CN117148010A - HPLC signal characteristic and voltage correlation-based platform region identification method - Google Patents
HPLC signal characteristic and voltage correlation-based platform region identification method Download PDFInfo
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Abstract
The invention discloses a station area identification method based on HPLC signal characteristics and voltage correlation, which comprises the following steps: step one: adopting an HPLC interconnection standard and adopting zero crossing NTB to carry out platform area identification; step two: waiting for the output of the identification result of the platform area, and outputting three results, namely the own platform area, the non-own platform area and the unrecognized platform area; step three: adopting a network clock to collect the voltage of all the ammeter in the transformer area in minutes; step four: performing voltage and SNR (signal to noise ratio) correlation secondary identification on the non-self-station area and the unidentified measuring point; step five: outputting the area identification result, and carrying out the re-correlation area identification on the unidentified measurement points. The method mainly adopts SNR (signal-to-noise ratio) and voltage correlation to secondarily identify the measurement points which are identified as non-local area and unidentified by an HPLC (high performance liquid chromatography) through an NTB zero crossing technology. The method solves the problem of realizing the identification of the platform area on the site of multi-chip mixed installation.
Description
Technical Field
The invention relates to the field of database security, in particular to a platform region identification method based on HPLC signal characteristics and voltage correlation.
Background
At present, the HPLC chips have larger kernel difference at the STA side, the M0-M4 sequences with ARM system architecture also adopt ARM9 cores, part of manufacturers also adopt Tensilica cores as a main control CPU, and meanwhile, the operating system also has larger difference, if FreeOs, uCosII is adopted, liteos (Hai Si) is also adopted. The basic principle of the zero-crossing peripheral circuit principle is different in magnitude, namely, after the power grid is stabilized, a certain voltage (for example, the sea is 7 v) of a rising or falling source of the power grid triggers zero-crossing pulse, pulse signals trigger NTB zero-crossing middle section response through the coordination connection between GPIO and the middle section of a main control chip, and a middle service program records the moment (NTB) of triggering the zero-crossing pulse. Because the main frequency of each chip and the response time sequence of the middle section of the operating system have larger difference, the service response time sequence of the middle section has larger difference, and the zero-crossing NTB data acquisition is affected.
Based on the problems, the existing HPLC (high performance liquid chromatography) zone identification function based on the single NTB zero crossing technology greatly reduces the field availability, basically only can meet the zone identification of a single HPLC chip scheme of a zone, and needs other technical assistance for the user-to-user relationship identification function of a hybrid chip scheme, so that a zone identification method based on the HPLC signal characteristics and the voltage correlation is provided.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: how to solve the problems that the prior HPLC (high performance liquid chromatography) station area identification function based on the single NTB zero crossing technology has greatly reduced field usability, can basically only meet the station area identification of a single HPLC chip scheme of a station area, and needs other technical assistance for the user-to-user relationship identification function of a mixed chip scheme.
The invention solves the technical problems through the following technical proposal, and the invention comprises the following steps:
step one: adopting an HPLC interconnection standard and adopting zero crossing NTB to carry out platform area identification;
step two: waiting for the output of the identification result of the platform area, and outputting three results, namely the own platform area, the non-own platform area and the unrecognized platform area;
step three: adopting a network clock to collect the voltage of all the ammeter in the transformer area in minutes;
step four: performing voltage and SNR (signal to noise ratio) correlation secondary identification on the non-self-station area and the unidentified measuring point;
step five: outputting the area identification result, and carrying out the re-correlation area identification on the unidentified measurement points.
The specific process of carrying out the station area identification by adopting the HPLC interconnection standard and adopting the zero crossing NTB in the first step further comprises the following steps:
step 1: the master node triggers the HPLC to identify the station area based on NTB according to the standard protocol;
step 2: waiting for the identification result of the HPLC platform area, and 3 states after the measurement points are identified, namely the platform area, the non-platform area and the unknown;
step 3: carrying out secondary voltage correlation identification on the non-self-station area which is not successfully identified;
step 4: synchronously collecting the minute voltage curves of all the electric meters according to the network clock NTB of the HPLC;
step 5: collecting 64-point curve data by each recognition algorithm;
step 6: performing correlation identification on the SNR of the neighbor measurement points of the surrounding communication and the in-phase voltage of the local area for the measurement points needing secondary identification;
step 7: the content of the correlation identification comprises the measurement points of strong correlation of the local area, the measurement points of weak correlation of the non-local area and other re-identification.
Further, the specific process of the zero crossing NTB is as follows: the time deviation of zero crossing points of carrier nodes in the same station area is very small, the zero crossing time deviation between different station areas is poor due to the differences of loads, noise and other power line environments, the zero crossing time signals and corresponding NTB clocks are collected through each power node, namely, the zero crossing NTB is used for comparing the similarity of the station areas, and stations with large similarity belong to the same station area.
The specific process of performing the voltage & SNR correlation secondary identification on the non-own station area and the unidentified measurement point in the fourth step is as follows:
step (1): only the measurement points of unidentified and non-own areas detected by adopting the zero crossing NTB are secondarily identified by adopting voltage & SNR correlation:
step (2): the secondary identification is based on the voltage correlation identification of measurement points with similar SNR
Step (3): in order to reduce the bandwidth occupied by an HPLC line channel, adopting neighbor measurement points which can be collected by the node and are measurement points of the platform area, and performing voltage sampling;
step (4): if the neighbor measuring points of the current identification measuring point are less than 16 measuring points of the own area, adopting brother measuring points of the superior father node to perform correlation calculation;
step (5): data screening selects average voltage difference for calculation
Step (6): data of 32 time points are adopted for carrying out stream storage and Pearson correlation coefficient calculation;
step (7): performing new data calculation by adopting and introducing new data to the measuring points with the modification coefficients of (0.5-0.8);
step (8): and outputting a platform area identification result, wherein the platform area identification result comprises a non-own platform area and an own platform area.
Further, the specific process of voltage correlation identification in the step (2) is as follows: based on the user side electrical data synchronously collected at high frequency of the transformer area, the electrical signals of the unified branch lines are equal everywhere with loop vector currents, the upper and lower branches of the same branch voltage have higher levels and more than or equal to descending, and the variation fluctuation has strong correlation and is combined with a Pearson correlation coefficient algorithm;
the correlation algorithm counts using two sets of correlation node data:
two sets of sequences are represented by X and Y, and the elements in the two sets of sequences are represented by X (i) and Y (i), and the correlation of length N is calculated as follows:
the degree of correlation between the count variables is as follows:
cov (X, Y) in the formula is the covariance of the two sets of variables, var [ X ] is the variance of variable X, var [ Y ] is the variance of variable Y
The variance Var [ x ] is given by:
variance Cov (X, Y) between two random variables X and Y for which the expected values are Ex and Ey, respectively;
the Cov (x, y) is calculated as follows:
Cov(X，Y)＝E[(X-E[X])(Y-E[Y])]。
further, the SNR signal differentiating process of the station area is as follows: the SNR value received by the adjacent nodes of the station area is obviously higher than the SNR of the adjacent nodes, the topology level and the SNR of the adjacent nodes are used as main judgment basis, meanwhile, the station environment information is utilized to record the communication success rate among the nodes, and the topology change of the station area is stabilized through long-time data statistics.
Further, the power supply relation characteristic of the station area in the station area identification process comprises: the load on the line is the phase line impedance, and after the power supply relation of the transformer area is confirmed, the distribution of the line impedance of the transformer area is fixed, namely the differential pressure is quite fixed;
the individual user voltage variation is inversely proportional to the power of the user load;
keeping the voltage change relation of the user consistent with the corresponding branch node voltage and the output voltage Un relation;
for a single load, where the load remains consistent, the voltage trend remains consistent throughout with nodes around the network, i.e., around the SNR.
Further, the line impedance is obtained as follows: due to Rx>>Rdn, and the current of the meter box is the vector sum of the meter boxesAnd voltage=ux- (Rx) ×in- (Rdn) ×idn (Idn) of each meter;
ux—total table voltage; the line resistance of the Rx-transformer to the meter box; current vector sum of each sub-table of the In-table box; rdn-line resistance from meter box to each intelligent meter; idn—the current of each meter, when (Rx) ×in > > (Rdn) ×idn, (Rdn) ×idn) the voltage drop can be ignored In the algorithm, the loop differential pressure of each meter is mainly composed of loop impedance and current, and the formula is as follows:
the voltage drop Rx of each intelligent ammeter line is equal to in=Ux- (Udn), and the total voltage of the UX-station area can be obtained; udn-the electricity voltage of each household meter can be obtained, in-the current of each ammeter can be obtained by confirmation;
the loop voltage drop is adopted as a sample point, and the voltage of a user load is as follows: the bay supply voltage-supply voltage drop has a strong correlation.
Compared with the prior art, the invention has the following advantages: the method for identifying the platform region based on the HPLC signal characteristics and the voltage correlation mainly adopts SNR (signal processor ratio) and voltage correlation to carry out secondary identification on the measurement points which are identified as non-local platform region and unidentified by an NTB zero crossing technology in the HPLC, solves the problem of realizing platform region identification on a multi-chip mixed installation site, adopts a GPIO (direct-current) signal (VDET) which is triggered by optical coupling to trigger an HPLC chip when a power grid voltage falling edge, triggers a middle section when an HPLC main chip receives the pulse and records the NTB moment triggered by the middle section as the zero crossing moment of a power grid sinusoidal signal, and is more worthy of popularization and use.
Drawings
FIG. 1 is an overall flow chart of the present invention;
FIG. 2 is a logic block diagram of zero crossing detection of the present invention;
fig. 3 is a plot of the SNR signal discrimination of the present invention;
FIG. 4 is a graph of the circuit impedance analysis of the present invention;
FIG. 5 is a process diagram of a secondary identification algorithm of a station area according to the present invention;
FIG. 6 is a graph of field and laboratory test results of the present invention;
FIG. 7 is a plot of the same area test results of the present invention;
fig. 8 is an effect diagram of zone 2 of the present invention;
fig. 9 is an effect diagram of the zone 1 of the present invention.
Detailed Description
The following describes in detail the examples of the present invention, which are implemented on the premise of the technical solution of the present invention, and detailed embodiments and specific operation procedures are given, but the scope of protection of the present invention is not limited to the following examples.
As shown in fig. 1, this embodiment provides a technical solution: a station area identification method based on HPLC signal characteristics and voltage correlation, the identification process includes:
the first step: zero-crossing NTB (network time base) is adopted for carrying out platform area identification by adopting HPLC interconnection standard
And a second step of: waiting for outputting the identification result of the platform area, and outputting three results, namely the own platform area, the non-own platform area and the unrecognized platform area
And a third step of: the network clock (NTB) is adopted to collect the voltage of all the ammeter (including the total meter) in the area for minutes
Fourth step: performing voltage & SNR correlation secondary identification on non-local area and unidentified measurement point
Fifthly, outputting a platform region identification result, and carrying out correlation platform region identification again on unidentified measurement points;
zero crossing NTB: the time deviation of zero crossing points of carrier nodes in the same station area is very small, and the zero crossing time deviation of different station areas is poor due to the differences of loads, noise and other power line environments, so that station area similarity comparison is carried out by collecting zero crossing time signals and corresponding NTB clocks (zero crossing NTB) through each power node, and stations with high similarity belong to the same station area;
1: the master node triggers the HPLC to identify the station area based on NTB according to the standard protocol;
2: waiting for the identification result of the HPLC platform area, and 3 states after the measurement points are identified, namely the platform area, the non-platform area and the unknown;
3: carrying out secondary voltage correlation identification on the non-self-station area which is not successfully identified;
4: synchronously collecting the minute voltage curves of all the electric meters (user meters and total meters) according to the network clock NTB of the HPLC;
5: collecting 64-point curve data by each recognition algorithm;
6: performing correlation identification on surrounding communication neighbor measurement points SNR and local area in-phase voltage of measurement points needing secondary identification
7: strongly correlated measurement points (home zone), weakly correlated measurement points (non-home zone), and others are identified again.
Zero crossing detection logic block diagram 2;
the scheme adopts a GPIO signal (VDET) of an optical coupling trigger HPLC chip when the power grid voltage falls, when the HPLC main chip receives the pulse, the middle section is triggered, the NTB moment triggered by the middle section is recorded, and the NTB moment is used as the zero crossing moment of a power grid sine signal.
As shown in fig. 3, the SNR SIGNAL is distinguished from the SIGNAL-to-NOISE RATIO (SNR) by the english name, which refers to the RATIO of SIGNAL to NOISE in an electronic system. The signal here refers to HPLC communication signal, noise refers to irregular extra signal which is not present in the original signal generated after passing through the device, and the signal does not change with the change of the original signal.
In general, the SNR value received by the neighboring node of the present area is significantly higher than the SNR of the node receiving the neighboring area, and the topology level and the SNR of the neighboring node are used as the main judgment basis. Meanwhile, the station environment information is utilized to record the communication success rate among the nodes, and the topology change of the station area is stabilized through long-time data statistics.
As in fig. 4: line loop impedance analysis:
since Rx > Rdn and the bin current is the vector sum in= of the bin sub-tables, and the voltage per meter = Ux- (Rx) In- (Rdn) (Idn)
(Ux-total voltage of the transformer area; line resistance of Rx-transformer to meter box; current vector sum of each sub-meter of In-meter box; line resistance of Rdn-meter box to each smart meter; current of Idn-meter) the voltage drop of (Rx) In > > (Rdn) In (Idn) and (Rdn) In (Idn) can be ignored In algorithm. The loop differential pressure of each household meter is composed mainly of loop impedance and current. The formula is as follows:
line drop (Rx) for each smart meter in=ux- (Udn) (Ux-block total voltage (available), udn-meter voltage (available), in-current (from a family of meters) for each meter
The loop voltage drop is adopted as a sample point, and the voltage of a user load is as follows: the bay supply voltage-supply voltage drop has a strong correlation.
Station power supply relation characteristics:
1: the load on the line is the phase line impedance, and after the power supply relation of the transformer area is confirmed, the distribution of the line impedance of the transformer area is fixed, namely the differential pressure is quite fixed.
2: the individual user voltage variation is inversely proportional to the power of the user load.
3: and keeping the voltage change relation of the user and the corresponding branch node voltage and output voltage Un relation as a whole to be consistent.
4: for a single load, the voltage trend is consistent with the overall vicinity of the nodes (SNR) around the network, while the load remains consistent.
The secondary identification algorithm process of the station area is as shown in fig. 5, 1: only if zero crossing NTB is adopted to detect unidentified and non-local area measurement points, voltage & SNR correlation is adopted to carry out secondary identification
2, performing voltage correlation recognition based on measurement points with similar SNR by secondary recognition
3: in order to reduce the bandwidth occupied by an HPLC line channel, adopting neighbor measurement points which can be collected by the node and are measurement points of the platform area, and performing voltage sampling;
4: if the neighbor measuring points of the current identification measuring point are less than 16 measuring points of the own area, adopting the brother measuring points of the superior father node to perform correlation calculation.
5: the data general selection selects the average voltage difference ((0.1-8V), the highest sign bit) to calculate
6: data of 32 time points are adopted for stream storage and Pearson correlation coefficient calculation
7: new data calculation is adopted and introduced for the measurement points with the modification coefficients between (0.5 and 0.8)
8: outputting the identification result of the platform region (non-own platform region, own platform region)
Algorithm of voltage correlation algorithm
Based on the user side electric data collected by the high frequency synchronization (NTB) of the station area, the electric signals of the unified branch lines are equal everywhere with loop vector currents, the upper and lower branches of the same branch voltage have higher levels and more than or equal to descending, and the fluctuation of the change has strong correlation, and the Pearson correlation coefficient algorithm is combined.
The correlation algorithm counts using two sets of correlation node data:
two sets of sequences are represented by X and Y, and the elements in the two sets of sequences are represented by X (i) and Y (i), and the correlation of length N is calculated as follows:
the degree of correlation between the count variables is as follows:
cov (X, Y) in the formula is the covariance of the two sets of variables, var [ X ] is the variance of variable X, var [ Y ] is the variance of variable Y
The variance Var [ x ] is given by:
variance Cov (X, Y) between two random variables X and Y for which the expected values are Ex and Ey, respectively;
the Cov (x, y) is calculated as follows:
Cov(X，Y)＝E[(X-E[X])(Y-E[Y])]
with relevant empirical knowledge, the detection threshold is set to 0.8, which is the home zone if about this choice.
By relevant empirical knowledge, the detection threshold is set to 0.5, if about this is chosen to be non-native
Setting the detection threshold to 0.5-0.8 through relevant experience knowledge is that the next calculation is needed by the data quality with improvement, and the 5 points of the streaming calculation are deleted for recalculation.
On-site and laboratory test effects are shown in FIG. 6, on-site laboratory test effects are shown in FIGS. 7, 8 and 9, the same on-site laboratory test effect
4.2 Mixed plot A phase Curve analysis
The mixed area is (two areas adjacent to each other are clustered, the measurement points with the same phase are searched, and the curves are compared), and the correctness of the technical route is verified
Combination station analysis: phase A: station area 2: 21. 22, 29, zones 1:105, 114.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (8)
1. The method for identifying the area based on the HPLC signal characteristics and the voltage correlation is characterized by comprising the following steps:
step one: adopting an HPLC interconnection standard and adopting zero crossing NTB to carry out platform area identification;
step two: waiting for the output of the identification result of the platform area, and outputting three results, namely the own platform area, the non-own platform area and the unrecognized platform area;
step three: adopting a network clock to collect the voltage of all the ammeter in the transformer area in minutes;
step four: performing voltage and SNR (signal to noise ratio) correlation secondary identification on the non-self-station area and the unidentified measuring point;
step five: outputting the area identification result, and carrying out the re-correlation area identification on the unidentified measurement points.
2. The method for identifying a region based on HPLC signal characteristics and voltage correlation of claim 1, wherein: the specific process of carrying out the identification of the station area by adopting the HPLC interconnection standard and adopting the zero-crossing NTB in the first step comprises the following steps:
step 1: the master node triggers the HPLC to identify the station area based on NTB according to the standard protocol;
step 2: waiting for the identification result of the HPLC platform area, and 3 states after the measurement points are identified, namely the platform area, the non-platform area and the unknown;
step 3: carrying out secondary voltage correlation identification on the non-self-station area which is not successfully identified;
step 4: synchronously collecting the minute voltage curves of all the electric meters according to the network clock NTB of the HPLC;
step 5: collecting 64-point curve data by each recognition algorithm;
step 6: performing correlation identification on the SNR of the neighbor measurement points of the surrounding communication and the in-phase voltage of the local area for the measurement points needing secondary identification;
step 7: the content of the correlation identification comprises the measurement points of strong correlation of the local area, the measurement points of weak correlation of the non-local area and other re-identification.
3. The method for identifying a region based on HPLC signal characteristics and voltage correlation of claim 1, wherein: the specific process of the zero crossing NTB is as follows: the time deviation of zero crossing points of carrier nodes in the same station area is very small, the zero crossing time deviation between different station areas is poor due to the differences of loads, noise and other power line environments, the zero crossing time signals and corresponding NTB clocks are collected through each power node, namely, the zero crossing NTB is used for comparing the similarity of the station areas, and stations with large similarity belong to the same station area.
4. The method for identifying a region based on HPLC signal characteristics and voltage correlation of claim 1, wherein: the specific process of performing the correlation secondary identification on the voltage & SNR of the non-own station area and the unidentified measurement point in the fourth step is as follows:
step (1): only the measurement points of unidentified and non-own areas detected by adopting the zero crossing NTB are secondarily identified by adopting voltage & SNR correlation:
step (2): the secondary identification is based on the voltage correlation identification of measurement points with similar SNR
Step (3): in order to reduce the bandwidth occupied by an HPLC line channel, adopting neighbor measurement points which can be collected by the node and are measurement points of the platform area, and performing voltage sampling;
step (4): if the neighbor measuring points of the current identification measuring point are less than 16 measuring points of the own area, adopting brother measuring points of the superior father node to perform correlation calculation;
step (5): data screening selects average voltage difference for calculation
Step (6): data of 32 time points are adopted for carrying out stream storage and Pearson correlation coefficient calculation;
step (7): performing new data calculation by adopting and introducing new data to the measuring points with the modification coefficients of (0.5-0.8);
step (8): and outputting a platform area identification result, wherein the platform area identification result comprises a non-own platform area and an own platform area.
5. The method for identifying a region based on HPLC signal characteristics and voltage correlation of claim 1, wherein: the specific process of voltage correlation identification in the step (2) is as follows: based on the user side electrical data synchronously collected at high frequency of the transformer area, the electrical signals of the unified branch lines are equal everywhere with loop vector currents, the upper and lower branches of the same branch voltage have higher levels and more than or equal to descending, and the variation fluctuation has strong correlation and is combined with a Pearson correlation coefficient algorithm;
the correlation algorithm counts using two sets of correlation node data:
two sets of sequences are represented by X and Y, and the elements in the two sets of sequences are represented by X (i) and Y (i), and the correlation of length N is calculated as follows:
the degree of correlation between the count variables is as follows:
cov (X, Y) in the formula is the covariance of the two sets of variables, var [ X ] is the variance of variable X, var [ Y ] is the variance of variable Y
The variance Var [ x ] is given by:
variance Cov (X, Y) between two random variables X and Y for which the expected values are Ex and Ey, respectively;
the Cov (x, y) is calculated as follows:
Cov(X，Y)＝E[(X-E[X])(Y-E[Y])]。
6. the method for identifying a region based on HPLC signal characteristics and voltage correlation of claim 1, wherein: the SNR signal distinguishing process of the station area is as follows: the SNR value received by the adjacent nodes of the station area is obviously higher than the SNR of the adjacent nodes, the topology level and the SNR of the adjacent nodes are used as main judgment basis, meanwhile, the station environment information is utilized to record the communication success rate among the nodes, and the topology change of the station area is stabilized through long-time data statistics.
7. The method for identifying a region based on HPLC signal characteristics and voltage correlation of claim 1, wherein: the power supply relation characteristics of the station area in the station area identification process comprise: the load on the line is the phase line impedance, and after the power supply relation of the transformer area is confirmed, the distribution of the line impedance of the transformer area is fixed, namely the differential pressure is quite fixed;
the individual user voltage variation is inversely proportional to the power of the user load;
keeping the voltage change relation of the user consistent with the corresponding branch node voltage and the output voltage Un relation;
for a single load, where the load remains consistent, the voltage trend remains consistent throughout with nodes around the network, i.e., around the SNR.
8. The method for identifying a region based on HPLC signal characteristics and voltage correlation as set forth in claim 7, wherein the line impedance obtaining process is as follows: due to Rx>>Rdn, and the current of the meter box is the vector sum of the meter boxesAnd voltage=ux- (Rx) ×in- (Rdn) ×idn (Idn) of each meter;
ux—total table voltage; the line resistance of the Rx-transformer to the meter box; current vector sum of each sub-table of the In-table box; rdn-line resistance from meter box to each intelligent meter; idn—the current of each meter, when (Rx) ×in > > (Rdn) ×idn, (Rdn) ×idn) the voltage drop can be ignored In the algorithm, the loop differential pressure of each meter is mainly composed of loop impedance and current, and the formula is as follows:
the voltage drop Rx of each intelligent ammeter line is equal to in=Ux- (Udn), and the total voltage of the UX-station area can be obtained; udn-the electricity voltage of each household meter can be obtained, in-the current of each ammeter can be obtained by confirmation;
the loop voltage drop is adopted as a sample point, and the voltage of a user load is as follows: the bay supply voltage-supply voltage drop has a strong correlation.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113630156A (en) * | 2021-08-06 | 2021-11-09 | 国网四川省电力公司营销服务中心 | Method for identifying starting area of crosstalk area by judging carrier signal |
| CN113746589A (en) * | 2021-09-01 | 2021-12-03 | 钜泉光电科技(上海)股份有限公司 | Station area identification method based on zero crossing NTB |
| CN113779751A (en) * | 2021-07-28 | 2021-12-10 | 天津大学 | Low-pressure HPLC (high performance liquid chromatography) platform area topology identification method and system |
| WO2022088192A1 (en) * | 2020-10-30 | 2022-05-05 | 福建网能科技开发有限责任公司 | Automatic topology identification method and system for electric power intelligent transformer area |
| CN114912526A (en) * | 2022-05-13 | 2022-08-16 | 北京市腾河电子技术有限公司 | Method and system for identifying distribution area, electronic device and storage medium |
| CN115800553A (en) * | 2023-01-09 | 2023-03-14 | 国网冀北电力有限公司承德供电公司 | HPLC-based platform area topology identification method and device, electronic equipment and storage medium |
| CN116436152A (en) * | 2022-12-13 | 2023-07-14 | 国网湖北省电力有限公司电力科学研究院 | Intelligent low-voltage distribution transformer area topology identification method based on characteristic information correlation |
-
2023
- 2023-09-01 CN CN202311125997.9A patent/CN117148010B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022088192A1 (en) * | 2020-10-30 | 2022-05-05 | 福建网能科技开发有限责任公司 | Automatic topology identification method and system for electric power intelligent transformer area |
| CN113779751A (en) * | 2021-07-28 | 2021-12-10 | 天津大学 | Low-pressure HPLC (high performance liquid chromatography) platform area topology identification method and system |
| CN113630156A (en) * | 2021-08-06 | 2021-11-09 | 国网四川省电力公司营销服务中心 | Method for identifying starting area of crosstalk area by judging carrier signal |
| CN113746589A (en) * | 2021-09-01 | 2021-12-03 | 钜泉光电科技(上海)股份有限公司 | Station area identification method based on zero crossing NTB |
| CN114912526A (en) * | 2022-05-13 | 2022-08-16 | 北京市腾河电子技术有限公司 | Method and system for identifying distribution area, electronic device and storage medium |
| CN116436152A (en) * | 2022-12-13 | 2023-07-14 | 国网湖北省电力有限公司电力科学研究院 | Intelligent low-voltage distribution transformer area topology identification method based on characteristic information correlation |
| CN115800553A (en) * | 2023-01-09 | 2023-03-14 | 国网冀北电力有限公司承德供电公司 | HPLC-based platform area topology identification method and device, electronic equipment and storage medium |
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