Computer data processing method, platform and storage medium based on internet of things

Abstract

This invention relates to the field of industrial equipment data processing technology, and more particularly to a computer data processing method, platform, and storage medium based on the Internet of Things (IoT). The method includes: acquiring sensor waveform data, channel data, and process criticality; analyzing data feature vectors based on the sensor waveform data; analyzing equipment health index and channel quality index based on the data feature vectors and channel data; constructing a priority scoring model based on the equipment health index, channel quality index, and process criticality to obtain a priority score; implementing dynamic response grading based on the priority score; acquiring equipment transmission data; determining preemption weights based on the equipment transmission data and priority scores; and constructing channel preemption decisions based on the preemption weights. This invention achieves precise analysis and processing of data transmission priority.

Description

Technical Field

[0001] This invention relates to the field of industrial equipment data processing technology, and in particular to a computer data processing method, platform and storage medium based on the Internet of Things. Background Technology

[0002] In the Industrial Internet of Things (IIoT), massive numbers of devices generate various types of data in real time, including production logs and status data. Traditional transmission methods are prone to network congestion and delays in critical data, impacting production. Therefore, it is necessary to introduce data transmission priority management, rationally allocate resources, ensure timely processing of critical data, and improve production efficiency and stability.

[0003] Current IoT-based computer data processing methods generally suffer from the following shortcomings: Traditional methods rely on device type or fixed rules to allocate transmission priorities, failing to dynamically respond to real-time changes in device health status and channel quality fluctuations, resulting in delays of high-value data due to queue congestion or channel degradation; Existing technologies ignore the topological interconnectivity of devices in the production chain, failing to quantify the impact of faults on upstream and downstream processes, potentially leading to excessive resource consumption by non-critical devices while data from actual high-risk nodes is not prioritized; Communication layer decisions are often independent of business logic and do not consider the urgency of data transmission, making it difficult to quickly seize resources in emergency scenarios, affecting the real-time performance of fault diagnosis and control; Sensor data analysis is mostly limited to time domain parameters, ignoring frequency domain characteristics and fluctuation features, reducing anomaly detection coverage and making it difficult to support accurate health assessments. Summary of the Invention

[0004] The purpose of this invention is to provide a computer data processing method, platform, and storage medium based on the Internet of Things (IoT) to solve at least one of the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A computer data processing method based on the Internet of Things (IoT) includes:

[0007] Collect sensor waveform data, channel data, and process criticality, and analyze data feature vectors based on sensor waveform data;

[0008] Based on data feature vectors and channel data analysis, device health index and channel quality index are calculated.

[0009] A priority scoring model is constructed based on equipment health index, channel quality index, and process criticality to obtain priority scores;

[0010] Dynamic response grading is achieved based on priority scoring;

[0011] The system collects data transmitted by the acquisition device, determines the preemption weight based on the data transmitted by the device and the priority score, and constructs a channel preemption decision based on the preemption weight.

[0012] Furthermore, based on sensor waveform data analysis, peak amplitude parameter, kurtosis coefficient, frequency band energy ratio, and amplitude fluctuation standard deviation are calculated. The peak amplitude parameter is the difference between the maximum and minimum values ​​of the sensor waveform data. The frequency band energy ratio is analyzed by performing a fast Fourier transform on the sensor waveform data to obtain the data spectrum, and the energy proportion of 3kHz-5kHz in the data spectrum is taken as the frequency band energy ratio. The amplitude fluctuation standard deviation is analyzed by dividing the sensor waveform data into multiple window sensor waveform data with a window size of 10ms, calculating the peak amplitude parameter of the sensor waveform data in each window, and taking the standard deviation of the peak amplitude parameter of the sensor waveform data in each window as the amplitude fluctuation standard deviation.

[0013] Furthermore, a data feature vector is constructed based on the peak amplitude parameter, kurtosis coefficient, frequency band energy ratio, and amplitude fluctuation standard deviation. The data feature vector is set as F, F=(A,K,E,DA), where F represents the data feature vector, A represents the peak amplitude parameter, K represents the kurtosis coefficient, E represents the frequency band energy ratio, and DA represents the amplitude fluctuation standard deviation.

[0014] Furthermore, the peak amplitude parameter, kurtosis coefficient, and frequency band energy ratio are weighted to obtain the equipment health index. The expression for the equipment health index is: H=w1×A / a+w2×|Kk|+w3×E, where H represents the equipment health index, w1 represents the first health weight, w2 represents the second health weight, w3 represents the third health weight, w1+w2+w3=1, a represents the peak amplitude threshold, and k represents the kurtosis threshold.

[0015] Furthermore, the channel quality index is analyzed based on packet loss rate, signal strength, and channel collision count. The expression for the channel quality index is: Q=[e -λ×PL +R / r+1 / (1+0.2×NC)] / 3, where Q represents the channel quality index, λ represents the preset packet loss parameter, PL represents the packet loss rate, R represents the signal strength, r represents the preset signal strength parameter, and NC represents the number of channel collisions.

[0016] Furthermore, dynamic weight allocation is performed on the equipment health index, channel quality index, and process criticality to obtain a priority score. The expression for the priority score is: Sc = α1 × H + α2 × Q + α3 × C, where Sc represents the priority score, α1 represents the first priority weight, α2 represents the second priority weight, α3 represents the third priority weight, α1 + α2 + α3 = 1, and C represents the process criticality.

[0017] Furthermore, based on the priority score analysis, the response level and response action are determined as follows: if the priority score is less than the first priority threshold, the response level is set to low, and the response action is normal batch processing; if the priority score is greater than or equal to the first priority threshold and less than the second priority threshold, the response level is set to medium, and the response action is to move the batch processing queue forward to ensure that the transmission delay is less than 200ms; if the priority score is greater than or equal to the second priority threshold and less than the third priority threshold, the response level is set to high, and the response action is to compress the sensor waveform data to retain data frames greater than 3kHz for transmission; if the priority score is greater than or equal to the third priority threshold, the response level is set to urgent, and the response action is to preempt a high-bandwidth channel for transmission.

[0018] Furthermore, based on the transmission rate, the amount of data to be transmitted, and the priority score, the preemption weight is analyzed. The expression for the preemption weight is: WP = Sc × e -G×1000 / (V×η) In the formula, WP represents the preemption weight, G represents the amount of data to be transmitted, V represents the transmission rate, and η represents the preset time parameter.

[0019] When the response level is emergency, if the preemption weight is greater than or equal to the preemption threshold, channel switching is triggered, and the channel of the industrial equipment with the lowest preemption weight is selected for data transmission.

[0020] When the response level is high, if the priority score is greater than or equal to [(second priority threshold + third priority threshold) / 2] and the channel quality index is less than or equal to the channel quality threshold, then dual-path parallel transmission is initiated; otherwise, the channel with the highest channel quality index is selected for transmission.

[0021] On the other hand, the present invention also provides a computer data processing platform based on the Internet of Things, comprising:

[0022] The data acquisition unit is used to acquire sensor waveform data, channel data, and process criticality, and to analyze data feature vectors based on the sensor waveform data.

[0023] The parameter analysis unit is used to analyze the device health index and channel quality index based on data feature vectors and channel data.

[0024] The model building unit is used to build a priority scoring model based on the equipment health index, channel quality index, and process criticality to obtain a priority score;

[0025] A hierarchical response unit is used to achieve dynamic response hierarchies based on priority scoring.

[0026] The decision analysis unit is used to collect data transmitted by the device, determine the preemption weight based on the data transmitted by the device and the priority score, and construct a channel preemption decision based on the preemption weight.

[0027] On the other hand, the present invention also provides a storage medium characterized in that it stores instructions that, when run on a computer, cause the computer to perform the method described in any of the preceding claims.

[0028] The beneficial effects of this invention are as follows: By dynamically coupling the equipment operating status, communication environment, and production logic, a priority scoring model is constructed, which solves the limitations of traditional methods that rely solely on equipment type or static rules. This achieves precise matching between data transmission strategies and real-time production needs. Through dynamic response grading and channel preemption mechanisms, low-latency transmission of critical data is ensured under limited bandwidth. Data compression strategies specifically retain fault characteristic frequency bands, reducing invalid bandwidth occupation and improving overall network utilization. Based on breadth-first search for process criticality calculation, core equipment that may cause cascading shutdowns can be identified in advance. The health index's sensitivity analysis of vibration characteristics supports early fault warning. The combination of these two aspects significantly reduces the risk of unplanned production stoppages and ensures continuous and stable operation of the production line. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0030] Figure 1 This is a flowchart of the computer data processing method based on the Internet of Things in this embodiment.

[0031] Figure 2 This is a flowchart illustrating the analysis method for the device health index and channel quality index in this embodiment.

[0032] Figure 3 This is a flowchart of the preemptive decision-making construction method in this embodiment.

[0033] Figure 4 This is a schematic diagram of the structure of the Internet of Things-based computer data processing platform in this embodiment. Detailed Implementation

[0034] To more clearly illustrate the present invention, the following description, in conjunction with preferred embodiments and accompanying drawings, further explains the invention. Similar components in the drawings are indicated by the same reference numerals. Those skilled in the art should understand that the specific description below is illustrative rather than restrictive and should not be construed as limiting the scope of protection of the present invention.

[0035] It should be noted that although the terms first, second, third, etc., may be used in the embodiments of this application for description, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of this application, first can also be referred to as second, and similarly, second can also be referred to as first.

[0036] Please see Figure 1 As shown, this embodiment illustrates a computer data processing method based on the Internet of Things, comprising:

[0037] Step S1: Collect sensor waveform data, channel data, and process criticality, and analyze the data feature vector based on the sensor waveform data. The sensor waveform data is the raw waveform data of the vibration sensor of the industrial equipment. In this embodiment, the acquisition frequency of the sensor waveform data is 8kHz. The channel data includes packet loss rate, signal strength, and number of channel collisions. The packet loss rate is the packet loss rate within the past 5 minutes. The number of channel collisions is the number of times multiple industrial devices attempt to communicate simultaneously using the same channel within 10 seconds. The unit of signal strength is dBm.

[0038] Specifically, in this embodiment, the process criticality is the maximum value of the node influence value, which is calculated based on the process topology network. The analysis method for the node influence value is as follows:

[0039] Each industrial device is abstracted as a network node, and terminal nodes are labeled as product output points. An industrial device flow is constructed, which is the sequential working order of each industrial device during the manufacturing process. The coupling strength of industrial devices with adjacent working orders is labeled. The coupling strength is a parameter based on the percentage reduction in the working efficiency of downstream devices caused by industrial device failure. For example, if a device failure causes 100% shutdown of downstream devices, the coupling strength is set to 1. If a device failure causes a 30% reduction in the working efficiency of downstream devices, the coupling strength is set to 0.3.

[0040] Key parameters are assigned based on the attributes of industrial equipment. These key parameters include equipment redundancy parameters, fault repair parameters, alternative equipment availability parameters, and historical impact parameters. The equipment redundancy parameter is determined based on whether the equipment data is backed up; for example, it is set to 1 if there is no backup and 0.3 if there are dual backups. The fault repair parameter is set based on the fault repair time; for example, it is set to 1 if the equipment fault repair time is greater than 4 hours and 0.2 if it is less than 30 minutes. The alternative equipment availability parameter is set based on whether there is a backup device; for example, it is set to 1 if there is no alternative device and 0.4 if there is an alternative device. The historical impact parameter is set based on the scope of equipment downtime caused by equipment failure; for example, it is set to 1 if the equipment failure causes a complete production line shutdown and 0.3 if only a partial shutdown occurs. Based on the key parameters, a basic key value for the industrial equipment is calculated, which is the average value of the key parameters.

[0041] A breadth-first search is used to traverse the downstream nodes of each network node and calculate the node influence value. The node influence value is calculated as the sum of the products of the network node's basic critical value and its coupling strength, multiplied by e. (-0.2×路径长度) The path length is an integer, which is the number of network nodes traversed between the network node and the product output point.

[0042] Specifically, in step S1 of this embodiment, the peak amplitude parameter, kurtosis coefficient, frequency band energy ratio, and amplitude fluctuation standard deviation are analyzed based on sensor waveform data. The peak amplitude parameter is the difference between the maximum and minimum values ​​of the sensor waveform data, and the expression for the kurtosis coefficient is: In the formula, K represents the kurtosis coefficient, and s i The data represents sensor waveform data, where i represents the acquisition sequence number, Ni represents the number of acquisition sequence numbers, μ() represents the average value of the data in parentheses, and σ() represents the standard deviation of the data in parentheses. The method for analyzing the frequency band energy ratio is to perform a fast Fourier transform on the sensor waveform data to obtain the data spectrum, and the energy ratio of 3kHz-5kHz in the data spectrum is taken as the frequency band energy ratio. The method for analyzing the amplitude fluctuation standard deviation is to divide the sensor waveform data into multiple window sensor waveform data with a window size of 10ms, calculate the peak amplitude parameter of the sensor waveform data in each window, and take the standard deviation of the peak amplitude parameter of the sensor waveform data in each window as the amplitude fluctuation standard deviation.

[0043] Specifically, in step S1 of this embodiment, a data feature vector is constructed based on the peak amplitude parameter, kurtosis coefficient, frequency band energy ratio, and amplitude fluctuation standard deviation. The data feature vector is set as F, F=(A,K,E,DA), where F represents the data feature vector, A represents the peak amplitude parameter, E represents the frequency band energy ratio, and DA represents the amplitude fluctuation standard deviation.

[0044] Specifically, in step S1 of this embodiment, the peak amplitude, kurtosis coefficient, frequency band energy ratio, and amplitude fluctuation standard deviation are analyzed by multi-dimensional sensor waveforms to achieve refined capture of the equipment's operating status. The frequency band energy ratio focuses on specific high-frequency bands to effectively identify potential fault characteristics such as mechanical wear. The windowed calculation mechanism of the amplitude fluctuation standard deviation enhances the detection sensitivity of transient anomalies in vibration signals. The process criticality is based on the dynamic calculation of node influence values ​​of the topology network, comprehensively considering equipment redundancy, repair difficulty, and production coupling, thus avoiding the risk of misjudging critical equipment by traditional static classification.

[0045] Please continue reading. Figure 1 As shown, the Internet of Things-based computer data processing method further includes:

[0046] Step S2: Analyze the device health index and channel quality index based on data feature vectors and channel data.

[0047] Please see Figure 2 As shown, this is the analysis method for device health index and channel quality index, including:

[0048] Step S21: Analyze the device health index based on data feature vectors.

[0049] Specifically, in step S21 of this embodiment, the peak amplitude parameter, kurtosis coefficient and frequency band energy ratio are weighted to obtain the equipment health index. The expression of the equipment health index is: H=w1×A / a+w2×|Kk|+w3×E, where H represents the equipment health index, w1 represents the first health weight, w2 represents the second health weight, w3 represents the third health weight, w1+w2+w3=1, a represents the peak amplitude threshold, and k represents the kurtosis threshold.

[0050] Specifically, in this embodiment, the first health weight is set to 0.5, the second health weight is set to 0.3, the third health weight is set to 0.2, the peak amplitude threshold is set to 30V, and the kurtosis threshold is set to 3. In this embodiment, the setting of the health weight threshold, the peak amplitude threshold, and the kurtosis threshold is not specifically limited, and those skilled in the art can set them freely.

[0051] Please continue reading. Figure 2 As shown, the analysis methods for the device health index and channel quality index also include:

[0052] Step S22: Analyze the channel quality index based on channel data.

[0053] Specifically, in step S22 of this embodiment, the channel quality index is analyzed based on packet loss rate, signal strength, and channel collision count. The expression for the channel quality index is: Q=[e -λ×PL+R / r+1 / (1+0.2×NC)] / 3, where Q represents the channel quality index, λ represents the preset packet loss parameter, PL represents the packet loss rate, R represents the signal strength, r represents the preset signal strength parameter, and NC represents the number of channel collisions.

[0054] Specifically, in this embodiment, the preset packet loss parameter is set to 0.05 and the preset signal strength parameter is set to -70dBm. This embodiment does not impose specific limitations on the settings of the preset packet loss parameter and the preset signal strength parameter, and those skilled in the art can set them freely.

[0055] Specifically, in step S2 of this embodiment, the device health index integrates physical characteristics and preset threshold differences, and highlights the contribution of core parameters through weight allocation to improve the accuracy of fault warning; the channel quality index innovatively integrates packet loss rate, signal strength and number of collisions, and uses the exponential decay function to weaken occasional packet loss interference, while strengthening the quality degradation characterization of high-collision channels, providing a reliable basis for dynamic channel selection.

[0056] Please continue reading. Figure 1 As shown, the Internet of Things-based computer data processing method further includes:

[0057] Step S3: Construct a priority scoring model based on the equipment health index, channel quality index, and process criticality to obtain a priority score.

[0058] Specifically, in step S3 of this embodiment, the device health index, channel quality index, and process criticality are dynamically weighted to obtain a priority score. The expression for the priority score is: Sc = α1 × H + α2 × Q + α3 × C, where Sc represents the priority score, α1 represents the first priority weight, α2 represents the second priority weight, α3 represents the third priority weight, α1 + α2 + α3 = 1, and C represents the process criticality.

[0059] Specifically, in this embodiment, the priority weights are preset according to the priority weights of different industrial equipment. For example, in industrial refrigeration equipment, the first priority weight of the compressor is set to 0.7, the second priority weight is set to 0.1, and the third priority weight is set to 0.2. The first priority weight of the cooling water pump is set to 0.5, the second priority weight is set to 0.3, and the third priority weight is set to 0.2, etc. In this embodiment, the setting of priority weights is not specifically limited. Those skilled in the art can set them freely, as long as they meet the analysis of priority scoring for different industrial equipment.

[0060] Specifically, in step S3 of this embodiment, a dynamic weight allocation model based on device health, channel quality, and process criticality is used to break through the limitations of traditional fixed weights. By configuring weights differently, the actual importance of different devices in the production chain is accurately adapted to ensure that high-value data is processed first.

[0061] Please continue reading. Figure 1 As shown, the Internet of Things-based computer data processing method further includes:

[0062] Step S4: Implement dynamic response grading based on priority scoring.

[0063] Specifically, in step S4 of this embodiment, the response level and response action are analyzed based on the priority score. If the priority score is less than the first priority threshold, the response level is set to low, and the response action is normal batch processing. If the priority score is greater than or equal to the first priority threshold and less than the second priority threshold, the response level is set to medium, and the response action is to move the batch processing queue forward to ensure that the transmission delay is less than 200ms. If the priority score is greater than or equal to the second priority threshold and less than the third priority threshold, the response level is set to high, and the response action is to compress the sensor waveform data to retain data frames greater than 3kHz for transmission. If the priority score is greater than or equal to the third priority threshold, the response level is set to urgent, and the response action is to preempt a high-bandwidth channel for transmission.

[0064] Specifically, in this embodiment, the first priority threshold is set to 0.3, the second priority threshold is set to 0.5, and the third priority threshold is set to 0.8. This embodiment does not impose specific limitations on the setting of the priority thresholds, and those skilled in the art can set them freely, as long as they meet the requirements for classifying the response levels.

[0065] Specifically, in step S4 of this embodiment, the four-level response mechanism triggers differentiated actions based on the scoring threshold: the medium level implements queue forwarding and delay control, the high level uses data compression to retain key frequency bands, and the emergency level triggers channel preemption, so as to balance resource efficiency and real-time requirements and avoid low-priority data blocking key transmission paths.

[0066] Please continue reading. Figure 1 As shown, the Internet of Things-based computer data processing method further includes:

[0067] Step S5: Collect device transmission data, determine the preemption weight based on the device transmission data and priority score, and construct a channel preemption decision based on the preemption weight.

[0068] Please see Figure 3 As shown, this is a method for constructing preemptive decision-making, including:

[0069] Step S51: Collect device transmission data. The device transmission data includes transmission rate and the amount of data to be transmitted. The unit of transmission rate is Kbps and the unit of the amount of data to be transmitted is KB.

[0070] Please continue reading. Figure 3 As shown, the method for constructing the preemption decision further includes:

[0071] Step S52: Analyze the preemption weight based on the transmission rate, the amount of data to be transmitted, and the priority score.

[0072] Specifically, in step S52 of this embodiment, the preemption weight is analyzed based on the transmission rate, the amount of data to be transmitted, and the priority score. The expression for the preemption weight is: WP = Sc × e -G×1000 / (V×η) In the formula, WP represents the preemption weight, G represents the amount of data to be transmitted, V represents the transmission rate, and η represents the preset time parameter.

[0073] Specifically, in this embodiment, the preset time parameter is set to 50ms. This embodiment does not impose specific limitations on the setting of the preset time parameter, and those skilled in the art can set it freely.

[0074] Please continue reading. Figure 3 As shown, the method for constructing the preemption decision further includes:

[0075] Step S53: Construct a channel preemption decision based on the preemption weight.

[0076] Specifically, in step S53 of this embodiment, when the response level is urgent, if the preemption weight is greater than or equal to the preemption threshold, channel switching is triggered, and the channel of the industrial equipment with the lowest preemption weight is selected for data transmission.

[0077] When the response level is high, if the priority score is greater than or equal to [(second priority threshold + third priority threshold) / 2] and the channel quality index is less than or equal to the channel quality threshold, then dual-path parallel transmission is initiated; otherwise, the channel with the highest channel quality index is selected for transmission.

[0078] Specifically, in this embodiment, the preemption threshold is set to 1.2 and the channel quality threshold is set to 0.6. This embodiment does not impose specific limitations on the setting of the preemption threshold and the channel quality threshold. Those skilled in the art can set them freely, as long as they meet the requirements for formulating the preemption decision.

[0079] Specifically, in step S5 of this embodiment, the preemption weight introduces a time-sensitive factor for the amount and rate of transmitted data. In an emergency, channel switching is implemented based on weight comparison to efficiently solve the sudden high-load transmission demand. The use of intelligent triggering conditions for high-level dual-path transmission significantly improves the robustness of data transmission.

[0080] Please see Figure 4 As shown, this is a computer data processing platform based on the Internet of Things in this embodiment, including:

[0081] The data acquisition unit is used to acquire sensor waveform data, channel data, and process criticality, and to analyze data feature vectors based on the sensor waveform data.

[0082] The parameter analysis unit is used to analyze the device health index and channel quality index based on data feature vectors and channel data.

[0083] The model building unit is used to build a priority scoring model based on the equipment health index, channel quality index, and process criticality to obtain a priority score;

[0084] A hierarchical response unit is used to achieve dynamic response hierarchies based on priority scoring.

[0085] The decision analysis unit is used to collect data transmitted by the device, determine the preemption weight based on the data transmitted by the device and the priority score, and construct a channel preemption decision based on the preemption weight.

[0086] This application also provides a computer-readable storage medium storing instructions that, when run on a computer, cause the computer to perform the Internet of Things-based computer data processing method as described in the above method embodiments.

[0087] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable programs, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable programs, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0088] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description. It is impossible to exhaustively list all the implementation methods here. All obvious variations or modifications derived from the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. A computer data processing method based on the Internet of Things, characterized in that, include: Collect sensor waveform data, channel data, and process criticality, and analyze data feature vectors based on sensor waveform data; The process criticality is the maximum value of the node influence value, which is calculated based on the process topology network. Each industrial device is abstracted as a network node, and terminal nodes are labeled as product output points. The flow of industrial devices is constructed, and the coupling strength of adjacent working sequences of industrial devices is labeled. The coupling strength is a parameter based on the proportion of downstream equipment efficiency reduction caused by industrial device failure. Key parameters are assigned based on the attributes of industrial devices, and the basic critical value of industrial devices is calculated based on the key parameters. The basic critical value is the average value of the key parameters. Breadth-first search is used to traverse the downstream nodes of each network node and calculate the node influence value. The node influence value = the sum of the products of the basic critical value and the coupling strength of the network node × e. (-0.2×路径长度) The path length is an integer, which is the number of network nodes traversed between the network node and the product output point; A data feature vector is constructed based on the peak amplitude parameter, kurtosis coefficient, frequency band energy ratio, and amplitude fluctuation standard deviation. The peak amplitude parameter is the difference between the maximum and minimum values ​​of the sensor waveform data. The frequency band energy ratio is analyzed by performing a fast Fourier transform on the sensor waveform data to obtain the data spectrum, and the energy ratio of 3kHz-5kHz in the data spectrum is taken as the frequency band energy ratio. The amplitude fluctuation standard deviation is analyzed by dividing the sensor waveform data into multiple window sensor waveform data with a window size of 10ms, calculating the peak amplitude parameter of the sensor waveform data in each window, and taking the standard deviation of the peak amplitude parameter of the sensor waveform data in each window as the amplitude fluctuation standard deviation. Based on data feature vectors and channel data analysis, device health index and channel quality index are calculated. The peak amplitude parameter, kurtosis coefficient, and bandwidth energy ratio are weighted and assigned to obtain the equipment health index; The expression for the channel quality index is: Q=[e -λ×PL +R / r+1 / (1+0.2×NC)] / 3, where Q represents the channel quality index, λ represents the preset packet loss parameter, PL represents the packet loss rate, R represents the signal strength, r represents the preset signal strength parameter, and NC represents the number of channel collisions; A priority scoring model is constructed based on equipment health index, channel quality index, and process criticality to obtain priority scores; The equipment health index, channel quality index, and process criticality are dynamically weighted to obtain a priority score. The expression for the priority score is: Sc = α1 × H + α2 × Q + α3 × C, where Sc represents the priority score, α1 represents the first priority weight, α2 represents the second priority weight, α3 represents the third priority weight, α1 + α2 + α3 = 1, and C represents the process criticality. Dynamic response grading is achieved based on priority scoring; Collect data transmitted by the acquisition device, determine the preemption weight based on the device's transmitted data and priority score, and construct a channel preemption decision based on the preemption weight; The preemption weight is analyzed based on transmission rate, amount of data to be transmitted, and priority score. The expression for the preemption weight is: WP = Sc × e -G×1000 / (V×η) In the formula, WP represents the preemption weight, G represents the amount of data to be transmitted, V represents the transmission rate, and η represents the preset time parameter.

2. The computer data processing method based on the Internet of Things according to claim 1, characterized in that, A data feature vector is constructed based on the peak amplitude parameter, kurtosis coefficient, frequency band energy ratio, and amplitude fluctuation standard deviation. The data feature vector is set as F, F=(A,K,E,DA), where F represents the data feature vector, A represents the peak amplitude parameter, K represents the kurtosis coefficient, E represents the frequency band energy ratio, and DA represents the amplitude fluctuation standard deviation.

3. The computer data processing method based on the Internet of Things according to claim 2, characterized in that, The peak amplitude parameter, kurtosis coefficient, and frequency band energy ratio are weighted to obtain the equipment health index. The expression of the equipment health index is: H=w1×A / a+w2×|Kk|+w3×E, where H represents the equipment health index, w1 represents the first health weight, w2 represents the second health weight, w3 represents the third health weight, w1+w2+w3=1, a represents the peak amplitude threshold, and k represents the kurtosis threshold.

4. The computer data processing method based on the Internet of Things according to claim 3, characterized in that, Based on the priority score analysis, the response level and response action are determined. If the priority score is less than the first priority threshold, the response level is set to low and the response action is normal batch processing. If the priority score is greater than or equal to the first priority threshold and less than the second priority threshold, the response level is set to medium, and the response action is to move the batch processing queue forward to ensure that the transmission delay is less than 200ms; if the priority score is greater than or equal to the second priority threshold and less than the third priority threshold, the response level is set to high, and the response action is to compress the sensor waveform data to retain data frames greater than 3kHz for transmission; if the priority score is greater than or equal to the third priority threshold, the response level is set to emergency, and the response action is to preempt the high bandwidth channel for transmission.

5. The computer data processing method based on the Internet of Things according to claim 4, characterized in that, When the response level is emergency, if the preemption weight is greater than or equal to the preemption threshold, channel switching is triggered, and the channel of the industrial equipment with the lowest preemption weight is selected for data transmission. When the response level is high, if the priority score is greater than or equal to [(second priority threshold + third priority threshold) / 2] and the channel quality index is less than or equal to the channel quality threshold, then dual-path parallel transmission is initiated; otherwise, the channel with the highest channel quality index is selected for transmission.

6. A computer data processing platform based on the Internet of Things (IoT), applied to the computer data processing method based on the IoT as described in any one of claims 1-5, characterized in that, include: The data acquisition unit is used to acquire sensor waveform data, channel data, and process criticality, and to analyze data feature vectors based on the sensor waveform data. The parameter analysis unit is used to analyze the device health index and channel quality index based on data feature vectors and channel data. The model building unit is used to build a priority scoring model based on the equipment health index, channel quality index, and process criticality to obtain a priority score; A hierarchical response unit is used to achieve dynamic response hierarchies based on priority scoring. The decision analysis unit is used to collect data transmitted by the device, determine the preemption weight based on the data transmitted by the device and the priority score, and construct a channel preemption decision based on the preemption weight.

7. A storage medium, characterized in that, The device stores instructions that, when executed on a computer, cause the computer to perform the Internet of Things-based computer data processing method as described in any one of claims 1-5.

Images