A temporary wireless bridge adjustment method and system for infrastructure construction site

Abstract

The present invention relates to the technical field of network equipment, and discloses a temporary wireless bridge adjustment method and system for infrastructure construction sites. The method comprises the following steps: collecting bridge adjustment information, the bridge adjustment information comprises status information of wireless bridge equipment, construction staff information, infrastructure construction site information and construction equipment information, the status information of wireless bridge equipment comprises wireless bridge equipment status and coverage range of wireless bridge equipment, wherein the coverage range of wireless bridge equipment comprises angle and height of wireless bridge equipment; the present invention enables wireless bridge equipment to automatically align antennas and adjust gain according to real-time environment through intelligent adjustment model, thereby avoiding errors caused by manual adjustment, and based on data-driven analysis, identifies and adapts to terrain changes and construction interference in complex infrastructure construction sites, and combines them into resource allocation decision-making, so as to improve efficient construction and adjustment of wireless bridges and ensure maximum signal strength and quality of communication.

Description

Technical Field

[0001] The present invention relates to the field of network equipment construction, and more specifically, to a temporary wireless bridge adjustment method and system for infrastructure construction sites. Background Art

[0002] A wireless bridge is a network device used to establish a wireless connection between two or more networks; it is often used to provide a network link across physical barriers (such as buildings, hills, or bodies of water) or when laying cables is impractical; a wireless bridge can extend the signal of one local area network (LAN) to another LAN, allowing the two networks to function as if they were the same network.

[0003] In complex infrastructure site environments, traditional network cabling is difficult due to numerous terrain obstacles and construction interference, and temporary wireless bridges need to be built to ensure that on-site staff and equipment can obtain critical information in a timely manner. When installing wireless bridges, traditional construction cannot correctly align the antenna and adjust the gain, and cannot guarantee optimal signal strength and quality. Summary of the invention

[0004] The present invention provides a temporary wireless bridge adjustment method and system for infrastructure construction sites, which solves the technical problem in the related art that the traditional construction cannot correctly align the antenna and adjust the gain, and cannot ensure the best signal strength and quality.

[0005] The present invention provides a temporary wireless bridge adjustment method for infrastructure construction sites, comprising the following steps: S100, collecting bridge adjustment information, the bridge adjustment information including status information of wireless bridge equipment, construction staff information, infrastructure construction site information and construction equipment information, the status information of wireless bridge equipment including wireless bridge equipment status and coverage of wireless bridge equipment, wherein the coverage of wireless bridge equipment includes angle and height of wireless bridge equipment; S200, generating No. 1 two-dimensional structural data based on the bridge adjustment information, the No. 1 two-dimensional structural data including No. 1 data matrix and No. 1 relationship matrix, a unit of the No. 1 data matrix represents No. 1 one-dimensional structural data of a wireless bridge equipment; the element of the No. 1 relationship matrix in the i-th row and the j-th column represents the association between the wireless bridge equipment represented by the i-th unit of the No. 1 data matrix and the j-th unit, if there is an association, the value of the element of the No. 1 relationship matrix is ​​1, otherwise it is 0; the association between wireless bridge equipment means that: the communication properties of the wireless bridge equipment are the same and the wireless bridge equipment is located at the same infrastructure construction site; the No. 1 one-dimensional structural data includes n According to the data items sorted by time, the tth data item represents the bridge adjustment information collected at the tth moment; S300, input the structural data into the adjustment model, the adjustment model includes a first feature fusion layer, a first hidden layer, a second hidden layer and an output layer, the one-dimensional structural data No. 1 is input into the first hidden layer, and the first hidden state of each node is output to the second hidden layer; the initialization information of the adjacent wireless bridge devices of each wireless bridge device at the current moment is input into the first feature fusion layer for splicing to obtain the first fusion state, which is input into the second hidden layer, the initialization information includes the wireless bridge device information of the wireless bridge device and its initial local policy, the second hidden layer outputs the second hidden state to the output layer, and the output layer outputs the local policy; the adjustment model is run twice, the initial local policy in the first run is represented by a vector with a component of 0, the output layer outputs the initial local policy, and the second run outputs the executed local policy; the executed local policy output by the adjustment model is parsed, and the configuration information of the wireless bridge device is adjusted, and the configuration information includes the data exchange angle of the wireless bridge device and the data exchange height of the wireless bridge device.

[0006] Furthermore, S300 also includes configuring a distributed model for each wireless bridge device, inputting structural data into the distributed model, generating an initial local strategy through multi-layer processing; and receiving the initial local strategy output by the model of the adjacent wireless bridge device through a short-range radio communication link.

[0007] Furthermore, the calculation formula of the first hidden layer is as follows: ; ; ; ;in, , , represents the weight parameter, , , represents the bias parameter, represents the dot product, where represents the tth sequence unit of the sequence data, and Represent the t-th and t-1-th hidden states respectively, and the n-th sequence hidden state is used as the node feature of the node, n≥t≥1, n represents the total number of sequence units of the sequence data, and t=1 , tanh is the hyperbolic tangent function, represents the sigmoid function; the sequence units are encoded into features through feature engineering before entering the second hidden layer.

[0008] Furthermore, the calculation formula of the first feature fusion layer is as follows: the device information of the u-th neighboring device at the current moment, the initial local strategy of the u-th neighboring device; ; ;in, represents the vth first fusion state, represents the concatenation function, represents the summation weight parameter, represents the summation bias parameter, represents the set of nodes that have edges with the vth node, Indicates the device information of the u-th neighboring device, represents the initial local policy of the u-th neighbor, Indicates the fusion information of the device information of the u-th neighboring device and the initial local policy of the u-th neighboring device.

[0009] Furthermore, the calculation formula of the second hidden layer is as follows: ;in, represents the hidden state of the i-th node, represents the aggregation coefficient of the i-th node, represents the set of nodes that have edges with the i-th node, represents the activation function, represents the state weight coefficient; ; ; ; ; ;in, and denote the i-th and j-th first hidden states of the n-th node, respectively. and denote the i-th and j-th first fusion states respectively, and Represent the node features of the i-th and j-th nodes respectively, and Represent the linear transformation features of the i-th and j-th nodes respectively, represents the aggregation weight coefficient, represents the splicing weight coefficient, T represents transposition, represents an exponential function with a natural constant as the base, LeakyRelu represents a modified linear unit function, Represents the set of nodes that have edges with the i-th node.

[0010] Furthermore, the calculation formula of the output layer is as follows: ;in, A component of the control vector represents the probability value of a local strategy. The set of all local strategies is called the local strategy space, which contains all possible local strategies that may be selected. represents the hidden state of the vth node, Indicates the concatenation of the hidden states of all nodes, N represents the set of nodes related to the recognition results, is the weight parameter, is the bias parameter, Represents a sigmoid function.

[0011] Furthermore, the training steps of the adjustment model include: step 301, initializing the parameters of the adjustment model; step 302, observing the bridge adjustment information at time t , the local policy executed at time t , Bridge adjustment information at time e+1 , execute local policy Rewards ; Step 303, then calculate the strategy error: ;in represents the policy error at time e, represents the discount factor, , Indicates adjustment of model input The maximum probability value in the first output vector output when Indicates adjustment of model input The first output vector outputted when the local strategy The probability value of ;in represents the user satisfaction value covered by the i-th wireless bridge device, Indicates the coverage of the infrastructure site. , They represent the actual communication strength and expected communication strength covered by the i-th wireless bridge device, , They represent the weight coefficients of coverage range and communication strength difference respectively. The sum of the two is 1. The default values ​​are 0.4 and 0.6.

[0012] Step 304: Update the adjustment model. The updated formula is as follows: ;in , represents the step size of deep learning, Indicates transfer update; step 305, iterate steps 302-304 until the adjustment model converges or the number of iterations reaches the set value, the default value of which is 30.

[0013] Furthermore, the local strategy in the local strategy space is obtained by randomly combining all possible values ​​of the control parameters. For data transmission resources with continuous values, the local strategy is obtained by discretizing the mean into point values ​​and then combining them with the optional values ​​of other data transmission resources.

[0014] The present invention also provides a temporary wireless bridge adjustment system for infrastructure construction sites, which is used to execute the steps in the aforementioned temporary wireless bridge adjustment method for infrastructure construction sites, including: a data acquisition module, which regularly collects status information, construction staff information and construction equipment information of wireless bridge equipment; a data generation module, which generates structured data from the collected information; a data preprocessing module, which preprocesses the structured data to ensure a unified input format; an adjustment model module, which includes a first feature fusion layer, a first hidden layer, a second hidden layer and an output layer, and outputs local policy recommendations to adjust the settings of the bridge; a policy execution module, which executes the local policy output by the adjustment model, and adjusts the angle and height parameters of the wireless bridge equipment according to the output local policy.

[0015] The present invention also provides a storage medium storing non-temporary computer-readable instructions for executing the steps in the aforementioned method for adjusting a temporary wireless bridge for use at an infrastructure site.

[0016] The beneficial effects of the present invention are as follows: the present invention uses an intelligent adjustment model to enable the wireless bridge device to automatically align the antenna and adjust the gain according to the real-time environment, avoiding errors caused by manual adjustment. Based on data-driven analysis, the system can quickly identify and adapt to terrain changes and construction interference in complex infrastructure sites, and at the same time be incorporated into resource allocation decisions to improve the efficient construction and adjustment of wireless bridges, ensuring that the signal strength and quality of communications are maximized. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention is a flowchart of a temporary wireless bridge adjustment method for infrastructure construction sites.

[0018] Figure 2The present invention is a structural block diagram of a temporary wireless bridge adjustment system for infrastructure construction sites.

[0019] In the figure: 101, data acquisition module; 102, data generation module; 103, data preprocessing module; 104, model adjustment module; 105, strategy execution module. DETAILED DESCRIPTION

[0020] The subject matter described herein will now be discussed with reference to example embodiments; it should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification; various examples may omit, replace or add various processes or components as needed; in addition, features described relative to some examples may also be combined in other examples.

[0021] See also Figure 1 As shown, a temporary wireless bridge adjustment method for infrastructure construction site includes the following steps: S100, collecting bridge adjustment information, the bridge adjustment information includes the status information of the wireless bridge device, the construction staff information, the infrastructure construction site information and the construction equipment information, the status information of the wireless bridge device includes the wireless bridge device status (available equipment and damaged equipment) and the coverage range of the wireless bridge device, wherein the coverage range of the wireless bridge device includes the angle and height of the wireless bridge device; collecting the bridge adjustment information once at the same time interval; wherein the collection of the status information of the wireless bridge device is to use sensors installed on the wireless bridge device, such as angle sensors and height sensors, to collect the current status of the device in real time, including the angle and height.

[0022] At the same time, the wireless bridge device has its own status detection function, which can regularly check its own working status, identify whether the device is available or damaged, and automatically record status information.

[0023] The collection of construction staff information of the wireless bridge device is accomplished by the construction staff manually inputting or confirming the information of relevant personnel and construction equipment through a mobile device or interface.

[0024] At the same time, the data acquisition terminal deployed on site receives sensor data and staff input, and uniformly records equipment status information.

[0025] At the same time, construction equipment information can use Wi-Fi or other wireless communication technologies to transmit collected information to a unified background system in real time to ensure the timeliness and accuracy of the information.

[0026] S200, generating structural data from the collected bridge adjustment information; generating No. 1 two-dimensional structural data based on the bridge adjustment information, the No. 1 two-dimensional structural data includes No. 1 data matrix and No. 1 relationship matrix, a unit of the No. 1 data matrix represents No. 1 one-dimensional structural data of a wireless bridge device; the element of the i-th row and j-th column of the No. 1 relationship matrix represents the association between the i-th unit and the j-th unit of the No. 1 data matrix, if there is an association, the element value of the No. 1 relationship matrix is ​​1, otherwise it is 0; the association between the wireless bridge devices means that: the communication properties of the wireless bridge devices are the same and the wireless bridge devices are located at the same infrastructure site; it should be supplemented that each wireless bridge device is equipped with corresponding construction staff and construction equipment, and the construction staff information and construction equipment information are spliced ​​into the status information of the wireless bridge device; the No. 1 one-dimensional structural data includes n data items sorted by time, and the t-th data item represents the bridge adjustment information collected at the t-th moment.

[0027] S300, input the structural data into the adjustment model, the adjustment model includes a first feature fusion layer, a first hidden layer, a second hidden layer and an output layer, input the one-dimensional structural data No. 1 into the first hidden layer, and output the first hidden state of each node to the second hidden layer; input the initialization information of the adjacent wireless bridge devices of each wireless bridge device at the current moment into the first feature fusion layer for splicing to obtain the first fusion state, and input it into the second hidden layer, the initialization information includes the wireless bridge device information of the wireless bridge device and its initial local policy; the second hidden layer outputs the second hidden state to the output layer, and the output layer outputs the local policy; the adjustment model runs twice, the initial local policy in the first run is represented by a vector with a component of 0, and the output layer outputs the initial local policy; the executed local policy is output when the model is run for the second time.

[0028] In one embodiment of the present invention, a distributed model is configured for each wireless bridge device, and the structural data is input into the distributed model.

[0029] In one embodiment of the present invention, an initial local policy output by a model of a neighboring wireless bridge device is received via a short-range radio communication link.

[0030] Analyze and adjust the execution local strategy of the model output, adjust the configuration information of the wireless bridge device, the configuration information includes the data exchange angle of the wireless bridge device and the data exchange height of the wireless bridge device; execute the local strategy including but not limited to the position, height and angle of the wireless bridge device to optimize the communication coverage and maximize the coverage.

[0031] In one embodiment of the present invention, adjacent wireless bridge devices can use short-range radio communication technology (such as Wi-Fi, Zigbee, Bluetooth, etc.) to directly exchange signals and data. The above-mentioned short-range radio communication link is suitable for situations where the distance between devices is relatively close, and factors such as frequency band, power and interference need to be considered; it should be supplemented that the wireless bridge device has the function of adjusting the height, angle and coverage of the wireless bridge device through parameters. The wireless bridge device adjusts the height and angle of the wireless bridge device. The coverage of the wireless bridge device is achieved through a mechanical structure. For example, the height is achieved through a lifting mechanism, and the angle is achieved through a rotating mechanism. The coverage can also be achieved with the assistance of a deflection mechanism to adjust the elevation angle and other parameters of the communication transmission of the wireless bridge device.

[0032] In one embodiment of the present invention, the calculation formula of the first hidden layer is as follows: ; ; ; ;in, , , represents the weight parameter, , , represents the bias parameter, represents the dot product, where represents the tth sequence unit of the sequence data, and Represent the t-th and t-1-th hidden states respectively, and the n-th sequence hidden state is used as the node feature of the node, n≥t≥1, n represents the total number of sequence units of the sequence data, and t=1 , tanh is the hyperbolic tangent function, Represents a sigmoid function.

[0033] In one embodiment of the present invention, the sequence units are encoded into features through feature engineering before being input into the second hidden layer.

[0034] In one embodiment of the present invention, the calculation formula of the first feature fusion layer is as follows: device information of the u-th neighboring device at the current moment, initial local strategy of the u-th neighboring device; ; ;in, represents the vth first fusion state, represents the concatenation function, represents the summation weight parameter, represents the summation bias parameter, represents the set of nodes that have edges with the vth node, Indicates the device information of the u-th neighboring device, represents the initial local policy of the u-th neighbor, Indicates the fusion information of the device information of the u-th neighboring device and the initial local policy of the u-th neighboring device.

[0035] In one embodiment of the present invention, the calculation formula of the second hidden layer is as follows: ;in, represents the hidden state of the i-th node, represents the aggregation coefficient of the i-th node, represents the set of nodes that have edges with the i-th node, represents the activation function, represents the state weight coefficient; ; ; ; ; ;in, and denote the i-th and j-th first hidden states of the n-th node, respectively. and denote the i-th and j-th first fusion states respectively, and Represent the node features of the i-th and j-th nodes respectively, and Represent the linear transformation features of the i-th and j-th nodes respectively, represents the aggregation weight coefficient, represents the splicing weight coefficient, T represents transposition, represents an exponential function with a natural constant as the base, LeakyRelu represents a modified linear unit function, Represents the set of nodes that have edges with the i-th node.

[0036] In one embodiment of the present invention, the calculation formula of the output layer is as follows: ;in, A component of the control vector represents the probability value of a local strategy. The set of all local strategies is called the local strategy space, which contains all possible local strategies that may be selected. represents the hidden state of the vth node, Indicates the concatenation of the hidden states of all nodes, N represents the set of nodes related to the recognition results, is the weight parameter, is the bias parameter, Represents a sigmoid function.

[0037] In one embodiment of the present invention, the local policy is represented as a matrix, and the value of the element in the i-th row and j-th column of the matrix represents the data transmission resources allocated by the i-th wireless bridge device to the j-th user. If the value is 0, it means that the i-th wireless bridge device has no connection with the j-th user.

[0038] The user is the target object served by the wireless bridge device.

[0039] Among them, the reasonable allocation of data transmission resources directly determines the data transmission time and throughput between wireless bridge equipment and users. The allocation of data transmission resources is directly related to key performance indicators such as stability and signal strength, and is the main reference for evaluating the utilization efficiency of bridge data and information transmission.

[0040] In one embodiment of the present invention, the steps of training the adjustment model include: step 301, initializing the parameters of the adjustment model; step 302, observing the bridge adjustment information at time t , the local policy executed at time t , Bridge adjustment information at time e+1 , execute local policy Rewards ; Step 303, then calculate the strategy error: ;in represents the policy error at time e, represents the discount factor, , Indicates adjustment of model input The maximum probability value in the first output vector output when Indicates adjustment of model input The first output vector outputted when the local strategy The probability value of ;in represents the user satisfaction value covered by the i-th wireless bridge device, Indicates the coverage of the infrastructure site. , They represent the actual communication strength and expected communication strength covered by the i-th wireless bridge device, , They represent the weight coefficients of coverage range and communication strength difference respectively. The sum of the two is 1. The default values ​​are 0.4 and 0.6.

[0041] Step 304: Update the adjustment model. The updated formula is as follows: ; , represents the step size of deep learning, Indicates transfer update; step 305, iterate steps 302-304 until the adjustment model converges or the number of iterations reaches the set value, the default value of which is 30.

[0042] In one embodiment of the present invention, the local policies in the local policy space are obtained by randomly combining possible values ​​of all control parameters.

[0043] For data transmission resources with continuous values, the local strategy is obtained by discretizing the mean into point values ​​and then combining them with optional values ​​of other data transmission resources.

[0044] like Figure 2 As shown, at least one embodiment of the present disclosure provides a temporary wireless bridge adjustment system for infrastructure sites, including: a data acquisition module 101: regularly collecting status information of wireless bridge equipment, construction staff information and construction equipment information; a data generation module 102: generating structured data from the collected information; a data preprocessing module 103: preprocessing the structured data to ensure a unified input format; an adjustment model module 104: including a first feature fusion layer, a first hidden layer, a second hidden layer and an output layer, outputting local policy recommendations to adjust the settings of the bridge; a policy execution module 105: executing the local policy output by the adjustment model, and adjusting the angle, height and other parameters of the wireless bridge device according to the output local policy.

[0045] At least one embodiment of the present disclosure provides a storage medium storing non-transitory computer-readable instructions for executing the steps in the aforementioned method for adjusting a temporary wireless bridge for use at an infrastructure site.

[0046] The computer program may be stored / distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. Any reference signs in the claims should not be construed as limiting the scope.

[0047] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are within the protection of the present embodiment.

Claims

1. A method for adjusting a temporary wireless bridge at a construction site, characterized in that: The following steps are involved: Step 100, collecting bridge adjustment information, the bridge adjustment information includes status information of wireless bridge equipment, construction staff information, infrastructure site information and construction equipment information, the status information of wireless bridge equipment includes wireless bridge equipment status and coverage of wireless bridge equipment, wherein the coverage of wireless bridge equipment includes angle and height of wireless bridge equipment; Step 200, generating structure data from the collected bridge adjustment information; Step 300, inputting the structure data into the adjustment model, the adjustment model includes a first feature fusion layer, a first hidden layer, a second hidden layer and an output layer, inputting the one-dimensional structure data No. 1 into the first hidden layer, and outputting the first hidden state of each node to the second hidden layer; Input the initialization information of the adjacent wireless bridge devices of each wireless bridge device at the current moment into the first feature fusion layer for splicing to obtain the first fusion state, and input it into the second hidden layer, the initialization information includes the wireless bridge device information of the wireless bridge device and its initial local strategy, the second hidden layer outputs the second hidden state to the output layer, and the output layer outputs the local strategy; The adjustment model is run twice. In the first run, the initial local policy is represented by a vector with a component of 0, and the output layer outputs the initial local policy. In the second run, the executed local policy is output. Analyze and adjust the execution local policy of the model output, and adjust the configuration information of the wireless bridge device, the configuration information includes the data exchange angle of the wireless bridge device and the data exchange height of the wireless bridge device; In step S200, the specific content of generating structure data from the collected bridge adjustment information is as follows: Generate No. 1 two-dimensional structure data based on the bridge adjustment information, the No. 1 two-dimensional structure data includes No. 1 data matrix and No. 1 relationship matrix, a unit of the No. 1 data matrix represents No. 1 one-dimensional structure data of a wireless bridge device; the element of the No. 1 relationship matrix in the i-th row and the j-th column represents the association between the wireless bridge device represented by the i-th unit of the No. 1 data matrix and the j-th unit, if there is an association, the element value of the No. 1 relationship matrix is ​​1, otherwise it is 0; The association between wireless bridge devices means that: the communication properties of the wireless bridge devices are the same and the wireless bridge devices are located at the same infrastructure site; The one-dimensional structure data No. 1 includes n data items sorted by time, and the t-th data item represents the bridge adjustment information collected at the t-th moment; In step 300, it also includes configuring a distributed model for each wireless bridge device, inputting the structure data into the distributed model, and generating an initial local policy through multi-layer processing; receiving an initial local policy outputted by a model of an adjacent wireless bridge device via a short-range radio communication link; The local policy is represented as a matrix. The value of the element in the i-th row and j-th column of the matrix represents the data transmission resource allocated by the i-th wireless bridge device to the j-th user. If the value is 0, it means that the i-th wireless bridge device has no connection with the j-th user. The user is the target object served by the wireless bridge device; Among them, the reasonable allocation of data transmission resources directly determines the data transmission time and throughput between the wireless bridge device and the user; The calculation formula of the first hidden layer is as follows: ； ； ； ； in, represents the weight parameter, represents the bias parameter, represents the dot product, where represents the tth sequence unit of the sequence data, and Represent the t-th and t-1-th hidden states respectively, and the n-th sequence hidden state is used as the node feature of the node, n≥t≥1, n represents the total number of sequence units of the sequence data, and t=1 , tanh is the hyperbolic tangent function, represents the sigmoid function; The sequence units are encoded into features through feature engineering before being input into the second hidden layer; The calculation formula of the first feature fusion layer is as follows: The device information of the u-th neighboring device at the current moment, and the initial local policy of the u-th neighboring device; ； ； in, represents the vth first fusion state, represents the concatenation function, represents the summation weight parameter, represents the summation bias parameter, represents the set of nodes that have edges with the vth node, Indicates the device information of the u-th neighboring device, represents the initial local policy of the u-th neighbor, The fusion information representing the device information of the u-th neighboring device and the initial local policy of the u-th neighboring device; The calculation formula of the second hidden layer is as follows: ； in, represents the hidden state of the i-th node, represents the aggregation coefficient of the i-th node, represents the set of nodes that have edges with the i-th node, represents the activation function, represents the state weight coefficient; ； ； ； ； ； in, and denote the i-th and j-th first hidden states of the n-th node, respectively. and denote the i-th and j-th first fusion states respectively, and Represent the node features of the i-th and j-th nodes respectively, and Represent the linear transformation features of the i-th and j-th nodes respectively, represents the aggregation weight coefficient, represents the splicing weight coefficient, T represents transposition, represents an exponential function with a natural constant as the base, LeakyRelu represents a modified linear unit function, Represents the set of nodes that have edges with the i-th node; The calculation formula of the output layer is as follows: ； in, A component of the control vector represents the probability value of a local strategy. The set of all local strategies is called the local strategy space, which contains all possible local strategies that may be selected. represents the hidden state of the vth node, Indicates the concatenation of the hidden states of all nodes, N represents the set of nodes related to the recognition results, is the weight parameter, is the bias parameter, Represents a sigmoid function.

2. A method for adjusting a temporary wireless bridge for infrastructure construction site according to claim 1, characterized in that: The steps to adjust the model training include: Step 101, initializing and adjusting the parameters of the model; Step 102: Observe the bridge adjustment information at time t , the local policy executed at time t , Bridge adjustment information at time e+1 , execute local policy Rewards ; Step 103, then calculate the policy error: ； in represents the policy error at time e, represents the discount factor, , Indicates adjustment of model input The maximum probability value in the first output vector output when Indicates adjustment of model input The first output vector outputted when the local strategy The probability value of ； in represents the user satisfaction value covered by the i-th wireless bridge device, Indicates the coverage of the infrastructure site. , They represent the actual communication strength and expected communication strength covered by the i-th wireless bridge device, , Respectively represent the weight coefficients of coverage and communication strength difference, the sum of the two is 1, and the default values ​​are 0.4 and 0.6; Step 104, update the adjustment model, the updated formula is as follows: ； in , represents the step size of deep learning, Indicates delivery update; Step 105, iterate steps 102-104 until the adjustment model converges or the number of iterations reaches a set value, the default value of which is 30.

3. A temporary wireless bridge adjustment method for infrastructure construction site according to claim 2, characterized in that: The local policies in the local policy space are obtained by random combinations of all possible values ​​of the control parameters; For data transmission resources with continuous values, the mean is discretized into point values, and then combined with optional values ​​of other data transmission resources to obtain the local strategy.

4. A temporary wireless bridge adjustment system for infrastructure construction site, characterized in that: The method for performing one or more steps of a temporary wireless bridge adjustment method for infrastructure construction site as claimed in any one of claims 1 to 3 comprises: Data collection module, which regularly collects status information of wireless bridge equipment, construction staff information and construction equipment information; The data generation module generates structured data from the collected information; Data preprocessing module, which preprocesses structured data to ensure uniform input format; An adjustment model module, including a first feature fusion layer, a first hidden layer, a second hidden layer, and an output layer, outputs a suggestion of a local strategy to adjust the setting of the bridge; The policy execution module executes the local policy output by the adjustment model and adjusts the angle and height parameters of the wireless bridge device according to the output local policy.

5. A storage medium, characterized in that: Non-transitory computer-readable instructions are stored therein for executing one or more steps in a temporary wireless bridge adjustment method for an infrastructure site as claimed in any one of claims 1-3.

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