Multi-source heterogeneous measurement data efficient acquisition and intelligent fusion processing system

Abstract

The invention discloses a multi-source heterogeneous measurement data efficient acquisition and intelligent fusion processing system, which comprises a data acquisition module, a data pre-analysis module, a data fusion processing module and a data early warning module, and is characterized in that the data acquisition module comprises a first data acquisition unit, a second data acquisition unit and a third data acquisition unit; the data fusion processing module comprises a first data fusion unit, a second data fusion unit, a third data fusion unit and a comprehensive data fusion unit, and the system collects equipment states, equipment images and measurement data in multiple directions and eliminates abnormal values in the collection process; whether early warning is carried out or not is judged according to the equipment state influence coefficient, the appearance image influence coefficient and the measurement influence coefficient, potential problems are found in time, and abnormal risk accidents of power grid equipment are avoided; and a comprehensive influence coefficient is constructed based on the equipment state influence coefficient, the appearance image influence coefficient and the measurement influence coefficient through a comprehensive data fusion unit, and data fusion processing is completed.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a system for efficiently collecting and intelligently fusion processing multi-source heterogeneous measurement data. Background Art

[0002] With the rapid development of smart grids, operating status monitoring and fault warning of power equipment have become key links in ensuring the safe and stable operation of the power grid. During actual operation, power equipment generates multi-source heterogeneous monitoring data, including equipment operating status data, appearance image data, and various measurement data. This data comes from diverse sources, has complex structures, and exhibits different temporal and spatial characteristics. Traditional data acquisition and processing methods struggle to achieve efficient integration and deep mining, resulting in insufficient accuracy and real-time performance in equipment status assessment. Some systems rely solely on electrical measurement data for analysis, while ignoring the influence of appearance image data or environmental factors. This results in high false alarm or missed alarm rates for fault warnings. A system for efficient acquisition and intelligent fusion processing of multi-source heterogeneous measurement data is urgently needed. Summary of the Invention

[0003] The purpose of the present invention is to provide a multi-source heterogeneous measurement data efficient acquisition and intelligent fusion processing system including a data acquisition module, a data pre-analysis module, a data fusion processing module and a data early warning module;

[0004] The data acquisition module includes a first data acquisition unit, a second data acquisition unit and a third data acquisition unit. The first data acquisition unit is used to collect the current operating status data of the power equipment, the second data acquisition unit is used to collect the appearance image data of the current power equipment, and the third data acquisition unit is used to collect the measurement data of the current power equipment.

[0005] The data pre-analysis module is used to perform pre-analysis processing on the current power equipment operation status data collected by the first data acquisition unit in the data acquisition module, and to eliminate abnormal values in the data of the first data acquisition unit in the data acquisition module, and to obtain the pre-processed current power equipment operation status data after the elimination is completed;

[0006] The data fusion processing module includes a first data fusion unit, a second data fusion unit, a third data fusion unit and a comprehensive data fusion unit. The first data fusion unit constructs an equipment state influence coefficient based on the pre-processed current power equipment operation status data output by the data pre-analysis module. The second data fusion unit constructs an appearance image influence coefficient based on the appearance image data of the current power equipment. The third data fusion unit constructs a measurement influence coefficient based on the measurement data of the current power equipment.

[0007] The data warning module includes a first warning unit and a second warning unit. The first warning unit determines whether to issue a warning based on the equipment status influence coefficient, the appearance image influence coefficient and the measurement influence coefficient. If no warning is issued, the first warning module outputs the corresponding information respectively and displays it visually. At the same time, the comprehensive data fusion unit is called to construct a comprehensive influence coefficient based on the equipment status influence coefficient, the appearance image influence coefficient and the measurement influence coefficient, and the second warning unit determines whether to issue a warning based on the comprehensive influence coefficient.

[0008] Preferably, the specific expression for the first data acquisition unit to collect the current state data of the power equipment operation is as follows:

[0009] ZTSJ=[ZTZ1,…,ZTZ i ,…,ZTZ I ]

[0010] Among them, ZTSJ represents the current state data set of power equipment operation, ZTZ1,…,ZTZ i ,…,ZTZ I They respectively represent the 1st state data set of the current power equipment, ..., the i-th state data set of the current power equipment, ..., the I-th state data set of the current power equipment, i∈[1,I], and a total of I state data sets are set by the staff, and a total of I state data sets are collected by corresponding sensors or devices.

[0011] Preferably, the specific expression for the second data acquisition unit to acquire the appearance image data of the current power equipment is as follows:

[0012] TXSJ=[TXCY1,…,TXCY j ,…,TXCY J ]

[0013] Among them, TXSJ represents the appearance image data of the current power equipment, TXCY1,…,TXCY j ,…,TXCY J They respectively represent the deviation values between the image during the current power equipment image detection process and the first sample image of the equipment when it leaves the factory stored in the historical database, ..., the deviation values between the j-th sample image of the equipment when it leaves the factory, ..., the deviation values between the J-th sample image of the equipment when it leaves the factory, and j∈[1,J]. The deviation value TXCY between the image during the current power equipment image detection process and the j-th sample image of the equipment when it leaves the factory stored in the historical database is j The specific expression is as follows:

[0014]

[0015] Among them, PCMJj PSMJ represents the deviation area between the image of the current power equipment during image detection and the j-th sample image of the equipment when it leaves the factory stored in the historical database. j Represents the sample image area of the jth device when it leaves the factory.

[0016] Preferably, the specific expression for the third data acquisition unit to acquire the measurement data of the current power equipment is as follows:

[0017] CLSJ=[CLPC1,…,CLPC j ,…,CLPC J ]

[0018] Among them, CLSJ represents the measurement data set of the current power equipment, CLPC1,…,CLPC j ,…,CLPC J They represent the deviation between the measured value of the current power equipment during measurement and the first qualified sample measurement value CLZ1 stored in the historical database, ..., the jth qualified sample measurement value CLZ j The deviation between, ..., the J-th qualified sample measurement value CLZ J The deviation between the measured value of the current power equipment during measurement and the measured value of the jth qualified sample stored in the historical database is CLPC j The specific expression is as follows:

[0019]

[0020] Among them, |CLZ0-CLZ j | indicates CLZ0-CLZ j For absolute value, CLZ0 represents the measurement value of the current power equipment during the measurement process.

[0021] Preferably, the data pre-analysis module is used to perform pre-analysis processing on the current power equipment operation status data collected by the first data acquisition unit in the data acquisition module, and the specific steps of eliminating abnormal values of the data of the first data acquisition unit in the data acquisition module are as follows:

[0022] Step A1: Calculate the i-th state data set ZTZ of the current power equipment i Each value in the distance from the i-th state data set ZTZ i The mean The difference between i ;

[0023] Step A2: When δ i When the abnormal value exceeds the set value, it will be eliminated and all values will be traversed until the traversal is completed.

[0024] Preferably, the first data fusion unit constructs a specific expression of the equipment status influence coefficient based on the pre-processed current power equipment operation status data output by the data pre-analysis module as follows:

[0025]

[0026] Among them, ZTYXXS represents the equipment status influence coefficient, ZTZ i,0 Indicates the standard value of the i-th state data, Express Perform the summation, Represents the i-th state data set ZTZ after preprocessing i The mean of .

[0027] Preferably, the second data fusion unit constructs a specific expression of the appearance image influence coefficient based on the appearance image data of the current power equipment as follows:

[0028]

[0029] Among them, TXYXXS represents the appearance image influence coefficient, Express your support for TXCY j Perform summation, TXCY j It represents the deviation between the image of the current power equipment during image detection and the j-th sample image of the equipment when it leaves the factory stored in the historical database.

[0030] Preferably, the third data fusion unit constructs a specific expression of the measurement influence coefficient based on the measurement data of the current power equipment as follows:

[0031]

[0032] Among them, CLYXXS represents the measurement influence coefficient, Indicates CLPC j Perform summation, CLPC j It represents the deviation between the measured value of the current power equipment during measurement and the measured value of the j-th qualified sample stored in the historical database.

[0033] Preferably, the specific steps for the first warning unit to determine whether to issue a warning based on the equipment status influence coefficient, the appearance image influence coefficient and the measurement influence coefficient are: when any value among the equipment status influence coefficient, the appearance image influence coefficient and the measurement influence coefficient exceeds the corresponding threshold, a warning is issued; if none of them exceeds the threshold, no warning is issued.

[0034] Preferably, the specific expression for constructing the comprehensive influence coefficient based on the device state influence coefficient, the appearance image influence coefficient and the measurement influence coefficient by calling the comprehensive data fusion unit is as follows:

[0035]

[0036] Among them, ZTYXXS represents the equipment status impact coefficient, ZHYXXS represents the comprehensive impact coefficient, TXYXXS represents the appearance image impact coefficient, and CLYXXS represents the measurement impact coefficient. When the comprehensive impact coefficient exceeds the comprehensive risk threshold, an early warning is issued. If it does not exceed the threshold, no early warning is issued.

[0037] To solve the problems raised in the above background technology.

[0038] By adopting the above technical solution, the fusion processing of multi-source heterogeneous measurement data during the operation of power grid equipment is realized.

[0039] Compared with the existing technology, the beneficial effects of the present invention are: the multi-source heterogeneous measurement data efficient acquisition and intelligent fusion processing system collects equipment status, equipment image and measurement data from multiple angles, and eliminates abnormal values during the acquisition process, and determines whether to issue an early warning based on the equipment status influence coefficient, appearance image influence coefficient and measurement influence coefficient, so as to timely discover potential problems and avoid the occurrence of equipment accidents. At the same time, the comprehensive data fusion unit constructs a comprehensive influence coefficient based on the equipment status influence coefficient, appearance image influence coefficient and measurement influence coefficient, completes the data fusion processing, and provides a more comprehensive evaluation index. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a schematic diagram of the framework of a system for efficient acquisition and intelligent fusion processing of multi-source heterogeneous measurement data according to the present invention. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] See also Figure 1 ,The present invention provides a technical solution: a multi-source heterogeneous measurement data efficient acquisition and intelligent fusion processing system includes a data acquisition module, a data pre-analysis module, a data fusion processing module and a data early warning module;

[0043] The data acquisition module includes a first data acquisition unit, a second data acquisition unit, and a third data acquisition unit. The first data acquisition unit is used to collect the current operating status data of the power equipment (such as temperature, vibration, current, voltage, etc.), the second data acquisition unit is used to collect the appearance image data of the current power equipment (such as infrared thermal image, visible light image), and the third data acquisition unit is used to collect the measurement data of the current power equipment (such as partial discharge, insulation resistance, etc.). It can simultaneously collect different types of data from multiple data sources, such as equipment status, equipment images, and measurement data, to achieve comprehensive data collection;

[0044] The data pre-analysis module is used to perform pre-analysis processing on the current power equipment operation status data collected by the first data acquisition unit in the data acquisition module, and to eliminate abnormal values in the data of the first data acquisition unit in the data acquisition module. After the elimination is completed, the pre-processed current power equipment operation status data is obtained. The data pre-analysis module pre-processes the collected equipment operation status data and eliminates abnormal values, thereby improving the accuracy and reliability of the data;

[0045] The data fusion processing module includes a first data fusion unit, a second data fusion unit, a third data fusion unit and a comprehensive data fusion unit. The first data fusion unit constructs an equipment state influence coefficient based on the pre-processed current power equipment operation status data output by the data pre-analysis module. The second data fusion unit constructs an appearance image influence coefficient based on the appearance image data of the current power equipment. The third data fusion unit constructs a measurement influence coefficient based on the measurement data of the current power equipment.

[0046] The data warning module includes a first warning unit and a second warning unit. The first warning unit determines whether to issue a warning based on the equipment status influence coefficient, the appearance image influence coefficient and the measurement influence coefficient. If no warning is issued, the first warning module outputs the corresponding information respectively and displays it visually. At the same time, the comprehensive data fusion unit is called to construct a comprehensive influence coefficient based on the equipment status influence coefficient, the appearance image influence coefficient and the measurement influence coefficient, and the second warning unit is used to determine whether to issue a warning based on the comprehensive influence coefficient. The data warning module determines whether to issue a warning based on the equipment status influence coefficient, the appearance image influence coefficient and the measurement influence coefficient, discovers potential problems in time, and avoids the occurrence of abnormal risk accidents of power grid equipment. At the same time, the comprehensive data fusion unit constructs a comprehensive influence coefficient based on the equipment status influence coefficient, the appearance image influence coefficient and the measurement influence coefficient, completes the data fusion processing, and provides a more comprehensive evaluation indicator.

[0047] Through the above technical solution, the specific expression for the first data acquisition unit to collect the current state data of the power equipment operation is as follows:

[0048] ZTSJ=[ZTZ1,…,ZTZ i ,…,ZTZ I ]

[0049] Among them, ZTSJ represents the current state data set of power equipment operation, ZTZ1,…,ZTZ i ,…,ZTZ I They respectively represent the 1st state data set of the current power equipment, ..., the i-th state data set of the current power equipment, ..., the I-th state data set of the current power equipment, i∈[1,I], and a total of I state data sets are set by the staff, and a total of I state data sets are collected by corresponding sensors or devices.

[0050] Through the above technical solution, the specific expression for the second data acquisition unit to collect the appearance image data of the current power equipment is as follows:

[0051] TXSJ=[TXCY1,…,TXCY j ,…,TXCY J ]

[0052] Among them, TXSJ represents the appearance image data of the current power equipment, TXCY1,…,TXCY j ,…,TXCY J They respectively represent the deviation values between the image during the current power equipment image detection process and the first sample image of the equipment when it leaves the factory stored in the historical database, ..., the deviation values between the j-th sample image of the equipment when it leaves the factory, ..., the deviation values between the J-th sample image of the equipment when it leaves the factory, and j∈[1,J]. The deviation value TXCY between the image during the current power equipment image detection process and the j-th sample image of the equipment when it leaves the factory stored in the historical database is j The specific expression is as follows:

[0053]

[0054] Among them, PCMJ j It represents the deviation area between the image of the current power equipment during image detection and the sample image of the equipment when it leaves the factory stored in the historical database. The deviation area can be obtained by image comparison. PSMJ j It represents the sample image area of the jth device when it leaves the factory, achieving accurate quantification of appearance anomalies, avoiding errors in subjective judgment, and improving the objectivity and repeatability of detection. At the same time, it converts complex image data into structured numerical values, which is convenient for fusion with other heterogeneous data such as device status data and measurement data, providing unified input for subsequent intelligent analysis.

[0055] Through the above technical solution, the specific expression for the third data acquisition unit to collect the measurement data of the current power equipment is as follows:

[0056] CLSJ=[CLPC1,…,CLPC j ,…,CLPC J ]

[0057] Among them, CLSJ represents the measurement data set of the current power equipment, CLPC1,…,CLPC j ,…,CLPC J They represent the deviation between the measured value of the current power equipment during measurement and the first qualified sample measurement value CLZ1 stored in the historical database, ..., the jth qualified sample measurement value CLZ j The deviation between, ..., the J-th qualified sample measurement value CLZ J The deviation between the measured value of the current power equipment during measurement and the measured value of the jth qualified sample stored in the historical database is CLPC j The specific expression is as follows:

[0058]

[0059] Among them, |CLZ0-CLZ j | indicates CLZ0-CLZ j For absolute value, CLZ0 represents the measurement value of the current power equipment during the measurement process.

[0060] Through the above technical solution, the data pre-analysis module is used to pre-analyze the current power equipment operation status data collected by the first data acquisition unit in the data acquisition module, and the specific steps of eliminating abnormal values in the data of the first data acquisition unit in the data acquisition module are as follows:

[0061] Step A1: Calculate the i-th state data set ZTZ of the current power equipment i Each value in the distance from the i-th state data set ZTZ i The mean The difference between i ;

[0062] Step A2: When δ i When the abnormal value exceeds the set value, it will be eliminated and all values will be traversed until the traversal is completed.

[0063] Through the above technical solution, the first data fusion unit constructs the specific expression of the equipment status influence coefficient based on the pre-processed current power equipment operation status data output by the data pre-analysis module as follows:

[0064]

[0065] Among them, ZTYXXS represents the equipment status influence coefficient, ZTZ i,0 Indicates the standard value of the i-th state data, Express Perform the summation, Represents the i-th state data set ZTZ after preprocessing i At the same time, the framework supports the rapid access of new data sources (such as ambient temperature and humidity, and noise monitoring), which only requires expanding a few cases without reconstructing the system architecture.

[0066] Through the above technical solution, the second data fusion unit constructs the specific expression of the appearance image influence coefficient based on the appearance image data of the current power equipment as follows:

[0067]

[0068] Among them, TXYXXS represents the appearance image influence coefficient, Express your support for TXCY j Perform summation, TXCY j It represents the deviation between the image of the current power equipment during image detection and the j-th sample image of the equipment when it leaves the factory stored in the historical database.

[0069] Through the above technical solution, the third data fusion unit constructs the specific expression of the measurement influence coefficient based on the measurement data of the current power equipment as follows:

[0070]

[0071] Among them, CLYXXS represents the measurement influence coefficient, CIPC j Perform summation, CLPC j It represents the deviation between the measured value of the current power equipment during measurement and the j-th qualified sample measurement value stored in the historical database. By comparing the measured value with the measured values of other situations, it can reflect the degree of influence on the current equipment status or working status.

[0072] Through the above technical solution, the specific steps for the first warning unit to determine whether to issue a warning based on the equipment status influence coefficient, the appearance image influence coefficient and the measurement influence coefficient are: when any value among the equipment status influence coefficient, the appearance image influence coefficient and the measurement influence coefficient exceeds the corresponding threshold, a warning is issued; if none of them exceeds the threshold, no warning is issued.

[0073] Through the above technical solution, the specific expression of the comprehensive influence coefficient constructed by calling the comprehensive data fusion unit based on the equipment status influence coefficient, the appearance image influence coefficient and the measurement influence coefficient is as follows:

[0074]

[0075] Among them, ZTYXXS represents the equipment status influence coefficient, ZHYXXS represents the comprehensive influence coefficient, TXYXXS represents the appearance image influence coefficient, and CLYXXS represents the measurement influence coefficient. When the comprehensive influence coefficient exceeds the comprehensive risk threshold, an early warning is issued. If it does not exceed the threshold, no early warning is issued. By constructing a comprehensive influence coefficient system, the operating status, visual defects and physical measurement data of power equipment can be comprehensively utilized to avoid the limitations of a single data source and significantly improve the comprehensiveness and accuracy of the assessment.

[0076] The threshold values in the above embodiments are set for ease of comparison. The threshold values depend on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data. As long as the proportional relationship between the parameter and the quantized value is not affected, the threshold values can be determined by those skilled in the art based on each sample data set and multiple rounds of experiments.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A system for efficient acquisition and intelligent fusion processing of multi-source heterogeneous measurement data, characterized by: It includes data acquisition module, data pre-analysis module, data fusion processing module and data early warning module; The data acquisition module includes a first data acquisition unit, a second data acquisition unit and a third data acquisition unit. The first data acquisition unit is used to collect the current operating status data of the power equipment, the second data acquisition unit is used to collect the appearance image data of the current power equipment, and the third data acquisition unit is used to collect the measurement data of the current power equipment. The data pre-analysis module is used to perform pre-analysis processing on the current power equipment operation status data collected by the first data acquisition unit in the data acquisition module, and to eliminate abnormal values in the data of the first data acquisition unit in the data acquisition module, and to obtain the pre-processed current power equipment operation status data after the elimination is completed; The data fusion processing module includes a first data fusion unit, a second data fusion unit, a third data fusion unit and a comprehensive data fusion unit. The first data fusion unit constructs an equipment state influence coefficient based on the pre-processed current power equipment operation status data output by the data pre-analysis module. The second data fusion unit constructs an appearance image influence coefficient based on the appearance image data of the current power equipment. The third data fusion unit constructs a measurement influence coefficient based on the measurement data of the current power equipment. The data warning module includes a first warning unit and a second warning unit. The first warning unit determines whether to issue a warning based on the equipment status influence coefficient, the appearance image influence coefficient and the measurement influence coefficient. If no warning is issued, the first warning module outputs the corresponding information respectively and displays it visually. At the same time, the comprehensive data fusion unit is called to construct a comprehensive influence coefficient based on the equipment status influence coefficient, the appearance image influence coefficient and the measurement influence coefficient, and the second warning unit determines whether to issue a warning based on the comprehensive influence coefficient.

2. The system for efficient acquisition and intelligent fusion processing of multi-source heterogeneous measurement data according to claim 1 is characterized by: The specific expression for the first data acquisition unit to collect the current state data of the power equipment operation is as follows: ZTSJ=[ZTZ1,…,ZTZ i ,…,ZTZ I ] Among them, ZTSJ represents the current state data set of power equipment operation, ZTZ1,…,ZTZ i ,…,ZTZ I They respectively represent the 1st state data set of the current power equipment, ..., the i-th state data set of the current power equipment, ..., the I-th state data set of the current power equipment, i∈[1,I], and a total of I state data sets are set by the staff, and a total of I state data sets are collected by corresponding sensors or devices.

3. The system for efficient acquisition and intelligent fusion processing of multi-source heterogeneous measurement data according to claim 2, characterized in that: The specific expression for the second data acquisition unit to acquire the appearance image data of the current power equipment is as follows: TXSJ=[TXCY1,…,TXCY j ,…,TXCY J ] Among them, TXSJ represents the appearance image data of the current power equipment, TXCY1,…,TXCY j ,…,TXCY J They respectively represent the deviation values between the image during the current power equipment image detection process and the first sample image of the equipment when it leaves the factory stored in the historical database, ..., the deviation values between the j-th sample image of the equipment when it leaves the factory, ..., the deviation values between the J-th sample image of the equipment when it leaves the factory, and j∈[1,J]. The deviation value TXCY between the image during the current power equipment image detection process and the j-th sample image of the equipment when it leaves the factory stored in the historical database is j The specific expression is as follows: Among them, PCMJ j PSMJ represents the deviation area between the image of the current power equipment during image detection and the j-th sample image of the equipment when it leaves the factory stored in the historical database. j Represents the sample image area of the jth device when it leaves the factory.

4. The system for efficient acquisition and intelligent fusion processing of multi-source heterogeneous measurement data according to claim 3 is characterized by: The specific expression for the third data acquisition unit to collect the measurement data of the current power equipment is as follows: CLSJ=[CLPC1,…,CLPC j ,…,CLPC J ] Among them, CLSJ represents the measurement data set of the current power equipment, CLPC1,…,CLPC j ,…,CLPC J They represent the deviation between the measured value of the current power equipment during measurement and the first qualified sample measurement value CLZ1 stored in the historical database, ..., the jth qualified sample measurement value CLZ j The deviation between, ..., the J-th qualified sample measurement value CLZ J The deviation between the measured value of the current power equipment during measurement and the measured value of the jth qualified sample stored in the historical database is CLPC j The specific expression is as follows: Among them, |CLZ0-CLZ j | indicates CLZ0-CLZ j For absolute value, CLZ0 represents the measurement value of the current power equipment during the measurement process.

5. The system for efficient acquisition and intelligent fusion processing of multi-source heterogeneous measurement data according to claim 4 is characterized by: The data pre-analysis module is used to pre-analyze the current power equipment operation status data collected by the first data acquisition unit in the data acquisition module, and to eliminate abnormal values of the data of the first data acquisition unit in the data acquisition module. The specific steps are as follows: Step A1: Calculate the i-th state data set ZTZ of the current power equipment i Each value in the distance from the i-th state data set ZTZ i The mean The difference between i ; Step A2: When δ i When the abnormal value exceeds the set value, it will be eliminated and all values will be traversed until the traversal is completed.

6. The system for efficient acquisition and intelligent fusion processing of multi-source heterogeneous measurement data according to claim 5, characterized in that: The first data fusion unit constructs a specific expression of the equipment status influence coefficient based on the pre-processed current power equipment operation status data output by the data pre-analysis module as follows: Among them, ZTYXXS represents the equipment status influence coefficient, ZTZ i,0 Indicates the standard value of the i-th state data, Express Perform the summation, Represents the i-th state data set ZTZ after preprocessing i The mean of .

7. The system for efficient acquisition and intelligent fusion processing of multi-source heterogeneous measurement data according to claim 6, characterized in that: The specific expression of the appearance image influence coefficient constructed by the second data fusion unit based on the appearance image data of the current power equipment is as follows: Among them, TXYXXS represents the appearance image influence coefficient, Express your support for TXCY j Perform summation, TXCY j It represents the deviation between the image of the current power equipment during image detection and the j-th sample image of the equipment when it leaves the factory stored in the historical database.

8. The system for efficient acquisition and intelligent fusion processing of multi-source heterogeneous measurement data according to claim 7, characterized in that: The specific expression of the measurement influence coefficient constructed by the third data fusion unit based on the measurement data of the current power equipment is as follows: Among them, CLYXXS represents the measurement influence coefficient, Indicates CLPC j Perform summation, CLPC j It represents the deviation between the measured value of the current power equipment during measurement and the measured value of the j-th qualified sample stored in the historical database.

9. The system for efficient acquisition and intelligent fusion processing of multi-source heterogeneous measurement data according to claim 8, characterized in that: The specific steps for the first warning unit to determine whether to issue a warning based on the equipment status influence coefficient, the appearance image influence coefficient and the measurement influence coefficient are: when any value among the equipment status influence coefficient, the appearance image influence coefficient and the measurement influence coefficient exceeds the corresponding threshold, a warning is issued; if none of them exceeds the threshold, no warning is issued.

10. The system for efficient acquisition and intelligent fusion processing of multi-source heterogeneous measurement data according to claim 9, characterized in that: The specific expression for constructing the comprehensive influence coefficient based on the device status influence coefficient, the appearance image influence coefficient and the measurement influence coefficient by calling the comprehensive data fusion unit is as follows: Among them, ZTYXXS represents the equipment status impact coefficient, ZHYXXS represents the comprehensive impact coefficient, TXYXXS represents the appearance image impact coefficient, and CLYXXS represents the measurement impact coefficient. When the comprehensive impact coefficient exceeds the comprehensive risk threshold, an early warning is issued. If it does not exceed the threshold, no early warning is issued.

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