Control and acquisition system based on multipath intelligent acquisition

Abstract

The invention relates to the technical field of data acquisition, in particular to a multi-channel intelligent acquisition-based control and acquisition system, which comprises a signal quality evaluation module, a signal optimization adjustment module, an abnormal signal detection module, a resource scheduling control module, a data synchronization adjustment module and a cloud backup strategy module. According to the invention, by analyzing the amplitude and frequency components of the signal in real time, noise and distortion can be identified and corrected in real time, signal processing is optimized, the accuracy and availability of data are improved, the real-time optimization processing of the signal improves the processing quality of the signal, and the high efficiency of data synchronization and cloud backup is ensured; by dynamically adjusting the synchronous bandwidth and period, data changes can be flexibly responded, the risk of data delay and loss is reduced, intelligent detection and resource scheduling of abnormal signals are achieved, the response speed and maintenance efficiency of the system are improved, and the stability and safety of the whole system are improved through accurate resource allocation and timely maintenance response.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition, and in particular to a control and acquisition system based on multi-channel intelligent acquisition. Background Art

[0002] The field of data acquisition technology involves the process of collecting information from physical or digital source systems using automated equipment. The technology includes various hardware and software configurations designed to optimize the input, storage, and processing of data. Common data acquisition systems include sensor networks, data acquisition cards, and supporting data management and analysis software. The key to this field is to efficiently convert various signals into electronic data for further data analysis and decision support. Data acquisition technology is widely used in many fields such as industrial automation, environmental monitoring, health monitoring, and intelligent transportation systems to improve operational efficiency and data accuracy.

[0003] Among them, the control and acquisition system based on multi-channel intelligent acquisition is a system designed to intelligently collect information from multiple data sources at the same time. By using multi-channel data acquisition technology, the system can process multiple signals or data streams from different sensors. By processing and integrating the data, the data set required for subsequent data analysis is constructed. Its main uses include industrial automation, environmental monitoring, medical health monitoring and security systems.

[0004] Traditional data collection systems use fixed-configuration sensors and synchronization strategies, which have the problem of degraded data quality in rapidly changing environments. Traditional systems lack flexibility in data synchronization and backup. Fixed synchronization frequency and bandwidth allocation cannot meet the needs of all situations, especially when the data volume is large or network conditions change, which will cause data delays or loss. Traditional systems usually lack automation and intelligence in anomaly detection and resource scheduling, and require manual intervention to adjust resources or respond to abnormal situations, which not only increases operating costs, but also prolongs response time and reduces the overall efficiency and reliability of the system. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a control and acquisition system based on multi-channel intelligent acquisition.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a control and acquisition system based on multi-channel intelligent acquisition, the system comprising: The signal quality assessment module is based on the data acquisition environment and multi-channel data acquisition targets. It implements data acquisition by deploying differentiated sensors, analyzes and identifies the noise level and distortion of the signal, evaluates the signal quality, and generates signal quality analysis results. The signal optimization and adjustment module performs noise filtering and distortion correction on the signal based on the signal quality analysis result, converts the electrical signal of the sensor into a digital signal, and locally stores the signal to obtain a signal optimization processing result; The abnormal signal detection module identifies signals with quality lower than a set threshold based on the signal quality analysis result, evaluates whether the sensor is abnormal, notifies the management personnel to perform maintenance and replacement, and obtains abnormal detection information; The resource scheduling control module dynamically adjusts the resource allocation of the sensor and the processing unit based on the signal optimization processing result, adjusts the processing order of the differentiated signal according to the urgency and resource requirements of the differentiated signal, and obtains the processing resource allocation result; The data synchronization adjustment module synchronously transmits the signal to the cloud server based on the processing resource allocation result, and adjusts the bandwidth in the process of differential signal synchronization transmission according to the importance of the signal to obtain a signal synchronization adjustment result; The cloud backup strategy module adjusts the number of backup versions of the signal in the cloud based on the signal synchronization adjustment result and the importance of the differentiated signal, performs signal backup, and obtains a backup execution record.

[0007] As a further solution of the present invention, the signal quality analysis result includes the noise level and signal distortion of the signal, the signal optimization processing result includes the adjusted amplification factor, updated filtering threshold and linearization parameter settings, the abnormality detection information includes the abnormality type, detection frequency and sensor maintenance measures, the processing resource allocation result includes signal priority, processing unit load and task allocation strategy, the signal synchronization adjustment result includes synchronization period, data transmission rate and storage location selection, and the backup execution record includes backup timestamp, backup version number and data integrity status.

[0008] As a further solution of the present invention, the signal quality assessment module includes: The signal acquisition submodule is based on the data acquisition environment and multi-channel data acquisition targets, selects and deploys sensors that adapt to the target data acquisition environment, collects the electrical signals captured by the sensors in the target environment, and obtains basic electrical signal data; The signal feature extraction submodule performs signal amplitude and frequency components based on the basic electrical signal data, extracts and records key signal features, including amplitude peaks and key frequency components, by decomposing the signal to obtain signal feature parameters; The quality analysis submodule analyzes and identifies the noise level and distortion of each signal based on the signal characteristic parameters, evaluates the quality index of the signal, and generates a signal quality analysis result.

[0009] As a further solution of the present invention, the signal optimization and adjustment module includes: The parameter adjustment submodule adjusts the signal amplification factor, updates the cutoff frequency of the filter, optimizes the signal processing process, and obtains the signal processing optimization parameters based on the signal quality analysis result, according to the quality of each signal and the differentiated signal quality requirements; The signal processing submodule performs noise filtering and distortion correction on the signal based on the signal processing optimization parameters to obtain a clarity optimized signal; The signal conversion submodule converts the signal based on the clarity optimization signal, converts the electrical signal of the sensor into a digital signal, compresses and formats the data, and locally stores the signal to obtain a signal optimization processing result.

[0010] As a further solution of the present invention, the abnormal signal detection module includes: The signal comparison submodule compares the quality of each signal with a preset quality threshold based on the signal quality analysis result, analyzes the deviation between the signal quality and the quality threshold, evaluates the quality deviation, and obtains a quality deviation analysis result; The abnormality identification submodule evaluates the change trend of the signal quality based on the quality deviation analysis result, calculates the abnormality degree of the signal in combination with the quality deviation, compares it with the preset abnormality threshold, identifies the abnormal signal, and obtains the abnormal signal identification result; The alarm generation submodule formulates corresponding alarm information based on the abnormal signal recognition result, including the type of abnormal signal and the corresponding sensor location, notifies the management personnel to implement maintenance and replacement, and obtains abnormal detection information.

[0011] As a further solution of the present invention, the formula for calculating the abnormality degree of the signal is:

[0012] in, is the abnormality of the signal, represents the signal quality deviation, Represents the trend of signal quality change. Represents the average quality of the signal, , and is the adjustment coefficient.

[0013] As a further solution of the present invention, the resource scheduling control module includes: The resource load analysis submodule evaluates the resource usage of each sensor and processing unit in the current system based on the signal optimization processing results and anomaly detection information, including monitoring the occupancy rate of processing resources, calculating resource consumption, analyzing the load of processing tasks, and obtaining resource usage information; The task priority analysis submodule calculates the priority score of each signal processing task based on the resource usage information, according to the urgency of the signal and the resource requirements, and performs priority sorting of the signal processing tasks to obtain task priority information; The scheduling execution submodule dynamically adjusts the order of signal processing and resource allocation based on the task priority information, including adjusting the working order of the processing unit and the sensor, allocating the required processing resources, and obtaining the processing resource allocation result.

[0014] As a further solution of the present invention, the formula for calculating the priority score of each signal processing task is:

[0015] in, is the priority score of the signal processing task, Represents the urgency of the signal processing task, represents the importance of the signal processing task, Represents the resource requirements of the signal processing task, Represents the total amount of available resources. , and is the weight coefficient.

[0016] As a further solution of the present invention, the data synchronization adjustment module includes: The synchronization bandwidth allocation submodule evaluates the bandwidth required for the differentiated signal data in the cloud synchronization process based on the processing resource allocation result and the signal data size, and adjusts the cloud synchronization bandwidth in combination with the importance of the differentiated signal to obtain bandwidth allocation information; The synchronization cycle adjustment submodule evaluates the update frequency of the differentiated signal based on the bandwidth allocation information, adjusts the time period of data synchronization in combination with network conditions, sets a matching synchronization interval for each signal, and obtains a cycle adjustment result; The data synchronization implementation submodule synchronously transmits the signal to the cloud server based on the period adjustment result, monitors the continuity and integrity of the transmission, and obtains the signal synchronization adjustment result.

[0017] As a further solution of the present invention, the cloud backup strategy module includes: The redundant backup strategy submodule evaluates the backup redundancy level required for the differentiated signal based on the signal synchronization adjustment result and the importance of the signal data, configures the corresponding redundant backup quantity for each signal, and obtains the backup redundancy configuration information; The backup location selection submodule selects a matching storage backup cloud node based on the backup redundancy configuration information by analyzing the availability, security and cost efficiency of the cloud node to obtain a backup node selection result; The backup status verification submodule performs a signal data backup process based on the backup node selection result, and regularly performs integrity and security tests on the backup data to verify the integrity and security of the backup and obtain a backup execution record.

[0018] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by real-time analysis of the amplitude and frequency components of the signal, it is possible to instantly identify and correct noise and distortion, optimize signal processing, and improve data accuracy and availability. The real-time optimization processing of the signal improves the signal processing quality and ensures the efficiency of data synchronization and cloud backup. By dynamically adjusting the synchronization bandwidth and period, it is possible to flexibly respond to data changes and reduce the risk of data delay and loss. The intelligent detection and resource scheduling of abnormal signals improves the response speed and maintenance efficiency of the system. Through precise resource allocation and timely maintenance response, the stability and security of the overall system are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a system flow chart of the present invention; Figure 2 It is a schematic diagram of the system framework of the present invention; Figure 3 is a flow chart of a signal quality assessment module of the present invention; Figure 4 This is a flow chart of the signal optimization and adjustment module of the present invention; Figure 5 This is a flow chart of the abnormal signal detection module of the present invention; Figure 6 It is a flow chart of the resource scheduling control module of the present invention; Figure 7 This is a flow chart of the data synchronization adjustment module of the present invention; Figure 8 Flow chart of the cloud backup strategy module of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0021] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0022] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0023] Example See also Figure 1 , a control and acquisition system based on multi-channel intelligent acquisition, the system includes: The signal quality assessment module is based on the data acquisition environment and multi-channel data acquisition targets. It implements data acquisition by deploying differentiated sensors, extracts the amplitude and frequency components of the signal, analyzes and identifies the noise level and distortion of the signal, evaluates the signal quality, and generates signal quality analysis results. The signal optimization and adjustment module adjusts the signal amplification factor based on the signal quality analysis results and the quality of each signal, updates the filter cutoff frequency, performs noise filtering and distortion correction on the signal, converts the sensor's electrical signal into a digital signal, and stores the signal locally to obtain the signal optimization processing result; Based on the signal quality analysis results, the abnormal signal detection module analyzes the quality of each signal, identifies signals whose quality is lower than the set threshold, and evaluates whether the sensor is abnormal based on the deviation between the signal quality and the preset threshold and the signal quality change trend, notifies the management personnel to implement maintenance and replacement, and obtains abnormal detection information; The resource scheduling control module evaluates the signal processing resource usage of the current system based on the signal optimization processing results, dynamically adjusts the resource allocation of sensors and processing units, prioritizes the differentiated signals according to their urgency and resource requirements, adjusts the processing order of the differentiated signals, and obtains the processing resource allocation results; The data synchronization adjustment module transmits the signal synchronously to the cloud server based on the processing resource allocation result, and adjusts the bandwidth in the process of differentiated signal synchronization transmission according to the importance of the signal, and adjusts the data synchronization update frequency according to the signal update frequency to obtain the signal synchronization adjustment result; The cloud backup strategy module adjusts the number of backup versions of the signal in the cloud based on the signal synchronization adjustment result and the importance of the differentiated signal, selects the matching cloud node, performs signal backup according to the preset period, and evaluates the integrity and security of the backup to obtain the backup execution record; The signal quality analysis results include the signal noise level and signal distortion. The signal optimization processing results include the adjusted amplification factor, updated filter threshold and linearization parameter settings. The anomaly detection information includes the anomaly type, detection frequency and sensor maintenance measures. The processing resource allocation results include signal priority, processing unit load and task allocation strategy. The signal synchronization adjustment results include the synchronization period, data transmission rate and storage location selection. The backup execution record includes the backup timestamp, backup version number and data integrity status.

[0024] See also Figure 2 and Figure 3 ,The signal quality assessment module includes a signal acquisition submodule, a signal feature extraction submodule, and a quality analysis submodule; The signal acquisition submodule is based on the data acquisition environment and multi-channel data acquisition targets, selects and deploys sensors that adapt to the target data acquisition environment, collects the electrical signals captured by the sensors in the target environment, and obtains basic electrical signal data; The signal acquisition submodule is based on the data acquisition environment and multi-channel data acquisition targets. It conducts a detailed analysis of the environment for specific application environments, such as industrial sites or outdoor natural environments, including environmental noise levels, temperature and humidity ranges, electromagnetic interference, etc. Based on these environmental characteristics, sensors with high anti-interference capabilities and suitable operating temperature ranges are selected. For example, sensors with high signal-to-noise ratios may be selected in high-noise industrial sites. Subsequently, according to the number and type of acquisition targets, such as temperature, pressure, or sound, the corresponding number and type of sensors are deployed, and the correct position and installation angle are ensured to maximize signal capture efficiency and accuracy to obtain basic electrical signal data.

[0025] The signal feature extraction submodule performs signal amplitude and frequency components based on basic electrical signal data, decomposes the signal, extracts and records key signal features, including amplitude peaks and key frequency components, and obtains signal feature parameters; The signal feature extraction submodule analyzes the amplitude and frequency components of the signal based on the basic electrical signal data. Through signal processing techniques such as fast Fourier transform or wavelet transform, the signal is decomposed to extract its frequency domain features. In the process, the energy of each frequency band of the signal is calculated, with special attention paid to key frequency components. For example, the peak of a specific frequency in the fault diagnosis of mechanical equipment may indicate a specific type of mechanical failure. In addition, the amplitude peak is evaluated, which helps to identify prominent events or anomalies in the signal and obtain signal characteristic parameters.

[0026] The quality analysis submodule analyzes and identifies the noise level and distortion of each signal based on the signal characteristic parameters, evaluates the quality indicators of the signal, and generates signal quality analysis results.

[0027] The quality analysis submodule analyzes and identifies the noise level and distortion of each signal based on signal characteristic parameters, uses signal-to-noise ratio and distortion indicators (such as total harmonic distortion) as the main evaluation indicators of signal quality, and uses signal-to-noise ratio and distortion indicators for weighted calculation to evaluate signal quality and generate signal quality analysis results.

[0028] See also Figure 2 and Figure 4 ,The signal optimization and adjustment module includes a parameter adjustment submodule, a signal processing submodule, and a signal conversion submodule; The parameter adjustment submodule adjusts the signal amplification factor, updates the filter cutoff frequency, optimizes the signal processing process, and obtains the signal processing optimization parameters based on the signal quality analysis results, the quality of each signal, and the differentiated signal quality requirements; Based on the signal quality analysis results, the parameter adjustment submodule identifies the signal features that need to be improved, such as excessive noise levels or excessive distortion, according to the quality of each signal, and adjusts the signal amplification factor to appropriately enhance the signal for further processing without excessive noise amplification. At the same time, according to the key frequency components determined in the signal, the cutoff frequency of the filter is adjusted to accurately remove unnecessary frequency components, thereby retaining the effective information in the signal. The process involves adjusting the cutoff frequencies of different types of filters (such as low-pass, high-pass or band-pass filters) to adapt to different signal characteristics and obtain signal processing optimization parameters.

[0029] The signal processing submodule performs noise filtering and distortion correction on the signal based on the signal processing optimization parameters to obtain a clarity optimized signal; The signal processing submodule performs noise filtering and distortion correction on the signal based on the signal processing optimization parameters, and uses signal processing technology, such as adaptive filtering technology, to dynamically adjust the filtering parameters according to the real-time characteristics of the signal to effectively reduce the noise level of the signal. Distortion correction is implemented through technologies such as dynamic range compression or linear predictive coding to correct signal distortion and improve the overall signal quality to obtain a clarity-optimized signal.

[0030] The signal conversion submodule optimizes the signal based on clarity, converts the signal, converts the electrical signal of the sensor into a digital signal, compresses and formats the data, and stores the signal locally to obtain the signal optimization processing result.

[0031] The signal conversion submodule optimizes the signal based on clarity. The signal is digitized through an analog-to-digital converter to ensure that the captured analog signal is faithfully converted into digital form. In order to effectively manage storage space and improve transmission efficiency, data compression algorithms such as Huffman coding or LZW coding are used to compress the signal data. The compressed data is formatted to ensure compatibility with downstream systems. For example, it is converted into common data formats such as CSV or XML and stored on local storage media to obtain signal optimization processing results.

[0032] See also Figure 2 and Figure 5 ,The abnormal signal detection module includes a signal comparison submodule, an abnormal identification submodule, and an alarm generation submodule; The signal comparison submodule compares the quality of each signal with a preset quality threshold based on the signal quality analysis result, analyzes the deviation between the signal quality and the quality threshold, evaluates the quality deviation, and obtains the quality deviation analysis result; The signal comparison submodule is based on the signal quality analysis results. By setting appropriate quality thresholds, the thresholds are formulated according to specific applications and expected signal processing requirements. For example, in communication systems, the signal-to-noise ratio may be of concern, while in data acquisition systems, signal integrity may be more of a concern. The quality of each acquired signal is evaluated and compared with the threshold. For each signal, its deviation from the threshold is recorded in detail. This deviation information will be used for subsequent abnormal signal identification and response measures to obtain quality deviation analysis results.

[0033] The abnormal identification submodule evaluates the changing trend of signal quality based on the quality deviation analysis results, calculates the abnormal degree of the signal in combination with the quality deviation, compares it with the preset abnormal threshold, identifies the abnormal signal, and obtains the abnormal signal identification result; The formula for calculating the abnormality of the signal is:

[0034] in, is the abnormality of the signal, represents the signal quality deviation, Represents the trend of signal quality change. Represents the average quality of the signal, , and is the adjustment coefficient.

[0035] formula:

[0036] Parameter details and how to obtain them Signal quality deviation: indicates the difference between the current signal quality and the preset threshold. Set to 80, current signal quality is 70, then This indicates that the signal quality is below the threshold, which should be set based on the expected performance of the signal.

[0037] Signal quality change trend: Indicates the change trend of signal quality, which can be determined by calculating the difference between the current signal quality and the average quality of the previous measurements. For example, if the quality of the previous three measurements was 75, 72, and 78, the average quality , current signal quality ,but , indicating that the signal quality is decreasing.

[0038] The average quality of the signal: represents the average value of the signal quality over a period of time, which is used to reflect the long-term performance of the signal. For example, if the signal quality values ​​measured in the past ten times are 70, 73, 75, 72, 76, 80, 78, 77, 74, 79, then .

[0039] , and The weight coefficient of the adjustment coefficient is obtained through experiments or data analysis, and is used to adjust the influence of different factors in the calculation of the abnormal index. Adjust the effect of signal deviation, Adjust the sensitivity of the signal quality change trend, To ensure that the denominator is not zero and balance the impact of average mass, all parameters are obtained through experiments or historical data analysis to ensure that the improved formula can improve calculation accuracy, operational flexibility or achieve other beneficial effects.

[0040] Calculation Example Set the following parameters: , , , , , .

[0041] Abnormal Index The calculation process is as follows:

[0042]

[0043]

[0044]

[0045]

[0046] Calculation result abnormality index The value of is approximately 0.093, which reflects the degree of deviation between the current state of the signal and the ideal state. The larger the value, the higher the degree of abnormality.

[0047] The alarm generation submodule formulates corresponding alarm information based on the abnormal signal recognition results, including the type of abnormal signal and the corresponding sensor location, notifies the management personnel to implement maintenance and replacement, and obtains abnormal detection information.

[0048] Based on the abnormal signal identification results, the alarm generation submodule describes in detail the specific type of each abnormal signal, such as excessive noise, signal interruption or distortion, and the sensor location information corresponding to the abnormal signal. This information is essential for quickly locating the source of the problem and taking corresponding maintenance measures. The alarm information is sent directly to the maintenance team's mobile device or workstation through the automation system to ensure timely response and handling of the problem, thereby minimizing the system's downtime and potential operational losses and obtaining abnormal detection information.

[0049] See also Figure 2 and Figure 6 ,The resource scheduling and control module includes a resource load analysis submodule, a task priority analysis submodule, and a scheduling execution submodule; The resource load analysis submodule evaluates the resource usage of each sensor and processing unit in the current system based on the signal optimization processing results and anomaly detection information, including monitoring the occupancy rate of processing resources, calculating resource consumption, analyzing the load of processing tasks, and obtaining resource usage information; The resource load analysis submodule collects the operating data of each sensor and processing unit based on the signal optimization processing results and anomaly detection information, including key indicators such as CPU usage, memory usage, and input and output operation frequency. Then, a resource monitoring tool is used to draw a resource usage trend chart to analyze the performance fluctuations and resource requirements of each component under different operating conditions. By comparing the resource usage data with the maximum and minimum resource usage thresholds predetermined by the system, the peaks and valleys of resource usage are identified to obtain resource usage information.

[0050] The task priority analysis submodule calculates the priority score of each signal processing task based on the resource usage information, the urgency of the signal and the resource requirements, and sorts the priorities of the signal processing tasks to obtain the task priority information; The formula for calculating the priority score of each signal processing task is:

[0051] in, is the priority score of the signal processing task, Represents the urgency of the signal processing task, represents the importance of the signal processing task, Represents the resource requirements of the signal processing task, Represents the total amount of available resources. , and is the weight coefficient.

[0052] formula:

[0053] Parameter details and how to obtain them: Represents the urgency of the signal processing task: The urgency of the signal is usually determined by the time sensitivity of the signal or the urgency of its impact on system operation. It can be determined through historical accident data analysis or system performance testing.

[0054] Represents the importance of the signal processing task: The importance of a signal is assessed based on the degree to which the signal affects the overall system stability and key operational objectives. It can be determined through system analysis that, for example, the signal involves the operational safety of critical equipment or the integrity of critical data, and the importance of these signals will be rated high.

[0055] Represents the resource demand of the signal processing task: The resource demand of the signal is evaluated based on the total amount of computing resources and storage resources required for signal processing, which can be directly obtained from the resource management system. For example, the computing resource demand is CPU time, memory usage, etc.

[0056] Represents the total amount of available resources: The total amount of available resources is obtained from system resource monitoring, including all currently allocatable computing and storage resources.

[0057] , and are weight coefficients: corresponding to the influence of urgency, importance and resource requirements, these weight coefficients are determined based on historical data analysis and management's strategies.

[0058] formula:

[0059] Calculation example: Set the parameters as follows: (Urgency High, 1-10 rating), (Medium importance, 1-10 rating), (resource demand), (the system's currently available resources), .

[0060] Task Priority The calculation process is as follows:

[0061]

[0062]

[0063]

[0064]

[0065] The obtained task priority score is 0.068, which reflects the priority of task processing. The larger the value, the higher the priority of the task. This score will be used to compare different tasks to determine which tasks should be executed first, thereby ensuring the effective allocation of resources and the smooth operation of the system.

[0066] The scheduling execution submodule dynamically adjusts the order of signal processing and resource allocation based on the task priority information, including adjusting the working order of processing units and sensors, allocating the required processing resources, and obtaining the processing resource allocation result.

[0067] The scheduling execution submodule dynamically adjusts the order of signal processing and resource allocation based on task priority information, and uses dynamic scheduling algorithms, such as priority-based polling or shortest job first, to manage resource allocation and task queues. The scheduler adjusts the task queues of each processing unit according to the task priority information to ensure that urgent or resource-intensive tasks are processed first. In addition, according to the current resource usage, the scheduler can also dynamically adjust the operating status of sensors and processing units, such as suspending certain non-critical tasks or increasing processing capacity to adapt to real-time operating needs and obtain processing resource allocation results.

[0068] See also Figure 2 and Figure 7 ,The data synchronization adjustment module includes a synchronization bandwidth allocation submodule, a synchronization period adjustment submodule, and a data synchronization implementation submodule; The synchronization bandwidth allocation submodule evaluates the bandwidth required for differentiated signal data in the cloud synchronization process based on the processing resource allocation result and the signal data size, and adjusts the cloud synchronization bandwidth based on the importance of differentiated signals to obtain bandwidth allocation information; The synchronization bandwidth allocation submodule evaluates bandwidth requirements based on the processing resource allocation results and the data volume and urgency of various types of signals. For example, large-capacity video surveillance data or critical real-time sensor data requires higher bandwidth to ensure transmission rate and real-time performance. Analyze the current network status and bandwidth capacity of cloud services, and dynamically allocate bandwidth according to the importance and real-time needs of the signal. Prioritize the instant synchronization of critical signals such as security monitoring or system alarm signals, and implement bandwidth restrictions on non-urgent data to optimize the overall network resource utilization efficiency and obtain bandwidth allocation information.

[0069] The synchronization cycle adjustment submodule evaluates the update frequency of the differentiated signal based on the bandwidth allocation information, adjusts the time period of data synchronization in combination with the network conditions, sets a matching synchronization interval for each signal, and obtains the cycle adjustment result; The synchronization cycle adjustment submodule evaluates the update frequency of the differentiated signal based on the bandwidth allocation information, and adjusts the synchronization cycle according to the bandwidth utilization and network stability by analyzing the data generation rate and processing rate in combination with the network conditions. For high-priority signals, a shorter synchronization cycle is set to quickly respond to system changes. For low-priority signals such as log data or statistical information, the synchronization interval is increased to reduce the network load. In addition, the synchronization cycle is automatically adjusted according to the real-time performance of the network to ensure efficient and economical data transmission and obtain the cycle adjustment result.

[0070] The data synchronization implementation submodule transmits the signal synchronously to the cloud server based on the period adjustment result, monitors the continuity and integrity of the transmission, and obtains the signal synchronization adjustment result; The data synchronization implementation submodule transmits the signal synchronously to the cloud server based on the periodic adjustment results, and ensures the transmission security and speed by deploying data compression and encryption technology. The monitoring system detects the transmission status of data packets in real time, such as packet loss rate and transmission delay, and ensures data integrity through an automatic retransmission mechanism. After the synchronization is completed, the synchronization log is automatically updated to record detailed information on data transmission and processing for system administrators or automated scripts to analyze and optimize resource configuration and obtain signal synchronization adjustment results.

[0071] See also Figure 2 and Figure 8 ,The cloud backup strategy module includes a redundant backup strategy submodule, a backup location selection submodule, and a backup status verification submodule; The redundant backup strategy submodule evaluates the backup redundancy level required for the differentiated signal based on the signal synchronization adjustment result and the importance of the signal data, configures the corresponding redundant backup quantity for each signal, and obtains the backup redundancy configuration information; The redundant backup strategy submodule evaluates the criticality of data and its impact on business continuity based on the signal synchronization adjustment results and the data classification. For critical data, a higher level of redundancy is set, such as multi-location backup or multi-version backup, to ensure data recoverability under any circumstances. For less sensitive data, such as historical logs, a lower level of backup strategy is implemented to save resources, and the corresponding number of redundant backups is configured for each signal to obtain backup redundancy configuration information.

[0072] The backup location selection submodule selects matching storage backup cloud nodes based on the backup redundancy configuration information by analyzing the availability, security, and cost efficiency of cloud nodes to obtain the backup node selection result; The backup location selection submodule selects matching storage backup cloud nodes based on the backup redundancy configuration information by analyzing the availability, security, and cost efficiency of cloud nodes. When selecting backup nodes, factors include the geographical location of the node, the security standards of the data center, the reliability of the service provider, and cost factors. By evaluating the performance and cost of each cloud node, ensure that the backup data is stored in a location that meets compliance requirements and business needs. For example, for critical data that needs to be restored quickly, select a nearby high-availability data center; for non-critical data that is stored for a long time, select a remote data center with a lower cost to obtain the backup node selection result.

[0073] The backup status verification submodule performs the signal data backup process based on the backup node selection result, and regularly performs integrity and security tests on the backup data to verify the integrity and security of the backup and obtain the backup execution record.

[0074] The backup status verification submodule performs the signal data backup process based on the backup node selection results. After the backup is executed, regular integrity checks are performed, such as verifying the data hash and integrity check code, to ensure that the data has not been tampered with or damaged during transmission or storage. At the same time, security tests are implemented, including access control audits and vulnerability scans, to ensure that the protection measures for backup data meet the latest security standards. Regular inspection results will be recorded and reported to relevant managers to ensure that any problems can be discovered and handled in a timely manner, and backup execution records can be obtained.

[0075] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0076] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A control and acquisition system based on multi-channel intelligent acquisition, characterized in that: The system comprises: The signal quality assessment module is based on the data acquisition environment and multi-channel data acquisition targets. It implements data acquisition by deploying differentiated sensors, analyzes and identifies the noise level and distortion of the signal, evaluates the signal quality, and generates signal quality analysis results. The signal optimization and adjustment module performs noise filtering and distortion correction on the signal based on the signal quality analysis result, converts the electrical signal of the sensor into a digital signal, and locally stores the signal to obtain a signal optimization processing result; The abnormal signal detection module identifies signals with quality lower than a set threshold based on the signal quality analysis result, evaluates whether the sensor is abnormal, notifies the management personnel to perform maintenance and replacement, and obtains abnormal detection information; The resource scheduling control module dynamically adjusts the resource allocation of sensors and processing units based on the signal optimization processing results and abnormality detection information, and adjusts the processing order of the differentiated signals according to the urgency and resource requirements of the differentiated signals to obtain the processing resource allocation results; The data synchronization adjustment module synchronously transmits the signal to the cloud server based on the processing resource allocation result, and adjusts the bandwidth in the process of differential signal synchronization transmission according to the importance of the signal to obtain a signal synchronization adjustment result; The cloud backup strategy module adjusts the number of backup versions of the signal in the cloud based on the signal synchronization adjustment result and the importance of the differentiated signal, performs signal backup, and obtains a backup execution record.

2. The control and acquisition system based on multi-channel intelligent acquisition according to claim 1 is characterized in that: The signal quality analysis result includes the noise level and signal distortion of the signal, the signal optimization processing result includes the adjusted amplification factor, updated filter threshold and linearization parameter setting, the anomaly detection information includes the anomaly type, detection frequency and sensor maintenance measures, the processing resource allocation result includes signal priority, processing unit load and task allocation strategy, the signal synchronization adjustment result includes the synchronization period, data transmission rate and storage location selection, and the backup execution record includes the backup timestamp, backup version number and data integrity status.

3. The control and acquisition system based on multi-channel intelligent acquisition according to claim 1 is characterized in that: The signal quality assessment module comprises: The signal acquisition submodule is based on the data acquisition environment and multi-channel data acquisition targets, selects and deploys sensors that adapt to the target data acquisition environment, collects the electrical signals captured by the sensors in the target environment, and obtains basic electrical signal data; The signal feature extraction submodule performs signal amplitude and frequency components based on the basic electrical signal data, extracts and records key signal features, including amplitude peaks and key frequency components, by decomposing the signal to obtain signal feature parameters; The quality analysis submodule analyzes and identifies the noise level and distortion of each signal based on the signal characteristic parameters, evaluates the quality index of the signal, and generates a signal quality analysis result.

4. The control and acquisition system based on multi-channel intelligent acquisition according to claim 1 is characterized in that: The signal optimization and adjustment module comprises: The parameter adjustment submodule adjusts the signal amplification factor, updates the cutoff frequency of the filter, optimizes the signal processing process, and obtains the signal processing optimization parameters based on the signal quality analysis result, according to the quality of each signal and the differentiated signal quality requirements; The signal processing submodule performs noise filtering and distortion correction on the signal based on the signal processing optimization parameters to obtain a clarity optimized signal; The signal conversion submodule converts the signal based on the clarity optimization signal, converts the electrical signal of the sensor into a digital signal, compresses and formats the data, and locally stores the signal to obtain a signal optimization processing result.

5. The control and acquisition system based on multi-channel intelligent acquisition according to claim 1 is characterized in that: The abnormal signal detection module comprises: The signal comparison submodule compares the quality of each signal with a preset quality threshold based on the signal quality analysis result, analyzes the deviation between the signal quality and the quality threshold, evaluates the quality deviation, and obtains a quality deviation analysis result; The abnormality identification submodule evaluates the change trend of the signal quality based on the quality deviation analysis result, calculates the abnormality degree of the signal in combination with the quality deviation, compares it with the preset abnormality threshold, identifies the abnormal signal, and obtains the abnormal signal identification result; The alarm generation submodule formulates corresponding alarm information based on the abnormal signal recognition result, including the type of abnormal signal and the corresponding sensor location, notifies the management personnel to implement maintenance and replacement, and obtains abnormal detection information.

6. The control and acquisition system based on multi-channel intelligent acquisition according to claim 5 is characterized in that: The formula for calculating the abnormality degree of the signal is: in, is the abnormality of the signal, represents the signal quality deviation, Represents the trend of signal quality change. Represents the average quality of the signal, , and is the adjustment coefficient.

7. The control and acquisition system based on multi-channel intelligent acquisition according to claim 1 is characterized in that: The resource scheduling control module includes: The resource load analysis submodule evaluates the resource usage of each sensor and processing unit in the current system based on the signal optimization processing results and anomaly detection information, including monitoring the occupancy rate of processing resources, calculating resource consumption, analyzing the load of processing tasks, and obtaining resource usage information; The task priority analysis submodule calculates the priority score of each signal processing task based on the resource usage information, according to the urgency of the signal and the resource requirements, and performs priority sorting of the signal processing tasks to obtain task priority information; The scheduling execution submodule dynamically adjusts the order of signal processing and resource allocation based on the task priority information, including adjusting the working order of the processing unit and the sensor, allocating the required processing resources, and obtaining the processing resource allocation result.

8. The control and acquisition system based on multi-channel intelligent acquisition according to claim 7 is characterized in that: The formula for calculating the priority score of each signal processing task is: in, is the priority score of the signal processing task, Represents the urgency of the signal processing task, represents the importance of the signal processing task, Represents the resource requirements of the signal processing task, Represents the total amount of available resources. , and is the weight coefficient.

9. The control and acquisition system based on multi-channel intelligent acquisition according to claim 1 is characterized in that: The data synchronization adjustment module includes: The synchronization bandwidth allocation submodule evaluates the bandwidth required for the differentiated signal data in the cloud synchronization process based on the processing resource allocation result and the signal data size, and adjusts the cloud synchronization bandwidth in combination with the importance of the differentiated signal to obtain bandwidth allocation information; The synchronization cycle adjustment submodule evaluates the update frequency of the differentiated signal based on the bandwidth allocation information, adjusts the time period of data synchronization in combination with network conditions, sets a matching synchronization interval for each signal, and obtains a cycle adjustment result; The data synchronization implementation submodule synchronously transmits the signal to the cloud server based on the period adjustment result, monitors the continuity and integrity of the transmission, and obtains the signal synchronization adjustment result.

10. The control and acquisition system based on multi-channel intelligent acquisition according to claim 1, characterized in that: The cloud backup strategy module includes: The redundant backup strategy submodule evaluates the backup redundancy level required for the differentiated signal based on the signal synchronization adjustment result and the importance of the signal data, configures the corresponding redundant backup quantity for each signal, and obtains the backup redundancy configuration information; The backup location selection submodule selects a matching storage backup cloud node based on the backup redundancy configuration information by analyzing the availability, security and cost efficiency of the cloud node to obtain a backup node selection result; The backup status verification submodule performs a signal data backup process based on the backup node selection result, and regularly performs integrity and security tests on the backup data to verify the integrity and security of the backup and obtain a backup execution record.

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