A charging pile data intelligent monitoring method and system based on the Internet of Things

Abstract

The present invention discloses a charging pile data intelligent monitoring method and system based on the Internet of Things, which belongs to the field of dynamic monitoring technology. Based on an Internet of Things terminal, the normal operation data log and the abnormal operation data log of the equipment of the charging pile are retrieved, and the power jump time is respectively obtained; a normal and abnormal perception jump time set of the equipment operation power data is established; a normal and abnormal perception jump time difference sequence of the equipment operation power data is constructed, and is sorted into a normal and abnormal perception jump time difference vector of the equipment operation power data; based on the normal and abnormal perception jump time difference vector of the equipment operation power data, a two-dimensional jump feature matrix is constructed; based on the two-dimensional jump feature matrix, the probability of the charging pile being abnormal is calculated; a threshold is preset, and an early warning is performed, which not only improves the monitoring accuracy, but also realizes the adaptive learning and optimization of the system, and effectively promotes the practicality and intelligentization process of charging pile intelligent monitoring.

Description

Technical Field

[0001] The present invention relates to the field of dynamic monitoring technology, and in particular to a charging pile data intelligent monitoring method and system based on the Internet of Things. Background Art

[0002] With the rapid adoption of new energy vehicles, charging piles, as a core piece of infrastructure, are experiencing exponential growth in both number and frequency of use. To improve the operational efficiency and safety of charging piles, the state monitoring and data management technologies for charging equipment are constantly evolving. Currently, IoT technology is widely used in remote monitoring, state perception, and data collection for charging piles, enabling real-time feedback of the operating status of massive amounts of equipment to the operations platform. Traditional monitoring systems primarily assess the operating status of charging piles by collecting basic operating data such as current, voltage, and power. However, in the real-world scenarios of large-scale deployment and dynamic operation, static or periodic data analysis alone cannot meet the requirements for accurate and timely fault identification. This is particularly true in the early stages of abnormal conditions or transient faults, where traditional technologies lack both agility and response efficiency.

[0003] Existing charging pile monitoring methods primarily focus on macroscopic assessments of device status or crude alarms based on anomaly thresholds, lacking the ability to deeply model and intelligently identify power fluctuation characteristics. This is particularly true when charging piles experience intermittent or sudden operational anomalies. Existing technologies struggle to accurately identify abnormal trends and their evolutionary patterns, often suffering from omissions, false alarms, or delayed responses. Furthermore, most current methods fail to effectively utilize historical data structures from charging piles under normal and abnormal operating conditions for comparative modeling, and are unable to extract discriminative identification indicators from time series features, resulting in a lack of refined capabilities for anomaly prediction and early warning. Summary of the Invention

[0004] The purpose of the present invention is to provide a charging pile data intelligent monitoring method and system based on the Internet of Things to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A method for intelligent monitoring of charging pile data based on the Internet of Things, the method comprising the following steps: step S1: based on an Internet of Things terminal, retrieving the normal operation data log and the abnormal operation data log of the charging pile equipment, and obtaining the power jump time respectively; establishing a normal and abnormal perception jump time set of the equipment operation power data; step S2: performing difference calculation on each two consecutive times contained in the time sets of the normal and abnormal perception jump time sets of the equipment operation power data, and constructing a normal and abnormal perception jump time difference sequence of the equipment operation power data; step S3: arranging the normal and abnormal perception jump time difference sequence of the equipment operation power data into a normal and abnormal perception jump time difference vector of the equipment operation power data; constructing a two-dimensional jump feature matrix based on the normal and abnormal perception jump time difference vector of the equipment operation power data; step S4: calculating the probability of abnormality of the charging pile based on the two-dimensional jump feature matrix; presetting a threshold, analyzing and issuing an early warning.

[0007] As a preferred solution of the method for intelligently monitoring charging pile data based on the Internet of Things described in the present invention, based on the Internet of Things terminal, a device normal operation data log of the charging pile when it is in a historical normal working state is retrieved from the charging pile operation and management platform. Based on the device normal operation data log, the time points corresponding to the maximum and minimum values of the device operating power data of the charging pile when it is in the historical normal working state are extracted, and the time points are recorded as the power jump time of the charging pile when it is in the historical normal working state;

[0008] Obtaining a device abnormal operation data log for a charging pile in a historical abnormal operating state; extracting, based on the device abnormal operation data log, the time points corresponding to the maximum and minimum values of the device operating power data of the charging pile in the historical abnormal operating state, and recording the time points as the power jump time of the charging pile in the historical abnormal operating state;

[0009] According to the power jump time of the charging pile in the historical normal working state and the historical abnormal working state, a normal and abnormal perception jump time set of the equipment operation power data is established, and the time set is recorded in chronological order.

[0010] In the present invention, this step compresses and models the originally massive and continuous equipment operation data through the key characteristic point of "jump", identifying time nodes that are significantly related to changes in equipment status. By focusing on the jump time, a large amount of invalid or redundant data is eliminated, providing more efficient data support for subsequent modeling and identification; through this step, complex and large operating data is simplified into a set of key jump events, thereby significantly reducing the complexity of data processing, improving the monitoring model's sensitivity to abnormal conditions and recognition efficiency, and helping to improve the system's real-time early warning capabilities.

[0011] As a preferred solution of the method for intelligent monitoring of charging pile data based on the Internet of Things described in the present invention, the difference between each two consecutive times in the time sets of normal and abnormal perception jump time of the device operation power data is calculated to obtain a normal perception jump time difference sequence of the device operation power data and a abnormal perception jump time difference sequence of the device operation power data, which are respectively recorded as and ,in, The ith difference calculated between two consecutive time periods in the set of time periods for which the power data of the device is normally sensed is represented. I represents the total number of differences calculated between two consecutive time periods in the set of time periods for which the power data of the device is normally sensed. The value represents the jth difference calculated between two consecutive time periods in the device operating power data anomaly perception jump time set. J represents the total number of differences calculated between two consecutive time periods in the device operating power data anomaly perception jump time set.

[0012] As a preferred solution of the method for intelligent monitoring of charging pile data based on the Internet of Things described in the present invention, the normal perception jump time difference sequence of the equipment operation power data is respectively and the equipment operating power data abnormality perception jump time difference sequence The normal and abnormal perception jump time difference vectors of the equipment operation power data are organized as follows: and ,in, , ;

[0013] Based on the normal and abnormal perception transition time difference vectors of the equipment operating power data, a two-dimensional transition feature matrix is constructed, as follows:

[0014] ;

[0015] in, represents the two-dimensional jump feature matrix, represents the I+Jth difference, Indicates the perceptual jump type of the I+Jth difference, where if the I+Jth difference The jump time difference sequence of the normal perception of the power data of the equipment is If the I+Jth difference is within the range of The jump time difference sequence of abnormal power data perception in equipment operation If , the perception jump type of the I+Jth difference value is abnormal perception jump and is marked as 1.

[0016] In the present invention, the time differences between normal and abnormal states are organized into vectors and uniformly constructed into a two-dimensional jump feature matrix, which contains the difference size and the label of its category (normal or abnormal); as a structured data representation, the matrix not only retains the time information of the jump difference, but also introduces the category label, which is conducive to the further application of machine learning, statistical analysis and other algorithms for identification and classification processing; through this step, the transformation of unstructured time series difference data into a structured feature space is realized, which enhances the system's pattern learning ability when processing complex state changes and further improves the insight into the evolution trend of abnormal states.

[0017] As a preferred solution of the method for intelligently monitoring charging pile data based on the Internet of Things described in the present invention, the probability of abnormality of the charging pile is calculated based on the two-dimensional jump characteristic matrix, and the calculation formula is as follows:

[0018] ;

[0019] in, Indicates the jump time difference of the time point corresponding to the current operating power data of the charging pile, Indicates the probability of abnormality of the charging pile;

[0020] Preset probability threshold, if the probability of abnormality of charging pile If the probability is greater than or equal to the threshold, it is determined that the charging pile is abnormal and an early warning is issued;

[0021] The jump time difference of the time point corresponding to the current operating power data of the charging pile Update to the two-dimensional jump feature matrix for dynamic update.

[0022] In the present invention, the constructed two-dimensional jump feature matrix is used to calculate the abnormal probability corresponding to the current charging pile jump difference, and compared with the preset threshold to determine whether to trigger an early warning. At the same time, the current difference is dynamically updated to the matrix to achieve online learning and adaptive optimization. This step introduces a probabilistic modeling method, which is different from the traditional single-point anomaly detection method based on threshold judgment. It can more comprehensively evaluate the degree of closeness between the current state of the charging pile and the historical normal / abnormal mode, and realize a more flexible and intelligent early warning mechanism. By dynamically calculating the abnormal probability and updating the feature matrix in real time, not only the timeliness and accuracy of the charging pile fault detection are improved, but also the system has adaptive learning capabilities, can continuously optimize the monitoring accuracy, reduce the probability of false alarms and missed alarms, and enhance the intelligence level of the entire monitoring system.

[0023] An intelligent monitoring system for charging pile data based on the Internet of Things, comprising: a data acquisition and time set construction module, a difference sequence construction module, a vector and matrix construction module, and a probability calculation and analysis warning module;

[0024] The data acquisition and time set construction module: based on the Internet of Things terminal, retrieves the normal operation data log and abnormal operation data log of the charging pile equipment, and obtains the power jump time respectively; establishes the normal and abnormal perception jump time set of the equipment operation power data;

[0025] The difference sequence construction module is configured to calculate the difference between each of two consecutive times in the time sets of the normal and abnormal perception jump time of the device operation power data, and to construct a normal and abnormal perception jump time difference sequence of the device operation power data;

[0026] The vector and matrix construction module: organizes the normal and abnormal perception jump time difference sequence of the device operation power data into a normal and abnormal perception jump time difference vector of the device operation power data; and constructs a two-dimensional jump feature matrix based on the normal and abnormal perception jump time difference vector of the device operation power data;

[0027] The probability calculation and analysis warning module calculates the probability of abnormality of the charging pile based on the two-dimensional jump feature matrix; presets a threshold, analyzes and issues a warning.

[0028] Furthermore, the data acquisition and time set construction module includes a data acquisition unit and a time set construction unit;

[0029] The data acquisition unit: based on the Internet of Things terminal, retrieves the normal operation data log of the charging pile when the device is in a historical normal working state from the charging pile operation management platform, extracts the time points corresponding to the maximum and minimum values of the device operation power data of the charging pile when the device is in a historical normal working state according to the normal operation data log, and records the time points as the power jump time of the charging pile when the device is in a historical normal working state;

[0030] Obtaining a device abnormal operation data log for a charging pile in a historical abnormal operating state; extracting, based on the device abnormal operation data log, the time points corresponding to the maximum and minimum values of the device operating power data of the charging pile in the historical abnormal operating state, and recording the time points as the power jump time of the charging pile in the historical abnormal operating state;

[0031] The time set construction unit: establishes a normal and abnormal perception jump time set of equipment operation power data according to the power jump time of the charging pile in the historical normal working state and the historical abnormal working state, and the time set is recorded in chronological order.

[0032] Furthermore, the difference sequence construction module includes a difference sequence construction unit;

[0033] The difference sequence construction unit calculates the difference between each two consecutive times contained in the normal and abnormal perception jump time sets of the device operation power data to obtain a normal perception jump time difference sequence of the device operation power data and a abnormal perception jump time difference sequence of the device operation power data.

[0034] Furthermore, the vector and matrix construction module includes a vector construction unit and a matrix construction unit;

[0035] The vector construction unit: respectively senses the normal jump time difference sequence of the device operation power data and the equipment operating power data abnormality perception jump time difference sequence Organize the data into a vector of the time difference between normal and abnormal perception jumps of the equipment's operating power data;

[0036] The matrix construction unit is configured to construct a two-dimensional transition feature matrix based on a normal and abnormal perception transition time difference vector of the equipment operation power data.

[0037] Furthermore, the probability calculation and analysis warning module includes a probability calculation unit and an analysis warning unit;

[0038] The probability calculation unit is configured to calculate the probability of an abnormality occurring in the charging pile based on the two-dimensional jump characteristic matrix;

[0039] The analysis and early warning unit: presets a probability threshold. If the probability of an abnormality occurring in the charging pile is greater than or equal to the probability threshold, it is determined that the charging pile currently has an abnormality, and an early warning is issued; the jump time difference of the time point corresponding to the current operating power data of the charging pile is updated to the two-dimensional jump feature matrix for dynamic update.

[0040] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in the method and system for intelligent monitoring of charging pile data based on the Internet of Things provided by the present invention, the charging pile operation log is retrieved through the Internet of Things terminal, the power jump time is extracted, and a jump time set is constructed, which effectively compresses the original data scale and improves the efficiency of key state recognition. Subsequently, the difference between adjacent jump times is calculated to form a difference sequence under normal and abnormal conditions, and further explores the equipment operation stability and power fluctuation characteristics. On this basis, the difference sequence is converted into a vector, and a two-dimensional jump feature matrix containing category labels is constructed to realize the conversion of unstructured time series to structured data and enhance pattern recognition capabilities. Finally, the abnormality probability is calculated based on the feature matrix, and a dynamic update mechanism is combined to perform real-time warning, which not only improves the monitoring accuracy, but also realizes the adaptive learning and optimization of the system. The overall solution has significant advantages such as efficient data processing, strong recognition sensitivity, and timely abnormal response, which effectively promotes the practicality and intelligentization of charging pile intelligent monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0042] Figure 1 This is a schematic diagram of the steps of a method for intelligently monitoring charging pile data based on the Internet of Things of the present invention;

[0043] Figure 2 It is a structural schematic diagram of a charging pile data intelligent monitoring system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] See also Figure 1 In the first embodiment of the present invention, a method for intelligently monitoring charging pile data based on the Internet of Things is provided, and the method includes the following steps:

[0046] Step S1: Based on the IoT terminal, retrieve the normal operation data log and abnormal operation data log of the charging pile, and obtain the power jump time respectively; establish the normal and abnormal perception jump time set of the device operation power data.

[0047] Specifically, based on the IoT terminal, a device normal operation data log of the charging pile when it is in a historical normal working state is retrieved from the charging pile operation management platform. Based on the device normal operation data log, the time points corresponding to the maximum and minimum values of the device operating power data of the charging pile when it is in a historical normal working state are extracted, and the time points are recorded as the power jump time of the charging pile when it is in a historical normal working state;

[0048] Obtaining a device abnormal operation data log for a charging pile in a historical abnormal operating state; extracting, based on the device abnormal operation data log, the time points corresponding to the maximum and minimum values of the device operating power data of the charging pile in the historical abnormal operating state, and recording the time points as the power jump time of the charging pile in the historical abnormal operating state;

[0049] According to the power jump time of the charging pile in the historical normal working state and the historical abnormal working state, a normal and abnormal perception jump time set of the equipment operation power data is established, and the time set is recorded in chronological order.

[0050] Step S2: Calculate the difference between each two consecutive times in the time sets of the normal and abnormal perception jump time of the device operation power data, and construct a normal and abnormal perception jump time difference sequence of the device operation power data.

[0051] Specifically, the difference between each two consecutive times in the time sets of the normal and abnormal perception jump time of the device operation power data is calculated to obtain the normal perception jump time difference sequence of the device operation power data and the abnormal perception jump time difference sequence of the device operation power data, which are respectively recorded as and ,in, The ith difference calculated between two consecutive time periods in the set of time periods for which the power data of the device is normally sensed is represented. I represents the total number of differences calculated between two consecutive time periods in the set of time periods for which the power data of the device is normally sensed. The value represents the jth difference calculated between two consecutive time periods in the device operating power data anomaly perception jump time set. J represents the total number of differences calculated between two consecutive time periods in the device operating power data anomaly perception jump time set.

[0052] Step S3: Arrange the normal and abnormal perception jump time difference sequence of the device operation power data into a normal and abnormal perception jump time difference vector of the device operation power data; and construct a two-dimensional jump feature matrix based on the normal and abnormal perception jump time difference vector of the device operation power data.

[0053] Specifically, the normal perception jump time difference sequence of the equipment operation power data is respectively and the equipment operating power data abnormality perception jump time difference sequence The normal and abnormal perception jump time difference vectors of the equipment operation power data are organized as follows: and ,in, , ;

[0054] Based on the normal and abnormal perception transition time difference vectors of the equipment operating power data, a two-dimensional transition feature matrix is constructed, as follows:

[0055] ;

[0056] in, represents the two-dimensional jump feature matrix, represents the I+Jth difference, Indicates the perceptual jump type of the I+Jth difference, where if the I+Jth difference The jump time difference sequence of the normal perception of the power data of the equipment is If the I+Jth difference is within the range of The jump time difference sequence of abnormal power data perception in equipment operation If , the perception jump type of the I+Jth difference value is abnormal perception jump and is marked as 1.

[0057] Step S4: Based on the two-dimensional jump characteristic matrix, calculate the probability of abnormality of the charging pile; preset a threshold, analyze and issue an early warning.

[0058] Specifically, based on the two-dimensional jump characteristic matrix, the probability of the charging pile being abnormal is calculated, and the calculation formula is as follows:

[0059] ;

[0060] in, Indicates the jump time difference of the time point corresponding to the current operating power data of the charging pile, Indicates the probability of abnormality of the charging pile;

[0061] It should be noted that this formula uses the distance metric method to calculate the current difference The average absolute distance from the historical normal difference (TN) and abnormal difference (TAN) is used to evaluate the abnormal probability by the ratio of the two; if Closer to the TN sequence (smaller molecule), If it approaches 0, it is considered normal; if Closer to the TAN sequence, If it approaches 1, it is considered abnormal. This formula can quantify the degree of deviation between the current state and the normal / abnormal mode, avoiding the "one-size-fits-all" defect of the traditional threshold method. Adding a matrix) enables online learning of the model. For example, after a charging pile has been running for a long time, it can automatically adapt to the new normal / abnormal mode to reduce false alarms.

[0062] Preset probability threshold, if the probability of abnormality of charging pile If the probability is greater than or equal to the threshold, it is determined that the charging pile is abnormal and an early warning is issued;

[0063] The jump time difference of the time point corresponding to the current operating power data of the charging pile Update to the two-dimensional jump feature matrix for dynamic update.

[0064] See also Figure 2In the second embodiment of the present invention, a charging pile data intelligent monitoring system based on the Internet of Things is provided, which includes: a data acquisition and time set construction module, a difference sequence construction module, a vector and matrix construction module, and a probability calculation and analysis warning module;

[0065] The data acquisition and time set construction module: based on the Internet of Things terminal, retrieves the normal operation data log and abnormal operation data log of the charging pile equipment, and obtains the power jump time respectively; establishes the normal and abnormal perception jump time set of the equipment operation power data;

[0066] The difference sequence construction module is configured to calculate the difference between each of two consecutive times in the time sets of the normal and abnormal perception jump time of the device operation power data, and to construct a normal and abnormal perception jump time difference sequence of the device operation power data;

[0067] The vector and matrix construction module: organizes the normal and abnormal perception jump time difference sequence of the device operation power data into a normal and abnormal perception jump time difference vector of the device operation power data; and constructs a two-dimensional jump feature matrix based on the normal and abnormal perception jump time difference vector of the device operation power data;

[0068] The probability calculation and analysis warning module calculates the probability of abnormality of the charging pile based on the two-dimensional jump feature matrix; presets a threshold, analyzes and issues a warning.

[0069] Furthermore, the data acquisition and time set construction module includes a data acquisition unit and a time set construction unit;

[0070] The data acquisition unit: based on the Internet of Things terminal, retrieves the normal operation data log of the charging pile when the device is in a historical normal working state from the charging pile operation management platform, extracts the time points corresponding to the maximum and minimum values of the device operation power data of the charging pile when the device is in a historical normal working state according to the normal operation data log, and records the time points as the power jump time of the charging pile when the device is in a historical normal working state;

[0071] Obtaining a device abnormal operation data log for a charging pile in a historical abnormal operating state; extracting, based on the device abnormal operation data log, the time points corresponding to the maximum and minimum values of the device operating power data of the charging pile in the historical abnormal operating state, and recording the time points as the power jump time of the charging pile in the historical abnormal operating state;

[0072] The time set construction unit: establishes a normal and abnormal perception jump time set of equipment operation power data according to the power jump time of the charging pile in the historical normal working state and the historical abnormal working state, and the time set is recorded in chronological order.

[0073] Furthermore, the difference sequence construction module includes a difference sequence construction unit;

[0074] The difference sequence construction unit calculates the difference between each two consecutive times contained in the normal and abnormal perception jump time sets of the device operation power data to obtain a normal perception jump time difference sequence of the device operation power data and a abnormal perception jump time difference sequence of the device operation power data.

[0075] Furthermore, the vector and matrix construction module includes a vector construction unit and a matrix construction unit;

[0076] The vector construction unit: respectively senses the normal jump time difference sequence of the device operation power data and the equipment operating power data abnormality perception jump time difference sequence Organize the data into a vector of the time difference between normal and abnormal perception jumps of the equipment's operating power data;

[0077] The matrix construction unit is configured to construct a two-dimensional transition feature matrix based on a normal and abnormal perception transition time difference vector of the equipment operation power data.

[0078] Furthermore, the probability calculation and analysis warning module includes a probability calculation unit and an analysis warning unit;

[0079] The probability calculation unit is configured to calculate the probability of an abnormality occurring in the charging pile based on the two-dimensional jump characteristic matrix;

[0080] The analysis and early warning unit: presets a probability threshold. If the probability of an abnormality occurring in the charging pile is greater than or equal to the probability threshold, it is determined that the charging pile currently has an abnormality, and an early warning is issued; the jump time difference of the time point corresponding to the current operating power data of the charging pile is updated to the two-dimensional jump feature matrix for dynamic update.

[0081] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0082] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A charging pile data intelligent monitoring method based on the Internet of Things, characterized in that: The method comprises the following steps: Step S1: Based on the IoT terminal, retrieve the normal operation data log and abnormal operation data log of the charging pile device, and obtain the power jump time respectively; establish the normal and abnormal perception jump time set of the device operation power data; Step S2: Calculating the difference between each two consecutive times in the time sets of the normal and abnormal sensing jump time of the device operation power data, and constructing a normal and abnormal sensing jump time difference sequence of the device operation power data; Step S3: Arranging the normal and abnormal perception transition time difference sequence of the device operation power data into a normal and abnormal perception transition time difference vector of the device operation power data; and constructing a two-dimensional transition feature matrix based on the normal and abnormal perception transition time difference vector of the device operation power data; Step S4: Calculate the probability of abnormality of the charging pile based on the two-dimensional jump characteristic matrix; preset a threshold, analyze and issue an early warning; The specific implementation process of step S2 includes: The difference between each two consecutive times in the time sets of the normal and abnormal perception jump time of the equipment operation power data is calculated to obtain the normal perception jump time difference sequence of the equipment operation power data and the abnormal perception jump time difference sequence of the equipment operation power data, which are respectively recorded as TN={TN i |i∈[1,I]} and TAN={TAN j |j∈[1,J]}, where TN i It represents the i-th difference value calculated for each two consecutive time periods in the time set of the normal perception jump time of the device operation power data. I represents the total number of differences calculated for each two consecutive time periods in the time set of the normal perception jump time of the device operation power data. TAN j The jth difference value calculated between two consecutive time periods in the device operating power data anomaly detection jump time set. J represents the total number of differences calculated between two consecutive time periods in the device operating power data anomaly detection jump time set. The specific implementation process of step S3 includes: The normal perception jump time difference sequence TN of the equipment operation power data and the abnormal perception jump time difference sequence TAN of the equipment operation power data are respectively sorted into the normal and abnormal perception jump time difference vectors of the equipment operation power data, which are recorded as and in, Based on the normal and abnormal perception transition time difference vectors of the equipment operating power data, a two-dimensional transition feature matrix is constructed, as follows: Where M represents the two-dimensional jump characteristic matrix, Δt I+J Indicates the I+Jth difference, s I+J Indicates the perceptual jump type of the I+Jth difference, where if the I+Jth difference Δt I+J In the normal perception jump time difference sequence TN of the equipment operation power data, the perception jump type of the I+Jth difference is a normal perception jump and is marked as 0. If the I+Jth difference Δt I+J In the abnormality perception jump time difference sequence TAN of the equipment operating power data, the perception jump type of the I+Jth difference is abnormality perception jump and is marked as 1; The specific implementation process of step S4 includes: Based on the two-dimensional jump characteristic matrix, the probability of the charging pile being abnormal is calculated, and the calculation formula is as follows: Where, Δt new Indicates the jump time difference of the time point corresponding to the current operating power data of the charging pile, P abn Indicates the probability of abnormality of the charging pile; The preset probability threshold is: if the probability of abnormality of the charging pile is P abn If the probability is greater than or equal to the threshold, it is determined that the charging pile is abnormal and an early warning is issued; The jump time difference Δt of the time point corresponding to the current operating power data of the charging pile new Update to the two-dimensional jump feature matrix for dynamic update.

2. The method for intelligent monitoring of charging pile data based on the Internet of Things according to claim 1, characterized in that: The specific implementation process of step S1 includes: Based on the IoT terminal, retrieve the normal operation data log of the charging pile device when it is in a historical normal working state from the charging pile operation management platform. According to the normal operation data log, extract the time points corresponding to the maximum and minimum values of the device operating power data of the charging pile when it is in a historical normal working state, and record the time points as the power jump time of the charging pile when it is in a historical normal working state; Obtaining a device abnormal operation data log for a charging pile in a historical abnormal operating state; extracting, based on the device abnormal operation data log, the time points corresponding to the maximum and minimum values of the device operating power data of the charging pile in the historical abnormal operating state, and recording the time points as the power jump time of the charging pile in the historical abnormal operating state; According to the power jump time of the charging pile in the historical normal working state and the historical abnormal working state, a normal and abnormal perception jump time set of the equipment operation power data is established, and the time set is recorded in chronological order.

3. An intelligent monitoring system for charging pile data based on the Internet of Things, which executes an intelligent monitoring method for charging pile data based on the Internet of Things as described in any one of claims 1-2, characterized in that: The system includes: a data acquisition and time set construction module, a difference sequence construction module, a vector and matrix construction module and a probability calculation and analysis warning module; The data acquisition and time set construction module: based on the Internet of Things terminal, retrieves the normal operation data log and abnormal operation data log of the charging pile equipment, and obtains the power jump time respectively; establishes the normal and abnormal perception jump time set of the equipment operation power data; The difference sequence construction module is configured to calculate the difference between each of two consecutive times in the time sets of the normal and abnormal sensing jump time of the device operation power data, and to construct a normal and abnormal sensing jump time difference sequence of the device operation power data; The vector and matrix construction module: organizes the normal and abnormal perception jump time difference sequence of the device operation power data into a normal and abnormal perception jump time difference vector of the device operation power data; and constructs a two-dimensional jump feature matrix based on the normal and abnormal perception jump time difference vector of the device operation power data; The probability calculation and analysis warning module calculates the probability of abnormality of the charging pile based on the two-dimensional jump feature matrix; presets a threshold, analyzes and issues a warning.

4. The charging pile data intelligent monitoring system based on the Internet of Things according to claim 3 is characterized by: The data acquisition and time set construction module includes a data acquisition unit and a time set construction unit; The data acquisition unit: based on the Internet of Things terminal, retrieves the normal operation data log of the charging pile when the device is in a historical normal working state from the charging pile operation management platform, extracts the time points corresponding to the maximum and minimum values of the device operation power data of the charging pile when the device is in a historical normal working state according to the normal operation data log, and records the time points as the power jump time of the charging pile when the device is in a historical normal working state; Obtaining a device abnormal operation data log for a charging pile in a historical abnormal operating state; extracting, based on the device abnormal operation data log, the time points corresponding to the maximum and minimum values of the device operating power data of the charging pile in the historical abnormal operating state, and recording the time points as the power jump time of the charging pile in the historical abnormal operating state; The time set construction unit: establishes a normal and abnormal perception jump time set of equipment operation power data according to the power jump time of the charging pile in the historical normal working state and the historical abnormal working state, and the time set is recorded in chronological order.

5. The charging pile data intelligent monitoring system based on the Internet of Things according to claim 4 is characterized in that: The difference sequence construction module includes a difference sequence construction unit; The difference sequence construction unit calculates the difference between each two consecutive times contained in the normal and abnormal perception jump time sets of the device operation power data to obtain a normal perception jump time difference sequence of the device operation power data and a abnormal perception jump time difference sequence of the device operation power data.

6. The charging pile data intelligent monitoring system based on the Internet of Things according to claim 5 is characterized by: The vector and matrix building module includes a vector building unit and a matrix building unit; The vector construction unit is configured to respectively organize the normal perception jump time difference sequence TN of the device operation power data and the abnormal perception jump time difference sequence TAN of the device operation power data into normal and abnormal perception jump time difference vectors of the device operation power data; The matrix construction unit is configured to construct a two-dimensional transition feature matrix based on a normal and abnormal perception transition time difference vector of the equipment operation power data.

7. The charging pile data intelligent monitoring system based on the Internet of Things according to claim 6 is characterized by: The probability calculation and analysis warning module includes a probability calculation unit and an analysis warning unit; The probability calculation unit is configured to calculate the probability of an abnormality occurring in the charging pile based on the two-dimensional jump characteristic matrix; The analysis and early warning unit: presets a probability threshold. If the probability of an abnormality occurring in the charging pile is greater than or equal to the probability threshold, it is determined that the charging pile currently has an abnormality, and an early warning is issued; the jump time difference of the time point corresponding to the current operating power data of the charging pile is updated to the two-dimensional jump feature matrix for dynamic update.

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