An IO-LINK bus panel for machine tools with IO expansion capabilities

Abstract

This invention relates to the field of adjustment and control technology, specifically to an IO-LINK bus panel for machine tools with IO expansion functionality. The bus panel includes a memory and a processor. The processor executes a computer program stored in the memory to perform the following steps: acquiring sensor data from different dimensions of the machine tool; evaluating the degree of deviation of the sensor data based on the sensor data of each dimension at each time point; determining the risk factor for each sensor based on the changing characteristics of the degree of deviation of the sensor data of each dimension within the neighborhood at the current time point; determining the potential risk value of each sensor and the type of sensor to be connected by combining the correlation between the degree of deviation of sensor data of different dimensions within the current time period and the risk factor; and determining recommended nodes based on the potential risk value and the path length between nodes. This invention improves the path expansion effect of the IO-LINK bus panel for machine tools.

Description

Technical Field

[0001] This invention relates to the field of adjustment and control technology, and more specifically to an IO-LINK bus panel for machine tools with IO expansion function. Background Technology

[0002] IO-LINK, a widely used intelligent sensor communication technology in industrial automation, boasts advantages such as simple installation, low cost, and stable communication, making it a crucial method for connecting sensors and actuators in modern machine tool systems. Current mainstream IO-LINK bus panels primarily employ a modular structure, connecting multiple slave nodes through a master station, which in turn connects to various sensors and actuators, enabling unified data acquisition and control command issuance. These panels typically possess basic status monitoring, communication fault identification, and device identification functions, meeting the needs of fixed sensor access in most operating conditions. However, during the operation of complex machine tools, subsystem states fluctuate frequently, and potential anomalies can evolve rapidly. A single-structure IO-LINK bus panel struggles to intelligently expand and dynamically deploy nodes based on actual operating conditions, thus limiting its further application in high-reliability monitoring and intelligent diagnostics.

[0003] Most existing IO-LINK mainline panels have a pre-defined structure with fixed sensor and actuator configurations, lacking the ability to dynamically perceive and adjust the structure based on actual operating conditions. This is especially problematic when subsystems exhibit potential risks or abnormal operating trends, as they cannot intelligently determine whether to expand detection equipment or reconfigure node resources based on the current system state. For example, when multiple sensors in a subsystem collaboratively reflect abnormal symptoms, the system cannot comprehensively consider the data correlation between sensors and potential risks to determine whether additional detection dimensions are needed. Simultaneously, the mainline panel cannot intelligently assess the device load and remaining capacity of each slave node to select the optimal expansion path. This structural rigidity and lack of perception strategies severely limit the adaptability of IO-LINK systems in complex and dynamic scenarios. Summary of the Invention

[0004] To address the issue of poor expansion performance in existing methods when expanding paths on machine tool IO-LINK bus panels, the present invention aims to provide a machine tool IO-LINK bus panel with IO expansion capabilities. The specific technical solution adopted is as follows:

[0005] This invention provides an IO-LINK bus panel for machine tools with IO expansion functionality. The bus panel includes a processor and a memory. The processor executes a computer program stored in the memory to perform the following steps:

[0006] Acquire sensor data from different dimensions of the machine tool equipment;

[0007] Based on the relationship between the sensor data values ​​of each dimension at each time point and the safe range, evaluate the degree of deviation of the sensor data of each dimension at each time point; based on the changing characteristics of the degree of deviation of the sensor data of each dimension in the time neighborhood of the current time point, determine the risk factor of each sensor.

[0008] By combining the correlation between the deviations of sensor data from different dimensions within the current time period and the aforementioned risk factors, the potential risk value of each sensor is determined, and the type of sensor to be connected is determined. Based on the potential risk value of the sensor and the path length between each node connected to the sensor and other nodes, the expansion optimization degree of each node is determined. The node is either the master station or the slave station of the IO-LINK bus panel.

[0009] Recommended nodes for the sensors to be connected are determined based on the degree of optimization.

[0010] Preferably, the step of evaluating the deviation of the sensor data for each dimension at each time point based on the relationship between the sensor data values ​​for each dimension at each time point and the safety range includes:

[0011] For any given moment:

[0012] Calculate the first difference between the sensor data of the candidate dimension at any given time and the median value of the safe range of the sensor data of the candidate dimension;

[0013] Based on the length of the safe range of the sensor data in the first difference and the candidate dimension, the degree of deviation of the sensor data in the candidate dimension at any given time is obtained;

[0014] The candidate dimension can be any dimension.

[0015] Preferably, the deviation degree of the sensor data of the candidate dimension at any given time is obtained based on the length of the safe range of the first difference and the sensor data of the candidate dimension, including:

[0016] The first feature value of the candidate dimension is defined as half the length of the safe range of the sensor data in the candidate dimension.

[0017] The ratio between the first difference and the first feature value is determined as the degree of deviation of the sensor data of the candidate dimension at any given time.

[0018] Preferably, determining the risk factor for each sensor based on the variation characteristics of the deviation of sensor data in each dimension within the current time neighborhood includes:

[0019] Based on the degree of deviation of the sensor data of each candidate dimension in the time neighborhood of the current time, a difference sequence of the corresponding degree of deviation is obtained;

[0020] If the element value in the difference sequence is greater than 0, then the element value at the corresponding position is taken as the change value of the deviation at the corresponding position; if the element value in the difference sequence is less than or equal to 0, then the change value of the deviation at the corresponding position is set to 0.

[0021] Based on the overall distribution of the deviation changes at all positions in the difference sequence corresponding to the current time neighborhood, the risk factors of the sensor in the candidate dimension are obtained.

[0022] Preferably, determining the potential risk value of each sensor by combining the correlation between the deviations of sensor data from different dimensions within the current time period and the risk factors includes:

[0023] For sensors with candidate dimensions:

[0024] Calculate the Pearson correlation coefficient between the sensor data of the candidate dimension and the sensor data of each other dimension in the current time period, and use it as the correlation value for each other dimension.

[0025] The product of the relevant value for each of the other dimensions and the corresponding risk factor is used as the second feature value for each of the other dimensions.

[0026] Based on the overall distribution of the second eigenvalues ​​of all other dimensions, the potential risk value of the sensor in the candidate dimension is determined.

[0027] Preferably, determining the potential risk value of the sensor in the candidate dimension based on the overall distribution of the second feature values ​​of all other dimensions includes: taking the average of the second feature values ​​of all other dimensions as the potential risk value of the sensor in the candidate dimension.

[0028] Preferably, determining the type of sensor to be accessed includes:

[0029] The sensor type corresponding to the dimension where the potential risk value is less than a preset first threshold and greater than a preset second threshold is used as the type of sensor to be connected; the preset first threshold is greater than the preset second threshold.

[0030] Preferably, determining the degree of expansion preference for each node based on the potential risk value of the sensor and the path length between each node connected to the sensor and other nodes includes:

[0031] For any node:

[0032] If the number of free ports in any node is 0, then the expansion preference of any node is set to 0.

[0033] If the number of free ports in any node is greater than 0, then obtain the shortest path length between any node and all other nodes connected to each sensor, and record it as the first distance for each sensor.

[0034] The degree of expansion preference for any given node is obtained based on the potential risk value of each sensor and the first distance corresponding to each sensor.

[0035] Preferably, the degree of expansion preference for any node is obtained based on the potential risk value of each sensor and the first distance corresponding to each sensor, including:

[0036] Calculate the first ratio between the potential risk value of each sensor and the corresponding first distance;

[0037] The sum of all the first ratios is determined as the degree of expansion preference for any given node.

[0038] Preferably, determining the recommended nodes for the sensors to be connected based on the extended preference level includes:

[0039] Based on the expansion optimization level of each node, prompts are pushed to the user via PLC or host system to determine the recommended nodes for the sensors to be connected.

[0040] The present invention has at least the following beneficial effects:

[0041] This invention first dynamically assesses the deviation of collected sensor data based on the relationship between the sensor data values ​​of each dimension at each time point and the safe range. Then, combined with the correlation analysis of multi-dimensional sensors, it achieves precise quantification of potential risks. Based on the risk quantification results, the types of sensors to be connected are determined, and optimal nodes are recommended based on the master-slave station layout of the existing IO-LINK bus panel, considering factors such as wiring convenience, node load, and sensor layout. This method not only avoids large-scale modifications to the existing bus structure, significantly improving the flexibility of system expansion and detection accuracy, but also reduces potential machine tool equipment failures, improves the stability of machine tool operation and maintenance efficiency, and has high application value and promising prospects for promotion. Attached Figure Description

[0042] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart illustrating a method performed by an IO-LINK bus panel for a machine tool with IO expansion functionality, as provided in an embodiment of the present invention. Detailed Implementation

[0044] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following detailed description, in conjunction with the accompanying drawings and preferred embodiments, describes an IO-LINK bus panel for machine tools with IO expansion function proposed according to the present invention.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0046] The following description, in conjunction with the accompanying drawings, details a specific solution for an IO-LINK bus panel for machine tools with IO expansion functionality provided by the present invention.

[0047] An embodiment of an IO-LINK bus panel for machine tools with IO expansion capabilities:

[0048] The specific scenario addressed in this embodiment is as follows: by intelligently classifying and managing the sensors connected to each subsystem of the machine tool, analyzing the potential risks of each subsystem in the machine tool in real time, and dynamically selecting the optimal expansion node based on the current load and scalability of each slave node, the system can achieve rapid I / O expansion of detection sensors and other devices, thereby improving the intelligent monitoring capability and operational safety of the overall machine tool system.

[0049] This embodiment proposes an IO-LINK bus panel for machine tools with IO expansion capabilities. The bus panel includes a memory and a processor. The processor executes a computer program stored in the memory to achieve, for example... Figure 1 The steps shown are as follows:

[0050] Step S1: Obtain sensor data from different dimensions of the machine tool.

[0051] In the IO-LINK bus panel standard, there are several main forms of data communication between sensors and devices:

[0052] (1) Periodic data: Real-time data is transmitted according to a fixed sampling period. It has high real-time performance and mainly transmits sensor data during operation.

[0053] (2) Non-periodic data: transmitted only when actively requested by the master station, such as equipment diagnostic information, configuration parameters, etc.;

[0054] (3) Event-triggered data: Actively uploaded when the sensor is abnormal or a specific event occurs, such as alarm, fault prompt and other data.

[0055] For data transmission on the IO-LINK bus panel of a machine tool, real-time detection uses periodic data, while configuration and alarms use non-periodic / event data.

[0056] The machine tool is equipped with various types of sensors, including vibration sensors, temperature sensors, current sensors, position sensors, air pressure sensors, and hydraulic pressure sensors, to collect data from different dimensions of each device. These sensors are used to collect sensor data from different dimensions during the operation of the machine tool. In this embodiment, the collection frequency of sensor data from different dimensions is set to once per second. In specific applications, the implementer can set the frequency according to the specific situation.

[0057] When performing anomaly detection on the same equipment inside a machine tool, multiple sensors often detect multi-dimensional data of the same equipment. Taking a five-axis machining center as an example, some of the devices and their included detection devices are as follows: Spindle system: spindle vibration sensor (for bearing failure and early wear detection), spindle temperature sensor, spindle current sensor (for real-time load detection), etc.; Tool magazine system: robotic arm position sensor (for motion control), air pressure sensor (for pneumatic device status monitoring); Cooling system: coolant level sensor (for detecting level changes), pressure sensor (for detecting coolant pump pressure changes). This embodiment uses one device as an example for illustration; the method provided in this embodiment can be used to process other devices.

[0058] In this embodiment, data for each dimension of the machine tool at each moment within the current time period are acquired during machine tool operation. Existing data denoising methods are used to denoise the sensor data for each dimension. The current time period is the set of all historical moments with a time interval less than or equal to a preset duration, plus the current moment. In this embodiment, the preset duration is set to 1 hour. In specific applications, the implementer can set it according to the specific situation. It should be noted that the sensor data mentioned below are all denoised data.

[0059] Thus, this embodiment has collected sensor data of different dimensions of the machine tool at each moment within the current time period.

[0060] Step S2: Evaluate the degree of deviation of sensor data in each dimension at each time point based on the relationship between the sensor data values ​​in each dimension at each time point and the safe range; determine the risk factor of each sensor based on the changing characteristics of the degree of deviation of sensor data in each dimension within the time neighborhood of the current time point.

[0061] Considering that the initial sensor layout of the machine tool is a "sufficient" design, it can effectively detect obvious anomalies and identify the type of anomaly. However, after precision machining, long-term operation under high load, and aging cycles, potential risks emerge. Therefore, it is necessary to add high-precision / high-frequency sensors locally based on the type of anomaly to achieve enhanced detection. For example, in a five-axis machining center spindle, after long-term precision machining, the temperature and current are normal, but due to factors such as micro-cracks in the spindle bearing, tiny lines appear on the workpiece surface. The existing vibration sensor has a sampling frequency of 5kHz, which cannot capture the high-frequency characteristics of early cracks (usually above 20kHz). Therefore, it is necessary to add a vibration sensor with a bandwidth of above 20kHz near the spindle (added via IO expansion nodes) to further analyze the source of the anomaly.

[0062] Therefore, this embodiment first evaluates the deviation of the collected sensor data based on the relationship between the sensor data values ​​of each dimension and the safe range, and then uses the evaluation results to conduct a preliminary detection of the potential operational risks of each sensor.

[0063] This embodiment will now be described using a single moment within the current time period as an example. The method provided in this embodiment can be used to process other moments within the current time period.

[0064] Specifically, for any given moment:

[0065] Designate any dimension as a candidate dimension. Calculate the absolute value of the difference between the sensor data of the candidate dimension at that moment and the median value of the safe range of the sensor data of the candidate dimension. Denote this absolute value as the first difference. Denote half the length of the safe range of the sensor data of the candidate dimension as the first eigenvalue of the candidate dimension. The ratio between the first difference and the first eigenvalue of the candidate dimension is determined as the degree of deviation of the sensor data of the candidate dimension at that moment.

[0066] In this embodiment, a specific formula for calculating the degree of deviation is given. The degree of deviation of the sensor data in the r-th dimension at the i-th time moment can be expressed as:

[0067]

[0068] in, This represents the degree of deviation of the sensor data in the r-th dimension at time i. This represents the sensor data in the r-th dimension at time i. This represents the upper limit of the safe range of the sensor data in the r-th dimension at time i. This represents the lower limit of the safe range for the sensor data in the r-th dimension at time i. Indicates the absolute value sign.

[0069] Indicates the first difference, This represents the length of the safe range for the sensor data in the r-th dimension. This represents the first feature value of the r-th dimension. It should be noted that the safety range for sensor data in each dimension is set manually based on the data type and actual conditions. The safety range differs for different dimensions of sensor data, which will not be elaborated upon here.

[0070] Using the above method, we can obtain the degree of deviation of the sensor data for each candidate dimension at each time point within the current time period.

[0071] As the deviation increases, the potential risk also rises. Therefore, based on the deviation of the sensor data in each candidate dimension within the current time's time neighborhood, a difference sequence of the corresponding deviation is obtained. It should be noted that the elements in the difference sequence are obtained by subtracting the deviation of the sensor data in the candidate dimension from the deviation of the sensor data in the candidate dimension of the previous time from the deviation of the sensor data in the candidate dimension of the next two adjacent time points within the current time's time neighborhood. In this embodiment, the time neighborhood of the current time is defined as follows: the last time point adjacent to the current time is taken as the last time point within the current time's time neighborhood, and a time period of a preset first duration is obtained as the current time's time neighborhood. The preset first duration is 10 minutes; in specific applications, the implementer can set this according to specific circumstances.

[0072] If the element value in the difference sequence is greater than 0, then the element value at the corresponding position is taken as the change in deviation at that position; if the element value in the difference sequence is less than or equal to 0, then the change in deviation at the corresponding position is set to 0. This yields the change in deviation for each element at each position in the difference sequence.

[0073] Next, based on the overall distribution of the deviation changes at all positions in the difference sequence corresponding to the current time's temporal neighborhood, the risk factor for the candidate dimension sensor is obtained. In this embodiment, the average deviation change value at all positions in the difference sequence corresponding to the current time's temporal neighborhood is used as the risk factor for the candidate dimension sensor.

[0074] Using the above method, the risk factors of the sensor in each dimension can be obtained.

[0075] Step S3: Combining the correlation between the deviation of sensor data in different dimensions within the current time period and the risk factors, determine the potential risk value of each sensor and determine the type of sensor to be connected; based on the potential risk value of the sensor and the path length between each node connected to the sensor and other nodes, determine the degree of expansion optimization for each node; the node is the master station or slave station of the IO-LINK bus panel.

[0076] Considering that different physical quantities do not change independently during actual machine tool operation—for example, when the spindle vibration frequency increases, its temperature will also rise slightly, or when the spindle current suddenly increases, its vibration amplitude will also increase—it is necessary to analyze the correlation of the degree of deviation of sensor data in different dimensions.

[0077] If a certain sensor has a high potential risk, but the risks of other highly correlated devices are low, the probability of measurement anomalies from a single sensor is high. In this case, there is no need to expand or add sensors based on the original sensor layout. Therefore, when judging potential risks, the potential risks of multiple sensors should be considered to further evaluate the operational potential risks of the equipment within the machine tool.

[0078] Next, we will continue to use the sensor of the candidate dimension as an example for explanation. The method provided in this embodiment can be used to process sensors of other dimensions.

[0079] Specifically, for sensors with candidate dimensions:

[0080] The Pearson correlation coefficients are calculated between the sensor data of the candidate dimension and the sensor data of each of the other dimensions within the current time period. These coefficients serve as the correlation values ​​for each of the other dimensions. Each dimension, except the candidate dimension, has a corresponding correlation value. The calculation method for the Pearson correlation coefficients is existing technology and will not be elaborated further here. The product of the correlation value for each of the other dimensions and the corresponding risk factor is used as the second eigenvalue for each of the other dimensions. The average of the second eigenvalues ​​for all other dimensions is used as the potential risk value of the sensor in the candidate dimension.

[0081] In this embodiment, a specific formula for calculating the potential risk value is given. The potential risk value of the sensor in the r-th dimension can be expressed as:

[0082]

[0083] in, This represents the potential risk value of the sensor in the r-th dimension. Indicates the number of dimensions of the sensor. It represents the Pearson correlation coefficient between the sensor data in the r-th dimension and the sensor data in the n-th dimension other than the r-th dimension within the current time period, that is, the correlation value corresponding to the n-th dimension other than the r-th dimension; This represents the risk factor in the nth dimension, excluding the rth dimension. This is the normalization function.

[0084] This represents the second eigenvalue of the nth dimension (excluding the rth dimension). A higher Pearson correlation coefficient between sensor data in the rth dimension and sensor data in the nth dimension (excluding the rth dimension) within the current time period indicates a stronger correlation between these two dimensions. If a higher Pearson correlation coefficient between sensor data in the rth dimension and sensor data in other dimensions (excluding the rth dimension) within the current time period, and a higher risk factor in the nth dimension (excluding the rth dimension), it indicates a greater likelihood of potential operational risks for the sensor in the rth dimension; that is, a higher potential risk value for the sensor in the rth dimension.

[0085] Using the above method, the potential risk value of the sensor in each dimension can be obtained. The higher the potential risk value, the more likely the corresponding sensor is to have a potential risk. Therefore, if the potential risk value is greater than or equal to a preset first threshold, it indicates that the risk of the corresponding sensor is too high, and there is a problem with the sensor's operation, requiring repair or replacement. If the potential risk value is less than or equal to a preset second threshold, it indicates that there is no potential risk and the equipment is in good condition. If the potential risk value is less than the preset first threshold but greater than the preset second threshold, it indicates that further high-precision sensor equipment needs to be added. The preset first threshold is greater than the preset second threshold. In this embodiment, the sensor type corresponding to the dimension where the potential risk value is less than the preset first threshold but greater than the preset second threshold is used as the type of sensor to be connected. In this embodiment, the preset first threshold is 0.9, and the preset second threshold is 0.7. In specific applications, the implementer can set these values ​​according to specific circumstances.

[0086] When adding high-precision sensor devices in practice, it is necessary to consider the differences in the node locations where the new sensors should be connected when expanding the IO. Different IO-LINK expansion modules have different numbers of ports, loads, and distances from the detected devices. Therefore, it is necessary to intelligently select the corresponding nodes for the new sensors based on the original IO-LINK.

[0087] The IO-LINK master panel communicates directly with the machine tool's PLC or industrial PC, typically managing 4 or 8 IO-LINK slave modules, and can also mount various sensors and actuators. The IO-LINK slave modules and the master panel communicate via IO-LINK and are located near the machine tool's physical detection or control areas, such as the spindle area, coolant area, feeding system, and pneumatic fixture area. They are mainly used to mount various sensors and actuators as sub-nodes. Sensors and actuators are directly connected to the IO-LINK master or slave ports.

[0088] Each IO-LINK site is treated as a node, and the sites include master sites and slave sites. That is, each master site is a node and each slave site is a node, which gives us the initial graph structure of IO-LINK.

[0089] For any node: if the number of free ports in the node is 0, then the expansion preference of the node is set to 0; if the number of free ports in the node is greater than 0, then the shortest path length between the node and all other nodes connected to each sensor is obtained, denoted as the first distance for each sensor; the path length is obtained by Dijkstra's algorithm. The ratio between the potential risk value of each sensor and its corresponding first distance is calculated, and this ratio is denoted as the first ratio; the sum of all first ratios is determined as the expansion preference of the node.

[0090] Using the above method, the degree of optimization of each node can be obtained.

[0091] Step S4: Determine the recommended nodes for the sensors to be connected based on the degree of expansion preference.

[0092] After determining the degree of expansion preference for each node, recommended nodes for connecting sensors are selected based on the degree of expansion preference.

[0093] Specifically, based on the optimal expansion level of each node, a prompt message is pushed to the user via PLC or upper-level system (such as HMI), clearly indicating the recommended node for the sensor to be connected, guiding the user to expand the sensor on the designated node, and recording relevant information for subsequent maintenance and traceability.

[0094] Thus, the intelligent I / O extension of the sensor detection module has been completed using the method provided in this embodiment.

[0095] This embodiment first dynamically assesses the deviation of the collected sensor data based on the relationship between the sensor data values ​​of each dimension at each time point and the safe range. Then, combined with the correlation analysis of multi-dimensional sensors, it achieves accurate quantification of potential risks. Based on the risk quantification results, the types of sensors to be connected are determined, and the optimal nodes are recommended based on the master-slave station layout of the existing IO-LINK bus panel, taking into account wiring convenience, node load, and sensor layout. This method not only avoids large-scale modifications to the existing bus structure, significantly improving the flexibility of system expansion and detection accuracy, but also reduces potential machine tool equipment failures, improves the stability of machine tool operation and maintenance efficiency, and has high application value and promising prospects for promotion.

[0096] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An IO-LINK bus panel for machine tools with IO expansion function, said bus panel comprising a processor and a memory, characterized in that, The processor executes the computer program stored in the memory to implement the following steps: Obtain sensor data of different dimensions of equipment of a machine tool; According to the size relationship between the value of the sensor data of each dimension at each time and the safety range, evaluate the deviation degree of the sensor data of each dimension at each time; determine the risk factor of each sensor according to the change characteristics of the deviation degree of the sensor data of each dimension in the time neighborhood of the current time; Determine the potential risk value of each sensor in combination with the correlation between the deviation degrees of the sensor data of different dimensions in the current time period and the risk factor, and determine the type of the sensor to be accessed; determine the expansion preference degree of each node according to the potential risk value of the sensor, the path length between each node connected by the sensor and other nodes, and the expansion preference degree of each node; the node is the master station or slave station of the IO-LINK bus panel; Determine the recommended node of the sensor to be accessed based on the expansion preference degree; The evaluation of the deviation degree of the sensor data of each dimension at each time according to the size relationship between the value of the sensor data of each dimension at each time and the safety range comprises: For any time: Calculate the first difference between the sensor data of the candidate dimension at the any time and the middle value of the safety range of the sensor data of the candidate dimension; According to the first difference and the length of the safety range of the sensor data of the candidate dimension, obtain the deviation degree of the sensor data of the candidate dimension at the any time; The candidate dimension is any dimension; The determination of the risk factor of each sensor comprises: Based on the deviation degree of the sensor data of the candidate dimension at each time in the time neighborhood of the current time, obtain the difference sequence corresponding to the deviation degree; If the element value in the difference sequence is greater than 0, the element value at the corresponding position is taken as the deviation degree change value at the corresponding position; if the element value in the difference sequence is less than or equal to 0, the deviation degree change value at the corresponding position is 0; According to the overall distribution of the deviation degree change values of all positions in the difference sequence corresponding to the time neighborhood of the current time, obtain the risk factor of the sensor of the candidate dimension; The determination of the potential risk value of each sensor comprises: For the sensor of the candidate dimension: Calculate the Pearson correlation coefficient between the sensor data of the candidate dimension and the sensor data of each dimension other than the candidate dimension in the current time period, as the correlation value corresponding to each dimension; The product between the correlation value corresponding to each dimension and the corresponding risk factor is taken as the second characteristic value of each dimension; According to the overall distribution of the second characteristic values of all other dimensions, determine the potential risk value of the sensor of the candidate dimension; The determination of the expansion preference degree of each node comprises: For any node: If the number of idle ports in the any node is 0, the expansion preference degree of the any node is 0; If the number of idle ports in the any node is greater than 0, obtain the shortest path length between the any node and all other nodes connected by each sensor, denoted as the first distance corresponding to each sensor; According to the potential risk value of each sensor and the first distance corresponding to each sensor, an expansion preference degree of the any node is obtained.

2. The IO-LINK bus panel with IO expansion function for machine tools according to claim 1, characterized in that, According to the first difference and the length of the safety range of the sensor data of the candidate dimension, a deviation degree of the sensor data of the candidate dimension at the any time is obtained, including: Half of the length of the safety range of the sensor data of the candidate dimension is recorded as a first characteristic value of the candidate dimension. A ratio between the first difference and the first characteristic value is determined as the deviation degree of the sensor data of the candidate dimension at the any time.

3. The IO-LINK bus panel with IO expansion function for machine tools according to claim 1, characterized in that, According to the overall distribution of the second characteristic values of all other dimensions, the potential risk value of the sensor of the candidate dimension is determined, including: taking an average value of the second characteristic values of all other dimensions as the potential risk value of the sensor of the candidate dimension.

4. The IO-LINK bus panel with IO expansion function for machine tools according to claim 1, characterized in that, The type of the sensor to be accessed is determined, including: When the potential risk value is less than a preset first threshold value and greater than a preset second threshold value, the type of the sensor corresponding to the dimension is taken as the type of the sensor to be accessed; the preset first threshold value is greater than the preset second threshold value.

5. The IO-LINK bus panel with IO expansion function for machine tools according to claim 1, characterized in that, According to the potential risk value of each sensor and the first distance corresponding to each sensor, an expansion preference degree of the any node is obtained, including: A first ratio between the potential risk value of each sensor and the first distance corresponding to each sensor is calculated respectively; An accumulated sum of all first ratios is determined as the expansion preference degree of the any node.

6. The IO-LINK bus panel with IO expansion function for machine tools according to claim 1, characterized in that, The recommended node of the sensor to be accessed is determined based on the expansion preference degree, including: Based on the expansion preference degree of each node, a prompt information is pushed to a user through a PLC or an upper system to determine a recommended access node of the sensor to be accessed.

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