A distribution network diagram data management system

Abstract

The present invention discloses a distribution network diagram data management system, which relates to the field of data management technology and solves the problem that the original synchronization method does not perform data synchronization based on the time characteristics of each different port. The present invention performs relevant confirmation on the data characteristics of different source ports, and subsequently based on the data characteristics associated with each different source port, based on the associated time characteristics of the different source ports, and the collected single-unit data, a group of standard intervals and a time line corresponding to a limited period are preferentially determined, and based on the specific time period division within this time line, each single-unit data is relatively consistent in terms of the overall number of data, so that when each data is synchronized, the correlation in the synchronization process can be effectively guaranteed, thereby achieving a better data synchronization effect and making the synchronization data packet have a strong synchronization.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a distribution network diagram data management system. Background Art

[0002] Provide a visual interface for the operation monitoring of the distribution network; through the distribution network diagram data, operators can intuitively understand the operating status of the distribution network, including the energized status of equipment, load distribution, voltage and current and other parameters.

[0003] The application with publication number CN112348295A discloses a distribution network information data management system and method, including a distribution network information input and modification module, which is used by users to import information based on the original graphic archive record data, and to add and modify distribution network information to edit and maintain the power grid information, and a distribution network query module, which is used to perform maintenance and auxiliary management of urban distribution networks in a map window according to the map query module, and display the query results on the map in a visual form; the invention can realize the management and analysis functions of the spatial database, and can utilize the programming advantages of other visual development languages ​​such as high efficiency and convenience, greatly improve the development efficiency of the application system, have good reliability, easy transplantation, and easy maintenance, and provide a fast, easy-to-use, and powerful mapping component, which is the most widely used and powerful, and ensures the security and reliability of data.

[0004] During the data management process of the graphic model data associated with its distribution network, generally based on the association characteristics between ports, the relevant data generated by the corresponding port are directly synchronized and processed, and the relevant synchronization data packets are confirmed. However, due to the method of data synchronization, the time characteristics of the data generated by each different port are different, which will cause some synchronization data packets to have missing data of certain ports. Therefore, the synchronization data contained in the synchronization data packet is not comprehensive. The original synchronization method does not synchronize data based on the time characteristics of each different port, and the relevant synchronization effect needs to be enhanced. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a distribution network diagram data management system, which solves the problem that the original synchronization method does not perform data synchronization based on the time characteristics of each different port.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a distribution network diagram data management system, comprising:

[0007] The source data feature collection end identifies the multi-source ports associated in the distribution network, and then calibrates each multi-source port based on the time interval feature interval of each multi-source port, collects the data generated by each different multi-source port, and transmits the collected source data to the source data calibration synchronization end. The specific method is as follows:

[0008] The data generated by each multi-source port is collected, and the collected data belonging to different multi-source ports are marked differently. When collecting, based on the header and the footer of the collected data, the data collected belonging to the same multi-source port are classified to confirm a plurality of classified monomer pattern data;

[0009] From the collected historical completion data, based on the completion time of the collection of the classified monomer pattern data corresponding to the multi-source port, the collection time interval of each multi-source port is identified from several groups of adjacent completion time, and then the minimum value and the maximum value are identified from several groups of collection time intervals to determine the time interval characteristic interval belonging to this multi-source port;

[0010] The source data calibration synchronization end confirms the associated multi-source ports, and based on the time interval characteristic interval confirmed by each different multi-source port, synchronizes and confirms several classified monomer image model data generated within a limited period, and locks the synchronization data packet. The specific method is as follows:

[0011] Based on the calibrated associated multi-source ports, the associated multi-source ports have been marked in advance by the operator, the time interval characteristic interval of the multi-source ports is confirmed, and then from different time interval characteristic intervals of different associated multi-source ports, the time interval characteristic interval with the interval endpoint value in the maximum state is selected as the standard interval, and a group of intervals are randomly selected as the standard interval;

[0012] Based on the set limited period T, where T is a preset value, the classified monomer pattern data generated within the limited period T is confirmed;

[0013] A timeline about the limited period T is preferentially generated, the initial endpoint of which is time 0. Based on the determined standard interval, a synchronization period is confirmed on this timeline, and the time length of the synchronization period is within the standard interval. The initial time of the first group of synchronization periods is time 0, and the initial time of each subsequent group of synchronization periods is the end time of the previous group of synchronization periods.

[0014] The length of the synchronization period in the timeline is adjusted, and the length of the synchronization period after adjustment is also within the standard range. The adjustment process can be performed for each different synchronization period, and the total number of classified monomer image model data contained in the adjusted synchronization period is confirmed and marked as G kThe generation time of the classified monomer image model data is all within this synchronization period, where k represents different synchronization periods. k Perform variance processing and determine the variance value. When the variance value is at the minimum state, the synchronization period corresponding to the minimum state is calibrated as the standard period.

[0015] Confirm the different classification monomer image model data associated with each standard time period, and bundle and confirm the synchronization data packets belonging to the corresponding standard time period;

[0016] The data format synchronization end, for the confirmed synchronization data packet, performs format confirmation on the classified monomer pattern data of different formats in the synchronization data packet, and unifies the format of the synchronization data packet based on the confirmed data format, and transmits the synchronization data packet after the format unification to the association verification end, and the specific method is as follows:

[0017] Check whether there are classified monomer image data of different formats in the synchronization data packet:

[0018] If it does not exist, no processing is performed, and the synchronization data packet is marked as a synchronization data packet with a unified format;

[0019] If it exists, identify the data format with the largest data capacity from the synchronization data packet, mark this data format as the format to be converted, and convert the classified monomer image model data of other formats to the format to be converted, so as to obtain the synchronization data packet after the format is unified;

[0020] The association verification end includes a feature recognition verification unit and an association abnormality assessment unit, and the association verification end, based on the different classification monomer pattern data of different multi-source ports associated in the synchronization data packet, preferentially performs the correlation verification of the association feature through the feature recognition verification unit, assesses whether there is abnormal data in the synchronization data packet, and transmits the assessed abnormal data to the association abnormality assessment unit, and the association abnormality assessment unit then performs the relevant confirmation of the port abnormality based on the assessed abnormal data;

[0021] Furthermore, the specific method in which the feature recognition and verification unit evaluates whether there is abnormal data in the synchronization data packet is:

[0022] Based on the preset association features between different multi-source ports, identify whether the classification monomer pattern data generated between the corresponding multi-source ports are consistent in the association features. If they are consistent, the relevant classification monomer pattern data are marked as normal data items. If they are not consistent, the corresponding classification monomer pattern data are marked as abnormal data items.

[0023] Based on the calibrated abnormal data items, identify whether the classified monomer image model data within such abnormal data items exist synchronously in other normal data items. If so, calibrate the classified monomer image model data as normal data. If not, calibrate the classified monomer image model data as abnormal data.

[0024] Further, for the determined abnormal data, the associated abnormality assessment unit identifies the multi-source ports associated with the abnormal data, and confirms the number of times the multi-source ports generate abnormal data within a preset period. Based on the confirmation process, the confirmation signal is locked and displayed, wherein the specific method of confirmation is:

[0025] Based on the abnormal data, determine the multi-source port, and then identify the number of times C that the multi-source port generates abnormal data within a preset period;

[0026] If C>Y1, where Y1 is a preset value, a multi-source port abnormal signal is generated and displayed directly;

[0027] If C≤Y1, a data anomaly signal is generated, and the data anomaly signal and the generated abnormal data are displayed.

[0028] The present invention provides a distribution network diagram data management system. Compared with the prior art, it has the following beneficial effects:

[0029] The present invention performs relevant confirmation on the data features of different source ports, and then preferentially determines a set of standard intervals and a time line corresponding to a limited period based on the data features associated with each different source port, the associated time features of the different source ports, and the collected monomer data. Based on the specific time period division within the time line, each monomer data is relatively consistent in terms of the overall number of data, so that when each data is synchronized, the correlation in the synchronization process can be effectively guaranteed, thereby achieving a better data synchronization effect and making the synchronization data packet have a strong synchronization.

[0030] For the confirmed synchronization data packets, the formats of related monomer data of different formats in the synchronization data packets are unified, and after the format unification is completed, data verification is performed. Specific verification is performed based on the correlation characteristics between the monomer data. From the specific verification data processing process, abnormal data is locked, and based on the source port of the abnormal data, specific analysis of data verification is performed, specific abnormal signals are locked and related displays are performed, so as to achieve better data management effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of the principle framework of the present invention;

[0032] Figure 2 It is a schematic diagram of confirming the synchronization period of the present invention. DETAILED DESCRIPTION

[0033] 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.

[0034] First embodiment

[0035] See also Figure 1 , the present application provides a distribution network diagram data management system, including a source data feature acquisition terminal, a source data calibration synchronization terminal, a data format synchronization terminal and an association verification terminal, wherein the source data feature acquisition terminal, the source data calibration synchronization terminal, the data format synchronization terminal and the association verification terminal are electrically connected from an output node to an input node;

[0036] The association verification end includes a feature recognition verification unit and an association abnormality assessment unit, wherein the feature recognition verification unit and the association abnormality assessment unit are electrically connected from the output node to the input node;

[0037] The source data feature collection end identifies the multi-source ports associated in the distribution network, and then calibrates each multi-source port based on the time interval feature interval of each multi-source port, collects the data generated by each different multi-source port, and transmits the collected source data to the source data calibration synchronization end. The specific method of collection is as follows:

[0038] The data generated by each multi-source port is collected, and the collected data belonging to different multi-source ports are marked differently. When collecting, the data collected from the same multi-source port is classified based on the header and the footer of the collected data, and several classified monomer pattern data are confirmed (the monomer data can be understood as a complete data, and the complete data can be confirmed for integrity based on the corresponding header and footer, and the corresponding complete data is locked);

[0039] From the collected historical completion data, based on the completion time of the collection of the classified monomer pattern data corresponding to the multi-source port, the collection time interval of each multi-source port is identified from several groups of adjacent completion time, and then the minimum value and the maximum value are identified from several groups of collection time intervals to determine the time interval characteristic interval belonging to this multi-source port;

[0040] Specifically, it is proposed that there are three multi-source ports, namely A, B and C. In the historical collection data of source port A, there are corresponding time intervals of 5.9, 6.0, 6.1, 6.11 and 6.13. Then the minimum value associated with the collected time interval belongs to 5.9, and the maximum value associated with the collected time interval belongs to 6.13. According to the corresponding time interval, the time interval characteristic interval [5.9, 6.13] belonging to this multi-source port can be determined.

[0041] Among them, the source data calibration synchronization end performs relevant confirmation on the associated multi-source ports, and based on the time interval characteristic interval confirmed by each different multi-source port, performs synchronization confirmation on several classified monomer pattern data generated within a limited period, and locks the synchronization data packet. Specifically, after the data is generated, based on the correlation between the corresponding data, the synchronization data needs to be confirmed. However, during the confirmation process of the synchronization data, due to the difference in the corresponding time interval characteristic interval, the data will be synchronized. There is a deviation, so it is necessary to select the largest time interval characteristic interval as the relevant benchmark, and perform relevant confirmation of the synchronization data to ensure the confirmed correlation accuracy. The specific method of locking the synchronization data packet is:

[0042] Based on the calibrated associated multi-source ports, the associated multi-source ports have been marked in advance by the operator, and the time interval characteristic interval of the multi-source ports is confirmed. Then, from different time interval characteristic intervals of different associated multi-source ports, the time interval characteristic interval with the maximum interval endpoint value is selected as the standard interval. If there are multiple time interval characteristic intervals with the maximum interval endpoint value (≥2), a group of intervals is randomly selected as the standard interval;

[0043] Based on the set limited period T, where T is a preset value, the specific value of which is determined by the operator based on experience, the classification monomer pattern data generated within the limited period T is confirmed;

[0044] Prioritize generating a timeline about this limited period T, whose initial endpoint is time 0, confirm the synchronization period on this timeline based on the determined standard interval, and the time length of the synchronization period is within the standard interval (that is, the proposed time length is CD, the determined standard interval is [Q1, Q2], and its time length CD∈[Q1, Q2]), the initial time of the first group of synchronization periods is time 0, and the initial time of each subsequent group of synchronization periods is the end time of the previous group of synchronization periods;

[0045] The time length of the synchronization period in the timeline is adjusted, and the time length of the adjusted synchronization period is also within the standard range. Each different synchronization period can perform the adjustment process (so after the adjustment, the time length associated with each different synchronization period may be inconsistent). The total number of classified monomer image model data contained in the adjusted synchronization period is confirmed and marked as G k The generation time of the classified monomer image data is all within this synchronization period, where k represents different synchronization periods. k Perform variance processing and determine the variance value. When the variance value is at the minimum state, the synchronization period corresponding to the minimum state is calibrated as the standard period.

[0046] The different classification monomer image model data associated with each standard time period are confirmed, and the synchronous data packets belonging to the corresponding standard time period are bundled and confirmed.

[0047] Combination Figure 2 After the standard interval and timeline are confirmed, the synchronization period is confirmed based on the location of the corresponding standard interval. The front-end endpoint of the subsequent synchronization period is consistent with the end endpoint of the front-end synchronization period. After the first synchronization period is confirmed, the second synchronization period, the third synchronization period and the fourth synchronization period are confirmed. The confirmation process is based on the number of data corresponding to each synchronization period for associated confirmation, so as to achieve a better confirmation effect between the number of data, and according to the associated variance value, to achieve a better time period confirmation effect. Subsequently, the synchronization data can be confirmed according to the confirmed synchronization period to lock the corresponding synchronization data packet.

[0048] For example, the time interval characteristic intervals associated with the three confirmed multi-source ports A, B and C are calibrated as: A[2, 4], B[3, 5] and C[4, 7] respectively, then C[4, 7] belongs to the standard interval, and the limited period is determined to be 40 minutes. The synchronization period is confirmed according to this standard interval. The time length of the first group of synchronization periods can be changed within [4, 7]. The second group of synchronization periods is confirmed from the end time of the first group of synchronization periods, and its time length can also be changed within [4, 7]. When each synchronization period is changed, the corresponding classification monomer image data can be locked. Then, each synchronization period can confirm the specific number of the corresponding classification monomer image data, so that the variance can be confirmed based on the corresponding number, and the corresponding variance parameter can be locked. Based on the corresponding time length change process, the corresponding data number will change, so the optimal time length can be locked to confirm the synchronization data packet, so that the synchronization data packet can be fully guaranteed in terms of synchronization, and a better data synchronization effect can be achieved.

[0049] Second embodiment

[0050] In the specific implementation process of this embodiment, for the confirmed synchronization data packet, whether there is data in other formats in the synchronization data packet is confirmed, and relevant conversion is performed, and its specific execution module is the data format synchronization end;

[0051] The data format synchronization end confirms the format of the classified monomer pattern data of different formats in the confirmed synchronization data packet, unifies the format of the synchronization data packet based on the confirmed data format, and transmits the synchronization data packet after the format unification to the associated verification end. The specific method of format unification is as follows:

[0052] Check whether there are classified monomer image data of different formats in the synchronization data packet:

[0053] If it does not exist, no processing is performed, and the synchronization data packet is marked as a synchronization data packet with a unified format;

[0054] If it exists, identify the data format with the largest data capacity from the synchronization data packet, mark this data format as the format to be converted, and convert the classified monomer image model data of other formats to the format to be converted, so as to obtain the synchronization data packet after the format is unified;

[0055] Specifically, when the data format is unified, it is necessary to confirm the overall capacity of data of different formats in the synchronization data packet, and confirm the relevant format with the maximum data capacity. This format is used as the format to be converted later, and the corresponding data is controlled to be converted to the format to be converted, thereby completing the corresponding data format conversion process.

[0056] Third embodiment

[0057] In the specific implementation process of this embodiment, data verification is mainly performed on the data packets after the data format is unified. The specific execution end is the association verification end, which performs association verification on the data in the synchronization data packet;

[0058] Among them, the associated verification end, based on the different classification monomer pattern data of different multi-source ports associated in the synchronization data packet, preferentially performs the related verification of the associated features through the feature recognition verification unit, evaluates whether there is abnormal data in the synchronization data packet, and transmits the evaluated abnormal data to the associated abnormal evaluation unit, and the associated abnormal evaluation unit then confirms the port abnormality based on the evaluated abnormal data, locks the abnormal cause of such abnormal data and displays it;

[0059] The specific method by which the feature recognition and verification unit evaluates whether there is abnormal data in the synchronization data packet is as follows:

[0060] Based on the preset association features between different multi-source ports, identify whether the classification monomer image model data generated between the corresponding multi-source ports are consistent in the association features. If they are consistent, the relevant classification monomer image model data is marked as a normal data item. If they are not consistent, the corresponding classification monomer image model data is marked as an abnormal data item. The association features are marked in advance by relevant operators.

[0061] Based on the marked abnormal data items, identify whether the classified monomer pattern data within such abnormal data items exist synchronously in other normal data items. If so, mark the classified monomer pattern data as normal data; if not, mark the classified monomer pattern data as abnormal data;

[0062] Example: The preset association feature can be understood as a conversion feature, and its conversion feature is generally the corresponding conversion factor. It is assumed that the data generated by A is A1, and the data generated by B is B1. Its association feature is A×C1=B, then the generated data must satisfy A1×C1=B1, where C1 is the preset conversion factor. If it satisfies, it means that A1 and B1 belong to normal data items. If it does not satisfy, it means that A1 and B1 belong to the corresponding abnormal data items. However, not all two sets of data A1 and B1 in the abnormal data item are abnormal. There may be normal data, and further confirmation is required to determine the corresponding abnormal data.

[0063] For the determined abnormal data, the associated abnormality assessment unit identifies the multi-source ports associated with the abnormal data, and confirms the number of times the multi-source ports generate abnormal data within a preset period. Based on the confirmation process, the confirmation signal is locked and displayed, wherein the specific method of confirmation is:

[0064] Based on the abnormal data, the multi-source port is determined, and then the number of times C that the multi-source port generates abnormal data within a preset period is identified. The preset period is prepared in advance by relevant operators based on experience, and is generally 30 minutes.

[0065] If C>Y1, where Y1 is a preset value, which is prepared by the operator in advance based on experience and is generally 3, a multi-source port abnormal signal is generated and displayed directly. Based on the multi-source port abnormal signal, the external operator evaluates and adjusts the abnormality of the multi-source port to change the corresponding multi-source port to a standard state, thereby avoiding the subsequent generation of abnormal data and achieving better data processing effects;

[0066] If C≤Y1, a data anomaly signal is generated, and the data anomaly signal and the generated abnormal data are displayed for external personnel to view. The external personnel can make data corrections to such abnormal data based on the abnormal data currently viewed, so as to achieve better data management effects.

[0067] Fourth embodiment

[0068] The specific implementation process of this embodiment includes all the implementation processes of the above three groups of embodiments.

[0069] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0070] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A distribution network diagram data management system, characterized in that: include: The source data feature collection end identifies the multi-source ports associated in the distribution network, and then calibrates each multi-source port based on the time interval feature interval of each multi-source port, collects the data generated by each different multi-source port, and transmits the collected source data to the source data calibration synchronization end. The specific method is as follows: The data generated by each multi-source port is collected, and the collected data belonging to different multi-source ports are marked differently. When collecting, based on the header and the footer of the collected data, the data collected belonging to the same multi-source port are classified to confirm a plurality of classified monomer pattern data; From the collected historical completion data, based on the completion time of the collection of the classified monomer pattern data corresponding to the multi-source port, the collection time interval of each multi-source port is identified from several groups of adjacent completion time, and then the minimum value and the maximum value are identified from several groups of collection time intervals to determine the time interval characteristic interval belonging to this multi-source port; The source data calibration synchronization end confirms the associated multi-source ports, and based on the time interval characteristic interval confirmed by each different multi-source port, synchronizes and confirms several classified monomer image model data generated within a limited period, and locks the synchronization data packet. The specific method is as follows: Based on the calibrated associated multi-source ports, the associated multi-source ports have been marked in advance by the operator, the time interval characteristic interval of the multi-source ports is confirmed, and then from different time interval characteristic intervals of different associated multi-source ports, the time interval characteristic interval with the interval endpoint value in the maximum state is selected as the standard interval, and a group of intervals are randomly selected as the standard interval; Based on the set limited period T, where T is a preset value, the classified monomer pattern data generated within the limited period T is confirmed; A timeline about the limited period T is preferentially generated, the initial endpoint of which is time 0. Based on the determined standard interval, a synchronization period is confirmed on this timeline, and the time length of the synchronization period is within the standard interval. The initial time of the first group of synchronization periods is time 0, and the initial time of each subsequent group of synchronization periods is the end time of the previous group of synchronization periods. The length of the synchronization period in the timeline is adjusted, and the length of the synchronization period after adjustment is also within the standard range. The adjustment process can be performed for each different synchronization period, and the total number of classified monomer image model data contained in the adjusted synchronization period is confirmed and marked as G k The generation time of the classified monomer image data is all within this synchronization period, where k represents different synchronization periods. k Perform variance processing and determine the variance value. When the variance value is at the minimum state, the synchronization period corresponding to the minimum state is calibrated as the standard period. Confirm the different classification monomer image model data associated with each standard time period, and bundle and confirm the synchronization data packets belonging to the corresponding standard time period; The data format synchronization end confirms the format of the classified monomer pattern data of different formats in the confirmed synchronization data packet, unifies the format of the synchronization data packet based on the confirmed data format, and transmits the synchronization data packet after the format unification to the association verification end; The associated verification end includes a feature recognition verification unit and an associated abnormality assessment unit, and the associated verification end, based on the different classification monomer pattern data of different multi-source ports associated in the synchronization data packet, preferentially performs related verification of the associated features through the feature recognition verification unit, assesses whether there is abnormal data in the synchronization data packet, and transmits the assessed abnormal data to the associated abnormality assessment unit, and the associated abnormality assessment unit then performs related confirmation of the port abnormality based on the assessed abnormal data.

2. A distribution network model data management system according to claim 1, characterized in that: The specific method of the data format synchronization end to unify the format of the synchronization data packet is: Check whether there are classified monomer image data of different formats in the synchronization data packet: If it does not exist, no processing is performed, and the synchronization data packet is marked as a synchronization data packet with a unified format; If it exists, identify the data format with the largest data capacity in the synchronization data packet, mark this data format as the format to be converted, and convert the classified monomer image model data of other formats to the format to be converted to obtain the synchronization data packet after the format unification is completed.

3. A distribution network model data management system according to claim 1, characterized in that: The specific method for the feature recognition and verification unit to evaluate whether there is abnormal data in the synchronization data packet is as follows: Based on the preset association features between different multi-source ports, identify whether the classification monomer pattern data generated between the corresponding multi-source ports are consistent in the association features. If they are consistent, the relevant classification monomer pattern data are marked as normal data items. If they are not consistent, the corresponding classification monomer pattern data are marked as abnormal data items. Based on the calibrated abnormal data items, it is identified whether the classified monomer pattern data within such abnormal data items exist synchronously in other normal data items. If so, the classified monomer pattern data is calibrated as normal data.

4. A distribution network model data management system according to claim 3, characterized in that: If the classified monomer pattern data within such abnormal data items does not exist synchronously in other normal data items, the classified monomer pattern data will be marked as abnormal data.

5. A distribution network model data management system according to claim 3, characterized in that: For the determined abnormal data, the associated abnormality assessment unit identifies the multi-source ports associated with the abnormal data, and confirms the number of times the multi-source ports generate abnormal data within a preset period. Based on the confirmation process, the confirmation signal is locked and displayed, wherein the specific method of confirmation is: Based on the abnormal data, determine the multi-source port, and then identify the number of times C that the multi-source port generates abnormal data within a preset period; If C>Y1, where Y1 is a preset value, a multi-source port abnormal signal is generated and displayed directly.

6. A distribution network model data management system according to claim 5, characterized in that: If C≤Y1, a data anomaly signal is generated, and the data anomaly signal and the generated abnormal data are displayed.

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