter, and the interception coefficient calculation module (303) obtains the current time interval as t. i The total number of preamble sequences M, and the number of sensor nodes N that need to initiate the initial access request, based on the total number of preamble sequences M and the number of sensor nodes N that need to initiate the initial access request. i Calculate t i Interception coefficient θ i ,in 2. The intelligent electronic information management system based on smart cities according to claim 1, characterized in that: The search particle determination unit (1) includes a group construction module (101), a search particle setting module (102), a vector value calculation module (103), a particle marking module (104), and a correlation coefficient analysis module (105). The group construction module (101) sets the total number of leading sequences for each access node, counts the position coordinates of all manhole cover sensor nodes around the access node, constructs multiple groups, generates multiple initial center points according to the position coordinates of the sensor nodes, the total number of leading sequences, and the number of groups, and transmits the position coordinates of the initial center points of each group to different groups. The search particle setting module (102) sets the corresponding search particles and observation particles for each group, and determines the number of exploration particles, the maximum number of iterations, the upper limit value and the lower limit value of each particle. After calculating the vector dimension of the search particles using the data dimension corresponding to the position coordinates and the number of initial center points in the group, it analyzes the vector values ​​of each dimension of the search particles according to the vector dimension and the position coordinate values ​​of each initial center point in the group. The vector value calculation module (103) counts the number of search particles O in the xth unmarked group. x The vector value o in the k-th dimension xk And the search particle O within the y-th group y The vector value o in the k-th dimension yk Then, according to O x The vector value o in the k-th dimension xk and O y The vector value o in the k-th dimension yk Calculate O x The new vector value o′ in the k-th dimension xk Where x, y, k represent the ordinal numbers, and o′ xk =o xk +rand(-1,1)(o xk -o yk The particle labeling module (104) uses a survival value analysis algorithm to label o xk and o′ xk Calculations are performed to obtain o xk The corresponding search particle survival rate and o′ xk The corresponding search particle survival rate, if o′ xk Survivability greater than o xk Survival rate is determined by o′ xk Replace the original o xk As the current search particle O x The vector value o in the k-th dimension xk And search for particle O x Mark the selection; otherwise, increment the selection count and apply the same to the search particle O. x The correlation coefficient analysis module (105) repeats the operation until all search particles are marked. After calculating the survival rate of search particles in different groups, the survival rates of all particles are summed to obtain the total survival rate. The ratio between the survival rate of search particles and the total survival rate is used as the correlation coefficient of the corresponding group.

3. The intelligent electronic information management system based on smart cities according to claim 1, characterized in that: The particle update unit (2) includes an observation particle analysis module (201), a survival calculation module (202), an optimal group selection module (203), and a position coordinate output module (204). The observation particle analysis module (201) calculates the correlation coefficient of each group, divides the correlation range of each group according to the magnitude of the correlation coefficient, and generates a corresponding correlation coefficient for each observation particle using a random function. If the current observation particle is within the correlation range of a group, the vector values ​​of each dimension of the current observation particle are determined using the search particle vector values ​​of the group. The survival calculation module (202) calculates the vector values ​​of the current observation particle, replaces the vector values ​​of the current observation particle with those of other search particles in the same dimension, and compares the survival of the observation particles before and after the replacement. If the survival after the replacement is greater than the survival before the replacement, a new search particle for the current group is determined based on the replaced observation particle; otherwise, the selection count is incremented by one. The optimal group selection module (203) calculates the survival and selection of each group. The number of selections is determined by the group with the highest survival rate. If no group is selected more than the threshold, no operation is performed. Otherwise, it is determined whether the group selected more than the threshold is the best group. If the group is the best group, it is retained. If the group is not the best group, it is deleted. A new group is generated according to the position coordinates of the sensor node, the total number of preceding sequences, and the number of groups. The vector values ​​of each dimension of the exploration particle are determined according to the position coordinates of the initial center point in the new group. The vector values ​​of the current exploration particle and other search particles in the same dimension are replaced. The survival rate of the exploration particle before and after the replacement is compared. If the survival rate after the replacement is greater than the survival rate before the replacement, the new search particle of the current group is determined according to the replacement exploration particle. Otherwise, the selection count is incremented by one. The position coordinate output module (204) increments the current iteration count by one. If the iteration count is greater than the maximum iteration count, the position coordinates of each initial center point in the current best group are output. Otherwise, the vector values ​​of the search particles are updated.

4. The intelligent electronic information management system based on smart cities according to claim 1, characterized in that: The relay node response unit (4) includes a random number comparison module (401), a preamble sequence allocation module (402), and a data transmission module (403). The random number comparison module (401) generates a random number after all sensor nodes that need to initiate an initial access request receive the interception coefficient corresponding to the current time interval. It compares the random number with the interception coefficient. If the random number is greater than or equal to the interception coefficient, the current sensor node is not allowed to initiate an initial access request, and its priority is incremented. If the random number is less than the interception coefficient, the current sensor node is allowed to initiate an initial access request. The preamble sequence allocation module (402) extracts all allowed access requests within the current time interval. After the sensor nodes are selected, the sensor node with the highest priority selects the preamble sequence first. After setting the corresponding preamble sequence for each sensor node according to priority, the preamble sequences of each sensor node are traversed. If there are identical preamble sequences between different sensor nodes, the duplicate preamble sequence is assigned to the sensor node with the higher priority. The priority of the remaining sensor nodes is increased by one and they are not allowed to issue an initial access request in the current time interval. After the data transmission module (403) receives the preamble sequence and the initial access request from the best relay node, it sends an adjustment command to the sensor node. After the sensor node returns the response information, it transmits the collected data to the best relay node.

5. The intelligent electronic information management system based on smart cities according to claim 1, characterized in that: The access node connection unit (5) includes a configuration information reading module (501), a node interception module (502), a resource reservation module (503), an access response module (504), a connection request sending module (505), and a connection confirmation module (506). The configuration information reading module (501) calculates the distance between each relay node and each access node, determines the best access node corresponding to each relay node according to the distance value, and reads the configuration information of the best relay node. The relay node that needs to send the initial resource reservation request waits to receive the interception coefficient broadcast by the best relay node. The node interception module (502) determines whether each relay node is allowed in the current time interval according to the interception coefficient. If the relay node is allowed, a preamble sequence is selected for the relay node according to the priority, and the preamble sequence and resource reservation request are sent to the best access node. If it is not allowed, the priority of the relay node is adjusted, and it continues to wait for the interception coefficient. The resource reservation module (503) After receiving the preamble sequence and resource reservation request from the relay node, the optimal access node sets a temporary access ID and a temporary access channel for the relay node. The access response module (504) sends a random access request to the optimal access node based on the temporary access ID and temporary access channel. After receiving the random access request, the optimal access node returns random access response information to the relay node. After receiving the random access response information, the connection request sending module (505) determines the connection ID and configuration information of the current relay node based on the random access response information and sends a connection request to the optimal access node. After receiving the connection request, the optimal access node returns setting response information to the relay node. After receiving the setting response information, the sensor node constructs a control channel and a user channel based on the setting response information and sends connection completion information to the optimal access node. After the optimal access node returns connection confirmation information, the data in the relay node is transmitted to the optimal access node.

6. The intelligent electronic information management system based on smart cities according to claim 1, characterized in that: The manhole cover status analysis unit (6) includes a type classification module (601), a time series data generation module (602), and a sliding window setting module (603). The type classification module (601) classifies the manhole cover status into four types: normal state, tilted state, water accumulation state, and empty state. The time series data generation module (602) calculates the location coordinates, sensor data, and recording time of different manhole covers based on the node data received by the access node. Then, it combines the sensor data and recording time of each manhole cover to generate the corresponding time series data. The sliding window setting module (603) sets the size of the sliding window and the movement step size. Then, it uses the sliding window to traverse the time series data of each manhole cover in turn. Before each movement, it determines the window range of the current sliding window, calculates the mean and standard deviation of the time series data within the window, stores them in the sliding set, and starts to move the sliding window according to the movement step size until all time series data have been traversed.

7. The intelligent electronic information management system based on a smart city according to claim 6, characterized in that: The manhole cover state analysis unit (6) further includes a synthetic vector calculation module (604), a state probability analysis module (605), and a state output module (606). The synthetic vector calculation module (604) calculates the tilt angle synthetic vector based on the tilt angle data of each manhole cover sensor data, and analyzes the vibration energy value using the manhole cover position offset data and vibration frequency data contained in the manhole cover sensor data. The state probability analysis module (605) combines the time series data, sliding set, tilt angle synthetic vector, and vibration energy value of each manhole cover to obtain the time series feature vector of each manhole cover. The time series feature vector of the manhole cover is transmitted to the LSTM model for analysis. Corresponding weight coefficients are set for different state types, and a weight vector is constructed using all the weight coefficients. The state prediction algorithm is used to analyze the weight vector and the time series feature vector to obtain the probability value of the manhole cover corresponding to different states. The state output module (606) takes the state type corresponding to the maximum probability value as the actual state type of the current manhole cover and transmits it to the visualization interface.

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