Intelligent electronic informatization management system based on smart city

Abstract

The invention discloses an intelligent electronic informatization management system based on a smart city, which relates to the technical field of data transmission and comprises a search particle determination unit, a particle updating unit, an interception coefficient analysis unit, a transfer node response unit, an access node connection unit and a well lid state analysis unit. After the position coordinates of all well lid sensor nodes around the access node are counted through the search particle determination unit, a plurality of groups containing different initial center point position coordinates are constructed, and the search particles corresponding to the groups are adjusted through the particle updating unit. Therefore, the position coordinates of the initial center points in each group can be optimized, the design is to determine the position coordinates of the initial center points in the optimal group in a short time, and the transfer nodes are arranged according to the position coordinates of the initial center points. Therefore, each sensor node can be more efficient in the data transmission process, and the load balance of the transfer node is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to an intelligent electronic information management system based on a smart city. Background Art

[0002] The intelligent electronic information management system based on smart cities realizes global intelligent management by integrating multiple data such as urban infrastructure and transportation. The system conducts real-time monitoring, dynamic analysis and intelligent decision-making on hardware facilities such as manhole covers, optimizes resource allocation, and thus improves public service efficiency and emergency response capabilities. The invention patent with 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: urban system layer, urban user layer, urban application layer and urban data layer. The core capabilities of the smart city management are set as the Internet of Things, cloud computing and big data. Smart city management consists of the urban user layer, urban system layer, urban application layer and urban data layer. The urban user layer is oriented to the public, relevant leaders and various professional units, and the urban application layer is oriented to all users of the smart city, covering communities, medical care, education, logistics and manufacturing. The core of smart city management relies on the Internet of Things, cloud computing and big data. Cloud computing is a key technology for realizing smart cities. Cloud computing provides powerful computing and storage capabilities for the business and applications of smart cities."

[0003] The above-mentioned existing technologies solve the problems of limited scope of business applications and lack of resource integration. However, when the system is running, due to the lack of reasonable grouping of sensor nodes, the layout of the transit nodes may not be scientific, and some nodes may have communication distances that are too far or load imbalances, thereby affecting data transmission efficiency. In addition, 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 only relies on the reporting of raw data and cannot quickly identify anomalies, which increases the operation and maintenance costs to a certain extent. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent electronic information management system based on a smart city to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solutions: an intelligent electronic information management system based on smart city, comprising an interception coefficient analysis unit, a transit node response unit, an access node connection unit and a manhole cover status analysis unit;

[0006] A search particle determination unit. After the search particle determination unit statistically analyzes the position coordinates of all manhole cover sensor nodes around an access node, it constructs multiple groups with different initial center point position coordinates, sets search particles and observation particles for each group, determines the number of exploration particles and the vector values of the search particles, and then calculates the corresponding new vector values according to the vector values of the search particles in different groups. If the survival degree 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 degree of each search particle.

[0007] A particle update unit. The particle update unit sets the correlation coefficient of each observation particle. After determining the corresponding group for each observation particle according to the correlation coefficient of each group and the observation particle, it analyzes the selection count of the group and the vector values of the search particles using the observation particle. If the selection count of the group is greater than the threshold and the survival degree 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, and the new search particles and selection count of the corresponding group are analyzed using the exploration particle. 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 maximum current survival degree are output.

[0008] Preferably, the search particle determination unit includes a group construction module, a search particle setting module, a vector value calculation module, a particle marking module, and a correlation coefficient analysis module. The group construction module sets the total number of preamble sequences of each access node. After statistically analyzing the position coordinates of all manhole cover sensor nodes around the access node, it constructs multiple groups, generates multiple sets of initial center points according to the position coordinates of the sensor nodes, the total number of preamble 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 the corresponding search particles and observation particles for each group, determines the number of exploration particles, the maximum iteration count, the upper and lower limit values 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 in the group, and analyzes the vector values of each dimension of the search particle according to the vector dimension and the position coordinate values of each initial center point in the group. The vector value calculation module statistically analyzes the vector value o of the k-th dimension of the search particle O in the x-th unmarked group x and the vector value o of the k-th dimension of the search particle O in the y-th group xk and then calculates O according to the vector value o of the k-th dimension of O in y and the vector value o of the k-th dimension of O in yk After that, according to the vector value o of the k-th dimension of O in x and the vector value o of the k-th dimension of O in xk and the vector value o of the k-th dimension of O in y calculate the vector value o of the k-th dimension of O in yk and calculate Ox The new vector value o′ of the kth dimension in xk , where x, y, k represent the sequence number, o′ xk =o xk +rand(-1,1)(o xk -o yk ), the particle labeling module uses a survival value analysis algorithm to xk and o′ xk Calculate to get o xk The corresponding search particle survival and o′ xk The corresponding search particle survival degree, if o′ xk The survival rate is greater than o xk The survival degree of o′ xk Replace the original o xk As the current search particle O x The vector value o of the kth dimension in xk , and search for particle O x Mark, otherwise increase the number of selections by one and search for particle O x Marking is performed, and the correlation coefficient analysis module repeats the operation until all search particles are marked. After counting the survival of search particles in different groups, all survivals are accumulated to obtain the total survival. The ratio between the survival of the search particle and the total survival is used as the correlation coefficient of the corresponding group. The survival value analysis algorithm is specifically as follows:

[0009]

[0010] Among them, f(a α ) represents the survival degree of the αth group, a αβ represents the value of the β-dimensional vector in the α-th group, a ρβ represents the value of the β-th dimension vector in the ρ-th group, ε max represents the maximum adjustment coefficient, represents the difference between the maximum adjustment coefficient and the minimum adjustment coefficient, L represents the maximum number of iterations, T represents the current number of iterations, α, β, ρ represent parameters, represents the adaptation coefficient, v represents the number of dimensions, a α represents the αth group.

[0011] Preferably, the particle update unit includes an observation particle analysis module, a survival degree calculation module, an optimal grouping selection module, and a position coordinate output module. The observation particle analysis module calculates the correlation coefficient of each group, divides the correlation range of each group according to the size of the correlation coefficient, and then uses a random function to generate corresponding correlation coefficients for each observation particle. If the current observation particle is within the correlation range of the group, the vector value of each dimension of the current observation particle is determined using the search particle vector value of the group. After the survival degree calculation module calculates the vector value of the current observation particle, it replaces the vector value of the same dimension of the current observation particle with that of other search particles, and compares the survival degree of the observation particle before and after replacement. If the survival degree after replacement is greater than that before replacement, a new search particle for the current group is determined based on the observation particle after replacement; otherwise, the selection count is incremented by one. The optimal grouping selection module calculates the survival degree and selection count of each group, and selects the group with the highest survival degree as the optimal group. If there is no group whose selection count is greater than the threshold, no operation is performed; otherwise, it is determined whether the group with a selection count greater than the threshold is the optimal group. If the group is the optimal group, the current group is retained; if the group is not the optimal group, the current group is deleted. A new group is generated according to the position coordinates of the sensor nodes, the total number of preamble sequences, and the number of groups. The vector value of each dimension of the exploration particle is determined according to the position coordinates of the initial center point in the new group. The vector value of the same dimension of the current exploration particle is replaced with that of other search particles, and the survival degree of the exploration particle before and after replacement is compared. If the survival degree after replacement is greater than that before replacement, a new search particle for the current group is determined based on the exploration particle after replacement; 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 optimal group are output; otherwise, the vector value of the search particle is continuously 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 according to the position coordinates of each initial center point, calculates the distance value 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 after reading 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 that the current time interval is t i and the correction coefficient ω, and calculates the number of sensor nodes n i-3 , n i-2 , n i-1 not intercepted within the first three time intervals t i-3 , n i-2 , n i-1and the corresponding interception coefficient θ i-3 , θ i-2 , θ i-1 After that, according to n i-3 , n i-2 , n i-1 and θ i-3 , θ i-2 , θ i-1 analyze that the number of all sensor nodes N that initiate the initial access request within the current time interval t i wherein i , where i represents a parameter, and the interception coefficient calculation module obtains the total number M of preambles in the current time interval t i , and calculates the interception coefficient θ of t i based on the total number M of preambles and the number N of all sensor nodes that need to initiate the initial access request i wherein i , where

[0013] Preferably, the relay node response unit includes a random number comparison module, a preamble allocation module, and a data transmission module. After all sensor nodes that need to initiate the initial access request receive the interception coefficient corresponding to the current time interval, the random number comparison module generates a random number, 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 send the initial access request, and the priority of the current node is incremented by one. If the random number is less than the interception coefficient, the current sensor node is allowed to send the initial access request. After the preamble allocation module extracts all the allowed sensor nodes within the current time interval, the sensor node with the highest priority selects the preamble first. After setting the corresponding preamble for each sensor node according to the priority, traverse the preambles of each sensor node. If there are consistent preambles between different sensor nodes, the repeated preambles are allocated to the sensor node with a higher priority, and the remaining sensor nodes have their priorities incremented by one and are not allowed to send the initial access request within the current time interval. After the best relay node receives the preamble and the initial access request, the data transmission module sends an adjustment command to the sensor node. After the sensor node returns the response information, the collected data is transmitted 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 values between each relay node and each access node, determines the optimal access node corresponding to each relay node according to the distance values, and after reading the configuration information of the optimal relay node, the relay node that needs to send an 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 slot according to the interception coefficient. If the relay node is allowed, it selects a preamble sequence for the relay node according to the priority, and sends the preamble sequence and the resource reservation request to the optimal access node. If not allowed, it adjusts the priority of the relay node and continues to wait for the interception coefficient. After the resource reservation module of the optimal access node receives the preamble sequence and the resource reservation request of the relay node, it sets a temporary access ID and a temporary access channel for the relay node. The access response module of the relay node sends a random access request to the optimal access node according to the temporary access ID and the temporary access channel. After the optimal access node receives the random access request, it returns a random access response message to the relay node. After the connection request sending module of the relay node receives the random access response message, it determines the connection ID and configuration information of the current relay node according to the random access response message, and sends a connection request to the optimal access node. After the connection confirmation module of the optimal access node receives the connection request, it returns a setting response message to the relay node. After the sensor node receives the setting response message, it constructs a control channel and a user channel according to the setting response message, sends a connection completion message to the optimal access node, and after the optimal access node returns a connection confirmation message, it transmits the data in the relay node 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, namely normal status, tilted status, water accumulation status, and vacant status. The time series data generation module combines the sensor data and recording time of each manhole cover after calculating the position coordinates, sensor data, and recording time of different manhole covers according to the node data received by the access node, so as to generate corresponding time series data. After the sliding window setting module sets the size and moving step of the sliding window, it uses the sliding window to sequentially traverse the time series data of each manhole cover. 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 moving the sliding window according to the moving step until all the time series data are traversed.

[0016] Preferably, the manhole cover status 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 an inclination synthetic vector based on the manhole cover inclination angle data included in each manhole cover sensor data, and analyzes the vibration energy value using the manhole cover position offset data and vibration frequency data included in the manhole cover sensor data. The state probability analysis module combines the time series data, sliding set, inclination synthetic vector, and vibration energy value of each manhole cover to obtain the time series feature vector of each manhole cover, transmits the time series feature vector of the manhole cover to the LSTM model for analysis, sets corresponding weight coefficients for different state types, constructs a weight vector using all the weight coefficients, and analyzes the weight vector and the time series feature vector using a state prediction algorithm to obtain the probability values of the manhole cover corresponding to different states. The state output module uses 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 as follows:

[0018] 1. After the search particle determination unit of the present invention statistically determines the position coordinates of all manhole cover sensor nodes around the access node, multiple groups including different initial center point position coordinates are constructed, and the particle update unit is used to adjust the search particles corresponding to the groups, so that the initial center point position coordinates in each group can be optimized. This design is to clarify the position coordinates of the initial center point in the best group in a short time, arrange the relay nodes according to these position coordinates of the initial center point, so that each sensor node can be more efficient in the process of transmitting data, and achieve the load balancing of the relay nodes;

[0019] 2. By setting an interception coefficient in the interception coefficient analysis unit, the present invention mainly aims to reduce the collision probability during data transmission. The interception coefficient can control the number of sensor nodes sending access requests within the same time interval to a certain extent, making the data transmission orderly, reducing the hardware requirements of the device. Since the interception coefficients in different time intervals are associated with the interception situations in the previous three time intervals, the interception coefficient in each time interval may change, so as to ensure that the number of nodes allowed to send requests can be 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 preambles to each node according to the priority, so that the nodes waiting for a long time can be preferentially responded, reducing the situation where nodes cannot transmit data in time. 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, enabling the monitoring personnel to promptly discover abnormal situations and ensuring the safety of residents when passing on the road. Description of the Drawings

[0020] Figure 1 This is a schematic diagram of the overall system process provided by the embodiments of the present invention;

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

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

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

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

[0025] Figure 6 This is an internal module block diagram of the manhole cover status analysis unit provided by the embodiments of the present invention.

[0026] In the figure: 1. Search particle determination unit; 101. Group 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. Observed particle analysis module; 202. Survival degree 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. Random number comparison module; 402. Preamble sequence allocation module; 403. Data transmission module; 5. Access node connection unit; 501. Configuration information reading module; 502. Node interception module; 503. Resource reservation module; 504. Access response module; 505. Connection request sending module; 506. Connection confirmation module; 6. Manhole cover status analysis unit; 601. Type classification module; 602. Timing data generation module; 603. Sliding window setting module; 604. Composite vector calculation module; 605. State probability analysis module; 606. State output module. Detailed implementation manners

[0027] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0028] Please refer to Figures 1-6 , the present invention provides a technical solution: an intelligent electronic information management system based on a smart city, 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] A search particle determination unit 1. After the search particle determination unit 1 statistically analyzes the position coordinates of all manhole cover sensor nodes around the access node, it constructs multiple groups containing different initial center point position coordinates, sets search particles and observation particles for each group, determines the number of exploration particles and the vector value of the search particles, and then calculates the corresponding new vector value according to the vector values of the search particles in different groups. If the survival degree 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 times of the group are incremented by one. The correlation coefficient of the group is analyzed based on the survival degree of each search particle;

[0030] A particle update unit 2. The particle update unit 2 sets the correlation coefficient of each observation particle. After determining the group corresponding to each observation particle according to the correlation coefficient of each group and the observation particle, it analyzes the selection times of the group and the vector value of the search particle using the observation particle. If the selection times of the group are greater than the threshold and the survival degree 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, and the new search particle and selection times of the corresponding group are analyzed using the exploration particle. The iteration times are incremented by one. If the iteration times are greater than the preset maximum iteration times, the position coordinates of each initial center point in the group with the maximum current survival degree are output.

[0031] 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 preamble sequences of each access node. After statistically analyzing the position coordinates of all manhole cover sensor nodes around the access node, it constructs multiple groups, generates multiple groups of initial center points according to the position coordinates of the sensor nodes, the total number of preamble 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, determines the number of exploration particles, the maximum iteration times, the upper limit value and the lower limit value of each particle, calculates the vector dimension of the search particle according to the data dimension corresponding to the position coordinates and the number of initial center points in the group, and analyzes the vector values of each current dimension of the search particle according to the vector dimension and the position coordinate values of each initial center point in the group. The vector value calculation module 103 statistically analyzes the vector value o of the k-th dimension of the search particle O in the x-th unmarked group x in xkand search particle O in the yth group y The vector value o of the kth dimension in yk Afterwards, according to O x The vector value o of the kth dimension in xk and O y The vector value o of the kth dimension in yk Calculate O x The new vector value o′ of the kth dimension in xk , where x, y, k represent the sequence number, o′ xk =o xk +rand(-1,1)(o xk -o yk ), the particle labeling module 104 uses a survival value analysis algorithm to xk and o′ xk Calculate to get o xk The corresponding search particle survival and o′ xk The corresponding search particle survival degree, if o′ xk The survival rate is greater than o xk The survival degree of o′ xk Replace the original o xk As the current search particle O x The vector value o of the kth dimension in xk , and search for particle O x Mark, otherwise increase the number of selections by one and search for particle O x Marking is performed, and the correlation coefficient analysis module 105 repeats the operation until all search particles are marked. After counting the survival rates of search particles in different groups, all survival rates are accumulated to obtain the total survival rate, and the ratio between the survival rate of the search particle and the total survival rate is used as the correlation coefficient of the corresponding group. The specific survival value analysis algorithm is:

[0032]

[0033] Among them, f(a α ) represents the survival degree of the αth group, a αβ represents the value of the β-dimensional vector in the α-th group, a ρβ represents the value of the β-th dimension vector in the ρ-th group, ε max represents the maximum adjustment coefficient, represents the difference between the maximum adjustment coefficient and the minimum adjustment coefficient, L represents the maximum number of iterations, T represents the current number of iterations, α, β, ρ represent parameters, represents the adaptation coefficient, v represents the number of dimensions, a α represents the αth group;

[0034] The particle update unit 2 includes an observed particle analysis module 201, a survival probability calculation module 202, an optimal grouping selection module 203, and a position coordinate output module 204. The observed 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 then uses a random function to generate corresponding correlation coefficients for each observed particle. If the current observed particle is within the correlation range of a group, the vector values of each dimension of the current observed particle are determined using the search particle vector value of the group. After the survival probability calculation module 202 calculates the vector values of the current observed particle, the vector values of the same dimension of the current observed particle and other search particles are replaced, and the survival probabilities of the observed particles before and after replacement are compared. If the survival probability after replacement is greater than that before replacement, new search particles for the current group are determined based on the observed particle after replacement; otherwise, the selection count is incremented by one. The optimal grouping selection module 203 calculates the survival probability and selection count of each group, and selects the group with the highest survival probability as the optimal group. If there is no group whose selection count is greater than the threshold, no operation is performed; otherwise, it is determined whether the group with a selection count greater than the threshold is the optimal group. If the group is the optimal group, the current group is retained; if the group is not the optimal group, the current group is deleted. New groups are generated according to the position coordinates of the sensor nodes, the total number of preamble sequences, and the number of groups, and the vector values of each dimension of the exploration particles are determined based on the position coordinates of the initial center points in the new groups. The vector values of the same dimension of the current exploration particle and other search particles are replaced, and the survival probabilities of the exploration particles before and after replacement are compared. If the survival probability after replacement is greater than that before replacement, new search particles for the current group are determined based on the exploration particle after replacement; 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 optimal group are output; otherwise, the vector values of the search particles are continuously 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 position information of the corresponding relay nodes according to the position coordinates of each initial center point, calculates the distance values between each manhole cover sensor node and each relay node, and determines the best relay node corresponding to each manhole cover sensor node according to the distance values. After reading the configuration information of the best relay node, the sensor nodes that need to send initial access requests wait to receive the interception coefficients broadcast by the best relay node. The sensor analysis module 302 determines that the current time slot is t i and the correction coefficient ω, and calculates the number of sensor nodes n i-3 , n i-2 , n i-1 not intercepted within the first three time slots t i-3 , n i-2 , ni-1 and the corresponding interception coefficient θ i-3 , θ i-2 , θ i-1 After that, according to n i-3 , n i-2 , n i-1 and θ i-3 , θ i-2 , θ i-1 analyze that the current time slot is t i the number N of all sensor nodes that initiate the initial access request within it i , where i represents a parameter, and the interception coefficient calculation module 303 obtains the total number M of preambles in the current time slot t i Based on the total number M of preambles and the number N of all sensor nodes that need to initiate the initial access request i calculate the interception coefficient θ of t i where i , where

[0036] The relay node response unit 4 includes a random number comparison module 401, a preamble allocation module 402, and a data transmission module 403. After all the sensor nodes that need to initiate the initial access request receive the interception coefficient corresponding to the current time slot, the random number comparison module 401 generates a random number and 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 send the initial access request, and the priority of the current node is incremented by one. If the random number is less than the interception coefficient, the current sensor node is allowed to send the initial access request. After the preamble allocation module 402 extracts all the allowed sensor nodes in the current time slot, the sensor node with the highest priority selects the preamble first. After setting the corresponding preamble for each sensor node according to the priority, traverse the preambles of each sensor node. If there are identical preambles among different sensor nodes, the duplicate preambles are allocated to the sensor node with a higher priority, and the priority of the remaining sensor nodes is incremented by one and they are not allowed to send the initial access request within the current time slot. After the data transmission module 403 receives the preamble 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 values between each relay node and each access node, determines the optimal access node corresponding to each relay node according to the distance values, 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 502 determines whether each relay node is allowed during the current time slot according to the interception coefficient. If the relay node is allowed, it selects a preamble sequence for the relay node according to the priority, and sends the preamble sequence and the resource reservation request to the optimal access node. If not allowed, it adjusts the priority of the relay node and continues to wait for the interception coefficient. After the resource reservation module 503, the optimal access node receives the preamble sequence and the resource reservation request of the relay node, and sets a temporary access ID and a temporary access channel for the relay node. After the access response module 504, the relay node sends a random access request to the optimal access node according to the temporary access ID and the temporary access channel. After the optimal access node receives the random access request, it returns a random access response message to the relay node. After the connection request sending module 505, the relay node receives the random access response message, determines the connection ID and the configuration information of the current relay node according to the random access response message, and then sends a connection request to the optimal access node. After the connection confirmation module 506, the optimal access node receives the connection request, and returns a setting response message to the relay node. After the sensor node receives the setting response message, it constructs a control channel and a user channel according to the setting response message, sends a connection completion message to the optimal access node, and after the optimal access node returns a connection confirmation message, it transmits the data in the relay node 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, namely normal status, inclined status, water accumulation status, and vacant status. After the time series data generation module 602 counts the position coordinates, sensor data, and recording time of different manhole covers according to the node data received by the access node, it combines the sensor data and the recording time of each manhole cover to generate the corresponding time series data. After the sliding window setting module 603 sets the size and moving step of the sliding window, 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 moving step until all the time series data are traversed;

[0039] The manhole cover status 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 inclination synthetic vector based on the manhole cover inclination angle data included in each manhole cover sensor data, analyzes the vibration energy value using the manhole cover position offset data and vibration frequency data included in the manhole cover sensor data. The state probability analysis module 605 combines the time series data, sliding set, inclination synthetic vector, and vibration energy value of each manhole cover to obtain the time series feature vector of each manhole cover, transmits the time series feature vector of the manhole cover to the LSTM model for analysis, sets corresponding weight coefficients for different state types, constructs a weight vector using all the weight coefficients, analyzes the weight vector and the time series feature vector using a state prediction algorithm to obtain the probability values 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 specifically:

[0040]

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

[0042] Working principle: In the present invention, the grouping construction module 101 in the particle search determination unit 1 generates multiple groups containing different initial center point position coordinates. The search particle setting module 102 analyzes the vector values of each dimension of the current search particle. The vector value calculation module 103 determines the new vector values of the search particles within the group. The particle marking module 104 calculates the survival degree and selection times of the search particles within the group. The correlation coefficient analysis module 105 calculates the correlation coefficient of the corresponding group based on the survival degree of the search particle. The observation particle analysis module 201 in the particle update unit 2 determines the vector values of each dimension of the observation particle. The survival degree calculation module 202 determines the new search particles of the current group based on the observation particle. The best group selection module 203 determines the vector values of the exploration particles through the position coordinates of the initial center points in the new group, and analyzes the new search particles of the current group using the exploration particles. The position coordinate output module 204 determines the position coordinates of each initial center point in the current best group. The transit node generation module 301 in the interception coefficient analysis unit 3 analyzes the position information of the corresponding transit node according to the position coordinates of the initial center point. The sensor analysis module 302 estimates the number of all sensor nodes that initiate the initial access request during the current time slot. The interception coefficient calculation module 303 calculates the interception coefficient of the current time slot. The random number comparison module 401 in the transit node response unit 4 generates corresponding random numbers for each sensor node. If the random number is less than the interception coefficient, the current sensor node is allowed to send the 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 best transit node. The configuration information reading module 501 in the access node connection unit 5 determines the best access node corresponding to each transit node according to the distance value. The node interception module 502 determines the priority of the transit node. The resource reservation module 503 sets a temporary access ID and a temporary access channel for the transit node. After the transit node in the access response module 504 sends a random access request to the best access node, the best access node returns the random access response information. After the transit node in the connection request sending module 505 sends a connection request to the best access node, the connection confirmation module 506 waits for the determination of the best access node and then transmits the data in the transit node to the best access node. The type division module 601 in the manhole cover status analysis unit 6 divides the manhole cover status. The time series data generation module 602 determines the time series data corresponding to the sensor. The sliding window setting module 603 sequentially traverses the time series data of each manhole cover using the sliding window. After the synthetic vector calculation module 604 analyzes the inclination synthetic vector and the vibration energy value, the state probability analysis module 605 obtains the probability values 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 text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0044] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent electronic information management system based on a smart city, comprising 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), characterized in that: A search particle determination unit (1). After the search particle determination unit (1) statistically determines the position coordinates of all manhole cover sensor nodes around the access node, it constructs multiple groups containing different initial center point position coordinates, sets search particles and observation particles for each group, determines the number of exploration particles and the vector value of the search particles, and then calculates the corresponding new vector value according to the vector values of the search particles in different groups. If the survival degree 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 times of the group are incremented by one. The correlation coefficient of the group is analyzed based on the survival degree of each search particle. A particle update unit (2). The particle update unit (2) sets the correlation coefficient of each observation particle. After determining the corresponding group of each observation particle according to the correlation coefficient of each group and the observation particle, it uses the observation particle to analyze the selection times of the group and the vector value of the search particle. If the selection times of the group are greater than the threshold and the survival degree 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, and the new search particle and selection times of the corresponding group are analyzed using the exploration particle. The iteration times are incremented by one. If the iteration times are greater than the preset maximum iteration times, the position coordinates of each initial center point in the group with the maximum current survival degree are output.

2. The intelligent electronic information management system based on a smart city according to claim 1, characterized in that: 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 preamble sequences of each access node. After statistically analyzing the position coordinates of all manhole cover sensor nodes around the access node, it constructs multiple groups, generates multiple groups of initial center points according to the position coordinates of the sensor nodes, the total number of preamble 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, determines the number of exploration particles, the maximum number of iterations, the upper and lower limit values of each particle, calculates the vector dimension of the search particles after using the data dimension corresponding to the position coordinates and the number of initial center points within the group, and analyzes the vector values of each dimension of the search particles currently according to the vector dimension and the position coordinate values of each initial center point in the group. The vector value calculation module (103) statistically analyzes the vector value o of the k-th dimension in the search particle O in the x-th unmarked group x and the vector value o of the k-th dimension in the search particle O in the y-th group xk After that, according to the vector value o of the k-th dimension in O y and the vector value o of the k-th dimension in O yk Calculate the new vector value o' of the k-th dimension in O x where x, y, and k represent serial numbers, and o' xk = o xk + rand(-1, 1)(o xk - o yk ). The particle marking module (104) uses the survival value analysis algorithm to calculate o xk and o', and thus obtains the survival degree of the search particle corresponding to o xk and the survival degree of the search particle corresponding to o'. If the survival degree of o' xk is greater than the survival degree of o xk , then use o' xk to replace the original o xk as the vector value o of the k-th dimension in the current search particle O xk , and mark the search particle O xk . Otherwise, increase the selection count by one and mark the search particle O x . Conversely, increase the selection count by one and mark the search particle O xk . If the survival degree of o' x is greater than the survival degree of o x , then use o' i to replace the original o i-3 as the vector value o of the k-th dimension in the current search particle O i-2 , and mark the search particle O i-1 . Otherwise, increase the selection count by one and mark the search particle O i-3 Make a mark, and the correlation coefficient analysis module (105) repeats the operation until all search particles are marked. After counting the survival degrees of search particles in different groups, accumulate all the survival degrees to obtain the total survival degree, and use the ratio between the survival degree of the search particle and the total survival degree as the correlation coefficient of the corresponding group.

3. An intelligent electronic information management system based on a smart city according to claim 1, wherein: The particle update unit (2) includes an observed particle analysis module (201), a survival degree calculation module (202), an optimal grouping selection module (203), and a position coordinate output module (204). The observed particle analysis module (201) calculates the correlation coefficient of each grouping, divides the correlation range of each grouping according to the magnitude of the correlation coefficient, and then uses a random function to generate corresponding correlation coefficients for each observed particle. If the current observed particle is within the correlation range of the grouping, the vector values of each dimension of the current observed particle are determined using the search particle vector value of the grouping. After the survival degree calculation module (202) calculates the vector value of the current observed particle, it replaces the vector value of the same dimension of the current observed particle with that of other search particles, and compares the survival degrees of the observed particles before and after replacement. If the survival degree after replacement is greater than that before replacement, the new search particle of the current grouping is determined according to the observed particle after replacement; otherwise, the selection count is incremented by one. The optimal grouping selection module (203) calculates the survival degree and selection count of each grouping, and selects the grouping with the maximum survival degree as the optimal grouping. If there is no grouping with a selection count greater than the threshold, no operation is performed; otherwise, it is determined whether the grouping with a selection count greater than the threshold is the optimal grouping. If the grouping is the optimal grouping, the current grouping is retained; if the grouping is not the optimal grouping, the current grouping is deleted. A new grouping is generated according to the position coordinates of the sensor nodes, the total number of preamble sequences, and the number of groupings, and the vector values of each dimension of the exploration particle are determined according to the position coordinates of the initial center points in the new grouping. The vector values of the same dimension of the current exploration particle are replaced with those of other search particles, and the survival degrees of the exploration particles before and after replacement are compared. If the survival degree after replacement is greater than that before replacement, the new search particle of the current grouping is determined according to the exploration particle after replacement; 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 optimal grouping are output; otherwise, the vector values of the search particles are continuously updated.

4. An intelligent electronic information management system based on a smart city according to claim 1, characterized in that: 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 according to the position coordinates of each initial center point, counts the distance values between each manhole cover sensor node and each relay node, determines the best relay node corresponding to each manhole cover sensor node according to the distance value, and after reading the configuration information of the best relay node, the sensor node that needs to send the initial access request waits to receive the interception coefficient broadcast by the best relay node. The sensor analysis module (302) determines that the current time interval is t i and the correction coefficient ω, and counts the previous three time intervals t i-3 , t i-2 , t i-1 the number of sensor nodes n i-3 , n i-2 , n i-1 not intercepted within and the corresponding interception coefficient θ i-3 , θ i-2 , θ i-1 After that, according to n i-3 , n i-2 , n i-1 and θ i-3 , θ i-2 , θ i-1 analyzes all the sensor nodes N i initiating the initial access request within the current time interval t i , where i represents a parameter. The interception coefficient calculation module (303) obtains the total number M of preambles in the current time interval t i , and calculates the interception coefficient θ i of t according to the total number M of preambles and all the sensor nodes N i that need to initiate the initial access request, where i , where 5. An intelligent electronic information management system based on a smart city 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). After the random number comparison module (401) receives the interception coefficient corresponding to the current time slot for all sensor nodes that need to initiate an initial access request, it generates a random number and 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 send an initial access request, and the priority of the current node is incremented by one. If the random number is less than the interception coefficient, the current sensor node is allowed to send an initial access request. After the preamble sequence allocation module (402) extracts all the allowed sensor nodes within the current time slot, the sensor node with the highest priority selects the preamble sequence first. After setting the corresponding preamble sequence for each sensor node according to the priority, it traverses the preamble sequences of each sensor node. If there are consistent preamble sequences between different sensor nodes, the repeated preamble sequences are allocated to the sensor node with a higher priority, and the remaining sensor nodes have their priorities incremented by one and are not allowed to send an initial access request within the current time slot. After the best relay node receives the preamble sequence and the initial access request, the data transmission module (403) sends an adjustment command to the sensor node. After the sensor node returns the response information, the collected data is transmitted to the best relay node.

6. An intelligent electronic information management system based on a smart city 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 values between each relay node and each access node, determines the optimal access node corresponding to each relay node according to the distance values, and after reading the configuration information of the optimal relay node, the relay nodes that need to send initial resource reservation requests wait to receive the interception coefficient broadcast by the optimal relay node. The node interception module (502) determines whether each relay node is allowed during the current time slot according to the interception coefficient. If the relay node is allowed, it selects a preamble sequence for the relay node according to the priority, and sends the preamble sequence and the resource reservation request to the optimal access node. If not allowed, it adjusts the priority of the relay node and continues to wait for the interception coefficient. After the resource reservation module (503) the optimal access node receives the preamble sequence and the resource reservation request from the relay node, it sets a temporary access ID and a temporary access channel for the relay node. The access response module (504) the relay node sends a random access request to the optimal access node according to the temporary access ID and the temporary access channel. After the optimal access node receives the random access request, it returns a random access response message to the relay node. After the connection request sending module (505) the relay node receives the random access response message, it determines the connection ID and the configuration information of the current relay node according to the random access response message, and then sends a connection request to the optimal access node. After the connection confirmation module (506) the optimal access node receives the connection request, it returns a setting response message to the relay node. After the sensor node receives the setting response message, it constructs a control channel and a user channel according to the setting response message, sends a connection completion message to the optimal access node, and after the optimal access node returns a connection confirmation message, it transmits the data in the relay node to the optimal access node.

7. An intelligent electronic information management system based on a smart city 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, namely, normal status, inclined status, water accumulation status, and vacancy status. The time series data generation module (602) combines the sensor data and the recording time of each manhole cover after calculating the position coordinates, sensor data, and recording time of different manhole covers according to the node data received by the access node, so as to generate corresponding time series data. After the sliding window setting module (603) sets the size and moving step of the sliding window, 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 moving step until all the time series data are traversed.

8. An intelligent electronic information management system based on a smart city according to claim 7, characterized in that: The manhole cover status analysis unit (6) further includes a composite vector calculation module (604), a state probability analysis module (605), and a state output module (606). The composite vector calculation module (604) calculates a dip angle composite vector based on the manhole cover tilt angle data included in each manhole cover sensor data, and analyzes the vibration energy value by using the manhole cover position offset data and vibration frequency data included in the manhole cover sensor data. The state probability analysis module (605) combines the time series data, sliding set, dip angle composite vector, and vibration energy value of each manhole cover to obtain the time series feature vector of each manhole cover, transmits the time series feature vector of the manhole cover to the LSTM model for analysis, sets corresponding weight coefficients for different state types, constructs a weight vector by using all the weight coefficients, analyzes the weight vector and the time series feature vector by using a state prediction algorithm, and obtains the probability values 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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