# Design and Application of Collaborative Experiment Management Platform for Innovation and Entrepreneurship Education Based on an Intelligent Sensor Network

Authors: Yongqiang Sun
Journal: Journal of Sensors
 (2022)

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## Abstract

Innovation and entrepreneurship is the soul of entrepreneurship, and realizing the integration of innovation and entrepreneurship education and collaborative experiment platform in colleges and universities is an important way to promote the development of entrepreneurship and facilitate higher quality employment. In this paper, the authors propose the 3D-IVF intelligent sensor overlay algorithm wisdom laboratory system structure and application mode through in-depth investigation of collaborative laboratory, build the macroenvironment and construction demand of collaborative experiment management platform of innovation and entrepreneurship education, comprehensively analyze the current situation, problems, and effectiveness of its integration of innovation and entrepreneurship education and professional education. In addition, we explore the operation mechanism of its organizational system, curriculum system, management system, and platform construction in the practice of integrating innovation and entrepreneurship education with professional education. The average coverage rate of the 3D-IVF-based 3D coverage algorithm is 92.15%, and the deployment time is 2.5857 s; the average coverage rate is improved by 0.76%, and the deployment time is reduced by 0.1712 s. Design FIR filter using MATLAB and Hilbert transform filter, generate HDL code, and implement it on FPGA. Use the designed low-pass and band-pass filters to filter the 100 KHz square wave signal sampled by the ADC. When the number of iterations of the algorithm reaches 30, the coverage rates of the four-node deployment strategies will stabilize, i.e., the forces between the nodes of the 3D-VF-based 3D coverage algorithm of this paper. The equilibrium state is reached. We use resources and real-time experimental data sharing to realize collaborative design among team members, further optimize the curriculum structure of innovation and entrepreneurship education, adopt diversified forms of curriculum implementation, and implement a diversified curriculum evaluation system, etc.


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## Body

## 1. Introduction

The integration of innovation and entrepreneurship education with professional education is an important way to cultivate the entrepreneurial spirit of college students and promote high-quality employment and entrepreneurship in college students. In addition to providing good employment and entrepreneurship opportunities and a market environment, it is more important to promote higher quality employment and entrepreneurship of college students, and more importantly, how to cultivate the entrepreneurial spirit of college students from the source of education, and effectively improve the innovation and entrepreneurial ability and social responsibility of college students [1]. Under the background of innovation-driven development strategy, the demand of society for innovative and entrepreneurial talents has become more and more urgent. Higher education is an important way to cultivate innovative and entrepreneurial talents, while higher vocational education, as an important part of higher education, aims to cultivate talents with high skills to meet the changes of market demand and face the front line of production, service, and management, so it is more relevant to carry out innovation and entrepreneurship education [2]. In the educational practice, we constantly enhance the consciousness of serving the society, improve the ability to promote regional economic development, and gradually form an educational model that adapts to social needs and deeply integrates industry and education, intending to cultivate highly skilled and innovative talents with high professionalism and strong technical ability. Among the many theoretical analysis models, multisource flow theory is a specialized tool that has been used for policy agenda analysis. By studying the problem and the policy direction, it can timely uncover the dilemma of the current innovation and entrepreneurship process of college students, provide more targeted and feasible solutions for the revision and improvement of the policy path, and achieve the effect of easing the social employment pressure and pulling the market economy from the top-level design [3].This paper combines the multistream theory with college students’ innovation and entrepreneurship policies and explores the whole process of college students’ innovation and entrepreneurship policies from triggering attention to policy introduction from three major sources: issue, policy, and politics. To avoid duplicate transmission of the same data, the sensor of the intermediate unit and the sensor of the top unit compare and judge the received data to eliminate invalid duplicate data and improve channel utilization. On the one hand, it expands the scope of application of multisource stream theory in the field of college students’ innovation and entrepreneurship policy through continuous revision and improvement in the process of framework application and at the same time enhances the flexibility and durability of model application; on the other hand, it researches and explores an intelligent laboratory management model by using the increasingly mature Internet of Things technology and conducts research on the construction of collaborative environment of virtual experiments, mainly for group-based remote control and data sharing of collaborative experiments and other issues, combining some of the structures and characteristics of the smart home aspects, designing a new management method of the laboratory, realizing intelligent monitoring and management of experimental equipment, and improving the management efficiency and level of collaborative experiments in innovation and entrepreneurship education [4]. The reform in terms of effective interface and integration between innovation and entrepreneurship education and collaborative experiment management system based on smart sensors can broaden the practical channels and ways of students’ professional ability cultivation, conduct relevant theoretical exploration on the relationship between innovation and entrepreneurship education and professional education, make the professional knowledge learned by students more relevant to practice, reinforce students’ internalization of knowledge, improve students’ practical and innovative abilities, and finally improve the quality of talent cultivation.


## 2. Related Work

In the process of developing innovation and entrepreneurship education, great importance is attached to the integration with professional education, and research is carried out mainly in terms of curriculum design, teaching methods, and project practice. Literature [5] argues that strengthening innovation and entrepreneurship teaching in professional education can enable students to learn about the specific activities of entrepreneurs and cultivate entrepreneurship in their activity participation, to finally realize the transformation from entrepreneurial consciousness to entrepreneurial behavior. Lima [6] suggests that the actual connotation of entrepreneurship should be reflected in the curriculum, and the innovation and entrepreneurship curriculum system should include basic courses, professional courses, and entrepreneurship courses to achieve interdisciplinary knowledge interaction and knowledge penetration to better cultivate students’ innovative thinking and practical skills. Jiang et al. [7] pointed out how effectively integrating innovation and entrepreneurship education into professional education is a very important direction for the comprehensive reform of education and teaching in European and American universities. Literature [8] suggested that the cultivation of entrepreneurial ability depends on professional education, and the combination of entrepreneurship education and professional education can better optimize the process of knowledge learning, and in the acquisition of knowledge, enhancing entrepreneurial ability is conducive to both the enrichment and improvement of professional education and the in-depth development of entrepreneurship education. Popescu et al. [9] suggests that to make the educated person a professional and innovative person, it is necessary to incorporate relevant pioneering ideas and techniques in the daily development of teaching. Literature [10] presents the development of entrepreneurship education as a philosophy of overall social development and progress, a change in mentality brought about by social development, which brings about a change in the paradigm of education and teaching.Distance laboratory education has been developing and changing for many years with the development of the Internet, information technology, and other scientific technologies as well as the change in people’s teaching philosophy. Sodhro et al.’s study [11] is based on a networked virtual experimental environment, and they designed a virtual networked online experimental system that combines Internet technology as well as distributed measurement and control technology, which marks the beginning of the development of online virtual experiments. Liu et al. [12] designed and built a lab called club lab for simulating control engineering experiments. This lab uses JavaApplet and some plug-ins to run the simulation, it can simulate real 3D experimental scenarios by using MATLAB/SIMULINK computational engine, students can go through this virtual lab, and it has some interactive capabilities. In literature [13], a remote experimental system is designed using B/S architecture and based on FPGA hardware logic structure; the object of the system is the actual hardware experimental equipment, which belongs to the second category of remote control remote experiments. Using distributed computer technology, this remote experiment system allows learners to remotely operate and control the actual experimental equipment, and at the same time, the system allows learners to design the CPU hardware independently and learn the actual logic design experiment remotely through the network. The Evlab system developed in literature [14] combines two technologies, QTVR and VRML, using the 3D realistic modeling feature of QTVR based on static images to display the instruments or environment in the laboratory and VRML to compensate for the lack of interactivity of QTVR. In this way, a 3D electronic circuit experimental environment based on virtual space is realized. Qiu et al. [15] developed a set of Internet-based remote control experiment platforms, which can allow students to remotely log in to this lab within the campus network for remote operation of physical experiments, and the remote operation experiments of the robotic arm, inverted pendulum, and bulbar system have been realized so far. Later, a portable trial web server was designed for the development of the remote lab system. And this web server was extended with a serial port to realize a many-to-many experiment model with multiple users for different experiments. Recently, a mobile lab-based system was designed to realize the operation of the implementation using cell phones. In literature [16], a virtual collaborative experiment on computer networks was designed using a constructivist-based instructional design model, proposing the “multiple tokens” + “operation queue” method to solve the concurrency problem of collaborative experiments, and using the dead reckoning algorithm to improve the “multiple tokens” + “operation queue” method. In literature [17], a synchronous collaborative virtual laboratory system with circuit experiments as a case experiment is designed and developed in Java language, which uses a token ring plus a message queue to control the concurrency problem.


## 3. Construction of an Experimental Platform for Collaborative Management Based on Intelligent Sensor Networks

### 3.1. Collaborative Management Experimental Platform Technology Foundation

In a collaborative system, not only synchronization problems but also concurrency problems need to be solved. In wide-area networks, there are often network delays. If the synchronization mechanism is not designed properly, it will not only cause lag but also lead to uncoordinated data and concurrency problems among users. When the same operation object is operated by multiple users at the same time, a concurrency problem will occur. If the concurrency problem is not controlled, errors such as data update loss, data dirty read, and data read nonreproducible will occur [18]. The synchronization mechanism of the cooperative system can be divided into the following three categories: (1) peer to peer, in which there is no special server for data processing and forwarding, and all clients are in the same position, and the most typical algorithm is lockstep algorithm. (2) In server-client, the server is at the core of this synchronization mechanism, and it is used to process and forward the data of each client; once the server is down, the whole cooperative system will be paralyzed, and typical algorithms are dead rocking algorithm and lockstep algorithm which uses timing without waiting. (3) In client-side prediction, which way is the improvement of the way two, all the operations of the client need to be confirmed by the server to operate.The lockstep algorithm does not have a dedicated server for data forwarding, it uses a turn-based approach, where each client broadcasts operation instructions to each other in a turn, and the computation and execution are in each client, so the amount of data in the network is generally not too large. The round here is a time slice turn defined by all clients, and the length of time is generally within the time range that is indistinguishable to the human eye. The instructions received in the same time slice do not need to be executed in the order they are received, because the operations in the same time slice are considered to happen at the same time [19]. The dead reckoning algorithm requires the establishment of a state protocol unit (PDU), in which the client sends PDUs to inform other clients of its information, and other clients correct the error value according to the received PDUs and the algorithm specified in the protocol and then predict the PDU information of the sending client. Figure 1 shows the construction framework of the collaborative management experiment platform.Figure 1
A framework of collaborative management experiment platform.A working collaborative system generally integrates multiple synchronization algorithms. This experimental platform uses Unity3D’s UNET technology to achieve data synchronization, combining the synchronization mechanisms of mode 1 and mode 2. The server only forwards instructions, and each client keeps a complete copy of the data, and the real computation and operation are done by the respective client. The algorithms to solve the concurrency problem of cooperative systems are lockstep, serialization, token ring, and “multiple tokens rings” + “operation queue” [20]. The locking method is to lock the common operation object, only the client with a lock operation authority can operate, and the client without lock authority can only wait; the disadvantage is easy to deadlock and does not allow multiple clients to operate at the same time. The serialization method is to treat the instruction sent to operate the shared object as an event and then define all the events into a global sequence in some way, and this shared object executes the operation in this global sequence order, but the disadvantage of this method is low operation efficiency and does not allow multiple clients to operate at the same time. From the point of view of the average moving distance, the optimized and initial positions of the nodes are less affected by the number of iterations: that is, as the number of iterations of the algorithm increases, the average moving distance is more stable when the number of nodes is M. The token ring method means that the system maintains a token and only the client that gets the token can operate the shared object, but the disadvantage is that the token can be easily lost and the system is down. The “multiple token ring” + “operation queue” method uses multiple tokens with different privileges and a server-controlled operation queue to operate the shared objects.Before describing the dynamics of controller nodes in Nonlinear-CN, we first review the controller node state update strategy in wireless control networks (WANs). The controller node state update strategy in WCN is as follows: at the kth moment, the controller node in WCN performs a linear operation on its state data at the k-1th moment with the data information received from its neighboring nodes and sensors; the result of the operation is the node’s state data at the kth moment. The Nonlinear-CN nodes of the neighboring sensors are responsible for receiving the sensed data from the sensors, while the controller nodes in the Nonlinear-CN communicate with each other and operate the control signals on the received data information (e.g., sensed data and node communication values) according to the Nonlinear-CN preset control policy, while in the Nonlinear-CN nodes of the neighboring actuators, Nonlinear-CN nodes are responsible for communicating with the actuators and transmitting the computed control signals to the corresponding actuators [21]. In the Nonlinear-CN, the controller node has a nonlinear relationship between the output variable and its input variable due to the node’s error in the calculation of the control quantity, i.e., the controller works in a nonlinear state. For this nonlinear characteristic, this paper describes it using an SNNM-based control model. Using this treatment, the stability analysis and control problems of linear systems under nonlinear controller control are discussed in a unified framework. And this nonlinear characteristic is specifically described by the following nonlinear equation.
(1)U⊗V=∑Hu⋅γ1/2+34μℏv,where Hu is the state of the nodes of the wireless nonlinear control network, γ is the measured value of the sensor Q responsible for detecting the output of the controlled object, v is the nonlinear function, and μ is the input variable of the nonlinear function ℏ of the nodes in the network. And U satisfies the following constraints:
(2)U=−k∑i−1npiln1pi,where pi portrays the nonlinear characteristics of the controller k and this nonlinear constraint becomes the fan condition. The Nonlinear-CN node iv of the neighboring actuator sends its state data to the actuator, which is responsible for computing a linear combination of the state data it receives and using it as a controlled quantity, where px,y is the control quantity applied by the actuator to the controlled object and λ is the scalar quantity:
(3)Hx=φ∑y∈γ∑x∈χpx,y⋅lnpx,y+Ax+Cy+λ.Because the controlled object studied in this paper is a linear model, the nonlinear variables of the closed-loop system are only the sectoral area constraints of Nonlinear-CN, so the dimensionality of the nonlinear part of the judgment matrix is relatively small. The controller network acting as an information processing system is responsible for the optimization of the state signal communication configuration and the calculation of the distributed control signals [22]. A controller network consisting of several controllers is designed for a physical process system composed of without loss of generality; it is assumed that the number of controllers is the same as the number of subphysical systems and that each controller is equipped with a state detection module and a communication module. On the one hand, the controller obtains the state information of the subphysical system through the state detection module, while with the help of the communication module, it can obtain the state information of its neighboring subphysical systems. On the other hand, the controller uses the state information of the subphysical system and its neighboring subphysical systems to calculate the control inputs.


### 3.2. Application of Intelligent Sensor Network on Collaborative Experiment Platform

Due to the advantages of smart sensors in terms of function and size, they can complete the ubiquitous intelligent sensing of all equipment in the laboratory, and then, the sensors need to form a wireless ubiquitous network among them to realize the long-distance transmission of fault diagnosis results or original vibration signals. The topology of the smart sensor network structure is shown in Figure2. The network is divided into two major areas: the fault diagnosis area and trend prediction area. The fault diagnosis area is responsible for the on-site diagnosis of equipment failure, and the trend prediction area is according to the equipment deterioration trend to complete the prediction of equipment failure. The fault diagnosis zone and the trend prediction zone correspond to the edge side and the center side of the cooperative computing architecture, respectively.Figure 2
Smart sensor network topology diagram.The smart sensors designed in this paper constitute each sensor unit in the network, and all smart sensor units can independently complete the signal acquisition, fault diagnosis, and communication functions; the gateway unit is the smart sensor unit to remove the signal acquisition part, the wireless communication, and wired communication with the host computer to optimize the implementation of the function. The sensor network adopts the form of a hierarchical network, and each sensor in the network is divided into end units (m-level units), intermediate units (m-1~1-level units), and top units (0-level units) according to whether they can communicate directly with the gateway, with m being the depth of the network. The diagnostic results of the end unit or the collected vibration data need to be forwarded by the intermediate unit and the top unit to reach the gateway unit; the data of the intermediate unit, including its data and the data from the lower unit, need to be forwarded by the higher intermediate unit or the top unit to be sent to the gateway unit; the data of the top unit, including its data and the data from the intermediate unit, are sent directly to the gateway unit; the gateway unit is responsible for the data aggregation [23]. In WAN, there are often network delays. If the synchronization mechanism is not designed properly, it will not only cause lag but also lead to data and concurrency problems among users. When the same operation object is operated by multiple users at the same time, a concurrency problem will occur. If the concurrency problem is not controlled, errors such as data update loss, dirty data read, and data read nonreproducible will occur. The data from the top-level unit, including its data and data from the intermediate unit, is sent directly to the gateway unit; the gateway unit is responsible for summarizing the data and sending it to the upper computer through wired transmission; the upper computer receives the data sent from the gateway and displays the operating status of each device visually through the graphical human-machine interface. By uploading the data step by step, the data of each sensor can finally converge to the gateway and be displayed in real-time on the upper computer. The commands sent by the upper computer are sent through the gateway oppositely and are sent down step by step until the corresponding sensor units have received the commands.If conditions permit, each sensor is connected to more than one top-level sensor to prevent safety accidents caused by the failure of the top-level sensor when the diagnosis results cannot be sent to the top unit in time. To avoid duplicate sending of the same data, the sensor of the intermediate unit and the sensor of the top unit compare and judge the received data to eliminate invalid duplicate data and improve channel utilization.To perform the master-slave motion unit pairing, it is necessary to establish the spatial position relationship between the base coordinate system of master and slave motion units and the coordinate system where the slave motion units make the cooperative motion concerning the master motion unit, i.e., the cooperative coordinate system [24]. After the master-slave coordinate system relationship is determined, the master-slave cotransformation formula can be derived, and the master-slave trajectory constraint equation can be solved for any given comovement trajectory by using the cotransformation formula. The following equation is shown.
(4)Rfx=∫∫gtdt=1+γn⋅∑x−1ft.Cooperative motion control requires trajectory constraints and time constraints. The trajectory constraint depends on the master-slave cotransformation, while the time constraint depends on the master-slave synchronous velocity planning. A coordinate system is a coordinate system for master-slave control in which the slave motion unit makes comovement relative to the master motion unit, which is defined by the user and can be generally fixed at the end of the master motion unit, similar to the tool coordinate system. Using covariant transformation formula, the master-slave linkage trajectory of a given section is constrained.(5)Iαfη=mziωαfω=ωαei2παfω.In practice, in addition to the unknown position of the base coordinate system, the kinematic parameters of the indexing machine often need to be established during the calibration process. Since the number of axes of the indexing machine is small, the base coordinate system and kinematic parameters can be established based on the joint axis method. Based on this, a tool coordinate system needs to be established on the indexing machine to facilitate the construction of theoretical contact points for error estimation. In this paper, we use the tip of the alignment pin (only position information, no attitude information) as the origin of the tool coordinate system and the direction of the end linkage of the alignment machine as the direction of the tool coordinate system and use the data from the base coordinate system calibration to construct a tool coordinate system, to avoid the complicated operation of a recalibration by the common tool coordinate system calibration method, and the problem of introducing new errors by recalibration. This avoids the complexity of recalibration by the common tool coordinate system calibration method and the introduction of new errors in recalibration. The errorSsigma is calculated as follows:
(6)Ssigma=1t∫0Txt−γtdt+C.To improve the performance of the coherent filtering of the sensor network, each sensor is responsible for sending its own and receiving observed measurements from neighboring sensors, in addition to measuring the observed measurements of the observed object. Relay nodes are added because, on the one hand, the wireless sensor/actuator network is deployed over a rather wide area such that collaborative processing by relay nodes (e.g., data relay forwarding) is required; on the other hand, the observation vectors of individual sensors are not the same due to the different sensor types, and relay nodes are required for data aggregation and preprocessing before generating control signals. By using algebraic graph theory and linear matrix inequalities, the operations of the relay node and the control algorithm in the actuator are processed in the same framework to obtain sufficient conditions to make the system stable.


## 3.1. Collaborative Management Experimental Platform Technology Foundation

In a collaborative system, not only synchronization problems but also concurrency problems need to be solved. In wide-area networks, there are often network delays. If the synchronization mechanism is not designed properly, it will not only cause lag but also lead to uncoordinated data and concurrency problems among users. When the same operation object is operated by multiple users at the same time, a concurrency problem will occur. If the concurrency problem is not controlled, errors such as data update loss, data dirty read, and data read nonreproducible will occur [18]. The synchronization mechanism of the cooperative system can be divided into the following three categories: (1) peer to peer, in which there is no special server for data processing and forwarding, and all clients are in the same position, and the most typical algorithm is lockstep algorithm. (2) In server-client, the server is at the core of this synchronization mechanism, and it is used to process and forward the data of each client; once the server is down, the whole cooperative system will be paralyzed, and typical algorithms are dead rocking algorithm and lockstep algorithm which uses timing without waiting. (3) In client-side prediction, which way is the improvement of the way two, all the operations of the client need to be confirmed by the server to operate.The lockstep algorithm does not have a dedicated server for data forwarding, it uses a turn-based approach, where each client broadcasts operation instructions to each other in a turn, and the computation and execution are in each client, so the amount of data in the network is generally not too large. The round here is a time slice turn defined by all clients, and the length of time is generally within the time range that is indistinguishable to the human eye. The instructions received in the same time slice do not need to be executed in the order they are received, because the operations in the same time slice are considered to happen at the same time [19]. The dead reckoning algorithm requires the establishment of a state protocol unit (PDU), in which the client sends PDUs to inform other clients of its information, and other clients correct the error value according to the received PDUs and the algorithm specified in the protocol and then predict the PDU information of the sending client. Figure 1 shows the construction framework of the collaborative management experiment platform.Figure 1
A framework of collaborative management experiment platform.A working collaborative system generally integrates multiple synchronization algorithms. This experimental platform uses Unity3D’s UNET technology to achieve data synchronization, combining the synchronization mechanisms of mode 1 and mode 2. The server only forwards instructions, and each client keeps a complete copy of the data, and the real computation and operation are done by the respective client. The algorithms to solve the concurrency problem of cooperative systems are lockstep, serialization, token ring, and “multiple tokens rings” + “operation queue” [20]. The locking method is to lock the common operation object, only the client with a lock operation authority can operate, and the client without lock authority can only wait; the disadvantage is easy to deadlock and does not allow multiple clients to operate at the same time. The serialization method is to treat the instruction sent to operate the shared object as an event and then define all the events into a global sequence in some way, and this shared object executes the operation in this global sequence order, but the disadvantage of this method is low operation efficiency and does not allow multiple clients to operate at the same time. From the point of view of the average moving distance, the optimized and initial positions of the nodes are less affected by the number of iterations: that is, as the number of iterations of the algorithm increases, the average moving distance is more stable when the number of nodes is M. The token ring method means that the system maintains a token and only the client that gets the token can operate the shared object, but the disadvantage is that the token can be easily lost and the system is down. The “multiple token ring” + “operation queue” method uses multiple tokens with different privileges and a server-controlled operation queue to operate the shared objects.Before describing the dynamics of controller nodes in Nonlinear-CN, we first review the controller node state update strategy in wireless control networks (WANs). The controller node state update strategy in WCN is as follows: at the kth moment, the controller node in WCN performs a linear operation on its state data at the k-1th moment with the data information received from its neighboring nodes and sensors; the result of the operation is the node’s state data at the kth moment. The Nonlinear-CN nodes of the neighboring sensors are responsible for receiving the sensed data from the sensors, while the controller nodes in the Nonlinear-CN communicate with each other and operate the control signals on the received data information (e.g., sensed data and node communication values) according to the Nonlinear-CN preset control policy, while in the Nonlinear-CN nodes of the neighboring actuators, Nonlinear-CN nodes are responsible for communicating with the actuators and transmitting the computed control signals to the corresponding actuators [21]. In the Nonlinear-CN, the controller node has a nonlinear relationship between the output variable and its input variable due to the node’s error in the calculation of the control quantity, i.e., the controller works in a nonlinear state. For this nonlinear characteristic, this paper describes it using an SNNM-based control model. Using this treatment, the stability analysis and control problems of linear systems under nonlinear controller control are discussed in a unified framework. And this nonlinear characteristic is specifically described by the following nonlinear equation.
(1)U⊗V=∑Hu⋅γ1/2+34μℏv,where Hu is the state of the nodes of the wireless nonlinear control network, γ is the measured value of the sensor Q responsible for detecting the output of the controlled object, v is the nonlinear function, and μ is the input variable of the nonlinear function ℏ of the nodes in the network. And U satisfies the following constraints:
(2)U=−k∑i−1npiln1pi,where pi portrays the nonlinear characteristics of the controller k and this nonlinear constraint becomes the fan condition. The Nonlinear-CN node iv of the neighboring actuator sends its state data to the actuator, which is responsible for computing a linear combination of the state data it receives and using it as a controlled quantity, where px,y is the control quantity applied by the actuator to the controlled object and λ is the scalar quantity:
(3)Hx=φ∑y∈γ∑x∈χpx,y⋅lnpx,y+Ax+Cy+λ.Because the controlled object studied in this paper is a linear model, the nonlinear variables of the closed-loop system are only the sectoral area constraints of Nonlinear-CN, so the dimensionality of the nonlinear part of the judgment matrix is relatively small. The controller network acting as an information processing system is responsible for the optimization of the state signal communication configuration and the calculation of the distributed control signals [22]. A controller network consisting of several controllers is designed for a physical process system composed of without loss of generality; it is assumed that the number of controllers is the same as the number of subphysical systems and that each controller is equipped with a state detection module and a communication module. On the one hand, the controller obtains the state information of the subphysical system through the state detection module, while with the help of the communication module, it can obtain the state information of its neighboring subphysical systems. On the other hand, the controller uses the state information of the subphysical system and its neighboring subphysical systems to calculate the control inputs.


## 3.2. Application of Intelligent Sensor Network on Collaborative Experiment Platform

Due to the advantages of smart sensors in terms of function and size, they can complete the ubiquitous intelligent sensing of all equipment in the laboratory, and then, the sensors need to form a wireless ubiquitous network among them to realize the long-distance transmission of fault diagnosis results or original vibration signals. The topology of the smart sensor network structure is shown in Figure2. The network is divided into two major areas: the fault diagnosis area and trend prediction area. The fault diagnosis area is responsible for the on-site diagnosis of equipment failure, and the trend prediction area is according to the equipment deterioration trend to complete the prediction of equipment failure. The fault diagnosis zone and the trend prediction zone correspond to the edge side and the center side of the cooperative computing architecture, respectively.Figure 2
Smart sensor network topology diagram.The smart sensors designed in this paper constitute each sensor unit in the network, and all smart sensor units can independently complete the signal acquisition, fault diagnosis, and communication functions; the gateway unit is the smart sensor unit to remove the signal acquisition part, the wireless communication, and wired communication with the host computer to optimize the implementation of the function. The sensor network adopts the form of a hierarchical network, and each sensor in the network is divided into end units (m-level units), intermediate units (m-1~1-level units), and top units (0-level units) according to whether they can communicate directly with the gateway, with m being the depth of the network. The diagnostic results of the end unit or the collected vibration data need to be forwarded by the intermediate unit and the top unit to reach the gateway unit; the data of the intermediate unit, including its data and the data from the lower unit, need to be forwarded by the higher intermediate unit or the top unit to be sent to the gateway unit; the data of the top unit, including its data and the data from the intermediate unit, are sent directly to the gateway unit; the gateway unit is responsible for the data aggregation [23]. In WAN, there are often network delays. If the synchronization mechanism is not designed properly, it will not only cause lag but also lead to data and concurrency problems among users. When the same operation object is operated by multiple users at the same time, a concurrency problem will occur. If the concurrency problem is not controlled, errors such as data update loss, dirty data read, and data read nonreproducible will occur. The data from the top-level unit, including its data and data from the intermediate unit, is sent directly to the gateway unit; the gateway unit is responsible for summarizing the data and sending it to the upper computer through wired transmission; the upper computer receives the data sent from the gateway and displays the operating status of each device visually through the graphical human-machine interface. By uploading the data step by step, the data of each sensor can finally converge to the gateway and be displayed in real-time on the upper computer. The commands sent by the upper computer are sent through the gateway oppositely and are sent down step by step until the corresponding sensor units have received the commands.If conditions permit, each sensor is connected to more than one top-level sensor to prevent safety accidents caused by the failure of the top-level sensor when the diagnosis results cannot be sent to the top unit in time. To avoid duplicate sending of the same data, the sensor of the intermediate unit and the sensor of the top unit compare and judge the received data to eliminate invalid duplicate data and improve channel utilization.To perform the master-slave motion unit pairing, it is necessary to establish the spatial position relationship between the base coordinate system of master and slave motion units and the coordinate system where the slave motion units make the cooperative motion concerning the master motion unit, i.e., the cooperative coordinate system [24]. After the master-slave coordinate system relationship is determined, the master-slave cotransformation formula can be derived, and the master-slave trajectory constraint equation can be solved for any given comovement trajectory by using the cotransformation formula. The following equation is shown.
(4)Rfx=∫∫gtdt=1+γn⋅∑x−1ft.Cooperative motion control requires trajectory constraints and time constraints. The trajectory constraint depends on the master-slave cotransformation, while the time constraint depends on the master-slave synchronous velocity planning. A coordinate system is a coordinate system for master-slave control in which the slave motion unit makes comovement relative to the master motion unit, which is defined by the user and can be generally fixed at the end of the master motion unit, similar to the tool coordinate system. Using covariant transformation formula, the master-slave linkage trajectory of a given section is constrained.(5)Iαfη=mziωαfω=ωαei2παfω.In practice, in addition to the unknown position of the base coordinate system, the kinematic parameters of the indexing machine often need to be established during the calibration process. Since the number of axes of the indexing machine is small, the base coordinate system and kinematic parameters can be established based on the joint axis method. Based on this, a tool coordinate system needs to be established on the indexing machine to facilitate the construction of theoretical contact points for error estimation. In this paper, we use the tip of the alignment pin (only position information, no attitude information) as the origin of the tool coordinate system and the direction of the end linkage of the alignment machine as the direction of the tool coordinate system and use the data from the base coordinate system calibration to construct a tool coordinate system, to avoid the complicated operation of a recalibration by the common tool coordinate system calibration method, and the problem of introducing new errors by recalibration. This avoids the complexity of recalibration by the common tool coordinate system calibration method and the introduction of new errors in recalibration. The errorSsigma is calculated as follows:
(6)Ssigma=1t∫0Txt−γtdt+C.To improve the performance of the coherent filtering of the sensor network, each sensor is responsible for sending its own and receiving observed measurements from neighboring sensors, in addition to measuring the observed measurements of the observed object. Relay nodes are added because, on the one hand, the wireless sensor/actuator network is deployed over a rather wide area such that collaborative processing by relay nodes (e.g., data relay forwarding) is required; on the other hand, the observation vectors of individual sensors are not the same due to the different sensor types, and relay nodes are required for data aggregation and preprocessing before generating control signals. By using algebraic graph theory and linear matrix inequalities, the operations of the relay node and the control algorithm in the actuator are processed in the same framework to obtain sufficient conditions to make the system stable.


## 4. Application of Collaborative Experiment Management Platform Based on Intelligent Sensor Network in Innovation and Entrepreneurship Education

Innovation and entrepreneurship education are sublimation and development. Innovation and entrepreneurship education focusing on the cultivation of students’ pioneering personalities is a supplement and improvement to the limited nature of current higher education to meet the new needs of social talents. It involves a wide range of knowledge and theories, which can promote the learning and complementation of various disciplines and specialties and stimulate new vitality in professional education. At the same time, innovation and entrepreneurship education based on professionalism can also supplement professional education with timely information on industry dynamics and market frontiers, update teaching contents, broaden teachers’ and students’ professional horizons, constantly discover their problems and shortcomings in professionalism, and continuously draw nutrients from professional education and interdisciplinary learning, thus also contributing to the innovative development of professional education and teaching system [25]. Common goals are the basis for the formation of symbiotic relationships. The integrated development of a symbiotic system requires the interaction of various subsystems.After the networked smart sensor joins the sensor network, it stays in the TCP server listening state, ready to accept the network master node corresponding to the networked smart sensor’s Mini node to establish an I/O channel connection request by the GET method of HTTP. When the networked smart sensor listens to a connection request from the network master node, it then accepts the request, at which point an I/O channel and communication link are established with the sensor network master node. The networked smart sensor and the Mini node in the network master node then monitor the information flow on the I/O channel and communicate with each other, respectively. A perfect curriculum system of innovation and entrepreneurship education can promote the all-round development of students’ moral, intellectual, physical, aesthetic, and labor and also help to explore students’ innovation potential, promote the development of students’ independence, and lay a good foundation for students’ development after they leave school, which is also in line with the requirements of Marx’s theory on the all-round development of human beings. Therefore, a scientific and complete curriculum system of innovation and entrepreneurship education must be established as soon as possible.The analysis and application of the multistream theoretical framework are to analyze the process of introducing the innovation and entrepreneurship policy for college students from multiple perspectives and levels, and how to evolve effective measures from the influencing elements of the three main streams to be applied in the “upgraded” follow-up policy is the further research of this paper. On the one hand, because the wireless sensor/actuator network is deployed over a fairly large area, collaborative processing by relay nodes (e.g., data relay forwarding) is required; on the other hand, because of the different sensor types, the observation vectors of individual sensors are not the same, and relay nodes are required for data aggregation and preprocessing before generating control signals. The ultimate goal is to develop a networked intelligent sensor based on the self-developed AgileSN network application layer protocol, which is capable of autonomous operation, self-identification, automatic discovery, and external network communication to evaluate the effect of innovation and entrepreneurship education and give timely feedback. To test the above functions and its network data transmission in real time, this paper uses a wired networked smart sensor based on the AgileSN protocol as a sensor network node and a PC running a VIP (Instant Reconfigurable Sensor Network Virtual Instrumentation Platform) software as the master node of the sensor network to build a sensor network through the laboratory internal 10 M Ethernet for testing [26]. During the system test, the Ethernet smart sensor based on AgileSN protocol is powered on to join the sensor network and waits for the I/O link and communication link to be established with VIP. After establishing the I/O link with IsVIP, the room temperature is collected, and after disambiguation and reaveraging filtering, the data is packaged and sent to VIP via Ethernet for display, analysis, and processing according to the Agilent protocol. The schematic diagram of the test system is shown in Figure 3.Figure 3
Schematic diagram of the test system.The integration of innovation and entrepreneurship education and professional education in colleges and universities is a long-term, complex, and systematic educational practice, and the integration of the two is the basic path to improving the innovation and entrepreneurship of college students. In the process of innovation and entrepreneurship education, although schools encourage and support students to carry out innovation and entrepreneurship and provide good opportunities and an environment for innovation and entrepreneurship, the motivation for innovation and entrepreneurship education for students is relatively lacking [27]. Motivation is an important precursor of behavior, and students’ innovation and entrepreneurship purpose directly affect their motivation to participate in innovation and entrepreneurship activities and practices on and off-campus. Students should be guided to strengthen their sense of self-responsibility and social responsibility and help them form a correct concept and spirit of innovation and entrepreneurship while cultivating their innovation and entrepreneurship ability.


## 5. Experimental Verification and Conclusion

### 5.1. Intelligent Sensor Collaborative Experiment Management Platform Performance Testing

The data collected by the smart sensor are 16-bit signed fixed-point numbers, while the data collected by the data acquisition card are floating-point numbers, which are converted to g units for effective comparison. The vibration signals under different faults are shown in Figure4. The horizontal axis is the sampling time and the vertical axis is the vibration amplitude in g. Comparing the waveform, peak value, and RMS value of the data collected by the standard acquisition card and the smart sensor, the results are shown in Figure 4, and the data collected by the standard acquisition card and the data collected by the smart sensor match from the two indicators of peak value and RMS value. The data collected by the standard acquisition card and the data collected by the smart sensor match. As the data is collected by the fitting method, there is a slight energy loss when the vibration is propagated, but it does not affect the validity of the data.Figure 4
Vibration waveforms under different states collected by smart sensors.MATLAB was used to design the FIR filter and the Hilbert transform filter, generate the HDL code, and implement it on the FPGA. The designed low-pass and band-pass filters are used to filter the 100 KHz square wave signal sampled by the ADC. The low-pass filter retains the DC component of the signal, while the band-pass filter retains only the frequency component within the passband; it also shows a significant delay in the signal after the filter.The black curve is the vibration signal of the bearing inner ring fault, and the impact signal and modulated signal can be seen clearly; the blue curve is the envelope curve obtained by using Hilbert transform directly, and the red curve is the envelope curve obtained by using the Hilbert transform. It can be seen that there are some differences between the envelope curves obtained by the filter and the envelope curves obtained by the direct Hilbert transform, but from the envelope spectrum curves in Figure5, these differences do not have an obvious negative impact on the extraction of the fault characteristic frequency (141 Hz, the theoretical fault frequency is 145.15 Hz), and the designed Hilbert transform filter can well realize the envelope demodulation function of the fault signal, and the characteristic frequency of the fault signal is distinguished after demodulation.Figure 5
Effect of FIR filter.The time and frequency domain features extracted using FPGA for different operating conditions are shown separately. Compared with the normal operating condition, most of the eigenvalues in the fault condition show an increasing trend; some of the eigenvalues in the same operating condition do not differ much from each other, which is because these features are redundant for bearing faults. The time-frequency characteristics of the bearing fault vibration signal, i.e., IMF components, and the results of the bearing outer ring fault IMF extraction are shown in Figure6. It can be seen that as the decomposition proceeds, the frequency components of the IMF components become lower and lower. The IMF components with high-frequency components, especially IMF1 and IMF2, contain obvious shock components. In constructing the feature vectors, the percentage of the first 5 IMF energies, which constitute the time-frequency features, is used. The variance filtering method selects the features that contribute more to the fault diagnosis to build the feature vectors reflecting the operation status of the equipment, including 6 peaks, standard deviation, effective value, impulse factor, margin factor, and cliff factor in the time domain features, 3 centers of gravity frequency, mean square frequency, and frequency variance in the frequency domain features, and 3 energy shares of IMF1, IMF2, and IMF3 in the time-frequency features. When the networked smart sensor listens to a connection request from the network master node, it accepts the request and establishes an I/O channel and communication link with the sensor network master node. The networked smart sensor and the Mini node in the network master node then monitor the flow of information on the I/O channel and communicate with each other. Finally, 12 features were selected from the 20 features to reconstruct the feature vectors, and the features were calculated in real time and compared with the preset thresholds in real time to obtain the equipment operation status.Figure 6
Hilbert transform filter implementation effect.


### 5.2. Numerical Simulation of Innovation and Entrepreneurship Education Based on Collaborative Experiment Management Platform of Intelligent Sensor Network

To study the controller gain of the nonvulnerable controller under different trigger probabilities and different actuator failure rates, respectively, six experiments were conducted on the medium networked control system according to different trigger conditions and different actuator failure rates, and a total of four different event trigger probabilities and two different actuator failure probabilities were set. According to Equation (5), the control gains 1 K and 2 K for different trigger conditions, and different actuator failure rates can be obtained and listed. The state response and event-triggered release time for each experiment are shown in Figure 7.Figure 7
State response and event trigger time test.The state response curves in Figure7 show that the states in experiment 3 converge to the equilibrium point faster than those in experiment 2. This is because the event-triggered control is implemented based on the difference between the current state and the previous event-triggered moment, which helps to reduce the transmission of data information compared to the fixed-cycle sampled-data control. In experiment 4, the event trigger probability is the same as that of experiment 3 while the actuator failure probability is 2. The corresponding state response curves and event trigger moments are obtained for the nonvulnerable controller gains of 1 K and 2 K. From Figure 7, it can be seen that the state response curves in experiment 3 converge to the equilibrium point faster than those in experiment 4 because the probability of actuator failure in experiment 4 is larger than that in experiment 3. The convergence time of the state response curve in experiment 2 is smaller than that in experiment 5, which shows that the smaller the actuator failure rate, the smaller the convergence time of the state response of the networked control system under the control of the proposed nonfragile control algorithm.From Figure8, it can be seen that there are concentrations and overlaps when nodes are deployed randomly, resulting in poor coverage; by redeploying the nodes through the 3D-IVF algorithm, the positions of the nodes all move, and the curve indicates the trajectory of the nodes during the iteration of the algorithm; after the deployment of the algorithm with a set number of iterations, the nodes can be distributed more uniformly in the monitored spatial area; with the increase of the number of iterations, the coverage increases gradually. The coverage increases gradually with the increase of iterations. The average coverage rate of the 3D-VF-based 3D coverage algorithm is 91.39% and the deployment time is 2.7569 s; the average coverage rate of the 3D-IVF-based 3D coverage algorithm is 92.15% and the deployment time is 2.5857 s; the average coverage rate is improved by 0.76% and the deployment time is reduced by 0.1712 s. When the number of iterations of the algorithm reaches 30, the coverage rate of the four-node deployment strategies will stabilize, i.e., the forces between the nodes of the 3D-VF based 3D coverage algorithm of this paper reach the equilibrium state. From the perspective of the average moving distance, the optimized position and the initial position of the nodes are less affected by the number of iterations: that is, the average moving distance is stable when the number of nodes is M as the number of iterations of the algorithm increases, while the average moving distance of the nodes of the other three strategies tends to increase with the number of iterations.Figure 8
3D spatial deployment process.


## 5.1. Intelligent Sensor Collaborative Experiment Management Platform Performance Testing

The data collected by the smart sensor are 16-bit signed fixed-point numbers, while the data collected by the data acquisition card are floating-point numbers, which are converted to g units for effective comparison. The vibration signals under different faults are shown in Figure4. The horizontal axis is the sampling time and the vertical axis is the vibration amplitude in g. Comparing the waveform, peak value, and RMS value of the data collected by the standard acquisition card and the smart sensor, the results are shown in Figure 4, and the data collected by the standard acquisition card and the data collected by the smart sensor match from the two indicators of peak value and RMS value. The data collected by the standard acquisition card and the data collected by the smart sensor match. As the data is collected by the fitting method, there is a slight energy loss when the vibration is propagated, but it does not affect the validity of the data.Figure 4
Vibration waveforms under different states collected by smart sensors.MATLAB was used to design the FIR filter and the Hilbert transform filter, generate the HDL code, and implement it on the FPGA. The designed low-pass and band-pass filters are used to filter the 100 KHz square wave signal sampled by the ADC. The low-pass filter retains the DC component of the signal, while the band-pass filter retains only the frequency component within the passband; it also shows a significant delay in the signal after the filter.The black curve is the vibration signal of the bearing inner ring fault, and the impact signal and modulated signal can be seen clearly; the blue curve is the envelope curve obtained by using Hilbert transform directly, and the red curve is the envelope curve obtained by using the Hilbert transform. It can be seen that there are some differences between the envelope curves obtained by the filter and the envelope curves obtained by the direct Hilbert transform, but from the envelope spectrum curves in Figure5, these differences do not have an obvious negative impact on the extraction of the fault characteristic frequency (141 Hz, the theoretical fault frequency is 145.15 Hz), and the designed Hilbert transform filter can well realize the envelope demodulation function of the fault signal, and the characteristic frequency of the fault signal is distinguished after demodulation.Figure 5
Effect of FIR filter.The time and frequency domain features extracted using FPGA for different operating conditions are shown separately. Compared with the normal operating condition, most of the eigenvalues in the fault condition show an increasing trend; some of the eigenvalues in the same operating condition do not differ much from each other, which is because these features are redundant for bearing faults. The time-frequency characteristics of the bearing fault vibration signal, i.e., IMF components, and the results of the bearing outer ring fault IMF extraction are shown in Figure6. It can be seen that as the decomposition proceeds, the frequency components of the IMF components become lower and lower. The IMF components with high-frequency components, especially IMF1 and IMF2, contain obvious shock components. In constructing the feature vectors, the percentage of the first 5 IMF energies, which constitute the time-frequency features, is used. The variance filtering method selects the features that contribute more to the fault diagnosis to build the feature vectors reflecting the operation status of the equipment, including 6 peaks, standard deviation, effective value, impulse factor, margin factor, and cliff factor in the time domain features, 3 centers of gravity frequency, mean square frequency, and frequency variance in the frequency domain features, and 3 energy shares of IMF1, IMF2, and IMF3 in the time-frequency features. When the networked smart sensor listens to a connection request from the network master node, it accepts the request and establishes an I/O channel and communication link with the sensor network master node. The networked smart sensor and the Mini node in the network master node then monitor the flow of information on the I/O channel and communicate with each other. Finally, 12 features were selected from the 20 features to reconstruct the feature vectors, and the features were calculated in real time and compared with the preset thresholds in real time to obtain the equipment operation status.Figure 6
Hilbert transform filter implementation effect.


## 5.2. Numerical Simulation of Innovation and Entrepreneurship Education Based on Collaborative Experiment Management Platform of Intelligent Sensor Network

To study the controller gain of the nonvulnerable controller under different trigger probabilities and different actuator failure rates, respectively, six experiments were conducted on the medium networked control system according to different trigger conditions and different actuator failure rates, and a total of four different event trigger probabilities and two different actuator failure probabilities were set. According to Equation (5), the control gains 1 K and 2 K for different trigger conditions, and different actuator failure rates can be obtained and listed. The state response and event-triggered release time for each experiment are shown in Figure 7.Figure 7
State response and event trigger time test.The state response curves in Figure7 show that the states in experiment 3 converge to the equilibrium point faster than those in experiment 2. This is because the event-triggered control is implemented based on the difference between the current state and the previous event-triggered moment, which helps to reduce the transmission of data information compared to the fixed-cycle sampled-data control. In experiment 4, the event trigger probability is the same as that of experiment 3 while the actuator failure probability is 2. The corresponding state response curves and event trigger moments are obtained for the nonvulnerable controller gains of 1 K and 2 K. From Figure 7, it can be seen that the state response curves in experiment 3 converge to the equilibrium point faster than those in experiment 4 because the probability of actuator failure in experiment 4 is larger than that in experiment 3. The convergence time of the state response curve in experiment 2 is smaller than that in experiment 5, which shows that the smaller the actuator failure rate, the smaller the convergence time of the state response of the networked control system under the control of the proposed nonfragile control algorithm.From Figure8, it can be seen that there are concentrations and overlaps when nodes are deployed randomly, resulting in poor coverage; by redeploying the nodes through the 3D-IVF algorithm, the positions of the nodes all move, and the curve indicates the trajectory of the nodes during the iteration of the algorithm; after the deployment of the algorithm with a set number of iterations, the nodes can be distributed more uniformly in the monitored spatial area; with the increase of the number of iterations, the coverage increases gradually. The coverage increases gradually with the increase of iterations. The average coverage rate of the 3D-VF-based 3D coverage algorithm is 91.39% and the deployment time is 2.7569 s; the average coverage rate of the 3D-IVF-based 3D coverage algorithm is 92.15% and the deployment time is 2.5857 s; the average coverage rate is improved by 0.76% and the deployment time is reduced by 0.1712 s. When the number of iterations of the algorithm reaches 30, the coverage rate of the four-node deployment strategies will stabilize, i.e., the forces between the nodes of the 3D-VF based 3D coverage algorithm of this paper reach the equilibrium state. From the perspective of the average moving distance, the optimized position and the initial position of the nodes are less affected by the number of iterations: that is, the average moving distance is stable when the number of nodes is M as the number of iterations of the algorithm increases, while the average moving distance of the nodes of the other three strategies tends to increase with the number of iterations.Figure 8
3D spatial deployment process.


## 6. Conclusion

The development of innovation and entrepreneurship education in colleges and universities is an inevitable requirement to cultivate the entrepreneurial spirit of college students, improve the employment and entrepreneurship system, and better serve high-quality development and modernization. Strengthening the integration of innovation and entrepreneurship education and professional education is an important guiding principle to carry out innovation and entrepreneurship education. In this paper, the realization of an intelligent laboratory management system function based on IOT technology is based on the core of IoT technology, using the relevant technology of IoT, proposing the intelligent laboratory system structure and application model, and building a complete intelligent laboratory management system for innovation and entrepreneurship education with the system hardware taking the cloud edge router as the core. Secondly, the number of nodes required to be deployed in the set three-dimensional space is calculated theoretically for four kinds of innovation and entrepreneurship education experimental equipment situations, and the 3D-VF three-dimensional coverage algorithm is used for simulation testing; finally, the improved algorithms 3D-IVF and 3D-VF three-dimensional coverage algorithm are compared and simulated under different initial deployment situations, and the changes of their coverage effects are analyzed. From the experimental results, it can be seen that the proposed four deployment strategies are experimentally verified, and the nodes with full spatial coverage are better deployed under the premise of full coverage; under different initial deployment cases, the optimized coverage is influenced by the initial deployment, and the 3D-IVF 3D coverage algorithm is better, but further improvement is still needed. The system fails to solve the problem: the management of hardware equipment cannot be fully intelligent, and future research and exploration should be strengthened in the management of equipment and strive to use new technologies in the process of software development to truly achieve intelligent management of the laboratory.



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*Source: 9542280-2022-08-25.xml*