Operation and maintenance data fusion method and system

Abstract

The invention relates to the technical field of information, and particularly discloses an operation and maintenance data fusion method and system.The operation and maintenance data fusion method comprises the steps that two sets of data are monitored in real time through monitoring equipment, data features are extracted, a data intersection ratio is obtained and compared with a threshold value, a data similarity signal is generated, and the similarity degree value of the two sets of data is obtained based on the data similarity signal; quick response to data change is ensured through real-time monitoring, if two groups of data are changed, the system can capture and analyze the data immediately, further calculate a data similarity degree value based on data similarity signals, and compare the data similarity degree value with the threshold value, so that the data correlation degree value is obtained. The correlation degree between the two groups of data can be quantitatively evaluated, so that the understanding of the data is deeper, and the description of the relationship between the data is facilitated.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an operation and maintenance data fusion method and system. Background Art

[0002] Data fusion technology was initially applied in the military field, and has now expanded to many fields, such as multi-source image compounding, robotics and intelligent instrument systems, battlefields and unmanned aircraft, image analysis and understanding, target detection and tracking, etc. In the field of operation and maintenance, data fusion technology is mainly used to integrate data from different sources and in different formats to provide more comprehensive and accurate information.

[0003] The Chinese invention patent with publication number CN117033734A discloses an operation and maintenance data fusion method and system, which obtains reported data reported through a message queue; performs a warehouse entry verification on the reported data based on a verification model in the operation and maintenance data center; when the reported data passes the warehouse entry verification, the reported data is operated and maintained based on the database in the operation and maintenance data center to obtain metadata; and the metadata is entered into the warehouse.

[0004] However, there is no monitoring equipment to monitor the two sets of data in real time, extract data features, obtain the data intersection ratio, compare it with the threshold, generate a signal of whether the data is similar, obtain the similarity value of the two sets of data based on the data similarity signal, compare it with the threshold, generate a data association degree signal, obtain the accurate impact value based on the large data association degree signal, compare it with the threshold, generate an impact degree signal, obtain the data fusion adjustment value based on the small fusion degree signal, complete the data fusion adjustment work, and analyze the poor fusion effect after the fusion of the data again, thereby achieving the effect of real-time adjustment of the fusion strategy. Summary of the invention

[0005] The purpose of the present invention is to provide an operation and maintenance data fusion method and system to solve the technical problems in the above background.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] In a first aspect, the present invention provides an operation and maintenance data fusion method, comprising the following method steps:

[0008] Step 1: Use monitoring equipment to monitor two sets of data in real time, extract data features, obtain data intersection ratio, compare it with the data intersection threshold, and generate a signal to determine whether the data is similar;

[0009] The data features include the number of data format repetitions and the number of data structure repetitions. The number of data format repetitions and the number of data structure repetitions are added together to obtain a data intersection ratio.

[0010] If the data intersection ratio is greater than or equal to the data intersection threshold, a data similarity signal is generated;

[0011] Step 2: Based on the data similarity signal, obtain the similarity values ​​of the two sets of data, compare them with the data similarity threshold, and generate a data association degree signal;

[0012] During the monitoring period, the same fluctuation coefficient of the data frame values ​​and the redundant data area ratio in the two sets of data are obtained, and the same fluctuation coefficient of the data frame values ​​and the redundant data area ratio in the two sets of data are added and summed to obtain the data similarity value;

[0013] If the data similarity value is greater than the data similarity threshold, a large signal of data association is generated;

[0014] Step 3: Based on the large signal of data correlation degree, obtain the accurate impact value, compare it with the accurate impact threshold, and generate the impact degree signal;

[0015] Obtain the data intersection ratio and the same data fusion ratio, add the data intersection ratio and the same data fusion ratio together to obtain the impact accuracy value;

[0016] If the impact accuracy value is greater than or equal to the impact accuracy threshold, a high impact signal is generated;

[0017] Step 4: Based on the large signal of influence, obtain the data fusion adjustment value and complete the adjustment of data fusion.

[0018] As a further solution of the present invention: the process of obtaining the same fluctuation coefficient of the data frame value is as follows:

[0019] During the monitoring period, two sets of data transmission data frame values ​​are obtained in real time, and an XY axis coordinate system is established. The X axis represents the monitoring time point, and the Y axis represents the two sets of data transmission data frame values ​​corresponding to each monitoring time point. The two sets of data transmission data frame values ​​are substituted into the XY axis coordinate system, and the output is the change curve of the two sets of data frame values;

[0020] Based on the two groups of data frame value change curves, each group of data frame value change curves is divided according to the monitoring time period to obtain multiple data frame value change sub-curves, and based on the two data frame value change sub-curves, the slope corresponding to each data frame value change sub-curve is obtained and marked as the change curve sub-slope;

[0021] Based on the sub-slopes of the change curve, the sub-slopes of the change curve corresponding to the same monitoring time period are calculated by ratio to obtain the slope coefficient;

[0022] Based on the slope coefficient, the number of the same monitoring time periods corresponding to the same slope coefficient in the two sets of data frame value change curves is obtained, marked as the number of time periods with the same slope, and the number of time periods with the same slope is ratioed with the number of time periods in the monitoring period to obtain the same fluctuation coefficient of the data frame value.

[0023] As a further solution of the present invention: in step 2, the process of obtaining the redundant data area ratio is as follows:

[0024] After fusing the two sets of data, new fused data is obtained, redundant data is extracted from the new fused data, and image processing is performed on the new fused data and the redundant data to obtain a fused new data image and a redundant data image;

[0025] Based on the fused new data image and the redundant data image, the fused new data image and the redundant data image are overlapped and compared to obtain the overlapping data image area, which is marked as the redundant data area;

[0026] The redundant data area is ratio-calculated to the area of ​​the fused new data image to obtain the redundant data area ratio.

[0027] As a further solution of the present invention: the process of obtaining the data intersection ratio is as follows:

[0028] The data format overlap ratio and the data structure duplication ratio are obtained, and the data format overlap ratio and the data structure duplication ratio are added together to obtain the data intersection ratio.

[0029] As a further solution of the present invention: in step 3, the data format repetition number ratio is obtained in the following manner:

[0030] Obtain the number of data format repetitions, calculate the ratio of the number of data format repetitions to the total number of data formats, and obtain the data format repetition number ratio;

[0031] The method for obtaining the ratio of the number of repetitions of the data structure is as follows:

[0032] The number of data structure repetitions is obtained, and the ratio of the number of data structure repetitions to the total number of data structures is calculated to obtain the data structure repetition number ratio.

[0033] As a further solution of the present invention: in step 3, the same data fusion ratio is obtained in the following manner:

[0034] During the fusion process of the two sets of data, the data fusion process is monitored to obtain the storage space ratio of the same data in different locations, which is marked as the data occupied space difference value;

[0035] The ratio of the difference in data space occupied to the total storage space is calculated to obtain the same data fusion ratio.

[0036] As a further solution of the present invention: in step 3, the process of obtaining the data occupied space difference value is as follows:

[0037] During the monitoring period, data in multiple storage units are obtained, the data in each storage unit is compared for overlap, and the storage units with the same storage data are extracted and marked as the same data storage units;

[0038] Based on the same data storage unit, the data storage space value in the same data storage unit is obtained, and the data storage space values ​​in the same data storage unit are added and summed to obtain the data occupied space difference value.

[0039] As a further solution of the present invention: in step 4, the data fusion adjustment value is obtained in the following manner:

[0040] Obtaining the coefficient affecting the accuracy value and the coefficient of the data similarity value, adding and summing the coefficient affecting the accuracy value and the coefficient of the data similarity value to obtain the data fusion adjustment coefficient;

[0041] The data fusion value is multiplied by the data fusion adjustment coefficient to obtain the data fusion adjustment value;

[0042] According to the data fusion adjustment value, the data fusion adjustment value is compared, the maximum value of the data fusion adjustment is extracted, the maximum value of the data fusion adjustment is adjusted first, and then adjusted in order from large to small until the data fusion adjustment work is completed.

[0043] As a further solution of the present invention: in step 4, the process of obtaining the coefficient affecting the accuracy value is as follows:

[0044] The influence accuracy value is calculated by subtracting the influence accuracy threshold value to obtain the influence accuracy deviation value;

[0045] The accuracy-affecting deviation value is calculated by ratio with the accuracy-affecting threshold value to obtain the accuracy-affecting value coefficient;

[0046] The process of obtaining the data similarity value coefficient is as follows:

[0047] Subtract the data similarity value from the data similarity threshold to obtain the data similarity deviation value;

[0048] The data similarity value coefficient is obtained by calculating the ratio of the data similarity deviation value to the data similarity threshold value.

[0049] In a second aspect, the present invention provides an operation and maintenance data fusion system, the system comprising:

[0050] Data monitoring module: monitors two sets of data in real time through monitoring equipment, extracts data features, obtains data intersection ratio, compares it with data intersection threshold, and generates data similarity signal;

[0051] The data features include the number of data format repetitions and the number of data structure repetitions. The number of data format repetitions and the number of data structure repetitions are added together to obtain a data intersection ratio.

[0052] If the data intersection ratio is greater than or equal to the data intersection threshold, a data similarity signal is generated;

[0053] Association analysis module: Based on the data similarity signal, obtain the similarity value of two sets of data, compare it with the data similarity threshold, and generate a data association degree signal;

[0054] During the monitoring period, the same fluctuation coefficient of the data frame values ​​and the redundant data area ratio in the two sets of data are obtained, and the same fluctuation coefficient of the data frame values ​​and the redundant data area ratio in the two sets of data are added and summed to obtain the data similarity value;

[0055] If the data similarity value is greater than the data similarity threshold, a large signal of data association is generated;

[0056] Impact assessment module: Based on the large signal of data correlation degree, the accurate impact value is obtained, compared with the accurate impact threshold, and the impact degree signal is generated;

[0057] Obtain the data intersection ratio and the same data fusion ratio, add the data intersection ratio and the same data fusion ratio together to obtain the impact accuracy value;

[0058] If the impact accuracy value is greater than or equal to the impact accuracy threshold, a high impact signal is generated;

[0059] Optimization and adjustment module: Based on the large signal of influence, obtain the data fusion adjustment value and complete the adjustment of data fusion.

[0060] Beneficial effects of the present invention:

[0061] (1) The present invention monitors two sets of data in real time through a monitoring device, extracts data features, obtains a data intersection ratio, compares it with a threshold, generates a data similarity signal, obtains a similarity value of the two sets of data based on the data similarity signal, compares it with a threshold, and generates a data association degree signal. Real-time monitoring ensures a rapid response to data changes. If the two sets of data change, the system can immediately capture and analyze it, and further calculate the data similarity value based on the data similarity signal and compare it with the threshold. The degree of association between the two sets of data can be quantitatively evaluated, which makes the understanding of the data more in-depth and helps to describe the relationship between the data.

[0062] (2) The present invention obtains the accurate impact value based on the large signal of data correlation degree, compares it with the threshold, generates an impact degree signal, obtains the data fusion adjustment value based on the large signal of impact degree, completes the adjustment of data fusion, and analyzes the poor fusion effect after the fusion of data again, thereby achieving the effect of real-time adjustment of the fusion strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The present invention will be further described below in conjunction with the accompanying drawings.

[0064] Figure 1 is a flow chart of Embodiment 1 of the present invention;

[0065] Figure 2 is a flow chart of Embodiment 2 of the present invention;

[0066] Figure 3 It is a schematic diagram of the system in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] Embodiment 1:

[0069] See also Figure 1 As shown, an operation and maintenance data fusion method according to an embodiment of the present invention comprises the following steps:

[0070] Step 1: Use monitoring equipment to monitor two sets of data in real time, extract data features, obtain data intersection ratio, compare it with the data intersection threshold, and generate a signal to determine whether the data is similar;

[0071] Wherein, the data similarity signal includes a data similarity signal and a data dissimilarity signal;

[0072] In some embodiments, two sets of data are monitored in real time by a monitoring device to extract data features, wherein the data features include the number of data format repetitions and the number of data structure repetitions, and the number of data format repetitions and the number of data structure repetitions are added and summed to obtain a data intersection ratio;

[0073] The data intersection ratio is compared with the data intersection threshold. The comparison process is as follows:

[0074] If the data intersection ratio is greater than or equal to the data intersection threshold, it means that the repeated data formats in the two sets of data are more similar, and the repeated data structures are more similar, and a data similarity signal is generated;

[0075] If the data intersection ratio is less than the data intersection threshold, it means that the similarity of the repeated data formats in the two sets of data is small, and the similarity of the repeated data structures is small, and a data dissimilarity signal is generated;

[0076] It needs further explanation that: monitoring equipment includes power monitoring instruments; the two sets of data specifically refer to: real-time operation data of the power system and substation data, where the real-time operation data of the power system includes voltage, current and power, and the substation data includes the switch status of the substation;

[0077] Step 2: Based on the data similarity signal, obtain the similarity values ​​of the two sets of data, compare them with the data similarity threshold, and generate a data association degree signal;

[0078] The data association degree signal includes a large data association degree signal and a small data association degree signal;

[0079] In some embodiments, when a data similarity signal is generated, a data similarity value is obtained, and the data similarity value is compared with a data similarity threshold. The comparison process is as follows:

[0080] If the data similarity value is greater than the data similarity threshold, it means that the fluctuation similarity of the two sets of data frame values ​​is high, and the edge contour similarity of the two sets of data images is high, generating a large data correlation signal;

[0081] If the data similarity value is less than the data similarity threshold, it means that the fluctuation similarity of the two sets of data frame values ​​is low, and the edge contour similarity of the two sets of data images is low, generating a small signal of data association degree;

[0082] The data similarity value is obtained as follows:

[0083] During the monitoring period, the same fluctuation coefficient of the data frame values ​​and the redundant data area ratio in the two sets of data are obtained, and the same fluctuation coefficient of the data frame values ​​and the redundant data area ratio in the two sets of data are added and summed to obtain the data similarity value;

[0084] Exemplarily, the process of obtaining the same fluctuation coefficient of the data frame value is as follows:

[0085] During the monitoring period, two sets of data transmission data frame values ​​are obtained in real time, and an XY axis coordinate system is established. The X axis represents the monitoring time point, and the Y axis represents the two sets of data transmission data frame values ​​corresponding to each monitoring time point. The two sets of data transmission data frame values ​​are substituted into the XY axis coordinate system, and the output is the change curve of the two sets of data frame values;

[0086] Based on the two groups of data frame value change curves, each group of data frame value change curves is divided according to the monitoring time period to obtain multiple data frame value change sub-curves, and based on the two data frame value change sub-curves, the slope corresponding to each data frame value change sub-curve is obtained and marked as the change curve sub-slope;

[0087] Based on the sub-slopes of the change curve, the sub-slopes of the change curve corresponding to the same monitoring time period are calculated by ratio to obtain the slope coefficient;

[0088] Based on the slope coefficient, the number of the same monitoring time periods corresponding to the same slope coefficient in the two sets of data frame value change curves is obtained, marked as the number of time periods with the same slope, and the number of time periods with the same slope is processed by ratio with the number of time periods in the monitoring period to obtain the same fluctuation coefficient of the data frame value;

[0089] It should be noted that the meaning of the transmission data frame value is: the data transmission rate of each of the two groups of data during the transmission process;

[0090] Exemplarily, the process of obtaining the redundant data area ratio is as follows:

[0091] After fusing the two sets of data, new fused data is obtained, redundant data is extracted from the new fused data, and image processing is performed on the new fused data and the redundant data to obtain a fused new data image and a redundant data image;

[0092] Based on the fused new data image and the redundant data image, the fused new data image and the redundant data image are overlapped and compared to obtain the overlapping data image area, which is marked as the redundant data area;

[0093] Calculate the ratio of the redundant data area to the area of ​​the fused new data image to obtain the redundant data area ratio;

[0094] The specific implementation plan of the embodiment of the present invention is: real-time monitoring of two sets of data is performed through a monitoring device, data features are extracted, a data intersection ratio is obtained, the data intersection ratio is compared with a threshold, a data similarity signal is generated, and based on the data similarity signal, a similarity value of the two sets of data is obtained, the data is compared with a threshold, and a data association degree signal is generated. Real-time monitoring ensures a rapid response to data changes. If the two sets of data change, the system can immediately capture and analyze it, and further calculate the data similarity value based on the data similarity signal and compare it with the threshold. The degree of association between the two sets of data can be quantitatively evaluated, which enables a deeper understanding of the data and helps to describe the relationship between the data.

[0095] Embodiment 2:

[0096] Based on Example 1, please refer to Figure 2As shown, the operation and maintenance data fusion method described in the embodiment of the present invention further includes the following steps:

[0097] Step 3: Based on the large correlation signal, obtain the accurate impact value, compare it with the threshold, and generate an impact signal;

[0098] In some embodiments, when a small fusion degree signal is generated, an accurate impact value is obtained, and the accurate impact value is compared with an accurate impact threshold. The comparison process is as follows:

[0099] If the impact accuracy value is greater than or equal to the impact accuracy threshold, it means that the two sets of data have a large degree of intersection, and there are many identical data fragments, generating a signal with a large impact degree;

[0100] If the impact accuracy value is less than the impact accuracy threshold, it means that the intersection of the two sets of data is small, the data has fewer identical fragments, and a small impact signal is generated;

[0101] Among them, the way to obtain the accurate value is:

[0102] Obtain the data intersection ratio and the same data fusion ratio, add the data intersection ratio and the data same fragmentation ratio together to obtain the impact accuracy value;

[0103] Exemplarily, the process of obtaining the data intersection ratio is as follows:

[0104] Obtaining the ratio of the number of data format overlaps and the ratio of the number of data structure duplications, adding the ratio of the number of data format overlaps and the ratio of the number of data structure duplications to obtain a data intersection ratio;

[0105] Specifically, the method for obtaining the data format repetition number ratio is as follows:

[0106] Obtain the number of data format repetitions, calculate the ratio of the number of data format repetitions to the total number of data formats, and obtain the data format repetition number ratio;

[0107] The method for obtaining the ratio of the number of repetitions of the data structure is as follows:

[0108] Obtain the number of data structure repetitions, calculate the ratio of the number of data structure repetitions to the total number of data structures, and obtain the data structure repetition number ratio;

[0109] The same data fusion ratio is obtained as follows:

[0110] During the fusion process of the two sets of data, the data fusion process is monitored to obtain the storage space ratio of the same data in different locations, which is marked as the data occupied space difference value;

[0111] Calculate the ratio of the difference in data space occupied to the total storage space to obtain the same data fusion ratio;

[0112] Among them, it is necessary to explain that: the meaning of the difference value of the data occupied space is: in the process of fusing two sets of data, the data are usually copied to different locations, resulting in the same data being stored in multiple different locations;

[0113] Specifically, the process of obtaining the difference value of the data occupied space is as follows:

[0114] During the monitoring period, data in multiple storage units are obtained, the data in each storage unit is compared for overlap, and the storage units with the same storage data are extracted and marked as the same data storage units;

[0115] Based on the same data storage unit, the data storage space value in the same data storage unit is obtained, and the data storage space values ​​in the same data storage unit are added and summed to obtain the data occupied space difference value;

[0116] Step 4: Based on the large signal with the highest degree of influence, obtain the data fusion adjustment value and complete the adjustment of data fusion;

[0117] In some embodiments, when a small fusion degree signal is generated, a data fusion adjustment value is obtained to reduce redundant data in the data fusion process and complete the adjustment work of the data fusion;

[0118] The data fusion adjustment value is obtained as follows:

[0119] Obtaining the coefficient affecting the accuracy value and the coefficient of the data similarity value, adding and summing the coefficient affecting the accuracy value and the coefficient of the data similarity value to obtain the data fusion adjustment coefficient;

[0120] The data fusion value is multiplied by the data fusion adjustment coefficient to obtain the data fusion adjustment value;

[0121] Exemplarily, the process of obtaining the coefficient affecting the accuracy is as follows:

[0122] The influence accuracy value is calculated by subtracting the influence accuracy threshold value to obtain the influence accuracy deviation value;

[0123] The accuracy-affecting deviation value is calculated by ratio with the accuracy-affecting threshold value to obtain the accuracy-affecting value coefficient;

[0124] The process of obtaining the data similarity value coefficient is as follows:

[0125] Subtract the data similarity value from the data similarity threshold to obtain the data similarity deviation value;

[0126] The data similarity value coefficient is obtained by calculating the ratio of the data similarity degree deviation value to the data similarity degree threshold value;

[0127] According to the data fusion adjustment value, the data fusion adjustment value is compared, the maximum value of the data fusion adjustment is extracted, the maximum value of the data fusion adjustment is adjusted first, and the adjustment is performed in order from large to small until the data fusion adjustment work is completed;

[0128] The specific scheme of the embodiment of the present invention is: based on the large signal of data correlation degree, the accurate impact value is obtained, compared with the threshold, and the impact degree signal is generated; based on the small signal of fusion degree, the data fusion adjustment value is obtained, the adjustment of data fusion is completed, and the poor fusion effect after the fusion of data is analyzed again, thereby achieving the effect of real-time adjustment of the fusion strategy.

[0129] Embodiment three:

[0130] Based on the first and second embodiments, please refer to Figure 3 As shown, an operation and maintenance data fusion system according to an embodiment of the present invention includes the following system modules:

[0131] Data monitoring module: monitors two sets of data in real time through monitoring equipment, extracts data features, obtains data intersection ratio, compares it with data intersection threshold, and generates data similarity signal;

[0132] The data features include the number of data format repetitions and the number of data structure repetitions. The number of data format repetitions and the number of data structure repetitions are added together to obtain a data intersection ratio.

[0133] If the data intersection ratio is greater than or equal to the data intersection threshold, a data similarity signal is generated;

[0134] Association analysis module: Based on the data similarity signal, obtain the similarity value of two sets of data, compare it with the data similarity threshold, and generate a data association degree signal;

[0135] During the monitoring period, the same fluctuation coefficient of the data frame values ​​and the redundant data area ratio in the two sets of data are obtained, and the same fluctuation coefficient of the data frame values ​​and the redundant data area ratio in the two sets of data are added and summed to obtain the data similarity value;

[0136] If the data similarity value is greater than the data similarity threshold, a large signal of data association is generated;

[0137] Impact assessment module: Based on the large signal of data correlation degree, the accurate impact value is obtained, compared with the accurate impact threshold, and the impact degree signal is generated;

[0138] Obtain the data intersection ratio and the same data fusion ratio, add the data intersection ratio and the same data fusion ratio together to obtain the impact accuracy value;

[0139] If the impact accuracy value is greater than or equal to the impact accuracy threshold, a high impact signal is generated;

[0140] Optimization and adjustment module: Based on the large signal of influence, obtain the data fusion adjustment value and complete the adjustment of data fusion.

[0141] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for fusion of operation and maintenance data, characterized in that: The method comprises the following steps: Step 1: Use monitoring equipment to monitor two sets of data in real time, extract data features, obtain data intersection ratio, compare it with the data intersection threshold, and generate a signal to determine whether the data is similar; The data features include the number of data format repetitions and the number of data structure repetitions. The number of data format repetitions and the number of data structure repetitions are added together to obtain a data intersection ratio. If the data intersection ratio is greater than or equal to the data intersection threshold, a data similarity signal is generated; Step 2: Based on the data similarity signal, obtain the similarity values ​​of the two sets of data, compare them with the data similarity threshold, and generate a data association degree signal; During the monitoring period, the same fluctuation coefficient of the data frame values ​​and the redundant data area ratio in the two sets of data are obtained, and the same fluctuation coefficient of the data frame values ​​and the redundant data area ratio in the two sets of data are added and summed to obtain the data similarity value; If the data similarity value is greater than the data similarity threshold, a large signal of data association is generated; Step 3: Based on the large signal of data correlation degree, obtain the accurate impact value, compare it with the accurate impact threshold, and generate the impact degree signal; Obtain the data intersection ratio and the same data fusion ratio, add the data intersection ratio and the same data fusion ratio together to obtain the impact accuracy value; If the impact accuracy value is greater than or equal to the impact accuracy threshold, a high impact signal is generated; Step 4: Based on the large signal of influence, obtain the data fusion adjustment value and complete the adjustment of data fusion.

2. The operation and maintenance data fusion method according to claim 1, characterized in that: In step 2, the process of obtaining the same fluctuation coefficient of the data frame value is as follows: During the monitoring period, two sets of data transmission data frame values ​​are obtained in real time, and an XY axis coordinate system is established. The X axis represents the monitoring time point, and the Y axis represents the two sets of data transmission data frame values ​​corresponding to each monitoring time point. The two sets of data transmission data frame values ​​are substituted into the XY axis coordinate system, and the output is the change curve of the two sets of data frame values; Based on the two groups of data frame value change curves, each group of data frame value change curves is divided according to the monitoring time period to obtain multiple data frame value change sub-curves, and based on the two data frame value change sub-curves, the slope corresponding to each data frame value change sub-curve is obtained and marked as the change curve sub-slope; Based on the sub-slopes of the change curve, the sub-slopes of the change curve corresponding to the same monitoring time period are calculated by ratio to obtain the slope coefficient; Based on the slope coefficient, the number of the same monitoring time periods corresponding to the same slope coefficient in the two sets of data frame value change curves is obtained, marked as the number of time periods with the same slope, and the number of time periods with the same slope is ratioed with the number of time periods in the monitoring period to obtain the same fluctuation coefficient of the data frame value.

3. The operation and maintenance data fusion method according to claim 1, characterized in that: In step 2, the process of obtaining the redundant data area ratio is as follows: After fusing the two sets of data, new fused data is obtained, redundant data is extracted from the new fused data, and image processing is performed on the new fused data and the redundant data to obtain a fused new data image and a redundant data image; Based on the fused new data image and the redundant data image, the fused new data image and the redundant data image are overlapped and compared to obtain the overlapping data image area, which is marked as the redundant data area; The redundant data area is ratio-calculated to the area of ​​the fused new data image to obtain the redundant data area ratio.

4. The operation and maintenance data fusion method according to claim 1, characterized in that: In step three, the process of obtaining the data intersection ratio is as follows: The data format overlap ratio and the data structure duplication ratio are obtained, and the data format overlap ratio and the data structure duplication ratio are added together to obtain the data intersection ratio.

5. The operation and maintenance data fusion method according to claim 3, characterized in that: In step 3, the method for obtaining the data format repetition ratio is as follows: Obtain the number of data format repetitions, calculate the ratio of the number of data format repetitions to the total number of data formats, and obtain the data format repetition number ratio; The method for obtaining the ratio of the number of repetitions of the data structure is as follows: The number of data structure repetitions is obtained, and the ratio of the number of data structure repetitions to the total number of data structures is calculated to obtain the data structure repetition number ratio.

6. The operation and maintenance data fusion method according to claim 1, characterized in that: In step 3, the same data fusion ratio is obtained as follows: During the fusion process of the two sets of data, the data fusion process is monitored to obtain the storage space ratio of the same data in different locations, which is marked as the data occupied space difference value; The ratio of the difference in data space occupied to the total storage space is calculated to obtain the same data fusion ratio.

7. The operation and maintenance data fusion method according to claim 6, characterized in that: In step 3, the process of obtaining the difference value of data space occupation is as follows: During the monitoring period, data in multiple storage units are obtained, the data in each storage unit is compared for overlap, and the storage units with the same storage data are extracted and marked as the same data storage units; Based on the same data storage unit, the data storage space value in the same data storage unit is obtained, and the data storage space values ​​in the same data storage unit are added and summed to obtain the data occupied space difference value.

8. The operation and maintenance data fusion method according to claim 1, characterized in that: In step 4, the data fusion adjustment value is obtained as follows: Obtaining the coefficient affecting the accuracy value and the coefficient of the data similarity value, adding and summing the coefficient affecting the accuracy value and the coefficient of the data similarity value to obtain the data fusion adjustment coefficient; The data fusion value is multiplied by the data fusion adjustment coefficient to obtain the data fusion adjustment value; According to the data fusion adjustment value, the data fusion adjustment value is compared, the maximum value of the data fusion adjustment is extracted, the maximum value of the data fusion adjustment is adjusted first, and then adjusted in order from large to small until the data fusion adjustment work is completed.

9. The operation and maintenance data fusion method according to claim 8, characterized in that: In step 4, the process of obtaining the coefficient affecting the accuracy value is as follows: The influence accuracy value is calculated by subtracting the influence accuracy threshold value to obtain the influence accuracy deviation value; The accuracy-affecting deviation value is calculated by ratio with the accuracy-affecting threshold value to obtain the accuracy-affecting value coefficient; The process of obtaining the data similarity value coefficient is as follows: Subtract the data similarity value from the data similarity threshold to obtain the data similarity deviation value; The data similarity value coefficient is obtained by calculating the ratio of the data similarity deviation value to the data similarity threshold value.

10. An operation and maintenance data fusion system, characterized in that: The system is used to execute the method described in any one of claims 1 to 9, and the system comprises: Data monitoring module: monitors two sets of data in real time through monitoring equipment, extracts data features, obtains data intersection ratio, compares it with data intersection threshold, and generates data similarity signal; The data features include the number of data format repetitions and the number of data structure repetitions. The number of data format repetitions and the number of data structure repetitions are added together to obtain a data intersection ratio. If the data intersection ratio is greater than or equal to the data intersection threshold, a data similarity signal is generated; Association analysis module: Based on the data similarity signal, obtain the similarity value of two sets of data, compare it with the data similarity threshold, and generate a data association degree signal; During the monitoring period, the same fluctuation coefficient of the data frame values ​​and the redundant data area ratio in the two sets of data are obtained, and the same fluctuation coefficient of the data frame values ​​and the redundant data area ratio in the two sets of data are added and summed to obtain the data similarity value; If the data similarity value is greater than the data similarity threshold, a large signal of data association is generated; Impact assessment module: Based on the large signal of data correlation degree, the accurate impact value is obtained, compared with the accurate impact threshold, and the impact degree signal is generated; Obtain the data intersection ratio and the same data fusion ratio, add the data intersection ratio and the same data fusion ratio together to obtain the impact accuracy value; If the impact accuracy value is greater than or equal to the impact accuracy threshold, a high impact signal is generated; Optimization and adjustment module: Based on the large signal of influence, obtain the data fusion adjustment value and complete the adjustment of data fusion.

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