Miniature intelligent bridge building machine system and control method

Abstract

This invention relates to the field of intelligent control technology for bridge-building machines, specifically to a micro intelligent bridge-building machine system and control method. The system mainly includes diagnosing the target equipment using a fault diagnosis model, outputting the current warning level using an early warning model, and configuring the system to, upon receiving a control signal from a user terminal requesting modification of the first equipment's operating parameters, acquire the user terminal's job level information. Based on the warning level and the user terminal's job level information, it determines whether to proceed with modifying the first equipment's operating parameters. Through this scheme, after a fault warning is issued and the modification of the first equipment's operating parameters is accepted, multiple levels of user terminal information are verified, and encrypted transmission is performed. This significantly prevents malicious tampering and reduces the overall project risk.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for bridge building machines, and more specifically, to a micro intelligent bridge building machine system and control method. Background Technology

[0002] In the field of modern bridge engineering, with the rapid development of technology, intelligent cantilever bridge-building machines have emerged as an advanced bridge construction equipment. They integrate cutting-edge technologies from multiple disciplines such as mechanical engineering, automation control, and information technology, bringing numerous advantages to bridge construction, including high efficiency, precision, and safety. At the same time, they have profoundly impacted and transformed traditional bridge construction concepts and methods. A thorough understanding of the technical characteristics, current applications, and future development trends of intelligent cantilever bridge-building machines is of paramount importance for promoting the advancement of bridge engineering technology.

[0003] However, current intelligent bridge-building machine control systems generally only provide fault warnings and send them to the corresponding processing terminal, without further follow-up. Since bridge construction is a large-scale project involving a vast amount of safety engineering, more careful handling is required when controlling equipment with fault warnings. Existing technology directly modifies the control terminal without other encryption and multi-level verification measures, which may result in the current bridge-building machines still posing significant risks. Summary of the Invention

[0004] The purpose of this invention is to provide a miniature intelligent bridge-building machine system and control method to solve the above-mentioned problems in the prior art.

[0005] This invention is achieved through the following technical solution:

[0006] In a first aspect, the present invention provides a control method for a miniature intelligent bridge-building machine system, comprising:

[0007] Acquire the basic data collected by the sensors in each target device of the current bridge building machine, establish a fault diagnosis model, diagnose the target devices through the fault diagnosis model, and output the first device with fault risk and the second device without fault risk.

[0008] Establish an early warning model, and output the current early warning level based on the number of the first device, the number of the second device, and the basic data of the first device.

[0009] When a control signal to modify the operating parameters of the first device is received from the user terminal, the user terminal's job level information is obtained. Based on the warning level and the user terminal's job level information, it is determined whether to modify the operating parameters of the first device.

[0010] If executed, the operating parameters of the first device are encrypted and sent to the task distribution end. The task distribution end determines whether the number of first devices whose operating parameters need to be modified is greater than 1. If it is greater than 1, a fault evaluation model for the first device is established, and the fault index of the first device is output through the fault evaluation model. The operating parameters of the first device are distributed to the corresponding first devices in order of the size of the fault index. If it is not greater than 1, the operating parameters of the first device are sent to the corresponding first device.

[0011] If not executed, a malicious tampering alarm signal will be sent.

[0012] Preferably, the establishment of the fault diagnosis model includes:

[0013] The historical data collected by the sensors in each target device of the bridge building machine is obtained, and the historical data is divided into test set and training set, and the data is normalized.

[0014] Based on the training set, a genetic algorithm is used to select the optimal fault features and obtain the optimal fault feature subset.

[0015] Construct a neural network model, train the neural network model with the optimal subset of fault features, test the trained neural network with a test set and update the learning rate, and output the optimal trained neural network model.

[0016] The optimal neural network model is used to diagnose faults in the basic data collected by sensors in each target device of the current bridge building machine, and the diagnostic results are output.

[0017] Preferably, the selection of optimal fault features using a genetic algorithm includes:

[0018] Several binary vectors are randomly generated and used as parent vectors, each of which represents a random set of equipment fault features;

[0019] The fitness function is used to evaluate individuals in the population, and tournament selection is performed based on the fitness function value. The failure feature set of the selected parents is cross-crossed and mutated according to a preset ratio to generate the failure feature set of the offspring.

[0020] After mutation, the best individual is compared with the best individual in history, and the worst individual is directly copied and replaced to obtain a new set of fault features for the offspring.

[0021] The iteration terminates when the genetic generation number is N; otherwise, it returns to the step of evaluating the population using the fitness function and continues the calculation until the optimal subset of fault features is found.

[0022] Preferably, the construction of the neural network model includes:

[0023] An input layer, a hidden layer, and an output layer are constructed. The input layer includes a hydraulic cylinder pressure data neuron unit, a temperature data neuron unit, a walking device motor speed data neuron unit, and a prestress tension data neuron unit. The output layer includes a confidence output neuron.

[0024] The input layer includes:

[0025]

[0026] The output layer includes:

[0027]

[0028] when When the current device has no risk of failure, output the result. When this occurs, output the result indicating that the current device is at risk of failure;

[0029] In the formula, For the normalized base data, For the first Layer input layer to the first The connection weights of the hidden layers, To input the number of layers, The threshold value for hidden layer nodes. For confidence level, For the first Hidden layer to the first The connection weights of the output layer, The threshold value for the output layer nodes. For the first The output of the hidden layer, This is the output layer number.

[0030] Preferably, the establishment of the early warning model includes:

[0031]

[0032]

[0033] when When this happens, the current warning level is not output, and a signal of no warning is sent simultaneously. At that time, a Level 1 warning will be issued. When a Level II warning is issued, the severity of the Level I warning is less than that of the Level II warning.

[0034] In the formula, As an early warning index, The number of the first equipment. For the number of second devices, For the first Confidence level of the device For the first Data fluctuation rate of this type of equipment For the first The first type of equipment Basic data values, For the first Type of equipment Historical averages of basic data To determine the threshold, The total number of types of basic data values.

[0035] Preferably, determining whether to modify the operating parameters of the first device based on the warning level and the user's job level information includes:

[0036] The job level system is set up, including Level 1, Level 2, and Level 3, with the scope of management authority increasing sequentially from Level 1 to Level 3.

[0037] If the current warning level is Level 1, and the user terminal sending the control signal is at Level 2 or Level 3, then the operating parameters of the first device are modified. If the current user terminal is at Level 1, then a permission notification is sent to the user terminal at Level 2. If a permission signal is received from the user terminal at Level 2, then the operating parameters of the first device are modified. If a permission rejection signal is received from the user terminal at Level 2, then the current permission notification is sent to the user terminal at Level 3. If a permission signal is received from the user terminal at Level 3, then the operating parameters of the first device are modified. If a permission rejection signal is received from the user terminal at Level 3, then the modification of the operating parameters of the first device is terminated.

[0038] Preferred options also include:

[0039] If the current warning level is Level 2 and the user terminal sending the control signal is Level 3, then the operation parameters of the first device will be modified. If the current user terminal is Level 1 or Level 2, then a permission notification will be sent to the Level 3 user terminal. If a permission signal is received from the Level 3 user terminal, then the operation parameters of the first device will be modified. If a permission rejection signal is received from the Level 3 user terminal, then the modification of the operation parameters of the first device will be terminated.

[0040] Set a reception time threshold for the control signal. If the task distribution terminal still does not receive the control signal after the reception time threshold is exceeded, it will receive the control signal from the user terminal of the first level or the second level.

[0041] Preferably, encrypting the operating parameters of the first device includes:

[0042] The operating parameters of the first device are encrypted using SM4-CTR at the edge node, and an HMAC-SM3 checksum is attached.

[0043] The data is transmitted to the cloud via a 5G private network, and then decrypted in the cloud using a KMS managed key and stored in a time-series database.

[0044] Upon receiving the call instruction, the encrypted data is sent to the task distribution terminal, and the key is sent to the user terminal. After receiving feedback information from the task distribution terminal that the first device's operating parameters have been received, the user terminal sends the key to the task distribution terminal to decrypt the encrypted first device's operating parameters.

[0045] Preferably, the first equipment fault evaluation model includes:

[0046]

[0047] In the formula, This is the failure index.

[0048] Secondly, the present invention also provides a control system for a miniature intelligent bridge-building machine system, comprising:

[0049] The early warning module is configured to acquire basic data collected by sensors in each target device of the current bridge building machine, establish a fault diagnosis model, diagnose the target devices through the fault diagnosis model, and output the first device with fault risk and the second device without fault risk; establish an early warning model, and output the current early warning level based on the number of the first device, the number of the second device, and the basic data of the first device.

[0050] The processing module is configured to, upon receiving a control signal from a user terminal to modify the operating parameters of the first device, obtain the user terminal's job level information and, based on the warning level and the user terminal's job level information, determine whether to execute the modification of the first device's operating parameters. If executed, the first device's operating parameters are encrypted and sent to the task distribution terminal. The task distribution terminal determines whether the number of first devices whose operating parameters need to be modified is greater than 1. If it is greater than 1, a first device fault evaluation model is established, and the fault index of the first device is output through the first device fault evaluation model. The first device's operating parameters are then distributed to the corresponding first devices in order of the fault index. If the number is not greater than 1, the first device's operating parameters are sent to the corresponding first devices. If not executed, a malicious tampering alarm signal is sent.

[0051] The main control module, connected to the early warning module and the processing module, is used to execute the above-described micro intelligent bridge-building machine system control method.

[0052] The technical solution of the present invention has at least the following advantages and beneficial effects:

[0053] The method provided by this invention mainly includes diagnosing the target equipment using a fault diagnosis model, outputting the current warning level using an early warning model, and configuring the system to obtain the user's job level information when a control signal for modifying the operating parameters of the first equipment is received from the user terminal. Based on the warning level and the user's job level information, it is determined whether to modify the operating parameters of the first equipment. Through this scheme, after a fault warning is issued and the modification of the first equipment's operating parameters is accepted, multiple levels of user terminal information are verified, and encrypted transmission is performed. This significantly prevents malicious tampering and reduces the overall project risk. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is the control flowchart of the present invention;

[0056] Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0058] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. The naming or numbering of steps in this application does not imply that the steps in the method flow must be executed in the chronological / logical order indicated by the naming or numbering. The execution order of named or numbered process steps can be changed according to the desired technical objective, as long as the same or similar technical effect is achieved.

[0059] The independently described modules or sub-modules may or may not be physically separated; they may be implemented in software or hardware, and some modules or sub-modules may be implemented in software, with the processor calling the software to implement the function of these modules or sub-modules, while other modules or sub-modules may be implemented in hardware, such as through hardware circuits. Furthermore, some or all of the modules can be selected to achieve the purpose of this application's solution according to actual needs.

[0060] Please refer to Figures 1-2 The present invention provides a control method for a miniature intelligent bridge-building machine system, comprising:

[0061] S101: Obtain the basic data collected by the sensors in each target device of the current bridge building machine, establish a fault diagnosis model, diagnose the target devices through the fault diagnosis model, and output the first device with fault risk and the second device without fault risk.

[0062] The fault diagnosis model is used to predict the data obtained by each target device of the current bridge building machine. The data used are mainly historical data and current data. The target devices can be hydraulic cylinders, travel motors, cranes, etc.

[0063] S102: Establish an early warning model, and output the current early warning level based on the number of the first device, the number of the second device, and the basic data of the first device;

[0064] S103: When a control signal sent by the user terminal to modify the operating parameters of the first device is received, the job level information of the user terminal is obtained, and the modification of the operating parameters of the first device is determined based on the warning level and the job level information of the user terminal.

[0065] In this embodiment, the control signal for modifying the operating parameters of the first device can only be sent normally based on the warning level and the corresponding job level information of the user terminal. This makes the control signal more professional and prevents more serious accidents.

[0066] S104: If executed, the first device's operating parameters are encrypted and sent to the task distribution end. The task distribution end determines whether the number of first devices whose operating parameters need to be modified is greater than 1. If it is greater than 1, a first device fault evaluation model is established, and the fault index of the first device is output through the first device fault evaluation model. The first device's operating parameters are distributed to the corresponding first devices in order of the size of the fault index. If it is not greater than 1, the first device's operating parameters are sent to the corresponding first devices.

[0067] In this embodiment, the operating parameters of the first device to be sent are encrypted to further protect the current data and reduce the possibility of accidents. Secondly, the fault index reflects the degree of failure of the first device. Different operating parameters of the first device are sent to the corresponding first device through the fault index, so that the device with more serious faults receives the control signal first.

[0068] S105: If not executed, a malicious tampering alarm signal will be sent.

[0069] The method provided by this invention mainly includes diagnosing the target equipment using a fault diagnosis model, outputting the current warning level using an early warning model, and configuring the system to obtain the user's job level information when a control signal for modifying the operating parameters of the first equipment is received from the user terminal. Based on the warning level and the user's job level information, it is determined whether to modify the operating parameters of the first equipment. Through this scheme, after a fault warning is issued and the modification of the first equipment's operating parameters is accepted, multiple levels of user terminal information are verified, and encrypted transmission is performed. This significantly prevents malicious tampering and reduces the overall project risk.

[0070] In one exemplary embodiment of the present invention, establishing a fault diagnosis model includes:

[0071] S201: Acquire historical data collected by sensors in each target device of the bridge building machine, divide the historical data into test set and training set, and normalize the data;

[0072] Data normalization can reduce data to the range of 0 to 1, obtaining a range of -1 to 1. In this embodiment, the range of 0 to 1 can be selected. Normalization eliminates the dimensional differences between features, making the distribution of input data more uniform, thereby optimizing the search path of gradient descent.

[0073] S202: Based on the training set, a genetic algorithm is used to select the optimal fault features and obtain the optimal fault feature subset;

[0074] S203: Construct a neural network model, train the neural network model with the optimal subset of fault features, test the trained neural network with the test set and update the learning rate, and output the optimal trained neural network model.

[0075] S204: Perform fault diagnosis on the basic data collected by sensors in each target device of the current bridge building machine through the optimal neural network model, and output the diagnosis results.

[0076] Specifically, the selection of optimal fault features using genetic algorithms includes:

[0077] Several binary vectors are randomly generated and used as parent vectors, each of which represents a random set of equipment fault features;

[0078] The fitness function is used to evaluate individuals in the population, and tournament selection is performed based on the fitness function value. The failure feature set of the selected parents is cross-crossed and mutated according to a preset ratio to generate the failure feature set of the offspring.

[0079] After mutation, the best individual is compared with the best individual in history, and the worst individual is directly copied and replaced to obtain a new set of fault features for the offspring.

[0080] The iteration terminates when the genetic generation number is N; otherwise, it returns to the step of evaluating the population using the fitness function and continues the calculation until the optimal subset of fault features is found.

[0081] In one exemplary embodiment of the present invention, constructing a neural network model includes:

[0082] An input layer, a hidden layer, and an output layer are constructed. The input layer includes a hydraulic cylinder pressure data neuron unit, a temperature data neuron unit, a walking device motor speed data neuron unit, and a prestress tension data neuron unit. The output layer includes a confidence output neuron. Each neuron is responsible for the input and output of the corresponding data.

[0083] The input layer includes:

[0084]

[0085] The output layer includes:

[0086]

[0087] when When the current device has no risk of failure, output the result. When this occurs, output the result indicating that the current device is at risk of failure;

[0088] In the formula, For the normalized base data, For the first Layer input layer to the first The connection weights of the hidden layers, To input the number of layers, The threshold value for hidden layer nodes. For confidence level, For the first Hidden layer to the first The connection weights of the output layer, The threshold value for the output layer nodes. For the first The output of the hidden layer, This is the output layer number.

[0089] In one exemplary embodiment of the present invention, establishing an early warning model includes:

[0090]

[0091]

[0092] when When this happens, the current warning level is not output, and a signal of no warning is sent simultaneously. At that time, a Level 1 warning will be issued. When a Level II warning is issued, the severity of the Level I warning is less than that of the Level II warning.

[0093] In the formula, As an early warning index, The number of the first equipment. For the number of second devices, For the first Confidence level of the device For the first Data fluctuation rate of this type of equipment For the first The first type of equipment Basic data values, For the first Type of equipment Historical averages of basic data To determine the threshold, The total number of types of basic data values.

[0094] In the above model, the data of each primary device was analyzed. A comprehensive calculation was performed based on historical averages and current data values ​​to obtain a relatively objective data fluctuation rate. This rate was then used to correct the final judgment, resulting in a more accurate early warning outcome. Those skilled in the art may freely configure the settings based on historical data; this invention will not describe them in detail.

[0095] In one exemplary embodiment of the present invention, determining whether to modify the operating parameters of the first device based on the warning level and the user's job level information includes:

[0096] The job level system is set up, including Level 1, Level 2, and Level 3, with the scope of management authority increasing sequentially from Level 1 to Level 3.

[0097] If the current warning level is Level 1, and the user terminal sending the control signal is at Level 2 or Level 3, then the operating parameters of the first device are modified. If the current user terminal is at Level 1, then a permission notification is sent to the user terminal at Level 2. If a permission signal is received from the user terminal at Level 2, then the operating parameters of the first device are modified. If a permission rejection signal is received from the user terminal at Level 2, then the current permission notification is sent to the user terminal at Level 3. If a permission signal is received from the user terminal at Level 3, then the operating parameters of the first device are modified. If a permission rejection signal is received from the user terminal at Level 3, then the modification of the operating parameters of the first device is terminated.

[0098] Specifically, it also includes:

[0099] If the current warning level is Level 2 and the user terminal sending the control signal is Level 3, then the operation parameters of the first device will be modified. If the current user terminal is Level 1 or Level 2, then a permission notification will be sent to the Level 3 user terminal. If a permission signal is received from the Level 3 user terminal, then the operation parameters of the first device will be modified. If a permission rejection signal is received from the Level 3 user terminal, then the modification of the operation parameters of the first device will be terminated.

[0100] Set a reception time threshold for the control signal. If the task distribution terminal still does not receive the control signal after the reception time threshold is exceeded, it will receive the control signal from the user terminal of the first level or the second level.

[0101] For example, the first-level client is the basic operator, the second-level client is the department head, and the third-level client is the chief engineer. Through the above solution in this embodiment, different levels of employees can handle different levels of early warning situations to a greater extent, so that the final handling result is more in line with the current situation.

[0102] In one exemplary embodiment of the present invention, encrypting the operating parameters of the first device includes:

[0103] The operating parameters of the first device are encrypted using SM4-CTR at the edge node and an HMAC-SM3 checksum is attached. The data is transmitted to the cloud via a 5G private network, where it is decrypted using a KMS-hosted key and stored in a time-series database. Upon receiving a call command, the encrypted data is sent to the task distribution end, and the key is sent to the user end. After receiving feedback from the task distribution end that the operating parameters of the first device have been received, the user end sends the key to the task distribution end to decrypt the encrypted operating parameters of the first device.

[0104] Secondly, the first equipment failure evaluation model includes:

[0105]

[0106] In the formula, This is the failure index.

[0107] Secondly, a control system for a miniature intelligent bridge-building machine includes:

[0108] The early warning module is configured to acquire basic data collected by sensors in each target device of the current bridge building machine, establish a fault diagnosis model, diagnose the target devices through the fault diagnosis model, and output the first device with fault risk and the second device without fault risk; establish an early warning model, and output the current early warning level based on the number of the first device, the number of the second device, and the basic data of the first device.

[0109] The processing module is configured to, upon receiving a control signal from a user terminal to modify the operating parameters of the first device, obtain the user terminal's job level information and, based on the warning level and the user terminal's job level information, determine whether to execute the modification of the first device's operating parameters. If executed, the first device's operating parameters are encrypted and sent to the task distribution terminal. The task distribution terminal determines whether the number of first devices whose operating parameters need to be modified is greater than 1. If it is greater than 1, a first device fault evaluation model is established, and the fault index of the first device is output through the first device fault evaluation model. The first device's operating parameters are then distributed to the corresponding first devices in order of the fault index. If the number is not greater than 1, the first device's operating parameters are sent to the corresponding first devices. If not executed, a malicious tampering alarm signal is sent.

[0110] The main control module, connected to the early warning module and the processing module, is used to execute the above-described micro intelligent bridge-building machine system control method.

[0111] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0112] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. This computer software product, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0113] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A control method for a miniature intelligent bridge-building machine system, characterized in that, include: Acquire the basic data collected by the sensors in each target device of the current bridge building machine, establish a fault diagnosis model, diagnose the target devices through the fault diagnosis model, and output the first device with fault risk and the second device without fault risk. Establish an early warning model, and output the current early warning level based on the number of the first device, the number of the second device, and the basic data of the first device. When a control signal to modify the operating parameters of the first device is received from the user terminal, the user terminal's job level information is obtained. Based on the warning level and the user terminal's job level information, it is determined whether to modify the operating parameters of the first device. If executed, the operating parameters of the first device are encrypted and sent to the task distribution end. The task distribution end determines whether the number of first devices whose operating parameters need to be modified is greater than 1. If it is greater than 1, a fault evaluation model for the first device is established, and the fault index of the first device is output through the fault evaluation model. The operating parameters of the first device are distributed to the corresponding first devices in order of the size of the fault index. If it is not greater than 1, the operating parameters of the first device are sent to the corresponding first device. If not executed, a malicious tampering alarm signal will be sent; The establishment of the early warning model includes: when When this happens, the current warning level is not output, and a signal of no warning is sent simultaneously. At that time, a Level 1 warning will be issued. When a Level II warning is issued, the severity of the Level I warning is less than that of the Level II warning. In the formula, As an early warning index, The number of the first equipment. For the number of second devices, For the first Confidence level of the device For the first Data fluctuation rate of this type of equipment For the first The first type of equipment Basic data values, For the first Type of equipment Historical averages of basic data To determine the threshold, The total number of types of basic data values.

2. The control method for a miniature intelligent bridge-building machine system according to claim 1, characterized in that, The establishment of the fault diagnosis model includes: The historical data collected by the sensors in each target device of the bridge building machine is obtained, and the historical data is divided into test set and training set, and the data is normalized. Based on the training set, a genetic algorithm is used to select the optimal fault features and obtain the optimal fault feature subset. Construct a neural network model, train the neural network model with the optimal subset of fault features, test the trained neural network with a test set and update the learning rate, and output the optimal trained neural network model. The optimal neural network model is used to diagnose faults in the basic data collected by sensors in each target device of the current bridge building machine, and the diagnostic results are output.

3. The control method for a miniature intelligent bridge-building machine system according to claim 2, characterized in that, The selection of optimal fault features using a genetic algorithm includes: Several binary vectors are randomly generated and used as parent vectors, each of which represents a random set of equipment fault features; The fitness function is used to evaluate individuals in the population, and tournament selection is performed based on the fitness function value. The failure feature set of the selected parents is cross-crossed and mutated according to a preset ratio to generate the failure feature set of the offspring. After mutation, the best individual is compared with the best individual in history, and the worst individual is directly copied and replaced to obtain a new set of fault features for the offspring. The iteration terminates when the genetic generation number is N; otherwise, it returns to the step of evaluating the population using the fitness function and continues the calculation until the optimal subset of fault features is found.

4. The control method for a miniature intelligent bridge-building machine system according to claim 2, characterized in that, The construction of the neural network model includes: An input layer, a hidden layer, and an output layer are constructed. The input layer includes a hydraulic cylinder pressure data neuron unit, a temperature data neuron unit, a walking device motor speed data neuron unit, and a prestress tension data neuron unit. The output layer includes a confidence output neuron. The input layer includes: The output layer includes: when When the current device has no risk of failure, output the result. When this occurs, output the result indicating that the current device is at risk of failure; In the formula, For the normalized base data, For the first Layer input layer to the first The connection weights of the hidden layers, To input the number of layers, The threshold value for hidden layer nodes. For confidence level, For the first Hidden layer to the first The connection weights of the output layer, The threshold value for the output layer nodes. For the first The output of the hidden layer, This is the output layer number.

5. The control method for a miniature intelligent bridge-building machine system according to claim 3, characterized in that, The step of determining whether to modify the operating parameters of the first device based on the warning level and the user's job level information includes: The job level system is set up, including Level 1, Level 2, and Level 3, with the scope of management authority increasing sequentially from Level 1 to Level 3. If the current warning level is Level 1, and the user terminal sending the control signal is at Level 2 or Level 3, then the operating parameters of the first device are modified. If the current user terminal is at Level 1, then a permission notification is sent to the user terminal at Level 2. If a permission signal is received from the user terminal at Level 2, then the operating parameters of the first device are modified. If a permission rejection signal is received from the user terminal at Level 2, then the current permission notification is sent to the user terminal at Level 3. If a permission signal is received from the user terminal at Level 3, then the operating parameters of the first device are modified. If a permission rejection signal is received from the user terminal at Level 3, then the modification of the operating parameters of the first device is terminated.

6. The control method for a miniature intelligent bridge-building machine system according to claim 5, characterized in that, Also includes: If the current warning level is Level 2 and the user terminal sending the control signal is Level 3, then the operation parameters of the first device will be modified. If the current user terminal is Level 1 or Level 2, then a permission notification will be sent to the Level 3 user terminal. If a permission signal is received from the Level 3 user terminal, then the operation parameters of the first device will be modified. If a permission rejection signal is received from the Level 3 user terminal, then the modification of the operation parameters of the first device will be terminated. Set a reception time threshold for the control signal. If the task distribution terminal still has not received the control signal after the reception time threshold is exceeded, it will receive the control signal from the user terminal of the first level or the second level.

7. The control method for a miniature intelligent bridge-building machine system according to claim 6, characterized in that, The encryption of the operating parameters of the first device includes: The operating parameters of the first device are encrypted using SM4-CTR at the edge node, and an HMAC-SM3 checksum is attached. The data is transmitted to the cloud via a 5G private network, and then decrypted in the cloud using a KMS managed key and stored in a time-series database. Upon receiving the call instruction, the encrypted data is sent to the task distribution terminal, and the key is sent to the user terminal. After receiving feedback information from the task distribution terminal that the first device's operating parameters have been received, the user terminal sends the key to the task distribution terminal to decrypt the encrypted first device's operating parameters.

8. The control method for a miniature intelligent bridge-building machine system according to claim 7, characterized in that, The first equipment fault evaluation model includes: In the formula, This is the failure index.

9. A miniature intelligent bridge-building machine system, characterized in that, include: The early warning module is configured to acquire basic data collected by sensors in each target device of the current bridge building machine, establish a fault diagnosis model, diagnose the target devices through the fault diagnosis model, and output the first device with fault risk and the second device without fault risk. Establish an early warning model, and output the current early warning level based on the number of the first device, the number of the second device, and the basic data of the first device. The processing module is configured to, upon receiving a control signal from a user terminal to modify the operating parameters of the first device, obtain the job level information of the user terminal, and determine whether to modify the operating parameters of the first device based on the warning level and the job level information of the user terminal; if so, encrypt the operating parameters of the first device and send them to the task distribution terminal. The task distribution terminal determines whether the number of first devices whose operating parameters need to be modified is greater than 1. If it is greater than 1, it establishes a fault evaluation model for the first device, outputs the fault index of the first device through the fault evaluation model, and distributes the operating parameters of the first device to the corresponding first device in order of the size of the fault index; if it is not greater than 1, it sends the operating parameters of the first device to the corresponding first device. If not executed, a malicious tampering alarm signal will be sent; The establishment of the early warning model includes: when When this happens, the current warning level is not output, and a signal of no warning is sent simultaneously. At that time, a Level 1 warning will be issued. When a Level II warning is issued, the severity of the Level I warning is less than that of the Level II warning. In the formula, As an early warning index, The number of the first equipment. For the number of second devices, For the first Confidence level of the device For the first Data fluctuation rate of this type of equipment For the first The first type of equipment Basic data values, For the first Type of equipment Historical averages of basic data To determine the threshold, The total number of categories of basic data values; The main control module, connected to the early warning module and the processing module, is used to execute the micro intelligent bridge building machine system control method according to any one of claims 1-8.

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