Data-driven site power supply abnormal status analysis method and system

Abstract

The present invention belongs to the field of power supply and relates to data analysis technology, which is used to solve the problem of low accuracy of site power abnormal status analysis results in the prior art. Specifically, it is a data-driven site power abnormal status analysis method and system, including an abnormality analysis platform, which is communicatively connected to a fault statistics module, a status analysis module, a status assessment module, a diagnosis optimization module and a database; the fault statistics module is used to perform statistical analysis on site power faults: generate a statistical cycle, and obtain a comprehensive data group and a fault data group of the analysis object at the end of the statistical cycle; the present invention can perform statistical analysis on site power faults, generate a fault data group in the form of fault type-state sequence-processing sequence, the fault data group reflects the fault frequency structure of a single site power supply, and then reflects the comprehensive fault frequency structure of all site power supplies through the comprehensive data group, providing data support for the status analysis process.

Description

Technical Field

[0001] The present invention belongs to the field of power supply and relates to data analysis technology, specifically to a data-driven site power supply abnormal status analysis method and system. Background Art

[0002] Site power anomaly analysis primarily involves identifying and analyzing common power failure types, such as power outages, voltage fluctuations, and frequency anomalies. These failures can be caused by a variety of reasons, including power line failures, aging power equipment, and unstable grid loads.

[0003] Patent publication CN115061061B discloses a method and device for predicting abnormal conditions in wireless site switching power supplies. This method analyzes, predicts, and reports abnormal conditions in the switching power supply, helping operations and maintenance personnel promptly discover, verify, and resolve potential abnormal conditions. It also analyzes and categorizes abnormal condition points and related causes, assisting frontline operations and maintenance personnel in quickly troubleshooting and resolving issues. This method addresses shortcomings in the switching power supply's own alarm module and improves the security and operation and maintenance capabilities of wireless sites. However, existing technologies generally use a unified state judgment basis for fault diagnosis. However, the operating phases of power supply components in different site power supplies vary, and their fault states when a fault occurs also vary. Therefore, the accuracy of state diagnosis results in existing technologies needs to be improved. Furthermore, existing technologies cannot assess the overlap between individual and overall device operating phases based on fault state statistics, resulting in difficulty in determining equipment maintenance timing for a single site power supply and controlling equipment failure rates.

[0004] In response to the above technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of the present invention is to provide a data-driven site power abnormality status analysis method and system to solve the problem of low accuracy of site power abnormality status analysis results in the prior art;

[0006] The technical problem to be solved by the present invention is: how to provide a data-driven site power abnormality status analysis method and system that can ensure the accuracy of the site power abnormality status analysis results.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A data-driven site power supply abnormality status analysis system includes an abnormality analysis platform, which is communicatively connected to a fault statistics module, a status analysis module, a status assessment module, a diagnosis and optimization module, and a database;

[0009] The fault statistics module is used to perform statistical analysis on site power supply faults: generate a statistical period, mark the site power supply connected to the abnormality analysis platform as an analysis object, and obtain a comprehensive data group and a fault data group of the analysis object at the end of the statistical period;

[0010] The state analysis module is used to analyze the equipment state of the site power supply: all fault data groups of the analysis objects are compared with the comprehensive data group one by one and the coincidence coefficient CH of the analysis objects is obtained;

[0011] The status assessment module is used to assess the equipment operation stage of the site power supply: obtain the coincidence threshold CHmin from the database, compare the coincidence coefficient CH of the analysis object within the statistical period with the coincidence threshold CHmin, and determine whether the equipment operation stage of the analysis object meets the overall characteristics based on the comparison result;

[0012] The diagnosis and optimization module is used to perform equipment maintenance optimization analysis on the site power supply.

[0013] Furthermore, the fault data group includes several fault types, each fault type corresponds to a state sequence, and the state sequence includes several fault states. When a fault occurs in the analysis object, the fault state is marked according to the fault type, and the fault states are arranged in order of the number of markings from large to small to obtain a state sequence; each fault state corresponds to a processing sequence, and the processing sequence includes several processing measures. The processing efficiency of the processing measures after the fault state marking of the analysis object is completed is obtained, and the processing efficiency of the fault processing using the same processing measure for the same fault state is summed and averaged to obtain the efficiency performance value, and the processing measures are arranged in order of the efficiency performance value from large to small to obtain the processing sequence; at the end of the statistical period, a comprehensive data group is obtained, and the statistical method of the comprehensive data group is the same as that of the fault data group, but the statistical object of the comprehensive data group is all analysis objects.

[0014] Furthermore, the specific process of comparing the fault data group of the analysis object with the comprehensive data group includes: taking the state sequence of the comprehensive data group under the same fault type as the state reference sequence, and taking the state sequence in the fault data group as the state comparison sequence; if the state reference sequence and the fault state corresponding to the same sequence number in the state comparison sequence are the same, then the corresponding sequence number is marked as the state coincidence sequence number; otherwise, the corresponding sequence number is marked as the state deviation sequence number, and the ratio of the number of state coincidence sequence numbers to the number of elements in the state reference sequence is marked as the state coincidence value of the fault type; taking the processing sequence of the comprehensive data group under the same fault state as the processing reference sequence, and taking the processing sequence of the fault data group as the processing comparison sequence; if the processing measures corresponding to the same sequence number in the processing reference sequence and the processing comparison sequence are the same, then the corresponding sequence number is marked as the processing coincidence sequence number; otherwise, the corresponding sequence number is marked as the processing deviation sequence number; and the ratio of the number of processing coincidence sequence numbers to the number of elements in the processing reference sequence is marked as the processing coincidence value of the fault state.

[0015] Furthermore, the process of obtaining the overlap coefficient CH of the analysis object includes: calculating the average value of the state overlap values of all fault types in the fault data group and marking it as the state overlap data ZC of the analysis object, calculating the average value of the processing overlap values of all fault states in the fault data group and marking it as the processing overlap data CC of the analysis object; performing numerical calculations on the state overlap data ZC and the processing overlap data CC to obtain the overlap coefficient CH of the analysis object within the statistical period.

[0016] Furthermore, the specific process of comparing the coincidence coefficient CH of the analysis object within the statistical period with the coincidence threshold CHmin includes: if the coincidence coefficient CH is less than or equal to the coincidence threshold CHmin, it is determined that the equipment operation stage of the analysis object within the statistical period does not conform to the overall characteristics, and the fault data group is used as the fault diagnosis basis for the analysis object in the next statistical period. At the same time, an equipment maintenance signal is generated and the equipment maintenance signal is sent to the mobile phone terminal of the manager through the abnormal analysis platform. After receiving the equipment maintenance signal, the manager performs maintenance analysis on the components in the analysis object and screens out the components that need to be replaced, and marks the components that need to be replaced as processing objects. After the processing object is replaced, the comprehensive data group is used as the fault diagnosis basis for the analysis object; if the coincidence coefficient CH is greater than the coincidence threshold CHmin, it is determined that the equipment operation stage of the analysis object within the statistical period conforms to the overall characteristics, and the comprehensive data group is used as the fault diagnosis basis for the analysis object in the next statistical period.

[0017] Furthermore, the specific process of the diagnostic optimization module performing equipment maintenance optimization analysis on the site power supply includes: marking the processing object marking process within the latest L1 statistical cycles as a maintenance process, obtaining the overlap coefficient CH of the analysis object corresponding to the maintenance process, forming an overlap range by the value zero and the overlap threshold CHmin, dividing the overlap range into several overlap intervals, marking the maintenance process with the overlap coefficient CH within the overlap interval as a matching process of the overlap interval, marking the number of times the same processing object is marked in all matching processes within the overlap interval as the priority value of the processing object relative to the overlap interval, marking the L2 processing objects with the largest priority value as priority screening objects of the overlap interval, forming a priority maintenance set of the overlap interval by the priority screening objects of the overlap interval, and sending the priority maintenance sets of all overlap intervals to the database for storage.

[0018] The data-driven site power supply abnormality analysis method includes the following steps:

[0019] Step 1: Perform statistical analysis on site power failures: Generate a statistical cycle, mark the site power supply connected to the anomaly analysis platform as the analysis target, and obtain the comprehensive data set and the failure data set of the analysis target at the end of the statistical cycle;

[0020] Step 2: Analyze the equipment status of the site power supply: compare the fault data groups of all analysis objects with the comprehensive data group one by one and obtain the coincidence coefficient CH of the analysis object within the statistical period;

[0021] Step 3: Evaluate the equipment operation phase of the site power supply: Use the coincidence coefficient CH to determine whether the equipment operation phase of the analysis object during the statistical period meets the overall characteristics;

[0022] Step 4: Perform equipment maintenance optimization analysis on the site power supply: Mark the processing object marking process within the most recent L1 statistical cycles as a maintenance process, obtain the overlap coefficient CH of the analysis object corresponding to the maintenance process, and divide the overlap range into several overlap intervals, which are composed of the value zero and the overlap threshold CHmin. Mark the priority maintenance set of the overlap interval.

[0023] The present invention has the following beneficial effects:

[0024] The fault statistics module allows for statistical analysis of site power supply faults. Fault data sets are generated using the fault type-state sequence-processing sequence format. These data sets reflect the fault frequency structure of a single site's power supply. The comprehensive data set then reflects the comprehensive fault frequency structure of all site power supplies, providing data support for the status analysis process.

[0025] The status analysis module analyzes the status of site power equipment. By comparing the fault data set with the comprehensive data set, the overlap coefficient is obtained. The overlap coefficient represents the overlap between the fault frequency structure of a single site power supply and that of the entire site. This overlap coefficient is then used to assess the necessity of site power equipment maintenance.

[0026] The status assessment module can evaluate the equipment operation stage of the site power supply, determine whether the equipment operation stage of the analyzed object meets the overall characteristics, and mark the fault diagnosis basis of the analyzed object in the next statistical cycle based on the judgment results, so as to carry out timely equipment maintenance while ensuring the accuracy of fault diagnosis results.

[0027] 4. The diagnostic optimization module can be used to perform equipment maintenance optimization analysis on the site power supply. The overlap interval and priority maintenance set are generated based on the overlap coefficient during component maintenance and the processing object marking results. Therefore, during subsequent equipment maintenance, priority screening objects can be extracted based on the overlap coefficient, thereby improving equipment maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0030] Figure 2 This is a flow chart of the method of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0031] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0032] Example 1: Figure 1 As shown, the data-driven site power abnormal status analysis system includes an abnormality analysis platform, which is communicatively connected to a fault statistics module, a status analysis module, a status evaluation module, a diagnosis optimization module, and a database.

[0033] The fault statistics module is used to perform statistical analysis on site power supply faults: generate a statistical cycle, mark the site power supply connected to the abnormal analysis platform as the analysis object, and obtain the fault data group of the analysis object at the end of the statistical cycle. The fault data group includes several fault types, each fault type corresponds to a state sequence, and the state sequence includes several fault states. When a fault occurs in the analysis object, the fault state is marked according to the fault type, and the fault states are arranged in order from the most to the least number of markings to obtain a state sequence; each fault state corresponds to a processing sequence, and the processing sequence includes several processing measures; fault types generally include power outages, voltage fluctuations, overcurrent faults, overvoltage faults, and power quality abnormalities; for example, when the fault type is a power outage, its corresponding fault states generally include circuit breaker tripping, AC power outage, power phase loss, power phase mismatch, and neutral line interruption, etc. Similarly, when the fault state is circuit breaker tripping, the corresponding processing measures generally include power supply maintenance processing, circuit load maintenance processing, leakage maintenance processing, short circuit maintenance processing, etc.

[0034] Obtain the processing efficiency of the post-processing measures after the fault status marking of the analysis object is completed, sum and average the processing efficiencies of the fault processing measures for the same fault status to obtain the efficiency performance value, and arrange the processing measures in descending order of efficiency performance value to obtain a processing sequence; obtain a comprehensive data group at the end of the statistical period, and the statistical method of the comprehensive data group is the same as that of the fault data group, but the statistical object of the comprehensive data group is all analysis objects; generate a fault data group in the form of fault type-state sequence-processing sequence, and the fault data group reflects the fault frequency structure of the power supply of a single site, and then the comprehensive data group reflects the comprehensive fault frequency structure of the power supply of all sites, providing data support for the state analysis process.

[0035] The status analysis module is used to analyze the equipment status of the site power supply: all fault data groups of the analysis objects are compared with the comprehensive data group one by one: the status sequence of the comprehensive data group under the same fault type is used as the status reference sequence, and the status sequence in the fault data group is used as the status comparison sequence. If the fault status corresponding to the same sequence number in the status reference sequence is the same as that in the status comparison sequence, the corresponding sequence number is marked as the status coincidence sequence number; otherwise, the corresponding sequence number is marked as the status deviation sequence number, and the ratio of the number of state coincidence sequence numbers to the number of elements in the status reference sequence is marked as the status coincidence value of the fault type; the processing sequence of the comprehensive data group under the same fault state is used as the processing reference sequence, and the processing sequence of the fault data group is used as the processing comparison sequence. If the processing measures corresponding to the same sequence number in the processing reference sequence and the processing comparison sequence are the same, the corresponding sequence number is marked as the processing measure. otherwise, mark the corresponding number as the processing deviation number; mark the ratio of the number of processing coincidence numbers to the number of elements in the processing reference sequence as the processing coincidence value of the fault state; calculate the average state coincidence value of all fault types in the fault data group and mark it as the state coincidence data ZC of the analysis object, calculate the average value of the processing coincidence value of all fault states in the fault data group and mark it as the processing coincidence data CC of the analysis object; obtain the coincidence coefficient CH of the analysis object within the statistical period through the formula CH=k1×ZC+k2×CC, where k1 and k2 are both proportional coefficients, and k1>k2>1; compare the fault data group with the comprehensive data group to obtain the coincidence coefficient, which represents the degree of overlap of the fault frequency structure of a single site power supply relative to the overall site, and then evaluate the necessity of equipment maintenance of the site power supply through the coincidence coefficient.

[0036] The status assessment module evaluates the equipment operation phase of the site power supply. The module obtains the coincidence threshold CHmin from the database and compares the coincidence coefficient CH of the analysis object during the statistical period with the coincidence threshold CHmin. If the coincidence coefficient CH is less than or equal to the coincidence threshold CHmin, the analysis object's equipment operation phase during the statistical period is determined to be inconsistent with the overall characteristics. The fault data set is used as the basis for fault diagnosis for the analysis object in the next statistical period. Simultaneously, an equipment maintenance signal is generated and sent to the administrator's mobile phone via the anomaly analysis platform. Upon receiving the maintenance signal, the administrator performs maintenance analysis on the components in the analysis object and identifies those requiring replacement. These components are marked as replacement targets. After the replacement of the replacement targets, the comprehensive data set is used as the basis for fault diagnosis for the analysis object. If the coincidence coefficient CH is greater than the coincidence threshold CHmin, the analysis object's equipment operation phase during the statistical period is determined to be consistent with the overall characteristics. The comprehensive data set is used as the basis for fault diagnosis for the analysis object in the next statistical period. Whether the analysis object's equipment operation phase meets the overall characteristics is determined, and based on the determination result, the fault diagnosis basis for the analysis object in the next statistical period is marked. This ensures timely equipment maintenance and ensures the accuracy of fault diagnosis results.

[0037] The diagnostic optimization module is used to perform equipment maintenance optimization analysis on the site power supply: the processing object marking process within the most recent L1 statistical cycles is marked as a maintenance process, the overlap coefficient CH of the maintenance process corresponding to the analysis object is obtained, the overlap range is composed of the value zero and the overlap threshold CHmin, the overlap range is divided into several overlap intervals, and the maintenance process with the overlap coefficient CH within the overlap interval is marked as a matching process in the overlap interval. The number of times the same processing object is marked in all matching processes within the overlap interval is marked as the processing object's priority value relative to the overlap interval. The L2 processing objects with the largest priority values are marked as priority screening objects in the overlap interval. L1 and L2 are both numerical constants, and the specific values of L1 and L2 are set by the management personnel. The priority screening objects in the overlap interval form a priority maintenance set for the overlap interval, and the priority maintenance sets of all overlap intervals are sent to the database for storage. The overlap interval and priority maintenance set are generated based on the overlap coefficient and processing object marking results during component maintenance. Therefore, during subsequent equipment maintenance, priority screening objects can be extracted based on the overlap coefficient, thereby improving equipment maintenance efficiency.

[0038] Example 2: Figure 2 As shown, the data-driven site power supply abnormality status analysis method includes the following steps:

[0039] Step 1: Perform statistical analysis on site power failures: Generate a statistical cycle, mark the site power supply connected to the anomaly analysis platform as the analysis target, and obtain the comprehensive data set and the failure data set of the analysis target at the end of the statistical cycle;

[0040] Step 2: Analyze the equipment status of the site power supply: compare the fault data groups of all analysis objects with the comprehensive data group one by one and obtain the coincidence coefficient CH of the analysis object within the statistical period;

[0041] Step 3: Evaluate the equipment operation phase of the site power supply: Use the coincidence coefficient CH to determine whether the equipment operation phase of the analysis object during the statistical period meets the overall characteristics;

[0042] Step 4: Perform equipment maintenance optimization analysis on the site power supply: Mark the processing object marking process within the most recent L1 statistical cycles as a maintenance process, obtain the overlap coefficient CH of the analysis object corresponding to the maintenance process, and divide the overlap range into several overlap intervals, which are composed of the value zero and the overlap threshold CHmin. Mark the priority maintenance set of the overlap interval.

[0043] The data-driven site power supply abnormal status analysis method and system generate a statistical cycle during operation, mark the site power supply connected to the abnormal analysis platform as the analysis object, and obtain the comprehensive data group and the fault data group of the analysis object at the end of the statistical cycle; compare the fault data groups of all analysis objects with the comprehensive data group one by one and obtain the overlap coefficient CH of the analysis object within the statistical cycle; determine whether the equipment operation stage of the analysis object within the statistical cycle meets the overall characteristics through the overlap coefficient CH; mark the processing object marking process within the latest L1 statistical cycles as a maintenance process, obtain the overlap coefficient CH of the maintenance process corresponding to the analysis object, and divide the overlap range into several overlap intervals consisting of the value zero and the overlap threshold CHmin. The priority maintenance set of the overlap interval is marked.

[0044] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

[0045] The above formulas are all obtained by collecting a large amount of data and performing software simulation to obtain a formula that is close to the actual value. The coefficients in the formula are set by those skilled in the art based on actual conditions; for example, in the formula CH=k1×ZC+k2×CC, those skilled in the art collect multiple sets of sample data and set corresponding coincidence coefficients for each set of sample data; the set coincidence coefficients and the collected sample data are substituted into the formula, and any two formulas form a system of linear equations of two variables. The calculated coefficients are screened and averaged, and the values of k1 and k2 are obtained as 3.74, 2.97, and 2.65, respectively;

[0046] The size of the coefficient is to quantify each parameter to obtain a specific numerical value, which is convenient for subsequent comparison. The size of the coefficient depends on the amount of sample data and the initial setting of the corresponding overlap coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value, such as the overlap coefficient is proportional to the value of the state overlap data.

[0047] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0048] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. Data-driven site power abnormality status analysis system, characterized by: It includes an abnormality analysis platform, which is communicatively connected to a fault statistics module, a state analysis module, a state assessment module, a diagnosis optimization module and a database; The fault statistics module is used to perform statistical analysis on site power supply faults: generate a statistical period, mark the site power supply connected to the abnormality analysis platform as an analysis object, and obtain a comprehensive data group and a fault data group of the analysis object at the end of the statistical period; The state analysis module is used to analyze the equipment state of the site power supply: all fault data groups of the analysis objects are compared with the comprehensive data group one by one and the coincidence coefficient CH of the analysis objects is obtained; The status assessment module is used to assess the equipment operation stage of the site power supply: obtain the coincidence threshold CHmin from the database, compare the coincidence coefficient CH of the analysis object within the statistical period with the coincidence threshold CHmin, and determine whether the equipment operation stage of the analysis object meets the overall characteristics based on the comparison result; The diagnostic optimization module is used to perform equipment maintenance optimization analysis on the site power supply; The fault data group includes several fault types, each fault type corresponds to a state sequence, and the state sequence includes several fault states. When a fault occurs in the analysis object, the fault state is marked according to the fault type, and the fault states are arranged in order of the number of markings from large to small to obtain the state sequence; each fault state corresponds to a processing sequence, and the processing sequence includes several processing measures. The processing efficiency of the processing measures after the fault state of the analysis object is marked is obtained, and the processing efficiency of the fault processing using the same processing measure for the same fault state is summed and averaged to obtain the efficiency performance value, and the processing measures are arranged in order of the efficiency performance value from large to small to obtain the processing sequence; at the end of the statistical period, a comprehensive data group is obtained. The statistical method of the comprehensive data group is the same as that of the fault data group, but the statistical object of the comprehensive data group is all analysis objects; The specific process of comparing the fault data group of the analysis object with the comprehensive data group includes: taking the state sequence of the comprehensive data group under the same fault type as the state reference sequence, and taking the state sequence in the fault data group as the state comparison sequence; if the fault state corresponding to the same sequence number in the state reference sequence is the same as that in the state comparison sequence, the corresponding sequence number is marked as the state coincidence sequence number; otherwise, the corresponding sequence number is marked as the state deviation sequence number, and the ratio of the number of state coincidence sequence numbers to the number of elements in the state reference sequence is marked as the state coincidence value of the fault type; taking the processing sequence of the comprehensive data group under the same fault state as the processing reference sequence, and taking the processing sequence of the fault data group as the processing comparison sequence; if the processing measures corresponding to the same sequence number in the processing reference sequence and the processing comparison sequence are the same, the corresponding sequence number is marked as the processing coincidence sequence number; otherwise, the corresponding sequence number is marked as the processing deviation sequence number; and the ratio of the number of processing coincidence sequence numbers to the number of elements in the processing reference sequence is marked as the processing coincidence value of the fault state.

2. The data-driven site power supply abnormality status analysis system according to claim 1, characterized in that: The process of obtaining the coincidence coefficient CH of the analysis object includes: calculating the average value of the state coincidence values of all fault types in the fault data group and marking it as the state coincidence data ZC of the analysis object, calculating the average value of the processing coincidence values of all fault states in the fault data group and marking it as the processing coincidence data CC of the analysis object; performing numerical calculation on the state coincidence data ZC and the processing coincidence data CC to obtain the coincidence coefficient CH of the analysis object within the statistical period.

3. The data-driven site power supply abnormality status analysis system according to claim 2, characterized in that: The specific process of comparing the coincidence coefficient CH of the analysis object within the statistical period with the coincidence threshold CHmin includes: if the coincidence coefficient CH is less than or equal to the coincidence threshold CHmin, it is determined that the equipment operation stage of the analysis object within the statistical period does not conform to the overall characteristics, and the fault data group is used as the basis for fault diagnosis of the analysis object in the next statistical period. At the same time, an equipment maintenance signal is generated and the equipment maintenance signal is sent to the mobile phone terminal of the manager through the abnormal analysis platform. After receiving the equipment maintenance signal, the manager performs maintenance analysis on the components in the analysis object and screens out the components that need to be replaced, and marks the components that need to be replaced as processing objects. After the processing object is replaced, the comprehensive data group is used as the basis for fault diagnosis of the analysis object; if the coincidence coefficient CH is greater than the coincidence threshold CHmin, it is determined that the equipment operation stage of the analysis object within the statistical period conforms to the overall characteristics, and the comprehensive data group is used as the basis for fault diagnosis of the analysis object in the next statistical period.

4. The data-driven site power supply abnormality status analysis system according to claim 3, characterized in that: The specific process of the diagnostic optimization module performing equipment maintenance optimization analysis on the site power supply includes: marking the processing object marking process in the latest L1 statistical cycles as a maintenance process, obtaining the overlap coefficient CH of the analysis object corresponding to the maintenance process, forming an overlap range by the value zero and the overlap threshold CHmin, dividing the overlap range into several overlap intervals, marking the maintenance process with the overlap coefficient CH within the overlap interval as a matching process of the overlap interval, marking the number of times the same processing object is marked in all matching processes in the overlap interval as the priority value of the processing object relative to the overlap interval, marking the L2 processing objects with the largest priority values as priority screening objects of the overlap interval, forming a priority maintenance set of the overlap interval by the priority screening objects of the overlap interval, and sending all priority maintenance sets of the overlap interval to the database for storage.

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