A data automatic collection method and system suitable for instrument equipment

Abstract

The present invention discloses a data automation acquisition method and system applicable to instruments and equipment, which relates to the field of equipment management technology, including an equipment management module, a dynamic protocol module, a multimodal sampling module, a correlation analysis module, a data compensation and reconstruction module, a load balancing module, a data enhancement module, a real-time feedback control module, and a time synchronization module. The data automation acquisition method and system applicable to instruments and equipment realize highly intelligent and flexible data acquisition capabilities, significantly improve the task allocation efficiency of the equipment through a two-way handshake protocol and an acquisition load balancing strategy, and reduce the risk of data loss; multimodal sampling and correlation analysis significantly improve data integrity and timeliness; and a parallelized time synchronization calibration mechanism ensures high precision of multi-device collaboration. The overall system can adapt to complex and changeable application scenarios, and provides efficient and reliable solutions for industrial control, environmental monitoring, medical instruments and other fields.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment management, and in particular to an automatic data collection method and system suitable for instruments and equipment. Background Art

[0002] In modern industry, scientific research and environmental monitoring, automated data acquisition of instruments and equipment has become a key link. However, traditional data acquisition systems usually rely on fixed sampling modes, single protocol standards and manual intervention. These systems often face many limitations in complex and changing scenarios: low acquisition efficiency, high risk of data loss, load imbalance, insufficient exception handling capabilities, etc. For example, traditional acquisition systems cannot quickly adapt to diverse hardware interfaces during device initialization, lack real-time capture and correlation analysis capabilities when abnormal data suddenly changes, and even have difficulty in achieving dynamic load regulation in large-scale equipment networks. These problems lead to insufficient data integrity and reliability, which seriously limits the scope of application of automated acquisition systems. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides an automatic data collection method and system suitable for instruments and equipment to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] In a first aspect, an embodiment of the present invention provides a method for automatic data collection applicable to an instrument, comprising:

[0006] Start the instrument and equipment. After the instrument and equipment are identified, the equipment management module dynamically adapts the equipment's collection parameters.

[0007] A two-way handshake protocol is used for the interaction between the instrument and the dynamic protocol module, and the instrument actively sends a signal to the dynamic protocol module;

[0008] Design a multimodal sampling strategy. The multimodal sampling module dynamically switches the sampling strategy according to different data types and priorities.

[0009] The correlation analysis module collects data based on the sampling strategy and analyzes the mutation points in the real-time data stream output by the instrument to determine whether to re-collect data;

[0010] The load balancing module builds a collection load balancing strategy according to the priority and dynamically allocates data collection tasks;

[0011] The real-time feedback control module collects and analyzes data quality in real time, sends feedback signals to instruments and equipment, and dynamically adjusts the collection parameters of instruments and equipment;

[0012] After data collection is completed, the time synchronization module aligns the time axes of different types of data.

[0013] To further optimize the technical solution, the two-way handshake protocol adopted by the instrument and the device management module includes:

[0014] Allow instruments to actively send key status signals, including "data congestion" and "abnormal over-temperature" status signals, and reduce acquisition delays through active signal mechanisms;

[0015] The device management module dynamically adjusts the collection load or pushes error information processing instructions to reduce device pressure.

[0016] To further optimize the technical solution, the multimodal sampling strategy includes:

[0017] For real-time data with high timeliness, a jumping high-frequency sampling strategy is adopted;

[0018] For stability data, an average delayed sampling strategy is adopted;

[0019] For abnormal data, an event-triggered sampling strategy is adopted;

[0020] A custom modal sampling strategy is also set to optimize sampling efficiency and reduce bandwidth pressure.

[0021] Further optimizing the technical solution, when analyzing the mutation points in the real-time data stream output by the instrument, the changes of the mutation points are detected by using a sliding window and a deep neural network;

[0022] Once an anomaly is detected, a multi-dimensional correlation analysis is performed based on historical data, equipment operation records, and environmental variables to determine whether the anomaly is related to equipment status, environmental conditions, or data interference, triggering corresponding response mechanisms, including alarms and re-collection.

[0023] To further optimize the technical solution, the acquisition load balancing strategy includes:

[0024] Quantification of equipment load status;

[0025] Task assignment priority calculation;

[0026] Dynamic transfer rules.

[0027] To further optimize the technical solution, in the quantification of the equipment load state, a device state function is constructed:

[0028] Define Instruments The real-time load status is , then the device status function is as follows:

[0029] ;

[0030] in,

[0031] :Instruments and Equipment Current sampling frequency;

[0032] : The maximum permissible sampling frequency of the instrument;

[0033] :Instruments and Equipment The data transmission bandwidth occupied;

[0034] : Maximum bandwidth of the instrument;

[0035] : Current hardware usage of instruments and equipment;

[0036] : Weight coefficient, which is flexibly adjusted according to the scenario. The sum of the three weights is 1;

[0037] , indicating the relative strength of the equipment load, The closer it is to 1, the higher the load on the instrument and equipment;

[0038] In the task allocation priority calculation, a task priority function is constructed:

[0039] For each data collection task , define the priority as , then the task priority function is as follows:

[0040] ;

[0041] in,

[0042] : Task timeliness requirement, the value is 0-1, the higher the value, the more urgent the need;

[0043] : The computational complexity of the task;

[0044] : The maximum computational complexity in the current network;

[0045] : The amount of data for the task;

[0046] : The maximum amount of task data in the current network;

[0047] : Weight coefficient, the sum of the three weights is 1;

[0048] In the dynamic transfer rule, if the instrument load , As the threshold, trigger load transfer and prioritize tasks Transfer to Adaptive Value Largest neighboring device .

[0049] To further optimize the technical solution, the method also includes virtual data inversion and compensation, including:

[0050] When the acquired data is lost or distorted due to environmental disturbances or instrument failure, virtual data generation is used for inversion and compensation;

[0051] Use historical equipment data and environmental models, combined with Bayesian inference and deep learning prediction algorithms, to reconstruct missing or abnormal data.

[0052] To further optimize the technical solution, the method also includes multi-channel data reorganization and fuzzy enhancement, including:

[0053] After data collection is completed, the heterogeneous data of multiple channels are reorganized, and the distorted or interfered data are enhanced through the signal reconstruction algorithm;

[0054] Fuzzy logic and context inference are used to extract effective information under low signal-to-noise ratio conditions and capture effective information in weak signals.

[0055] A data automation collection system suitable for instruments and equipment is constructed based on the above-mentioned data automation collection method suitable for instruments and equipment. The functional modules of the system include equipment management module, dynamic protocol module, multimodal sampling module, correlation analysis module, data compensation and reconstruction module, load balancing module, data enhancement module, real-time feedback control module, and time synchronization module.

[0056] Further optimizing the technical solution, in the system:

[0057] Device management module, used to automatically configure acquisition frequency, power mode and environmental adaptability parameters;

[0058] Dynamic protocol module, used to realize two-way communication between the acquisition system and the instrument equipment, and dynamically adjust the communication parameters through the two-way handshake protocol;

[0059] Multimodal sampling module, used to switch different sampling strategies;

[0060] The correlation analysis module is used to detect mutation points in data in real time through edge computing and identify abnormal points and trends;

[0061] Data compensation and reconstruction module, used to use historical data and environmental models to virtually compensate and reconstruct missing or abnormal data;

[0062] Load balancing module, used to dynamically adjust task allocation;

[0063] Data enhancement module, used to improve the quality of data in low signal-to-noise ratio environments;

[0064] Real-time feedback control module, used to control the operating parameters of instruments and equipment in real time;

[0065] The time synchronization module is used to provide high-precision time synchronization function for the data collected by the device.

[0066] In a second aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of an automatic data acquisition system suitable for instruments and equipment as described in the first aspect of the present invention are implemented.

[0067] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of an automatic data acquisition system suitable for instruments and equipment as described in the first aspect of the present invention are implemented.

[0068] Compared with the prior art, the present invention provides an automatic data acquisition system suitable for instruments and equipment, which has the following beneficial effects:

[0069] This data automatic collection method and system for instruments and equipment realizes highly intelligent and flexible data collection capabilities. Through the two-way handshake protocol and collection load balancing strategy, the task allocation efficiency of the equipment is significantly improved, and the risk of data loss is reduced; multi-modal sampling and correlation analysis significantly improve data integrity and timeliness; the parallelized time synchronization calibration mechanism ensures the high precision of multi-device collaboration. The overall system can adapt to complex and changeable application scenarios, and provides efficient and reliable solutions for industrial control, environmental monitoring, medical instruments and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0071] Figure 1 A schematic diagram of a flow chart of an automated data collection method applicable to instruments and equipment proposed by the present invention;

[0072] Figure 2A type diagram of a multimodal sampling strategy in a data automation acquisition method for instruments and equipment proposed by the present invention;

[0073] Figure 3 A composition and flow chart of a collection load balancing strategy in a data automation collection method for instruments and equipment proposed by the present invention;

[0074] Figure 4 This is a schematic diagram of the functional modules of an automatic data acquisition system suitable for instruments and equipment proposed by the present invention. DETAILED DESCRIPTION

[0075] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0076] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0077] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0078] Embodiment 1:

[0079] Reference Figure 1 to Figure 3 , which is the first embodiment of the present invention, provides a data automatic collection method applicable to instruments and equipment, comprising:

[0080] Start the device, and after device identification, dynamically adapt the device parameters.

[0081] In this embodiment, the collection period, sampling frequency and power mode are automatically adjusted according to the serial number, service life and environmental parameters (such as temperature, humidity and vibration level) of the device.

[0082] For example, for environmental sensors, the acquisition system will prioritize running the "adaptive mode" to adjust the working state in high humidity or high vibration environments to avoid hardware failures.

[0083] The device and the acquisition system interact using a two-way handshake protocol.

[0084] In this embodiment, the two-way handshake protocol adopted by the device and the acquisition system includes:

[0085] Different from the traditional one-way polling mode, this protocol allows instruments to actively send key status signals, including "data congestion" and "abnormal over-temperature" status signals, reducing acquisition delays through active signal mechanisms;

[0086] The acquisition system dynamically adjusts the acquisition load or pushes error information processing instructions to reduce equipment pressure.

[0087] Design a multimodal sampling strategy and dynamically switch according to different data types and priorities.

[0088] In this embodiment, the multimodal sampling strategy includes:

[0089] For real-time data with high timeliness, a jumping high-frequency sampling strategy is adopted.

[0090] Capture data points intermittently at a very high frequency (e.g., sampling interval 0.01 seconds), but only record points of sudden or critical changes. Filter out redundant data in a stable state and only save fluctuation values ​​in the interval of drastic changes.

[0091] For stability data, an average delayed sampling strategy is adopted.

[0092] A sample is collected every fixed time window (such as every 5 seconds or 1 minute), and the mean of all data points in the window is used as the representative value. The length of the time window can be dynamically adjusted according to the historical fluctuation trend.

[0093] For abnormal data, an event-triggered sampling strategy is adopted.

[0094] Preset multiple trigger conditions (such as threshold, mutation rate, logical event). When the trigger conditions are met, the system starts high-frequency sampling mode and records complete emergency event data.

[0095] A custom modal sampling strategy is also set to optimize sampling efficiency and reduce bandwidth pressure.

[0096] The system allows users to predefine special sampling rules through the interface, such as high-frequency sampling monitoring in a fixed time period (such as 8:00-10:00 am), or adding specific parameter sampling under specific experimental conditions (such as high-temperature operation). Cooperate with parameter constraints (such as single-channel high-precision, multi-channel synchronous sampling).

[0097] Analyze the sudden changes in the real-time data stream output by the device.

[0098] In this embodiment, when analyzing the mutation point in the real-time data stream output by the device, the change of the mutation point is detected by using a sliding window and a deep neural network;

[0099] Once an anomaly is detected, the system conducts multi-dimensional correlation analysis based on historical data, equipment operation records, and environmental variables to determine whether the anomaly is related to equipment status, environmental conditions, or data interference, and triggers corresponding response mechanisms, including alarms and re-collection.

[0100] Based on the collection load balancing strategy, data collection tasks are dynamically allocated.

[0101] In this embodiment,

[0102] The acquisition load balancing strategy includes:

[0103] Quantification of equipment load status;

[0104] Task assignment priority calculation;

[0105] Dynamic transfer rules.

[0106] Furthermore, in the device load state quantification, a device state function is constructed:

[0107] Defining devices The real-time load status is , then the device status function is as follows:

[0108] ;

[0109] in,

[0110] :equipment Current sampling frequency;

[0111] : The maximum allowed sampling frequency of the device;

[0112] :equipment The data transmission bandwidth occupied;

[0113] : Maximum bandwidth of the device;

[0114] : Current hardware usage of the device;

[0115] : Weight coefficient, which is flexibly adjusted according to the scenario. The sum of the three weights is 1;

[0116] , indicating the relative strength of the equipment load, The closer it is to 1, the higher the device load;

[0117] In the task allocation priority calculation, a task priority function is constructed:

[0118] For each data collection task , define the priority as , then the task priority function is as follows:

[0119] ;

[0120] in,

[0121] : Task timeliness requirement, the value is 0-1, the higher the value, the more urgent the need;

[0122] : The computational complexity of the task;

[0123] : The maximum computational complexity in the current network;

[0124] : The amount of data for the task;

[0125] : The maximum amount of task data in the current network;

[0126] : Weight coefficient, the sum of the three weights is 1;

[0127] In the dynamic transfer rule, if the device load , As the threshold, trigger load transfer and prioritize tasks Transfer to Adaptive Value Largest neighboring device .

[0128] When used, the model includes:

[0129] S1: Equipment status monitoring and quantification:

[0130] Regularly monitor each device parameter.

[0131] calculate Value, evaluate the current load status of the device.

[0132] S2: Task priority calculation:

[0133] For all pending tasks , calculate the priority based on the formula and clarify the order of importance of tasks.

[0134] Adjust weights according to the scene , for example, in high-time scenarios, adding .

[0135] S3: Dynamic task adaptation calculation:

[0136] Calculate tasks based on formulas On the device The adaptation value on .

[0137] For all devices and tasks, an adaptation matrix is ​​generated, where each row represents the degree of adaptation of a device to each task.

[0138] S4: Task Allocation and Execution:

[0139] According to the matrix, the device with the highest fitness value is selected for each task.

[0140] Ensure that high-priority tasks are assigned to low-load devices to avoid resource overload.

[0141] S5: Load transfer and dynamic regulation:

[0142] If the device Load :

[0143] Marking equipment It is in overload state.

[0144] Retrieve the neighboring device with the highest fitness value , transfer some tasks .

[0145] If the device network is large, the task transfer range can be limited by regional grouping to reduce the delay caused by cross-region transmission.

[0146] S6: Dynamic adjustment and feedback optimization:

[0147] Periodic evaluation and The set value optimizes the trade-off between device status and task priority.

[0148] Combine historical data to update collection cycles and allocation strategies to improve system adaptability.

[0149] In summary, the model can quickly respond to changes in device status and task requirements through real-time monitoring and dynamic adaptation matrix calculation. It combines device load and task priority to ensure efficient task allocation and prioritize key requirements. When the device load is too high, tasks can be transferred through neighboring devices to reduce the risk of data loss due to overload.

[0150] The acquisition system analyzes the data quality in real time and sends feedback signals to the instrument.

[0151] In this embodiment, while collecting data, the collection system analyzes the data quality in real time and sends feedback signals to the instrument to dynamically adjust the collection parameters of the device (such as sensor sensitivity).

[0152] For example, when the air quality sensor is detected to have signal overload under high pollution conditions, the system will immediately lower the sensitivity to avoid distortion of the sampling data and ensure the effectiveness of the next round of collection.

[0153] After data collection is completed, the time axes of data from different sources are aligned.

[0154] In this embodiment, after data collection is completed, a parallel timing calibration algorithm is used to align the time axis of data from different sources. Accurate timestamps and event-based sequence alignment algorithms are used to solve the time inconsistency problem caused by multi-device clock offset or network transmission delay.

[0155] For example, in industrial control systems, data from pressure sensors and temperature sensors need to be precisely synchronized to ensure the reliability of subsequent analysis.

[0156] The method also includes virtual data inversion and compensation, including:

[0157] When the acquired data is lost or distorted due to environmental disturbances or instrument failure, virtual data generation is used for inversion and compensation;

[0158] Use historical equipment data and environmental models, combined with Bayesian inference and deep learning prediction algorithms, to reconstruct missing or abnormal data.

[0159] For example, if a water quality analyzer has some spectral data distortion due to sensor aging, the real data can be simulated based on the changing trends of adjacent wavelengths to ensure data integrity.

[0160] The method also includes multi-channel data reorganization and fuzzy enhancement, including:

[0161] After data collection is completed, the heterogeneous data of multiple channels are reorganized, and the distorted or interfered data are enhanced through the signal reconstruction algorithm;

[0162] Fuzzy logic and context inference are used to extract effective information under low signal-to-noise ratio conditions and capture effective information in weak signals.

[0163] Embodiment 2:

[0164] Reference Figure 4 , which is the second embodiment of the present invention, and this embodiment provides a data automation acquisition system suitable for instruments and equipment. The functional modules of the system include an equipment management module, a dynamic protocol module, a multimodal sampling module, an association analysis module, a data compensation and reconstruction module, a load balancing module, a data enhancement module, a real-time feedback control module, and a time synchronization module.

[0165] In the system:

[0166] The device management module is used to automatically configure the acquisition frequency, power mode and environmental adaptability parameters.

[0167] Responsible for identifying and managing various types of connected instruments and equipment, and initializing them by reading the device's serial number, hardware characteristics, and current health status (such as temperature and vibration).

[0168] Automatically configure the acquisition frequency, power mode and environmental adaptability parameters to ensure stable operation of the device.

[0169] The dynamic protocol module is used to realize two-way communication between the acquisition system and the instrument equipment, and dynamically adjust the communication parameters through the two-way handshake protocol.

[0170] Realize two-way communication between the acquisition system and instruments and equipment, and dynamically adjust communication parameters (such as acquisition frequency and data transmission bandwidth) through active perception protocol handshake.

[0171] Supports devices to actively send status information (such as overload, environmental abnormality) to the system, and the system adjusts task allocation accordingly.

[0172] Multimodal sampling module, used to switch different sampling strategies.

[0173] Implement scenario-based multimodal sampling strategies, including jumping high-frequency sampling, average value delayed sampling, event-triggered sampling, etc.

[0174] The correlation analysis module is used to detect real-time mutation points in data through edge computing and identify abnormal points and trends.

[0175] Edge computing is used to detect real-time mutations in device-collected data and identify anomalies and trends.

[0176] Using the association analysis algorithm, the mutation data is compared with the historical operating status of the equipment, environmental parameters, etc. to determine the cause of the abnormality.

[0177] The data compensation and reconstruction module is used to use historical data and environmental models to perform virtual compensation and reconstruct missing or abnormal data.

[0178] When the collected data is incomplete due to equipment failure or external interference, historical data and environmental models are used for virtual compensation.

[0179] Use deep learning models to reconstruct missing or distorted data and ensure the integrity of your data stream.

[0180] Load balancing module, used to dynamically adjust task distribution.

[0181] Monitor device load status (sampling frequency, transmission bandwidth, hardware occupancy) in real time and dynamically adjust task allocation.

[0182] If a device is overloaded, task transfer is triggered and the task is reallocated to a device with lower load.

[0183] Data enhancement module is used to improve the quality of data in low signal-to-noise ratio environments.

[0184] The collected multi-channel heterogeneous data are reorganized and the quality of the data in a low signal-to-noise ratio environment is improved through fuzzy logic algorithm.

[0185] Supports simultaneous analysis of multi-channel data to improve the ability to extract weak signals.

[0186] The real-time feedback control module is used to control the operating parameters of the instrument and equipment in real time.

[0187] The operating parameters (sensitivity, sampling frequency) of the instrument are controlled in real time through the acquisition system.

[0188] Adjust the working status of the equipment according to the real-time feedback information during the data collection process to ensure stable data quality.

[0189] The time synchronization module is used to provide high-precision time synchronization function for the data collected by the device.

[0190] Provide high-precision time synchronization function for data collected by multiple devices to ensure the time consistency of different data streams.

[0191] Use precise timestamps and event alignment algorithms to resolve time deviation issues caused by network delays or device clock drift.

[0192] Embodiment three:

[0193] This embodiment also provides a computer device, which is applicable to a data automatic acquisition system applicable to instruments and equipment, and includes a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to realize the data automatic acquisition proposed in the above embodiment.

[0194] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the automatic data collection proposed in the above embodiment is realized.

[0195] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0196] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0197] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0198] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0199] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one or combination of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0200] It should be noted that the above embodiments are only used to illustrate the technical solutions 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 solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A data automation collection method suitable for instruments and equipment, characterized in that: include: Start the instrument and equipment. After the instrument and equipment are identified, the equipment management module dynamically adapts the equipment's collection parameters. A two-way handshake protocol is used for the interaction between the instrument and the dynamic protocol module, and the instrument actively sends a signal to the dynamic protocol module; Design a multimodal sampling strategy. The multimodal sampling module dynamically switches the sampling strategy according to different data types and priorities. The correlation analysis module collects data based on the sampling strategy and analyzes the mutation points in the real-time data stream output by the instrument to determine whether to re-collect data; The load balancing module builds a collection load balancing strategy according to the priority and dynamically allocates data collection tasks; The acquisition load balancing strategy includes: Quantification of equipment load status; Task assignment priority calculation; Dynamic transfer rules; In the device load state quantification, a device state function is constructed: Define Instruments The real-time load status is , then the device status function is as follows: ； in, :Instruments and Equipment Current sampling frequency; : The maximum permissible sampling frequency of the instrument; :Instruments and Equipment The data transmission bandwidth occupied; : Maximum bandwidth of the instrument; : Current hardware usage of instruments and equipment; : Weight coefficient, which is flexibly adjusted according to the scenario. The sum of the three weights is 1; , indicating the relative strength of the equipment load, The closer it is to 1, the higher the load on the instrument and equipment; In the task allocation priority calculation, a task priority function is constructed: For each data collection task , define the priority as , then the task priority function is as follows: ； in, : Task timeliness requirement, the value is 0-1, the higher the value, the more urgent the need; : The computational complexity of the task; : The maximum computational complexity in the current network; : The amount of data for the task; : The maximum amount of task data in the current network; : Weight coefficient, the sum of the three weights is 1; In the dynamic transfer rule, if the instrument load , As the threshold, trigger load transfer and prioritize tasks Transfer to Adaptive Value Largest neighboring device ; The real-time feedback control module collects and analyzes data quality in real time, sends feedback signals to instruments and equipment, and dynamically adjusts the collection parameters of instruments and equipment; After data collection is completed, the time synchronization module aligns the time axes of different types of data.

2. The method for automatic data collection suitable for instruments and equipment according to claim 1, characterized in that: The two-way handshake protocol adopted by the instrument and the device management module includes: Allows instruments to actively send key status signals, including "data congestion" and "abnormal over-temperature" status signals, and reduces acquisition delays through active signal mechanisms; The device management module dynamically adjusts the collection load or pushes error information processing instructions to reduce device pressure.

3. The method for automatic data collection suitable for instruments and equipment according to claim 1, characterized in that: The multimodal sampling strategy includes: For real-time data with high timeliness, a jumping high-frequency sampling strategy is adopted; For stability data, an average delayed sampling strategy is adopted; For abnormal data, an event-triggered sampling strategy is adopted; A custom modal sampling strategy is also set to optimize sampling efficiency and reduce bandwidth pressure.

4. The method for automatic data collection applicable to instruments and equipment according to claim 1, characterized in that: When analyzing the mutation points in the real-time data stream output by the instrument, the changes in the mutation points are detected through sliding windows and deep neural networks; Once an anomaly is detected, a multi-dimensional correlation analysis is performed based on historical data, equipment operation records, and environmental variables to determine whether the anomaly is related to equipment status, environmental conditions, or data interference, triggering corresponding response mechanisms, including alarms and re-collection.

5. The method for automatic data collection applicable to instruments and equipment according to claim 1, characterized in that: The method also includes virtual data inversion and compensation, including: When the acquired data is lost or distorted due to environmental disturbances or instrument failure, virtual data generation is used for inversion and compensation; Use historical equipment data and environmental models, combined with Bayesian inference and deep learning prediction algorithms, to reconstruct missing or abnormal data.

6. The method for automatic data collection applicable to instruments and equipment according to claim 1, characterized in that: The method also includes multi-channel data reorganization and fuzzy enhancement, including: After data collection is completed, the heterogeneous data of multiple channels are reorganized, and the distorted or interfered data are enhanced through the signal reconstruction algorithm; Fuzzy logic and context inference are used to extract effective information under low signal-to-noise ratio conditions and capture effective information in weak signals.

7. An automatic data collection system applicable to instruments and equipment, constructed based on an automatic data collection method applicable to instruments and equipment as claimed in any one of claims 1 to 6, characterized in that: The functional modules of the system include device management module, dynamic protocol module, multimodal sampling module, correlation analysis module, data compensation and reconstruction module, load balancing module, data enhancement module, real-time feedback control module, and time synchronization module.

8. The automatic data acquisition system suitable for instruments and equipment according to claim 7, characterized in that: In the system: Device management module, used to automatically configure acquisition frequency, power mode and environmental adaptability parameters; Dynamic protocol module, used to realize two-way communication between the acquisition system and the instrument equipment, and dynamically adjust the communication parameters through the two-way handshake protocol; Multimodal sampling module, used to switch different sampling strategies; The correlation analysis module is used to detect mutation points in data in real time through edge computing and identify abnormal points and trends; Data compensation and reconstruction module, used to use historical data and environmental models to virtually compensate and reconstruct missing or abnormal data; Load balancing module, used to dynamically adjust task allocation; Data enhancement module, used to improve the quality of data in low signal-to-noise ratio environments; Real-time feedback control module, used to control the operating parameters of instruments and equipment in real time; The time synchronization module is used to provide high-precision time synchronization function for the data collected by instruments and equipment.

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