A cloud platform control system for data acquisition and remote monitoring

Abstract

The present invention discloses a cloud platform control system for data acquisition and remote monitoring. The present invention relates to the field of platform monitoring technology and solves the problem of load caused by insufficient computing resources during the acquisition process of multiple devices. The present invention accurately locks the computing resources required for each acquisition port as the port feature based on the monitoring data of the acquisition port and the acquisition speed range of the terminal device. The acquisition logic confirmation end intelligently allocates computing resources based on the port features and the inherent computing resources of the processor. When the total resource features exceed the computing power of the processor, the combined device is selected through frequency error processing, the computing power of other devices is preferentially allocated, and the computing resources of the combined device are re-analyzed to ensure that each acquisition port can still operate effectively under limited computing power, thereby ensuring the smooth progress of data acquisition.

Description

Technical Field

[0001] The present invention relates to the field of platform monitoring technology, and in particular to a cloud platform control system for data acquisition and remote monitoring. Background Art

[0002] In today's era of rapid digitalization and intelligent advancements, industries are increasingly demanding real-time control of equipment operating status and efficient data collection and analysis. Whether it's mechanical equipment in industrial production, power generation equipment in the energy sector, or specialized medical equipment, advanced technologies are required to achieve comprehensive monitoring and management of these devices.

[0003] Traditional data collection and monitoring methods have numerous limitations. For one thing, there's often a lack of targeted data collection strategies for different types and specifications of terminal devices. The output frequencies and data capacities of different devices vary widely, making traditional data collection methods difficult to accurately adapt. This often leads to excessive data load or unbalanced load on a particular port, resulting in incomplete or inaccurate data collection, impacting subsequent analysis and decision-making. For example, in an industrial automation production line, equipment in different production links operates at varying paces and data output. Traditional data collection methods can't flexibly adjust the frequency and speed of data collection, easily leading to missed critical data or redundant data collection.

[0004] On the other hand, computing resource allocation lacks intelligence and refinement. In complex environments with multiple devices operating simultaneously, traditional methods struggle to rationally allocate processor computing resources based on their actual needs. When total resource demands exceed the inherent computing power of the processor, techniques such as frequency offset cannot be effectively utilized to optimize allocation, hindering data collection for some devices and reducing the efficiency and stability of the entire monitoring system.

[0005] Furthermore, when it comes to monitoring equipment operating status, traditional monitoring systems struggle to quickly and accurately identify equipment anomalies. Due to the lack of pre-set standard value ranges and effective anomaly detection mechanisms, fluctuations in equipment operating data cannot be detected promptly. Even when an anomaly is detected, it is difficult to pinpoint the specific data item involved. Often, the anomaly is only discovered after a serious equipment failure has occurred, resulting in production interruptions, financial losses, and safety risks.

[0006] In summary, existing data collection and remote monitoring technologies have obvious shortcomings in terms of accurate data collection, reasonable allocation of computing resources, and equipment anomaly monitoring. An innovative cloud platform control system is urgently needed to solve these problems and meet the needs of various industries for efficient equipment management and in-depth data mining. Summary of the Invention

[0007] In response to the shortcomings of the existing technology, the present invention provides a cloud platform control system for data collection and remote monitoring, which solves the problem of load caused by insufficient computing resources during the data collection process of multiple devices.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a cloud platform control system for data acquisition and remote monitoring, comprising:

[0009] The collection center is used to collect data from multiple terminal devices, and the collection center includes a device feature analysis terminal, a port feature confirmation terminal, and a collection logic confirmation terminal;

[0010] The monitoring center assesses whether there is data fluctuation or equipment failure in the terminal device based on the collected data;

[0011] The device feature analysis end determines the characteristic time associated with the corresponding terminal device based on the output frequency set by different terminal devices. Then, based on the historical data generated by the corresponding terminal device in the historical process, it identifies the data capacity output each time. Based on the data capacity and characteristic time, it determines the sampling rate range associated with the corresponding terminal device. The specific method is as follows:

[0012] Confirm the output frequency set by different terminal devices in the cloud platform and record it as P i , where i represents different terminal devices, and then confirm the time parameters within the adjacent output frequencies of the corresponding terminal devices, which are recorded as characteristic time;

[0013] Confirm the historical data generated by different terminal devices, identify the total amount of data output in each data output process from the historical data, and select the minimum total amount of data R from the multiple sets of total amount of data identified for the same terminal device. i min and the maximum value of the total amount of data R i max;

[0014] Based on the characteristic time SJ associated with the corresponding terminal device i , using: R i min÷SJ i =V i min and R i max÷SJ i =V i Max locks the sampling speed interval associated with the corresponding terminal device [V i min, V i max];

[0015] The port feature confirmation terminal uses the monitoring data associated with each collection port to confirm the collection feature associated with each collection port. Then, based on the collection speed range confirmed by the corresponding terminal device, it locks the computing power resources associated with the corresponding collection port and records them as the port feature of this port. The specific method is as follows:

[0016] From each collection port's past collection processes, identify the monitoring data associated with the most recent collection process. Then, identify the average collection speed associated with that collection process. Then, identify the computing resources associated with that collection process. Use the formula: average collection speed ÷ computing resources = collection characteristics to identify the collection characteristics associated with that collection port.

[0017] Then, based on the terminal device associated with the corresponding acquisition port, identify the acquisition speed interval confirmed by the corresponding terminal device [V i min, V i max], according to the acquisition characteristics associated with the corresponding acquisition port, use: V i Max ÷ Collection Characteristics = Total Computing Power Resources. The determined total computing power resources are recorded as the port characteristics of this collection port.

[0018] The acquisition logic confirmation end allocates the computing resources required by each acquisition port based on the port characteristics confirmed by each acquisition port and the inherent computing resources of the processor, confirms the allocation logic, and then performs real-time acquisition based on this allocation logic. The specific method is as follows:

[0019] The fixed computing power resources of the processor are calibrated as GL. Then, based on the port characteristics confirmed by each collection port, the port characteristics associated with several collection ports are summed to lock the total resource characteristics.

[0020] If the total resource characteristics are less than or equal to GL, the required computing power resources are directly allocated based on the port characteristics confirmed by each acquisition port.

[0021] If the total resource feature is greater than GL, then based on the feature times associated with different terminal devices, terminal devices with the same feature time and collected data in different time periods are selected. The selected terminal devices are marked as combined devices, and the computing power resources associated with the corresponding collection ports of other terminal devices are preferentially allocated. Then, computing power resources are allocated to the combined devices. Based on the specific allocation process, it is verified whether several collection ports can evenly divide the inherent computing power resources of the processor. If so, the allocation logic is confirmed. If not, a computing power resource shortage signal is directly generated for relevant display;

[0022] The specific method for calibrating the modular device is:

[0023] Based on the characteristic time SJ associated with different terminal devices i , select characteristic time SJ iTerminal devices with the same value are recorded as pending devices. The current time is used as the calibration time to confirm the next output time of other terminal devices in the pending device list. The next collection period is locked based on the next output time. The next collection period associated with other terminal devices in the pending device list is confirmed one by one. Multiple next collection periods are cross-confirmed in groups of two. The remaining offset periods are locked, excluding the interleaved periods. The two groups of next collection periods with the longest offset time range are recorded as characteristic periods. The terminal devices associated with the two groups of characteristic periods are recorded as combined devices.

[0024] Other terminal devices that are not part of the combined device are recorded as other terminal devices. Based on the port characteristics associated with the corresponding acquisition ports of the other terminal devices, the sum of multiple groups of port characteristics is determined, and it is determined whether the sum satisfies the following condition: sum < GL. If so, the inherent computing power resources of the processor are evenly divided, and the computing power resources of the combined device are re-analyzed.

[0025] If the conditions are not met, the sum of the port characteristics of the other pending device columns will be confirmed, and the computing power resources of the combined devices associated with the satisfied conditions will be re-analyzed. If no set of conditions are met, a computing power resource shortage signal will be directly generated for display;

[0026] The specific method of re-analyzing the computing resources of the combined device is as follows:

[0027] The remaining computing power resources are marked as ZY, and the two groups of next collection periods associated with the combined device are confirmed. The two groups of next collection periods are summed up to determine the sum period, and the R generated by the combined device during a single output is locked. i The min sum is recorded as the total output. The mean of the collection characteristics of the two collection ports associated with the modular device is then determined and recorded as the mean characteristic. The formula is: ZY × mean characteristic = mean collection speed. If the mean collection speed, sum period, and total output satisfy the following conditions: mean collection speed × sum period ≥ total output, the remaining computing power resources are directly allocated to the modular device, and synchronous collection is performed on the modular device. If not, correlation analysis is performed on other modular devices.

[0028] If all combined devices do not meet the following conditions: average sampling rate × total period ≥ total output, a computing power resource shortage signal is directly generated and displayed.

[0029] Preferably, the monitoring center includes an abnormality check terminal and a database, wherein the abnormality check terminal performs abnormality check and evaluation on the relevant operation data collected by the designated terminal device to identify whether the corresponding terminal device is operating normally. The evaluated standard data is provided by the database. The specific sub-steps are:

[0030] The relevant data collected by the corresponding terminal device is calibrated as XJ i-k ,where i represents different terminal devices and k represents different data items;

[0031] Extract the standard value interval of this data item of this terminal device from the database. The standard value interval is a preset interval. If XJ i-k ∈ standard value range, no processing is performed and continuous monitoring is sufficient. If XJ i-k ∉ standard value interval, the abnormal state is recorded and the duration of the abnormal state is confirmed. If the duration exceeds 3 minutes, it means that the data-related item of this terminal device is abnormal, and an abnormal signal of the data item of this terminal device is generated for display.

[0032] The present invention provides a cloud platform control system for data acquisition and remote monitoring. Compared with the existing technology, it has the following advantages:

[0033] The present invention determines the characteristic time based on the output frequency of the terminal device and identifies the data capacity in combination with historical data, thereby accurately locking the acquisition rate range. This ensures that the acquisition rate of the acquisition port can adapt to different devices, avoids the imbalance between the acquisition load and the port load, ensures the comprehensiveness and stability of data acquisition, and enables each port to achieve good acquisition results.

[0034] Based on the monitoring data of the collection port and the sampling speed range of the terminal device, the computing power resources required for each collection port are accurately identified as the port characteristics. The collection logic confirmation end intelligently allocates computing power resources based on the port characteristics and the inherent computing power resources of the processor. When the total resource characteristics exceed the computing power of the processor, the combined device is selected through frequency error processing, the computing power of other devices is preferentially allocated, and the computing power resources of the combined device are re-analyzed to ensure that each collection port can still operate effectively under limited computing power, ensuring the smooth progress of data collection work;

[0035] The collected terminal equipment operation data is compared with the preset standard value range in the database; once the data exceeds the range, the abnormal state is recorded and the duration is confirmed. If the duration exceeds 3 minutes, a data item abnormality signal is immediately generated and displayed to external personnel, which can timely detect and warn of terminal equipment operation abnormalities so that staff can quickly handle them, reduce the risk of equipment failure, and ensure stable equipment operation;

[0036] The system can collect and monitor data from a variety of different devices, flexibly adjust collection characteristics, and adapt to the output frequency characteristics of different devices; it also has good adaptability in computing resource allocation and anomaly verification assessment, and can be dynamically adjusted and optimized according to actual conditions, facilitating the application and expansion of the system in different scenarios to meet diverse business needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a schematic diagram of the principle framework of the present invention;

[0038] Figure 2 A schematic diagram of the process of locking total computing power resources in the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0040] See also Figure 1 The present application provides a cloud platform control system for data collection and remote monitoring, including a collection center and a monitoring center. The collection center is used to collect data from multiple terminal devices and transmit the collected data to the monitoring center. The monitoring center assesses whether the terminal devices have data fluctuations or equipment failures based on the collected data.

[0041] The acquisition center includes a device feature analysis terminal, a port feature confirmation terminal, and an acquisition logic confirmation terminal, and the device feature analysis terminal, the port feature confirmation terminal, and the acquisition logic confirmation terminal are electrically connected from the output node to the input node in sequence, and the monitoring center includes an anomaly verification terminal and a database, wherein the database and the anomaly verification terminal are electrically connected from the output node to the input node;

[0042] Among them, the device feature analysis end confirms the feature time associated with the corresponding terminal device based on the output frequency set by different terminal devices, and then identifies the data capacity output each time based on the historical data generated by the corresponding terminal device in the historical process, and confirms the sampling speed range associated with the corresponding terminal device based on the data capacity and feature time. Specifically, when its cloud platform is used for data collection and monitoring of a variety of different devices, different devices have different output frequencies. Therefore, when performing data collection, the collection features associated with each collection port can be adjusted to make the adjusted collection features more consistent with the output frequency features of the corresponding device, so as to ensure that the corresponding terminal device can achieve better collection effects. Each port can achieve better collection effects during collection, and not only will there be no collection load, but there will also be no unbalanced load on a certain port.

[0043] The specific method for confirming the speed range of the corresponding terminal device is:

[0044] Confirm the output frequency set by different terminal devices in the cloud platform and record it as Pi , where i represents different terminal devices. Then confirm the time parameters within the adjacent output frequencies of the corresponding terminal devices and record them as characteristic time (generally, if the frequency is 5s / time, then the characteristic time is 5s; if it is 10min / time, then the characteristic time is 10min. Different terminal devices have different associated characteristic times).

[0045] Confirm the historical data generated by different terminal devices, identify the total amount of data output in each data output process from the historical data, and select the minimum total amount of data R from the multiple sets of total amount of data identified for the same terminal device. i min and the maximum value of the total amount of data R i max, when selecting historical data, there is generally a selection period, which is generally 30 days, that is, the historical data generated by this terminal device in the past 30 days;

[0046] Based on the characteristic time SJ associated with the corresponding terminal device i , using: R i min÷SJ i =V i min and R i max÷SJ i =V i Max locks the sampling speed interval associated with the corresponding terminal device [V i min, V i max], the sampling speed range is the sampling speed that the subsequent acquisition port needs to reach, and the subsequent value is generally only V i max, in order to ensure that the collection process does not lose data, it is necessary to ensure that the collection rate of the corresponding port is not lower than this maximum collection rate.

[0047] Among them, the port feature confirmation end confirms the collection feature associated with each collection port through the monitoring data associated with each collection port, and then locks the computing power resources associated with the corresponding collection port according to the collection speed range confirmed by the corresponding terminal device as the port feature of this port, and transmits the locked port feature to the collection logic confirmation end. Specifically, each collection port corresponds to a group of terminal devices, and collects the data output by the corresponding terminal devices. The collection rate inside the collection port is related to the computing power resources allocated by the processor. The more computing power resources are allocated, the stronger the data processing capability of the collection port is, which can ensure the collection rate of the corresponding collection port, so that the corresponding collection rate can be improved.

[0048] Combine Figure 2 , the specific methods for locking computing power resources are:

[0049] From each collection port's past collection processes, identify the monitoring data associated with the most recent collection process. Also identify the average collection speed associated with that collection process (that is, the average of the collection rates across several groups of different times within that collection process). Then, identify the computing resources associated with that collection process (the computing resources allocated to each collection process are fixed and can be reallocated in subsequent collection processes). Use the formula: average collection speed ÷ computing resources = collection characteristics to identify the collection characteristics associated with that collection port.

[0050] Then, based on the terminal device associated with the corresponding acquisition port, identify the acquisition speed interval confirmed by the corresponding terminal device [V i min, V i max], according to the acquisition characteristics associated with the corresponding acquisition port, use: V i Max ÷ Collection Characteristics = Total Computing Power Resources. The determined total computing power resources are recorded as the port characteristics of this collection port.

[0051] Specifically, based on the corresponding collection process associated with the last time, the collection average speed and the corresponding computing power resources associated with the corresponding port in the collection process can be identified. Subsequently, based on the computing power resources associated with the corresponding collection average speed, the computing power resource value associated with the corresponding collection average speed in a single characteristic value can be locked. Based on the data collection situation of the corresponding terminal device, the total port characteristics can be locked. Based on the locked total port characteristics, the subsequent collection logic can be confirmed, so that in the subsequent collection process, there will be no collection load or other situations, and the collected data belonging to the terminal device will not be lost.

[0052] The acquisition logic confirmation end allocates the computing resources required by each acquisition port based on the port characteristics confirmed by each acquisition port and the inherent computing resources of the processor, confirms the allocation logic, and subsequently performs real-time acquisition based on this allocation logic. The specific method for confirmation is as follows:

[0053] The fixed computing power resources of the processor are calibrated as GL. Then, based on the port characteristics confirmed by each collection port, the port characteristics associated with several collection ports are summed to lock the total resource characteristics.

[0054] If the total resource characteristics are less than or equal to GL, the required computing power resources are directly allocated based on the port characteristics confirmed by each acquisition port.

[0055] If the total resource feature is greater than GL (that is, the computing power resources are insufficient based on the equal distribution method), then according to the feature time associated with different terminal devices, terminal devices with the same feature time and staggered collection time periods are selected, and the selected terminal devices are calibrated as combined devices. The computing power resources associated with the corresponding collection ports of other terminal devices are preferentially allocated, and then the computing power resources are allocated to the combined device. Based on the specific allocation process, it is verified whether several collection ports can evenly divide the inherent computing power resources of the processor. If so, the allocation logic is confirmed. If not, a computing power resource shortage signal is directly generated for relevant display (when the total resource feature exceeds the inherent computing power resources of the corresponding processor, it means that the original equal distribution method is not feasible. When the computing power resources are fixed, how to ensure that the collection ports associated with each terminal device can achieve the specific collection effect? ​​In this case, the staggered frequency processing method can be used to determine the combined device. Based on the staggered frequency time period, the computing power resources of the corresponding devices are aggregated and uniformly collected, and the corresponding collection logic is changed). The specific method of calibrating the combined device is as follows:

[0056] Based on the characteristic time SJ associated with different terminal devices i , select characteristic time SJ i Terminal devices with the same value are recorded as a pending device list (terminal devices with the same different characteristic times are associated with different pending device lists). The current time is used as the calibration time to confirm the next output time of other terminal devices in the pending device list (if the current time is 1, its characteristic time is 3, and the next output time is 2, then the next collection period of this terminal device is 2-5. If the next output time of another group of terminal devices is 3, then the next collection period of the other group of terminal devices is 3-6. Therefore, there are staggered periods between 2-5 and 3-6, namely 2-3 and 5-6, which are staggered periods with no relevant intersection). The next collection period is locked based on the next output time. The next collection periods associated with other terminal devices in the pending device list are confirmed one by one. Multiple next collection periods are cross-confirmed in pairs, and the remaining staggered periods excluding the intersecting periods are locked (staggered periods are other periods that do not belong to the intersecting periods and are synchronously included in the two-by-two collection periods). The two groups of next collection periods with the longest staggered time range are recorded as characteristic periods. The terminal devices associated with the two groups of characteristic periods are recorded as combined devices.

[0057] Other terminal devices that are not part of the combined device are recorded as other terminal devices. Based on the port characteristics associated with the corresponding acquisition ports of the other terminal devices, the sum of multiple groups of port characteristics is determined, and it is determined whether the sum satisfies the following condition: sum < GL. If so, the inherent computing power resources of the processor are evenly divided, and the computing power resources of the combined device are re-analyzed.

[0058] If the conditions are not met, the sum of the port characteristics of the other pending device columns will be confirmed, and the computing power resources of the combined devices associated with the satisfied conditions will be re-analyzed. If no set of conditions are met, a computing power resource shortage signal will be directly generated for display;

[0059] For example: When the confirmed total resource characteristics are greater than GL, there are related devices with the same characteristic time values ​​associated with their terminal devices. Such devices belong to the corresponding pending device column. There are multiple groups of different terminal devices in the pending device column. The crossover time periods generated between each terminal device are inconsistent, so the associated staggered time periods are also inconsistent. Therefore, the two groups of next collection time periods with the longest staggered time range are recorded as characteristic time periods (A's collection time period is 1-3, B's collection time period is 2-4, and C's collection time period is 2.5-4.5. It can be seen that the crossover time period between A and C is shorter, and their staggered time period is longer, so A and C belong to the combined devices selected from the pending device column).

[0060] For the confirmed combined equipment and the remaining computing resources, the specific method for re-analyzing computing resources is as follows:

[0061] The remaining computing power resources are marked as ZY, and the two groups of next collection periods associated with the combined device are confirmed. The two groups of next collection periods are summed up (the two groups of next collection periods are 1-3 and 2-4 respectively, so the sum is 1-4). The sum period is determined, and the R generated by the combined device during a single output is locked. i The min sum is recorded as the total output. The average of the collection characteristics of the two collection ports associated with the combined device is then determined and recorded as the average characteristic. The formula is: ZY × average characteristic = average collection speed. If the average collection speed, the sum period, and the total output satisfy the following conditions: average collection speed × sum period (duration) ≥ total output, the remaining computing power resources are directly allocated to the combined device, and synchronous collection is performed on the combined device. If not, correlation analysis is performed on other combined devices.

[0062] If all combined devices do not meet the following conditions: average sampling rate × total period ≥ total output, a computing power resource shortage signal will be generated and displayed.

[0063] Specifically, after the combined device is confirmed, the sum of the time periods between A and C is 1-4.5, so the duration of the sum period is 3.5. Then confirm the sum of the output of the two A and C in a single output, and then combine the remaining computing power resources to determine the average acquisition speed associated with A and C. Identify whether the relevant data collection of the total output can be completed within the duration of 3.5 based on this average acquisition speed. If it can be completed, it means that this allocation logic is feasible, and it can be executed directly. If it cannot be completed, analyze the other confirmed combined devices until they are confirmed.

[0064] Example 2: The abnormality check terminal within the monitoring center performs abnormality check and assessment on the relevant operating data collected by the designated terminal device to identify whether the corresponding terminal device is operating normally. The assessed standard data is provided by the database. The specific sub-steps of performing the abnormality check and assessment are as follows:

[0065] The relevant data collected by the corresponding terminal device is calibrated as XJ i-k ,where i represents different terminal devices and k represents different data items;

[0066] Extract the standard value range of this data item of this terminal device from the database. The standard value range is a preset range, which is prepared in advance by relevant personnel based on experience. If XJ i-k ∈ standard value range, no processing is performed and continuous monitoring is sufficient. If XJ i-k If the value is greater than the standard value range, the abnormal state is recorded and the duration of the abnormal state is confirmed. If the duration exceeds 3 minutes, it means that the data associated with this terminal device is abnormal, and an abnormal signal about the data item of this terminal device is generated and displayed for external personnel to view;

[0067] Among them, each terminal device has multiple data items to be monitored, such as voltage, current, power, temperature, etc. Different data items have different numerical standards. When the corresponding monitoring data fluctuates, an abnormal state will occur. If the abnormal state lasts too long, it means that there is a specific abnormality in the corresponding data item of the corresponding terminal device, and timely signal display and processing are required.

[0068] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0069] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A cloud platform control system for data acquisition and remote monitoring, characterized in that: include: The collection center is used to collect data from multiple terminal devices, and the collection center includes a device feature analysis terminal, a port feature confirmation terminal, and a collection logic confirmation terminal; The monitoring center assesses whether there is data fluctuation or equipment failure in the terminal device based on the collected data; The device feature analysis end determines the characteristic time associated with the corresponding terminal device based on the output frequency set by the terminal device. Then, based on the historical data generated by the terminal device in the historical process, it identifies the data capacity output each time. Based on the data capacity and characteristic time, it determines the sampling speed range associated with the terminal device. The port feature confirmation terminal confirms the collection features associated with each collection port through the monitoring data associated with each collection port, and then locks the computing power resources associated with the corresponding collection port according to the collection speed range confirmed by the corresponding terminal device, recording it as the port feature of this port; The acquisition logic confirmation end allocates the computing resources required by each acquisition port based on the port characteristics confirmed by each acquisition port and the inherent computing resources of the processor, confirms the allocation logic, and then performs real-time acquisition based on this allocation logic. The specific method is as follows: The fixed computing power resources of the processor are calibrated as GL. Then, based on the port characteristics confirmed by each collection port, the port characteristics associated with several collection ports are summed to lock the total resource characteristics. If the total resource characteristics are less than or equal to GL, the required computing power resources are directly allocated based on the port characteristics confirmed by each acquisition port. If the total resource feature is greater than GL, terminal devices with the same feature time and collected data in different time periods are selected based on the feature time associated with different terminal devices. The selected terminal devices are marked as combined devices, and the computing power resources associated with the corresponding collection ports of other terminal devices are allocated first. Then, computing power resources are allocated to the combined devices. Based on the specific allocation process, it is checked whether several collection ports can evenly divide the computing power resources inherent in the processor. If so, the allocation logic is confirmed. If not, a computing power resource shortage signal is directly generated for relevant display.

2. A cloud platform control system for data acquisition and remote monitoring according to claim 1, characterized in that: The device feature analysis terminal determines the speed range of the terminal device in the following manner: Confirm the output frequency set by different terminal devices in the cloud platform and record it as P i , where i represents different terminal devices, and then confirm the time parameters within the adjacent output frequencies of the corresponding terminal devices, which are recorded as characteristic time; Confirm the historical data generated by different terminal devices, identify the total amount of data output in each data output process from the historical data, and select the minimum total amount of data R from the multiple sets of total amount of data identified for the same terminal device. i min and the maximum value of the total amount of data R i max; Based on the characteristic time SJ associated with the corresponding terminal device i , using: R i min÷SJ i =V i min and R i max÷SJ i =V i Max locks the sampling speed interval associated with the corresponding terminal device [V i min, V i max].

3. A cloud platform control system for data acquisition and remote monitoring according to claim 2, characterized in that: The specific method of locking the port feature at the port feature confirmation end is as follows: From each collection port's past collection processes, identify the monitoring data associated with the most recent collection process. Then, identify the average collection speed associated with that collection process. Then, identify the computing resources associated with that collection process. Use the formula: average collection speed ÷ computing resources = collection characteristics to identify the collection characteristics associated with that collection port. Then, based on the terminal device associated with the corresponding acquisition port, identify the acquisition speed interval confirmed by the corresponding terminal device [V i min, V i max], according to the acquisition characteristics associated with the corresponding acquisition port, use: V i Max ÷ collection characteristics = total computing power resources. The determined total computing power resources are recorded as the port characteristics of this collection port.

4. A cloud platform control system for data acquisition and remote monitoring according to claim 1, characterized in that: The specific method of calibrating the combined device at the acquisition logic confirmation end is: Based on the characteristic time SJ associated with different terminal devices i , select characteristic time SJ i Terminal devices with the same value are recorded as pending devices. The current time is used as the calibration time to confirm the next output time of other terminal devices in the pending device list. The next collection period is locked based on the next output time. The next collection period associated with other terminal devices in the pending device list is confirmed one by one. Multiple next collection periods are cross-confirmed in groups of two. The remaining offset periods are locked, excluding the interleaved periods. The two groups of next collection periods with the longest offset time range are recorded as characteristic periods. The terminal devices associated with the two groups of characteristic periods are recorded as combined devices. Other terminal devices that are not part of the combined device are recorded as other terminal devices. Based on the port characteristics associated with the corresponding acquisition ports of the other terminal devices, the sum of multiple groups of port characteristics is determined, and it is determined whether the sum satisfies the following condition: sum < GL. If so, the inherent computing power resources of the processor are evenly divided, and the computing power resources of the combined device are re-analyzed. If not, the sum of the port characteristics of other pending device columns will be confirmed, and the computing power resources of the combined devices associated with the satisfied conditions will be re-analyzed. If no set of satisfied conditions exists, a computing power resource shortage signal will be directly generated for display.

5. A cloud platform control system for data acquisition and remote monitoring according to claim 4, characterized in that: The specific method of the acquisition logic confirmation end to re-analyze the computing power resources of the combined device is: The remaining computing power resources are marked as ZY, and the two groups of next collection periods associated with the combined device are confirmed. The two groups of next collection periods are summed up to determine the sum period, and the R generated by the combined device during a single output is locked. i The min sum is recorded as the total output. The mean of the collection characteristics of the two collection ports associated with the modular device is then determined and recorded as the mean characteristic. The formula is: ZY × mean characteristic = mean collection speed. If the mean collection speed, sum period, and total output satisfy the following conditions: mean collection speed × sum period ≥ total output, the remaining computing power resources are directly allocated to the modular device, and synchronous collection is performed on the modular device. If not, correlation analysis is performed on other modular devices. If all combined devices do not meet the following conditions: average sampling rate × total period ≥ total output, a computing power resource shortage signal is directly generated and displayed.

6. A cloud platform control system for data acquisition and remote monitoring according to claim 1, characterized in that: The monitoring center includes an abnormality check terminal and a database, wherein the abnormality check terminal performs abnormality check and evaluation on relevant operation data collected by the designated terminal equipment to identify whether the corresponding terminal equipment is operating normally. The evaluated standard data is provided by the database.

7. A cloud platform control system for data acquisition and remote monitoring according to claim 6, characterized in that: The specific sub-steps of the abnormality verification evaluation at the abnormality verification end are as follows: The relevant data collected by the corresponding terminal device is calibrated as XJ i-k ,where i represents different terminal devices and k represents different data items; Extract the standard value interval of this data item of this terminal device from the database. The standard value interval is a preset interval. If XJ i-k ∈ standard value range, no processing is performed and continuous monitoring is sufficient. If XJ i-k ∉ standard value interval, the abnormal state is recorded and the duration of the abnormal state is confirmed. If the duration exceeds 3 minutes, it means that the data-related item of this terminal device is abnormal, and an abnormal signal of the data item of this terminal device is generated for display.

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