Miniature intelligent bridge fabrication machine system and control method

Abstract

The invention relates to the technical field of bridge fabrication machine intelligent control, in particular to a miniature intelligent bridge fabrication machine system and a control method, and mainly comprises the steps of diagnosing target equipment through a fault diagnosis model, outputting a current early warning level through an early warning model, and controlling the bridge fabrication machine. And the processor is configured to obtain the level information of the user side when receiving a control signal which is sent by the user side and is used for modifying the operation parameters of the first equipment, and judge whether the operation parameters of the first equipment are modified or not according to the early warning level and the level information of the user side. According to the scheme, after the equipment has the fault early warning and is notified and the operation parameters of the first equipment are modified, checking of user side information of multiple levels and encrypted transmission are carried out in the process, so that malicious tampering can be prevented to a great extent, and the risk of the whole project is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control of bridge-building machines, and in particular to a micro intelligent bridge-building machine system and a control method. Background Art

[0002] In the field of modern bridge engineering, with the rapid development of science and technology, intelligent cantilever bridge-building machines have emerged as an advanced bridge construction equipment. Integrating cutting-edge technologies from multiple disciplines, including mechanical engineering, automated control, and information technology, they offer numerous advantages in bridge construction, including efficiency, precision, and safety. They have also profoundly impacted and transformed traditional bridge construction concepts and methods. In-depth consideration of the technical characteristics, current applications, and future development trends of intelligent cantilever bridge-building machines is crucial for advancing bridge engineering technology.

[0003] However, the current intelligent bridge-building machine control system generally only provides fault warnings and sends the fault warnings to the corresponding processing end, without any further follow-up. Since bridge construction is a large project and the safety engineering involved is also huge, more cautious handling is required when controlling equipment with fault warnings. The existing technology directly modifies the control end and lacks other encryption and multi-level verification measures, which may result in the current risk of bridge-building machines still being relatively high. Summary of the Invention

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

[0005] The present invention is achieved through the following technical solutions: In a first aspect, the present invention provides a method for controlling a micro intelligent bridge-building machine system, comprising: Obtain basic data collected by sensors in each target device of the current bridge-building machine, establish a fault diagnosis model, diagnose the target device using the fault diagnosis model, and output the first device currently at risk of failure and the second device not at risk of failure; Establishing an early warning model, and outputting a current early warning level through the early warning model according to the number of first devices, the number of second devices, and basic data of the first device; When receiving a control signal from a user terminal to modify the operating parameters of the first device, obtaining the rank information of the user terminal, and determining whether to modify the operating parameters of the first device according to the warning level and the rank information of the user terminal; 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 that currently need to modify the operating parameters is greater than 1. If it is greater than 1, a first device fault evaluation model is established, and a fault index of the first device is output through the first device 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.

[0006] Preferably, the establishing of the fault diagnosis model includes: Obtain historical data collected by sensors in each target device of the bridge construction machine, divide the historical data into test sets and training sets, and normalize the data; Based on the training set, a genetic algorithm is used to select the optimal fault features and obtain the optimal fault feature subset; Build a neural network model, train the neural network model with the optimal fault feature subset, test the trained neural network with the test set and update the learning rate, and output the optimal trained neural network model; The optimal neural network model is used to perform fault diagnosis on the basic data collected by sensors in each target device of the current bridge-building machine and output the diagnosis results.

[0007] Preferably, the selection of the optimal fault feature using a genetic algorithm includes: Randomly generate a number of binary vectors as parents, each of which represents a random device fault feature set; The fitness function is used to evaluate the individuals in the population, and tournament selection is performed based on the fitness function value. The fault feature set of the selected parent generation is cross-crossed and mutated according to the preset ratio to generate the fault feature set of the offspring generation. After the mutation operation, the current best individual is compared with the historical best individual, and the worst individual is directly copied and replaced to obtain a new fault feature set for the offspring; When the condition that the genetic generation number is N is met, the iteration is terminated, otherwise the process returns to the method of evaluating the population using the fitness function and continues to calculate until the optimal fault feature subset is found.

[0008] Preferably, the constructing of the neural network model includes: Constructing an input layer, a hidden layer, and an output layer, wherein the input layer includes a hydraulic cylinder pressure data neural unit, a temperature data neural unit, a walking device motor speed data neural unit, and a prestressed tension data neural unit, and the output layer includes a confidence output neuron; The input layer includes:

[0009] The output layer includes:

[0010] when When , the output is that there is no risk of failure for the current device. When , the output is that the current device has a failure risk; Where, is the normalized basic data, For the Layer input layer to the The connection weights of the hidden layers, is the number of input layers, is the threshold of the hidden layer nodes, is the confidence level, For the Hidden layer to the The connection weights of the layer output layer, is the threshold of the output layer node, For the The output of the hidden layer.

[0011] Preferably, the establishing of the early warning model includes:

[0012]

[0013] when When the current warning level is not output, a no-warning signal is sent. When the first level warning is issued, When the second level warning level is issued, the severity of the first level warning level is less than the second level warning level; Where, is the early warning index, is the number of the first device, is the number of the second device, For the The confidence level of the device, For the The data floating rate of the device, For the The first Basic data values, For the Type of equipment The historical average of basic data, is the judgment threshold.

[0014] Preferably, the determining whether to modify the operating parameters of the first device according to the warning level and the rank information of the user terminal includes: Establishing job grades, including first, second and third grades, with the scope of management authority of the first, second and third grades being expanded in sequence; If the current warning level is the first warning level and the user terminal currently sending the control signal is the second level or the third level, the operating parameters of the first device are modified. If the current user terminal is the first level, a permission notification is sent to the second level user terminal. If a permission signal is received from the second level user terminal, the operating parameters of the first device are modified. If a signal of rejection of permission is received from the second level user terminal, the current permission is notified to the third level user terminal. If a permission signal is received from the third level user terminal, the operating parameters of the first device are modified. If a signal of rejection of permission is received from the third level user terminal, the modification of the operating parameters of the first device is terminated.

[0015] Preferably, it also includes: If the current warning level is the second warning level and the user terminal currently sending the control signal is at the third level, then the operating parameters of the first device are modified; if the current user terminal is at the first or second level, then a permission notification is sent to the user terminal at the third level; if a permission signal is received from the user terminal at the third level, then the operating parameters of the first device are modified; if a rejection signal is received from the user terminal at the third level, then the modification of the operating parameters of the first device is terminated; A control signal reception time threshold is set. If the task distribution terminal still does not receive the control signal when the reception time threshold is exceeded, the control signal from the first level or second level user terminal is received.

[0016] Preferably, encrypting the operating parameters of the first device includes: Encrypt the first device operating parameters using SM4-CTR at the edge node and append an HMAC-SM3 check code; The data is transmitted to the cloud via the 5G private network. The cloud uses the KMS managed key to decrypt the data and store it in the time series database. After receiving the calling instruction, the encrypted data is sent to the task distribution end and the key is sent to the user end. After receiving the feedback information that the task distribution end has received the operating parameters of the first device, the user end sends the key to the task distribution end to decrypt the encrypted operating parameters of the first device.

[0017] Preferably, the first equipment fault evaluation model includes:

[0018] Where, is the failure index.

[0019] In a second aspect, the present invention further provides a micro intelligent bridge-building machine system control system, comprising: The early warning module is configured to obtain basic data collected by sensors in each target device of the current bridge-building machine, establish a fault diagnosis model, diagnose the target device using the fault diagnosis model, and output the first device currently at risk of failure and the second device not at risk of failure; establish an early warning model, and output the current warning level using the early warning model based on the number of first devices, the number of second devices, and the basic data of the first device; The processing module is configured to, upon receiving a control signal from a user terminal for modifying an operating parameter of a first device, obtain the rank information of the user terminal, and determine whether to execute the modification of the operating parameter of the first device based on the warning level and the rank information of the user terminal; if so, encrypt the operating parameter of the first device and send it to the task distribution terminal, the task distribution terminal determining whether the number of first devices whose operating parameters currently need to be modified is greater than 1; if so, establish a first device fault evaluation model, output a fault index of the first device through the first device fault evaluation model, distribute the first device operating parameter to the corresponding first device in order of the magnitude of the fault index; if not greater than 1, send the first device operating parameter to the corresponding first device; if not, send a malicious tampering alarm signal; The main control module is connected to the early warning module and the processing module, and is used to execute the above-mentioned micro intelligent bridge-building machine system control method.

[0020] The technical solution of the present invention has at least the following advantages and beneficial effects: The method provided by the present invention mainly includes diagnosing the target device through a fault diagnosis model, outputting the current warning level through an early warning model, and being configured to obtain the user's rank information when receiving a control signal sent by the user to modify the operating parameters of the first device, and judging whether to execute the modification of the operating parameters of the first device based on the warning level and the user's rank information. Through the above scheme, after the device fault warning is notified, the user's information is checked at multiple levels and encrypted transmission is performed during this process. This can greatly prevent malicious tampering and reduce the risk of the entire project. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 is a control flow chart of the present invention; Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0024] The terms "first," "second," and so on, in the specification and claims of this application and the accompanying drawings 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 necessarily imply that the steps in the method flow must be executed in the chronological or logical order indicated by the naming or numbering. Named or numbered process steps may be executed in a different order based on the desired technical objectives, as long as the same or similar technical effects are achieved.

[0025] Independently described modules or submodules may or may not be physically separate; they may be implemented in software or hardware. Some modules or submodules may be implemented in software, with the processor invoking the software to implement the functionality of these modules or submodules, while other modules or submodules may be implemented in hardware, such as hardware circuits. Furthermore, some or all of the modules may be selected based on actual needs to achieve the objectives of the present application.

[0026] Please refer to Figure 1-Figure 2 The present invention provides a micro intelligent bridge-building machine system control method, comprising: S101: Obtain basic data collected by sensors in each target device of the current bridge-building machine, establish a fault diagnosis model, diagnose the target device using the fault diagnosis model, and output a first device currently at risk of failure and a second device not at risk of failure; The fault diagnosis model is used to predict the data obtained from 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.

[0027] S102: Establishing an early warning model, and outputting a current early warning level through the early warning model according to the number of first devices, the number of second devices, and basic data of the first device; S103: upon receiving a control signal from the user terminal to modify the operating parameters of the first device, obtaining the user terminal's rank information, and determining whether to modify the operating parameters of the first device based on the warning level and the user terminal's rank information; In this embodiment, the control signal for modifying the operating parameters of the first device can be sent normally based on the warning level and the corresponding user terminal's job grade information, which makes the control signal more professional to a large extent and prevents larger accidents.

[0028] S104: If executed, the operating parameters of the first device are encrypted and sent to the task distribution terminal. The task distribution terminal determines whether the number of first devices that currently need to modify the operating parameters is greater than 1. If it is greater than 1, a first device fault evaluation model is established. The fault index of the first device is output through the first device fault evaluation model. The operating parameters of the first device are distributed to the corresponding first devices in order of the fault index. If it is not greater than 1, the operating parameters of the first device are sent to the corresponding first devices. In this embodiment, the operating parameters of the first device to be sent are encrypted, and the current data is further protected to reduce the possibility of accidents. Secondly, the fault index reflects the fault level of the current first device. Different first device operating parameters are sent to the corresponding first devices through the fault index, so that the device with a more serious fault receives the control signal first.

[0029] S105: If not executed, a malicious tampering warning signal is sent.

[0030] The method provided by the present invention mainly includes diagnosing the target device through a fault diagnosis model, outputting the current warning level through an early warning model, and being configured to obtain the user's rank information when receiving a control signal sent by the user to modify the operating parameters of the first device, and judging whether to execute the modification of the operating parameters of the first device based on the early warning level and the user's rank information. Through the above scheme, after the device fault early warning is notified, it waits until the modification of the operating parameters of the first device is accepted. During this process, multiple levels of user-side information are checked and encrypted transmission is performed, which can prevent malicious tampering to a large extent and reduce the risk of the entire project. In an exemplary embodiment of the present invention, establishing a fault diagnosis model includes: S201: Obtain historical data collected by sensors in each target device of the bridge construction machine, divide the historical data into a test set and a training set, and normalize the data; Data normalization processing can aggregate data into a 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 to make the distribution of input data more uniform, thereby optimizing the search path of gradient descent.

[0031] S202: Based on the training set, a genetic algorithm is used to select the optimal fault feature to obtain the optimal fault feature subset; S203: Build a neural network model, train the neural network model with the optimal fault feature subset, test the trained neural network with the test set and update the learning rate, and output the optimal trained neural network model; 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.

[0032] Specifically, the selection of optimal fault features using genetic algorithms includes: Randomly generate a number of binary vectors as parents, each of which represents a random device fault feature set; The fitness function is used to evaluate the individuals in the population, and tournament selection is performed based on the fitness function value. The fault feature set of the selected parent generation is cross-crossed and mutated according to the preset ratio to generate the fault feature set of the offspring generation. After the mutation operation, the current best individual is compared with the historical best individual, and the worst individual is directly copied and replaced to obtain a new fault feature set for the offspring; When the condition that the genetic generation number is N is met, the iteration is terminated, otherwise the process returns to the method of evaluating the population using the fitness function and continues to calculate until the optimal fault feature subset is found.

[0033] In an exemplary embodiment of the present invention, constructing a neural network model includes: Construct an input layer, a hidden layer and an output layer, wherein the input layer includes a hydraulic cylinder pressure data neural unit, a temperature data neural unit, a walking device motor speed data neural unit, and a prestressed tension data neural unit, and the output layer includes a confidence output neuron, wherein each neuron is responsible for the input and output of the corresponding data.

[0034] The input layer consists of:

[0035] The output layer consists of:

[0036] when When , the output is that there is no risk of failure for the current device. When , the output is that the current device has a failure risk; Where, is the normalized basic data, For the Layer input layer to the The connection weights of the hidden layers, is the number of input layers, is the threshold of the hidden layer nodes, is the confidence level, For the Hidden layer to the The connection weights of the layer output layer, is the threshold of the output layer node, For the The output of the hidden layer.

[0037] In an exemplary embodiment of the present invention, establishing an early warning model includes:

[0038]

[0039] when When the current warning level is not output, a no-warning signal is sent. When the first level warning is issued, When the second level warning level is issued, the severity of the first level warning level is less than the second level warning level; Where, is the early warning index, is the number of the first device, is the number of the second device, For the The confidence level of the device, For the The data floating rate of the device, For the The first Basic data values, For the Type of equipment The historical average of basic data, is the judgment threshold.

[0040] In the above model, the data of each first device is analyzed, and a comprehensive calculation is performed based on the historical average value and the current data value to obtain a relatively objective data fluctuation rate, which is used to correct the final judgment result and obtain a more accurate early warning result. Those skilled in the art may freely set it according to historical data, and the present invention will not describe it in detail.

[0041] In an exemplary embodiment of the present invention, determining whether to modify the operating parameters of the first device based on the warning level and the rank information of the user terminal includes: Establishing job grades, including first, second and third grades, with the scope of management authority of the first, second and third grades being expanded in sequence; If the current warning level is the first warning level and the user terminal currently sending the control signal is the second level or the third level, the operating parameters of the first device are modified. If the current user terminal is the first level, a permission notification is sent to the second level user terminal. If a permission signal is received from the second level user terminal, the operating parameters of the first device are modified. If a signal of rejection of permission is received from the second level user terminal, the current permission is notified to the third level user terminal. If a permission signal is received from the third level user terminal, the operating parameters of the first device are modified. If a signal of rejection of permission is received from the third level user terminal, the modification of the operating parameters of the first device is terminated.

[0042] Specifically, it also includes: If the current warning level is the second warning level and the user terminal currently sending the control signal is at the third level, then the operating parameters of the first device are modified; if the current user terminal is at the first or second level, then a permission notification is sent to the user terminal at the third level; if a permission signal is received from the user terminal at the third level, then the operating parameters of the first device are modified; if a rejection signal is received from the user terminal at the third level, then the modification of the operating parameters of the first device is terminated; A control signal reception time threshold is set. If the task distribution terminal still does not receive the control signal when the reception time threshold is exceeded, the control signal from the first level or second level user terminal is received.

[0043] For example, the first-level client is a grassroots operator, the second-level client is a department head, and the third-level client is a chief engineer. Through the above-mentioned scheme of this embodiment, different levels of warning situations can be handled to a large extent by employees of different levels, so that the final processing result is more in line with the current situation.

[0044] In an exemplary embodiment of the present invention, encrypting the operating parameters of the first device includes: The operating parameters of the first device are encrypted through the edge node using SM4-CTR, and an HMAC-SM3 verification code is attached; the parameters are transmitted to the cloud through the 5G private network, and the cloud decrypts them with the KMS managed key and stores them in the time series database; after receiving the call instruction, the encrypted data is sent to the task distribution end, and the key is sent to the user end. After receiving the feedback information that the task distribution end has received the operating parameters of the first device, the user end sends the key to the task distribution end to decrypt the encrypted operating parameters of the first device.

[0045] Secondly, the first equipment failure evaluation model includes:

[0046] Where, is the failure index.

[0047] In the second aspect, a micro intelligent bridge-building machine system control system includes: The early warning module is configured to obtain basic data collected by sensors in each target device of the current bridge-building machine, establish a fault diagnosis model, diagnose the target device using the fault diagnosis model, and output the first device currently at risk of failure and the second device not at risk of failure; establish an early warning model, and output the current warning level using the early warning model based on the number of first devices, the number of second devices, and the basic data of the first device; The processing module is configured to, upon receiving a control signal from a user terminal for modifying an operating parameter of a first device, obtain the rank information of the user terminal, and determine whether to execute the modification of the operating parameter of the first device based on the warning level and the rank information of the user terminal; if so, encrypt the operating parameter of the first device and send it to the task distribution terminal, the task distribution terminal determining whether the number of first devices whose operating parameters currently need to be modified is greater than 1; if so, establish a first device fault evaluation model, output a fault index of the first device through the first device fault evaluation model, distribute the first device operating parameter to the corresponding first device in order of the magnitude of the fault index; if not greater than 1, send the first device operating parameter to the corresponding first device; if not, send a malicious tampering alarm signal; The main control module is connected to the early warning module and the processing module, and is used to execute the above-mentioned micro intelligent bridge-building machine system control method.

[0048] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0049] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored on a computer-readable storage medium. This computer software product, stored on a storage medium, includes instructions for causing a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0050] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A micro intelligent bridge-building machine system control method, characterized in that: include: Obtain basic data collected by sensors in each target device of the current bridge-building machine, establish a fault diagnosis model, diagnose the target device using the fault diagnosis model, and output the first device currently at risk of failure and the second device not at risk of failure; Establishing an early warning model, and outputting a current early warning level through the early warning model according to the number of first devices, the number of second devices, and basic data of the first device; When receiving a control signal from a user terminal to modify the operating parameters of the first device, obtaining the rank information of the user terminal, and determining whether to modify the operating parameters of the first device according to the warning level and the rank information of the user terminal; 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 that currently need to modify the operating parameters is greater than 1. If it is greater than 1, a first device fault evaluation model is established, and a fault index of the first device is output through the first device 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.

2. A micro intelligent bridge-building machine system control method according to claim 1, characterized in that: Described establishment fault diagnosis model comprises: Obtain historical data collected by sensors in each target device of the bridge construction machine, divide the historical data into test sets and training sets, and normalize the data; Based on the training set, a genetic algorithm is used to select the optimal fault features and obtain the optimal fault feature subset; Build a neural network model, train the neural network model with the optimal fault feature subset, test the trained neural network with the test set and update the learning rate, and output the optimal trained neural network model; The optimal neural network model is used to perform fault diagnosis on the basic data collected by sensors in each target device of the current bridge-building machine and output the diagnosis results.

3. A micro intelligent bridge-building machine system control method according to claim 2, characterized in that: The selection of the optimal fault feature by using a genetic algorithm includes: Randomly generate a number of binary vectors as parents, each of which represents a random device fault feature set; The fitness function is used to evaluate the individuals in the population, and tournament selection is performed based on the fitness function value. The fault feature set of the selected parent generation is cross-crossed and mutated according to the preset ratio to generate the fault feature set of the offspring generation. After the mutation operation, the current best individual is compared with the historical best individual, and the worst individual is directly copied and replaced to obtain a new fault feature set for the offspring; When the condition that the genetic generation number is N is met, the iteration is terminated, otherwise the process returns to the method of evaluating the population using the fitness function and continues to calculate until the optimal fault feature subset is found.

4. A micro intelligent bridge-building machine system control method according to claim 2, characterized in that: The construction of the neural network model includes: Constructing an input layer, a hidden layer, and an output layer, wherein the input layer includes a hydraulic cylinder pressure data neural unit, a temperature data neural unit, a walking device motor speed data neural unit, and a prestressed tension data neural unit, and the output layer includes a confidence output neuron; The input layer includes: The output layer includes: when When , the output is that there is no risk of failure for the current device. When , the output is that the current device has a failure risk; Where, is the normalized basic data, For the Layer input layer to the The connection weights of the hidden layers, is the number of input layers, is the threshold of the hidden layer nodes, is the confidence level, For the Hidden layer to the The connection weights of the layer output layer, is the threshold of the output layer node, For the The output of the hidden layer.

5. A micro intelligent bridge-building machine system control method according to claim 4, characterized in that: The establishment of the early warning model includes: when When the current warning level is not output, a no-warning signal is sent. When the first level warning is issued, When the second level warning level is issued, the severity of the first level warning level is less than the second level warning level; Where, is the early warning index, is the number of the first device, is the number of the second device, For the The confidence level of the device, For the The data floating rate of the device, For the The first Basic data values, For the Type of equipment The historical average of basic data, is the judgment threshold.

6. A micro intelligent bridge-building machine system control method according to claim 5, characterized in that: The determining whether to modify the operating parameters of the first device according to the warning level and the rank information of the user terminal includes: Establishing job grades, including first, second and third grades, with the scope of management authority of the first, second and third grades being expanded in sequence; If the current warning level is the first warning level and the user terminal currently sending the control signal is the second level or the third level, the operating parameters of the first device are modified. If the current user terminal is the first level, a permission notification is sent to the second level user terminal. If a permission signal is received from the second level user terminal, the operating parameters of the first device are modified. If a signal of rejection of permission is received from the second level user terminal, the current permission is notified to the third level user terminal. If a permission signal is received from the third level user terminal, the operating parameters of the first device are modified. If a signal of rejection of permission is received from the third level user terminal, the modification of the operating parameters of the first device is terminated.

7. A micro intelligent bridge-building machine system control method according to claim 6, characterized in that: Also includes: If the current warning level is the second warning level and the user terminal currently sending the control signal is at the third level, then the operating parameters of the first device are modified; if the current user terminal is at the first or second level, then a permission notification is sent to the user terminal at the third level; if a permission signal is received from the user terminal at the third level, then the operating parameters of the first device are modified; if a rejection signal is received from the user terminal at the third level, then the modification of the operating parameters of the first device is terminated; A control signal reception time threshold is set. If the task distribution terminal still does not receive the control signal when the reception time threshold is exceeded, the control signal from the first level or second level user terminal is received.

8. A micro intelligent bridge-building machine system control method according to claim 7, characterized in that: The encrypting the first device operating parameter includes: Encrypt the first device operating parameters using SM4-CTR at the edge node and append an HMAC-SM3 check code; The data is transmitted to the cloud via the 5G private network. The cloud uses the KMS managed key to decrypt the data and store it in the time series database. After receiving the calling instruction, the encrypted data is sent to the task distribution end and the key is sent to the user end. After receiving the feedback information that the task distribution end has received the operating parameters of the first device, the user end sends the key to the task distribution end to decrypt the encrypted operating parameters of the first device.

9. A micro intelligent bridge-building machine system control method according to claim 8, characterized in that: The first equipment fault evaluation model includes: Where, is the failure index.

10. A micro intelligent bridge-building machine system control, characterized in that: include: The early warning module is configured to obtain basic data collected by sensors in each target device of the current bridge-building machine, establish a fault diagnosis model, diagnose the target device using the fault diagnosis model, and output a first device that is currently at risk of failure and a second device that is not at risk of failure; Establishing an early warning model, and outputting a current early warning level through the early warning model according to the number of first devices, the number of second devices, and basic data of the first device; The processing module is configured to, upon receiving a control signal from a user terminal for modifying an operating parameter of a first device, obtain the rank information of the user terminal, and determine whether to execute the modification of the operating parameter of the first device based on the warning level and the rank information of the user terminal; if so, encrypt the operating parameter of the first device and send it to the task distribution terminal, the task distribution terminal determining whether the number of first devices whose operating parameters currently need to be modified is greater than 1; if so, establish a first device fault evaluation model, output a fault index of the first device through the first device fault evaluation model, distribute the first device operating parameter to the corresponding first device in order of the magnitude of the fault index; if not greater than 1, send the first device operating parameter to the corresponding first device; if not, send a malicious tampering alarm signal; The main control module is connected to the early warning module and the processing module, and is used to execute the micro-intelligent bridge-building machine system control method described in any one of claims 1-9.

Images