Cloud platform control system for data acquisition and remote monitoring

Abstract

The invention discloses a cloud platform control system for data acquisition and remote monitoring, relates to the technical field of platform monitoring, and solves the problem that a load is easy to occur due to insufficient computing power resources in the acquisition process of multi-party equipment. And the computing power resource required by each acquisition port is accurately locked as a port feature. And the acquisition logic confirmation end intelligently allocates computing power resources according to port characteristics and inherent computing power resources of the processor. And when the total resource characteristics exceed the computing power of the processor, the combined equipment is selected through frequency staggering processing, the computing power of other equipment is preferentially allocated, and then the combined equipment is subjected to computing power resource reanalysis, so that each acquisition port can still operate effectively under the condition that the computing power is limited, and the data acquisition work is ensured to be carried out smoothly.

Description

Technical Field

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

[0002] In today's era of rapid development of digitalization and intelligence, various industries have an increasingly urgent need for real-time control of equipment operation status and efficient data collection and analysis. Whether it is mechanical equipment in industrial production, power generation equipment in the energy field, or professional instruments in the medical industry, advanced technical means are needed to achieve comprehensive monitoring and management of equipment.

[0003] Traditional data collection and monitoring methods have many limitations. On the one hand, there is often a lack of targeted data collection strategies for terminal devices of different types and specifications. The output frequency and data capacity of different devices vary greatly, and traditional collection methods are difficult to accurately adapt. There are often problems such as excessive collection load or unbalanced load on a certain port, resulting in incomplete or inaccurate data collection, affecting subsequent analysis and decision-making. For example, in industrial automation production lines, the equipment operation rhythm and data output of different production links are different. Traditional collection methods cannot flexibly adjust the collection frequency and speed, which easily leads to omission of key data or collection redundancy.

[0004] On the other hand, the allocation of computing resources lacks intelligence and refinement. In a complex environment where multiple devices are running simultaneously, traditional methods make it difficult to reasonably allocate the processor's computing resources according to the actual needs of the devices. When the total resource demand exceeds the inherent computing power of the processor, technical means such as frequency error cannot be effectively used to optimize the allocation, resulting in data collection of some devices being blocked, reducing the operating efficiency and stability of the entire monitoring system.

[0005] In addition, in terms of equipment operation status monitoring, traditional monitoring systems are difficult to quickly and accurately identify equipment operation anomalies. Due to the lack of preset standard value intervals and effective anomaly verification mechanisms, fluctuations in equipment operation data cannot be detected in a timely manner. Even if anomalies are found, it is difficult to accurately locate specific data-related items. They are often discovered only after serious equipment failures occur, causing production interruptions, economic losses, and safety hazards.

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

[0007] In view of the shortcomings of the prior art, 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 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: A 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 evaluates whether the terminal device has data fluctuations or equipment failures based on the collected data; 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. The specific method is as follows: 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 amounts 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 speed interval associated with the corresponding terminal device [V i min,V i max]; 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 and records them as the port feature of this port. The specific method is as follows: From the past collection processes of each collection port, confirm the monitoring data associated with the most recent collection process from the current moment, confirm the average collection speed associated with the corresponding collection process, and then confirm the computing power resources associated with the corresponding collection process. Use: average collection speed ÷ computing power resources = collection characteristics to confirm the collection characteristics associated with this collection port; Based on the terminal device associated with the corresponding acquisition port, the acquisition speed interval confirmed by the corresponding terminal device is identified. i min,V i max], according to the acquisition characteristics associated with the corresponding acquisition port, use: V i max÷collection feature=total computing power resources, the determined total computing power resources are recorded as the port feature of this collection port; The collection logic confirmation end allocates the computing resources required by each collection port based on the port characteristics confirmed by each collection port and the inherent computing resources of the processor, confirms the allocation logic, and then performs real-time collection based on this allocation logic. The specific method is as follows: The fixed computing power resources of the processor are calibrated as GL, and 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 ≤ GL, the port characteristics confirmed by each collection port are used as the required computing resources and directly allocated; If the total resource feature is greater than GL, 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 marked as combined devices. The computing power resources associated with the corresponding collection ports of other terminal devices are allocated first, and then the 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; The specific method for calibrating the combined equipment 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 device columns. The current time is used as the calibration time to confirm the next output time of other terminal devices in the pending device column. The next collection time period is locked based on the next output time. The next collection time periods associated with other terminal devices in the pending device column are confirmed one by one, and multiple next collection time periods are cross-confirmed in groups of two. Other staggered time periods excluding the cross-time periods are locked. The two groups of next collection time periods with the longest staggered time range are recorded as characteristic time periods, and the terminal devices associated with the two groups of characteristic time periods are recorded as combined devices. Other terminal devices that do not belong to 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 whether the sum satisfies: the 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 port characteristics is confirmed for other pending device columns, and the computing power resources of the combined devices associated with the satisfied conditions are re-analyzed. If no group of satisfied conditions exists, a computing power resource shortage signal is directly generated for display; The specific method of reanalyzing the computing resources of the combined device is as follows: The remaining computing 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 process is locked. i min sum, recorded as the sum of outputs, and then confirm the mean of the collection features of the two collection ports associated with the combined device, recorded as the mean feature, using: ZY×mean feature=mean collection speed. If the mean collection speed, sum period and output sum meet: mean collection speed×sum period ≥ output sum, the remaining computing power resources are directly allocated to the combined device, and the combined device is collected synchronously. If not, other combined devices are analyzed. 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 accordingly.

[0009] Preferably, the monitoring center includes an abnormality check terminal and a database, wherein the abnormality check terminal performs abnormality check evaluation on the relevant operation data collected by the designated terminal device to identify whether the corresponding terminal device is operating normally, and the evaluated standard data is provided by the database. The specific sub-steps are: 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, and the standard value interval is a preset interval. If XJ i-k ∈ standard value interval, 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.

[0010] The present invention provides a cloud platform control system for data collection and remote monitoring. Compared with the prior art, it has the following beneficial effects: The present invention determines the characteristic time according to the output frequency of the terminal device and identifies the data capacity in combination with historical data, thereby accurately locking the acquisition rate interval, ensuring that the acquisition rate of the acquisition port can adapt to different devices, avoiding the situation where the acquisition load and the port load are unbalanced, ensuring the comprehensiveness and stability of data acquisition, and allowing each port to achieve a better acquisition effect; Then, based on the monitoring data of the collection port and the speed range of the terminal device, the computing power resources required for each collection port are accurately locked 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 processor computing power, the combined device is selected through frequency error processing, the computing power of other devices is allocated preferentially, 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; Compare the collected terminal equipment operation data with the preset standard value range in the database; once the data exceeds the range, record the abnormal state and confirm the duration. If the duration exceeds 3 minutes, immediately generate a data item abnormal signal to display to external personnel, which can timely discover and warn the abnormal operation of the terminal equipment, so that the staff can quickly handle it, reduce the risk of equipment failure, and ensure the stable operation of the equipment; 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, making it easy to apply and expand the system in different scenarios to meet diverse business needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic diagram of the principle framework of the present invention; Figure 2 A schematic diagram of the process of locking total computing power resources according to the present invention. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0013] See also Figure 1The present application provides a cloud platform control system for data collection and remote monitoring, including a collection center and a monitoring center, wherein the collection center is used to collect data from multiple terminal devices and transmit the collected data to the monitoring center, and the monitoring center evaluates whether the terminal device has data fluctuations or equipment failures based on the collected data; Wherein, the collection center includes a device feature analysis terminal, a port feature confirmation terminal and a collection logic confirmation terminal, and the device feature analysis terminal, the port feature confirmation terminal and the collection logic confirmation terminal are electrically connected from the output node to the input node in sequence, and the monitoring center includes an abnormality check terminal and a database, wherein the database and the abnormality check terminal are electrically connected from the output node to the input node; 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 interval associated with the corresponding terminal device based on the data capacity and feature time. Specifically, when its cloud platform collects and monitors data from 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. The specific method for confirming the speed range of the corresponding terminal device is: 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 (generally, if the frequency is 5s / time, then the characteristic time is 5s, and if it is 10min / time, then the characteristic time is 10min. Different terminal devices are associated with different characteristic times); 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 amounts 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; Based on the characteristic time SJ associated with the corresponding terminal device i , using: R i min÷SJi =V i min and R i max÷SJ i =V i max locks the 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 will not lose data, it is necessary to ensure that the collection rate of the corresponding port is not lower than this maximum collection rate.

[0014] 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 interval 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 guarantee the collection rate of the corresponding collection port, so that the corresponding collection rate can be improved; Combination Figure 2 , the specific way to lock computing resources is: From the past collection processes of each collection port, confirm the monitoring data associated with the most recent collection process from the current moment, confirm the average collection speed associated with the corresponding collection process (that is, the average of the collection rates of several groups at different moments in the corresponding collection process), and then confirm the computing power resources associated with the corresponding collection process (the computing power resources allocated to each collection process are fixed values, and the computing power resources can be reallocated in subsequent collection processes). Use: average collection speed ÷ computing power resources = collection features to confirm the collection features associated with this collection port; Based on the terminal device associated with the corresponding acquisition port, the acquisition speed interval confirmed by the corresponding terminal device is identified. i min,V i max], according to the acquisition characteristics associated with the corresponding acquisition port, use: V i max÷collection feature=total computing power resources, the determined total computing power resources are recorded as the port feature of this collection port; Specifically, according to the 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 overall port characteristics can be locked. Based on the locked overall 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.

[0015] Among them, 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 of confirmation is: The fixed computing power resources of the processor are calibrated as GL, and 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 ≤ GL, the port characteristics confirmed by each collection port are used as the required computing resources and directly allocated; If the total resource feature is greater than GL (that is, the computing power resources are insufficient according to 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 marked as combined devices. The computing power resources associated with the corresponding collection ports of other terminal devices are allocated first, 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 a specific collection effect? ​​Then 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 summed up and uniformly collected, and the corresponding collection logic is changed). The specific method of calibrating the combined device is as follows: 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 device columns (terminal devices with the same characteristic time are associated with different pending device columns), and the current time is used as the calibration time to confirm the next output time of other terminal devices in the pending device column (the current time is 1, its characteristic time is 3, and the next output time is 2, then the next collection time period of this terminal device is 2-5, and the next output time of another group of terminal devices is 3, then the next collection time period of the other group of terminal devices is 3-6, then there is a staggered time period between 2-5 and 3-6, that is, 2-3 and 5-6, that is, staggered time periods without relevant intersection), lock the next collection time period based on the next output time, confirm the next collection time periods associated with other terminal devices in the pending device column one by one, and cross-confirm multiple next collection time periods in a group of two, lock other staggered time periods except the crossing time period (staggered time periods are other time periods that do not belong to the crossing time period, and synchronously belong to the collection time period of two groups), record the two groups of next collection time periods with the longest staggered time range as characteristic time periods, and then record the terminal devices associated with the two groups of characteristic time periods as combined devices; Other terminal devices that do not belong to 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 whether the sum satisfies: the 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 port characteristics is confirmed for other pending device columns, and the computing power resources of the combined devices associated with the satisfied conditions are re-analyzed. If no group of satisfied conditions exists, a computing power resource shortage signal is directly generated for display; For example: when the confirmed total resource characteristics are greater than GL, there are related devices with the same characteristic time associated with its 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 equipment selected from the pending device column).

[0016] For the confirmed combined equipment and the remaining computing resources, the specific method of re-analyzing the computing resources is as follows: 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 after summing up, it is 1-4), the sum period is determined, and the R generated by the combined device in a single output process is locked. i min sum, recorded as the sum of outputs, and then confirm the mean of the collection features of the two collection ports associated with the combined device, recorded as the mean feature, using: ZY×mean feature=mean collection speed. If the mean collection speed, sum period and sum of outputs meet: mean collection speed×sum period (duration) ≥ sum of outputs, the remaining computing power resources are directly allocated to the combined device, and the combined device is collected synchronously. If not, other combined devices are analyzed. If all combined devices do not meet the following conditions: average sampling rate × total period ≥ total output, a signal of insufficient computing resources will be directly generated and displayed; 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, and then the sum of the output of A and C in a single output is confirmed, and then combined with the remaining computing power resources, the average collection speed associated with A and C is determined, and it is identified within the duration of 3.5. Based on this average collection speed, whether the relevant data collection of the total output can be completed. If it can be completed, it means that this allocation logic is feasible, and it can be executed directly. If it cannot be completed, the other confirmed combined devices are analyzed and confirmed.

[0017] Embodiment 2: The abnormality check terminal in the monitoring center 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, and the specific sub-steps of performing abnormality check and evaluation 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 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 interval, 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 associated item of this terminal device is abnormal, and an abnormal signal of the data item of this terminal device is generated for display and for external personnel to view; 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.

[0018] 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.

[0019] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. 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: A 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 evaluates whether the terminal device has data fluctuations or equipment failures based on the collected data; 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 interval associated with the corresponding terminal device based on the data capacity and feature time; 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 and records them as the port feature of this port; The collection logic confirmation end allocates the computing resources required by each collection port based on the port characteristics confirmed by each collection port and the computing resources inherent in the processor, confirms the allocation logic, and then performs real-time collection based on this allocation logic.

2. A cloud platform control system for data acquisition and remote monitoring according to claim 1, characterized in that: The specific method of confirming the speed range of the terminal device at the device feature analysis end is: 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 amounts 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 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: From the past collection processes of each collection port, confirm the monitoring data associated with the most recent collection process from the current moment, confirm the average collection speed associated with the corresponding collection process, and then confirm the computing power resources associated with the corresponding collection process. Use: average collection speed ÷ computing power resources = collection characteristics to confirm the collection characteristics associated with this collection port; Based on the terminal device associated with the corresponding acquisition port, the acquisition speed interval confirmed by the corresponding terminal device is identified. i min,V i max], according to the acquisition characteristics associated with the corresponding acquisition port, use: V i max÷collection feature=total computing power resources. The determined total computing power resources are recorded as the port feature of this collection port.

4. A cloud platform control system for data acquisition and remote monitoring according to claim 3, characterized in that: The specific method of confirming the allocation logic at the acquisition logic confirmation end is: The fixed computing power resources of the processor are calibrated as GL, and 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 ≤ GL, the port characteristics confirmed by each collection port are used as the required computing resources and directly allocated; If the total resource feature is greater than GL, then according to the feature time associated with different terminal devices, terminal devices with the same feature time and collected data in different time periods are selected, and the selected terminal devices are marked as combined devices. The computing power resources associated with the corresponding collection ports of other terminal devices are preferentially allocated, and 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 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.

5. A cloud platform control system for data acquisition and remote monitoring according to claim 4, 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 device columns. The current time is used as the calibration time to confirm the next output time of other terminal devices in the pending device column. The next collection time period is locked based on the next output time. The next collection time periods associated with other terminal devices in the pending device column are confirmed one by one, and multiple next collection time periods are cross-confirmed in groups of two. Other staggered time periods excluding the cross-time periods are locked. The two groups of next collection time periods with the longest staggered time range are recorded as characteristic time periods, and the terminal devices associated with the two groups of characteristic time periods are recorded as combined devices. Other terminal devices that do not belong to 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 whether the sum satisfies: the 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 it is not satisfied, the sum of the port characteristics of other pending device columns is confirmed, and the computing power resources of the combined devices associated with the satisfied situation are re-analyzed. If no group of satisfied situations exists, a computing power resource shortage signal is directly generated for display.

6. A cloud platform control system for data acquisition and remote monitoring according to claim 5, 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 resources are marked as ZY, and the two sets of next collection periods associated with the combined device are confirmed. The two sets of next collection periods are summed up to determine the sum period, and the R generated by the combined device during a single output process is locked. i min sum, recorded as the sum of outputs, and then confirm the mean of the collection features of the two collection ports associated with the combined device, recorded as the mean feature, using: ZY×mean feature=mean collection speed. If the mean collection speed, sum period and output sum meet: mean collection speed×sum period ≥ output sum, the remaining computing power resources are directly allocated to the combined device, and the combined device is collected synchronously. If not, other combined devices are analyzed. 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 accordingly.

7. A cloud platform control system for data collection 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 the relevant operation data collected by the designated terminal equipment to identify whether the corresponding terminal equipment is operating normally, and the evaluated standard data is provided by the database.

8. A cloud platform control system for data collection and remote monitoring according to claim 7, 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, and the standard value interval is a preset interval. If XJ i-k ∈ standard value interval, 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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