Intelligent detection system and method applied to power adapter

Abstract

The invention discloses an intelligent detection system and method applied to a power adapter, and relates to the technical field of equipment intelligent detection, and the method comprises the steps: analyzing the equipment abnormity degree of the power adapter in a historical equipment detection record; obtaining an abnormal historical equipment detection record of the power adapter, obtaining feature operation data of the power adapter, and evaluating equipment operation parameters in the feature operation data and the equipment matching degree between the power adapter; acquiring a historical charging record and a historical charging priority setting record of the power adapter, and constructing a charging priority evaluation model of the power adapter based on the historical charging priority setting record; and charging the electronic equipment by using the power adapter, performing priority evaluation on the electronic equipment by using the charging priority evaluation model, performing equipment abnormal risk detection on the power adapter in combination with the operation data of the target equipment, and adjusting the charging strategy of the power adapter.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent equipment detection, and in particular to an intelligent detection system and method applied to a power adapter. Background Art

[0002] A power adapter is a device used to convert alternating current into direct current. It is mainly used to convert power into a specific voltage and current suitable for electronic devices to provide the required power for the electronic devices. Different electronic devices have very different requirements for the current and voltage of the power supply. The voltage and current of the power adapter must match the charged electronic device. Therefore, usually, a power adapter of one model only has a good charging effect on one or several specific models of electronic devices. It is impossible to intelligently detect the running power adapter according to the actual situation of the electronic device and the power adapter, so as to adjust the power supply of the power adapter to several connected electronic devices. This causes users to need to purchase power adapters of different models, resulting in a waste of resources, or users use incompatible power chargers to charge electronic devices, causing damage to the electronic devices and the power charger. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent detection system and method for a power adapter to solve the problems in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solution: an intelligent detection method for a power adapter, the method comprising: Step S100: Obtain historical device detection records of the power adapter, analyze the device abnormality degree of the power adapter in the historical device detection records, and obtain abnormal historical device detection records; Step S200: Obtain the abnormal historical device detection record of the power adapter, obtain the characteristic operation data of the power adapter, evaluate the device operation parameters in the characteristic operation data, and the device compatibility between the power adapter and the power adapter, and obtain the target device operation data; Step S300: Acquire historical charging records and historical charging priority setting records of the power adapter, and construct a charging priority evaluation model for the power adapter based on the historical charging priority setting records; Step S400: Use a power adapter to charge the electronic device, and use a charging priority evaluation model to perform priority evaluation on the electronic device, combine the target device operation data, perform device abnormality risk detection on the power adapter, and adjust the charging strategy of the power adapter.

[0005] Furthermore, step S100 includes: Step S101: acquiring each historical device detection record of the power adapter, and acquiring data corresponding to the output voltage of the power adapter from the historical device detection record; Step S102: Analyze the device abnormality degree of the power adapter in the historical device detection record, wherein the specific analysis process includes: Obtain an average value of the output voltage of the power adapter from the historical device detection records, and obtain a mean value μ of the average values ​​of the output voltage of the power adapter in each historical device detection record; Step S103: Calculate the characteristic dispersion value B of the power adapter: , Where j is the total number of historical device detection records; B i is the average value of the output voltage of the power adapter in the i-th historical device detection record; Step S104: Obtain a characteristic operating range C∈[μ-k×B,μ+k×B] of the output voltage of the power adapter, where k is a preset characteristic range coefficient. When the average value of the output voltage of the power adapter in the historical device detection record is not within the characteristic operating range C, it is determined that the power adapter in the historical device detection record is abnormal, and the historical device detection record is recorded as an abnormal historical device detection record of the power adapter.

[0006] Further, step S200 includes: Step S201: Acquire each abnormal historical device detection record of the power adapter, and acquire the average value of each device operating parameter in the power adapter from the abnormal historical device detection record; Step S202: Acquire characteristic operation data of the power adapter, wherein the characteristic operation data includes preset thresholds of various device operation parameters in the power adapter; Step S203: Acquire characteristic thresholds of various device operating parameters in the power adapter, wherein the characteristic threshold acquisition process of the fth device operating parameter in the power adapter is as follows: From the characteristic operation data, obtain the preset threshold value Q of the fth device operation parameter of the power adapter f , calculate the characteristic value of the f-th equipment operation parameter in each abnormal historical equipment detection record, where the characteristic value G of the f-th equipment operation parameter in the α-th abnormal historical equipment detection record is f α =Q f / |D f α -Q f |, where D f αis the average value of the fth equipment operating parameter in the αth abnormal historical equipment detection record; Obtain the maximum value of the characteristic value of the f-th device operation parameter in each abnormal historical device detection record, obtain the average value of the f-th device operation parameter in the abnormal historical device detection record corresponding to the maximum value, and record it as the characteristic threshold of the f-th device operation parameter; Step S204: Evaluate the compatibility of each device operation parameter in the characteristic operation data with the power adapter, wherein the specific process of evaluating the compatibility of the hth device operation parameter in the characteristic operation data with the power adapter is as follows: Get the characteristic threshold U of the hth device operating parameter in the power adapter h , calculate the hth item of equipment operation parameters in the characteristic operation data, and the equipment compatibility degree V of the power adapter h =Q h / |Q h -U h |, where Q h is the preset threshold value of the hth equipment operation parameter in the characteristic operation data; Step S205: When the device compatibility level V h is less than or equal to the preset device compatibility threshold, and determines that the hth device operation parameter in the characteristic operation data does not match the power adapter, and increases the characteristic threshold U h , recorded as the target threshold of the h-th equipment operating parameter; When the device compatibility level V h If the value is greater than the device compatibility threshold, the hth device operation parameter in the characteristic operation data is determined to be compatible with the power adapter. h , recorded as the target threshold of the h-th equipment operating parameter; Step S206: Obtain target thresholds of various device operation parameters of the power adapter, and aggregate them to obtain target device operation data of the power adapter.

[0007] Furthermore, step S300 includes: Step S301: obtaining each historical charging record of the power adapter, obtaining the historical charging priority setting record of the power adapter, extracting the priority score set by the user for the historical electronic device from the historical charging priority setting record, obtaining the historical time period to which the historical charging priority setting record belongs, and recording the historical time period as the characteristic historical time period of the power adapter; Step S302: obtaining a historical charging record of the power adapter within a characteristic historical period, and recording it as a characteristic historical charging record of the power adapter; obtaining a historical electronic device that the user has prioritized from the characteristic historical charging record, and recording it as a characteristic historical electronic device in the characteristic historical charging record; Step S303: extracting charging data of the characteristic history electronic device from the characteristic history charging record, where the charging data includes data corresponding to various characteristic charging indicators of the characteristic history electronic device; Step S304: obtaining several characteristic historical charging records of the power adapter and constructing a charging priority evaluation model for the power adapter. The specific construction process includes: Obtaining average values ​​of various characteristic charging indicators in a plurality of characteristic historical electronic devices in characteristic historical charging records; The average value of each characteristic charging index of the characteristic historical electronic device in the characteristic historical charging record is used as the input data of the charging priority evaluation model, and the priority score of the characteristic historical electronic device is used as the target output data of the charging priority evaluation model; Step S305: Based on a preset ratio, several characteristic historical charging records are divided to obtain a model training set and a model test set. The model training set is used to train the charging priority evaluation model to obtain a characteristic activation function f(x) of the charging priority evaluation model: , Get the preset weight matrix W and bias p of the charging priority evaluation model, and get the output S of the mth layer of the charging priority evaluation model (m) : , Among them, W (m) is the weight matrix of the mth layer of the charging priority evaluation model; S (m-1) is the output of the m-1th layer of the charging priority evaluation model; p (m) Bias for the mth layer of the charge-first evaluation model; Get the loss function L of the charging priority evaluation model: , Where n is the total number of training samples in the model training set; y i is the true value of the priority score of the i-th training sample in the model training set; y' i is the predicted value of the priority score of the i-th training sample in the training set; Get the gradient δ of the bias of the mth layer of the charge priority evaluation model （m） =∂L / ∂p (m) , calculate the gradient of the weight matrix of the mth layer of the charging priority evaluation model. The specific formula is: , Among them, S (m-1) The output of the m-1th layer of the charging priority evaluation model; Based on the gradient of the weight matrix and the bias, the model training set is used to update the weight matrix and the bias, and the model test set is used to test and verify the charging priority evaluation model to obtain the verified charging priority evaluation model; In the above steps, because in real life, when the power adapter is charging various electronic devices, the risk of equipment failure of the power adapter is likely to be caused by too many electronic devices being charged. As long as the charging strategy of the electronic device is adjusted, equipment failure of the power adapter can be avoided. By establishing a charging priority evaluation model, it is possible to accurately evaluate which electronic devices' charging power can be adjusted, which not only ensures the normal operation of the power adapter, but also minimizes the impact of the charging adjustment of the electronic device on the user.

[0008] Furthermore, step S400 includes: Step S401: using a charging priority evaluation model, evaluating the priorities of a power adapter and a plurality of electronic devices being charged in a current cycle, and obtaining priority scores of the plurality of electronic devices; Step S402: Monitor the device operation status of the power adapter, and perform device abnormality risk detection on the power adapter in combination with the target device operation data, and analyze the device abnormality degree of the power adapter. The specific intelligent detection process is as follows: When any device operating parameter in the power adapter is greater than the target threshold of any device operating parameter, it is determined that the power adapter has a device abnormality risk; Step S403: Based on the priority scores of the several electronic devices charged by the power adapter in the current cycle, the charging powers of the several electronic devices are adjusted in turn according to the scores until there are no device operating parameters in the power adapter that are greater than the target threshold, and the charging strategy of the power adapter is adjusted.

[0009] In order to better implement the above method, an intelligent detection system for a power adapter is also proposed, the system includes an abnormality analysis module, a device fit evaluation module, an evaluation model building module, and an intelligent detection module; An abnormality analysis module is used to analyze the abnormality degree of the power adapter in the historical device detection record to obtain the abnormal historical device detection record; The device compatibility evaluation module is used to evaluate the device operation parameters in the characteristic operation data and the device compatibility between the power adapter and the target device operation data; An evaluation model building module, used to build a charging priority evaluation model for a power adapter; The intelligent detection module is used to perform intelligent detection on the power adapter, analyze the degree of device abnormality of the power adapter, and adjust the charging strategy of the power adapter.

[0010] Further, the abnormality analysis module includes a characteristic operation range unit and an abnormality analysis unit; A characteristic operating range unit, used to obtain a characteristic operating range of an output voltage of a power adapter; The abnormality analysis unit is used to analyze the device abnormality degree of the power adapter in the historical device detection record according to the characteristic operation range, and obtain the abnormal historical device detection record.

[0011] Further, the device fit assessment module includes a feature threshold unit and a device fit assessment unit; A characteristic threshold unit is used to obtain characteristic thresholds of various equipment operating parameters in the power adapter; The device compatibility evaluation unit is used to evaluate the device operation parameters in the characteristic operation data and the degree of device compatibility between the power adapter and the target device operation data according to the characteristic threshold value.

[0012] Furthermore, the evaluation model construction module includes a data acquisition unit and an evaluation model construction unit; A data acquisition unit, used to acquire historical charging records and historical charging priority setting records of the power adapter; The evaluation model building unit is used to build a charging priority evaluation model for the power adapter.

[0013] Further, the intelligent detection module includes an intelligent detection unit; The intelligent detection unit is used to monitor the device operating status of the power adapter in the current cycle, and combine the target device operating data to perform intelligent device detection on the power adapter, analyze the degree of device abnormality of the power adapter, and adjust the charging strategy of the power adapter.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention realizes intelligent detection of the power adapter, by analyzing the device abnormality degree of the power adapter in the historical device detection records, obtaining the abnormal historical device detection records with device abnormality, obtaining the target device operation data of the power adapter, and establishing a charging priority evaluation model, not only the device failure of the power adapter can be accurately and timely detected, but also the charging strategy of the power adapter can be adjusted by the priority of the obtained electronic equipment, so that the normal use of the power adapter can be guaranteed, and the adjustment of the power adapter strategy can minimize the impact on the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a method flow chart of an intelligent detection method applied to a power adapter of the present invention; Figure 2 The present invention is a module schematic diagram of an intelligent detection system applied to a power adapter. DETAILED DESCRIPTION

[0016] 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.

[0017] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution, an intelligent detection method applied to a power adapter, the method comprising: Step S100: Obtain historical device detection records of the power adapter, analyze the device abnormality degree of the power adapter in the historical device detection records, and obtain abnormal historical device detection records; Wherein, step S100 includes: Step S101: acquiring each historical device detection record of the power adapter, and acquiring data corresponding to the output voltage of the power adapter from the historical device detection record; Step S102: Analyze the device abnormality degree of the power adapter in the historical device detection record, wherein the specific analysis process includes: Obtain an average value of the output voltage of the power adapter from the historical device detection records, and obtain a mean value μ of the average values ​​of the output voltage of the power adapter in each historical device detection record; Step S103: Calculate the characteristic dispersion value B of the power adapter: , Where j is the total number of historical device detection records; B i is the average value of the output voltage of the power adapter in the i-th historical device detection record; For example, j is 5; B 1 =10; B 2 is 11; B 3 is 13; B 4 is 12; B 5 is 15; calculate the characteristic dispersion value B of the power adapter: , Step S104: obtaining a characteristic operating range C∈[μ-k×B,μ+k×B] of the output voltage of the power adapter, where k is a preset characteristic range coefficient. When the average value of the output voltage of the power adapter in the historical device detection record is not within the characteristic operating range C, it is determined that the power adapter in the historical device detection record is abnormal, and the historical device detection record is recorded as an abnormal historical device detection record of the power adapter. Step S200: Obtain the abnormal historical device detection record of the power adapter, obtain the characteristic operation data of the power adapter, evaluate the device operation parameters in the characteristic operation data, and the device compatibility between the power adapter and the power adapter, and obtain the target device operation data; Wherein, step S200 includes: Step S201: Acquire each abnormal historical device detection record of the power adapter, and acquire the average value of each device operating parameter in the power adapter from the abnormal historical device detection record; For example, various equipment operating parameters include equipment temperature, equipment voltage, etc.; Step S202: Acquire characteristic operation data of the power adapter, wherein the characteristic operation data includes preset thresholds of various device operation parameters in the power adapter; Step S203: Acquire characteristic thresholds of various device operating parameters in the power adapter, wherein the characteristic threshold acquisition process of the fth device operating parameter in the power adapter is as follows: From the characteristic operation data, obtain the preset threshold value Q of the fth device operation parameter of the power adapter f , calculate the characteristic value of the f-th equipment operation parameter in each abnormal historical equipment detection record, where the characteristic value G of the f-th equipment operation parameter in the α-th abnormal historical equipment detection record is f α =Q f / |D f α -Q f |, where D f α is the average value of the fth equipment operating parameter in the αth abnormal historical equipment detection record; Obtain the maximum value of the characteristic value of the f-th device operation parameter in each abnormal historical device detection record, obtain the average value of the f-th device operation parameter in the abnormal historical device detection record corresponding to the maximum value, and record it as the characteristic threshold of the f-th device operation parameter; Step S204: Evaluate the compatibility of each device operation parameter in the characteristic operation data with the power adapter, wherein the specific process of evaluating the compatibility of the hth device operation parameter in the characteristic operation data with the power adapter is as follows: Get the characteristic threshold U of the hth device operating parameter in the power adapter h , calculate the hth item of equipment operation parameters in the characteristic operation data, and the equipment compatibility degree V of the power adapter h =Q h / |Q h -U h |, where Q h is the preset threshold value of the hth equipment operation parameter in the characteristic operation data; Step S205: When the device compatibility level V h is less than or equal to the preset device compatibility threshold, and determines that the hth device operation parameter in the characteristic operation data does not match the power adapter, and increases the characteristic threshold U h , recorded as the target threshold of the h-th equipment operating parameter; When the device compatibility level V h If the value is greater than the device compatibility threshold, the hth device operation parameter in the characteristic operation data is determined to be compatible with the power adapter. h , recorded as the target threshold of the h-th equipment operating parameter; Step S206: Obtain target thresholds of various device operation parameters of the power adapter, and collect them to obtain target device operation data of the power adapter; Step S300: Acquire historical charging records and historical charging priority setting records of the power adapter, and construct a charging priority evaluation model for the power adapter based on the historical charging priority setting records; Wherein, step S300 includes: Step S301: obtaining each historical charging record of the power adapter, obtaining the historical charging priority setting record of the power adapter, extracting the priority score set by the user for the historical electronic device from the historical charging priority setting record, obtaining the historical time period to which the historical charging priority setting record belongs, and recording the historical time period as the characteristic historical time period of the power adapter; Step S302: obtaining a historical charging record of the power adapter within a characteristic historical period, and recording it as a characteristic historical charging record of the power adapter; obtaining a historical electronic device that the user has prioritized from the characteristic historical charging record, and recording it as a characteristic historical electronic device in the characteristic historical charging record; Step S303: extracting charging data of the characteristic history electronic device from the characteristic history charging record, where the charging data includes data corresponding to various characteristic charging indicators of the characteristic history electronic device; For example, various characteristic charging indicators include the remaining power of characteristic historical electronic devices, the total battery power, etc.; Step S304: obtaining several characteristic historical charging records of the power adapter and constructing a charging priority evaluation model for the power adapter. The specific construction process includes: Obtaining average values ​​of various characteristic charging indicators in a plurality of characteristic historical electronic devices in characteristic historical charging records; The average value of each characteristic charging index of the characteristic historical electronic device in the characteristic historical charging record is used as the input data of the charging priority evaluation model, and the priority score of the characteristic historical electronic device is used as the target output data of the charging priority evaluation model; Step S305: Based on a preset ratio, several characteristic historical charging records are divided to obtain a model training set and a model test set. The model training set is used to train the charging priority evaluation model to obtain a characteristic activation function f(x) of the charging priority evaluation model: , Get the preset weight matrix W and bias p of the charging priority evaluation model, and get the output S of the mth layer of the charging priority evaluation model (m) : , Among them, W (m) is the weight matrix of the mth layer of the charging priority evaluation model; S (m-1) is the output of the m-1th layer of the charging priority evaluation model; p (m) Bias for the mth layer of the charge-first evaluation model; Get the loss function L of the charging priority evaluation model: , Where n is the total number of training samples in the model training set; y i is the true value of the priority score of the i-th training sample in the model training set; y' i is the predicted value of the priority score of the i-th training sample in the training set; Get the gradient δ of the bias of the mth layer of the charge priority evaluation model （m） =∂L / ∂p (m) , calculate the gradient of the weight matrix of the mth layer of the charging priority evaluation model. The specific formula is: , Among them, S (m-1) The output of the m-1th layer of the charging priority evaluation model; Based on the gradient of the weight matrix and the bias, the model training set is used to update the weight matrix and the bias, and the model test set is used to test and verify the charging priority evaluation model to obtain the verified charging priority evaluation model; Step S400: Use a power adapter to charge the electronic device, and use a charging priority evaluation model to perform priority evaluation on the electronic device, combine the target device operation data, perform device abnormality risk detection on the power adapter, and adjust the charging strategy of the power adapter.

[0018] Wherein, step S400 includes: Step S401: using a charging priority evaluation model, evaluating the priorities of a power adapter and a plurality of electronic devices being charged in a current cycle, and obtaining priority scores of the plurality of electronic devices; Step S402: Monitor the device operation status of the power adapter, and perform device abnormality risk detection on the power adapter in combination with the target device operation data, and analyze the device abnormality degree of the power adapter. The specific intelligent detection process is as follows: When any device operating parameter in the power adapter is greater than the target threshold of any device operating parameter, it is determined that the power adapter has a device abnormality risk; Step S403: According to the priority scores of the plurality of electronic devices charged by the power adapter in the current cycle, the charging powers of the plurality of electronic devices are adjusted in sequence according to the scores, until there is no device operating parameter in the power adapter that is greater than the target threshold, and the charging strategy of the power adapter is adjusted; In order to better implement the above method, an intelligent detection system for a power adapter is also proposed, the system includes an abnormality analysis module, a device fit evaluation module, an evaluation model building module, and an intelligent detection module; An abnormality analysis module is used to analyze the abnormality degree of the power adapter in the historical device detection record to obtain the abnormal historical device detection record; The device compatibility evaluation module is used to evaluate the device operation parameters in the characteristic operation data and the device compatibility between the power adapter and the target device operation data; An evaluation model building module, used to build a charging priority evaluation model for a power adapter; An intelligent detection module is used to perform intelligent detection on the power adapter, analyze the abnormality of the power adapter, and adjust the charging strategy of the power adapter; Among them, the abnormal analysis module includes a characteristic operation range unit and an abnormal analysis unit; A characteristic operating range unit, used to obtain a characteristic operating range of an output voltage of a power adapter; An abnormality analysis unit, used to analyze the device abnormality degree of the power adapter in the historical device detection record according to the characteristic operation range, and obtain the abnormal historical device detection record; Among them, the device fit assessment module includes a feature threshold unit and a device fit assessment unit; A characteristic threshold unit is used to obtain characteristic thresholds of various equipment operating parameters in the power adapter; The device compatibility evaluation unit is used to evaluate the device operation parameters in the characteristic operation data and the degree of device compatibility between the power adapter and the target device operation data according to the characteristic threshold value; Among them, the evaluation model construction module includes a data acquisition unit and an evaluation model construction unit; A data acquisition unit, used to acquire historical charging records and historical charging priority setting records of the power adapter; An evaluation model building unit, used to build a charging priority evaluation model for a power adapter; Wherein, the intelligent detection module includes an intelligent detection unit; The intelligent detection unit is used to monitor the device operating status of the power adapter in the current cycle, and combine the target device operating data to perform intelligent device detection on the power adapter, analyze the degree of device abnormality of the power adapter, and adjust the charging strategy of the power adapter.

[0019] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An intelligent detection method for a power adapter, characterized in that: The method comprises: Step S100: acquiring a historical device detection record of a power adapter, analyzing the device abnormality degree of the power adapter in the historical device detection record, and obtaining an abnormal historical device detection record; Step S200: Obtain the abnormal historical device detection record of the power adapter, obtain the characteristic operation data of the power adapter, evaluate the device operation parameters in the characteristic operation data and the device compatibility between the power adapter and the power adapter, and obtain the target device operation data; Step S300: Acquire historical charging records and historical charging priority setting records of the power adapter, and construct a charging priority evaluation model for the power adapter based on the historical charging priority setting records; Step S400: Use the power adapter to charge the electronic device, and use the charging priority evaluation model to perform priority evaluation on the electronic device, combine the target device operation data to perform device abnormality risk detection on the power adapter, and adjust the charging strategy of the power adapter.

2. The intelligent detection method for a power adapter according to claim 1, characterized in that: The step S100 includes: Step S101: acquiring each historical device detection record of the power adapter, and acquiring data corresponding to the output voltage of the power adapter from the historical device detection records; Step S102: analyzing the device abnormality degree of the power adapter in the historical device detection record, wherein the specific analysis process includes: Obtaining an average value of the output voltage of the power adapter from the historical device detection records, and obtaining a mean value μ of the average values ​​of the output voltage of the power adapter in each of the historical device detection records; Step S103: Calculate the characteristic dispersion value B of the power adapter: ， Wherein, j is the total number of the historical device detection records; B i is the average value of the output voltage of the power adapter in the i-th historical device detection record; Step S104: Obtain a characteristic operating range C∈[μ-k×B,μ+k×B] of the output voltage of the power adapter, where k is a preset characteristic range coefficient. When the average value of the output voltage of the power adapter in the historical device detection record is not within the characteristic operating range C, it is determined that the power adapter in the historical device detection record is abnormal, and the historical device detection record is recorded as an abnormal historical device detection record of the power adapter.

3. The intelligent detection method for a power adapter according to claim 2, characterized in that: The step S200 includes: Step S201: Acquire each abnormal historical device detection record of the power adapter, and acquire the average value of each device operating parameter in the power adapter from the abnormal historical device detection record; Step S202: Acquire characteristic operation data of the power adapter, wherein the characteristic operation data includes preset thresholds of various device operation parameters in the power adapter; Step S203: Acquire characteristic thresholds of various equipment operating parameters in the power adapter, wherein the characteristic threshold acquisition process of the fth equipment operating parameter in the power adapter is as follows: From the characteristic operation data, obtain the preset threshold value Q of the fth device operation parameter of the power adapter f , calculate the characteristic value of the f-th equipment operation parameter in each abnormal historical equipment detection record, wherein the characteristic value G of the f-th equipment operation parameter in the α-th abnormal historical equipment detection record is f α =Q f / |D f α -Q f |, where D f α is the average value of the f-th equipment operating parameter in the α-th abnormal historical equipment detection record; Obtaining the maximum value of the characteristic value of the f-th device operating parameter in each abnormal historical device detection record, obtaining the average value of the f-th device operating parameter in the abnormal historical device detection record corresponding to the maximum value, and recording it as the characteristic threshold value of the f-th device operating parameter; Step S204: Evaluate the compatibility of each device operation parameter in the characteristic operation data with the power adapter, wherein the specific process of evaluating the compatibility of the hth device operation parameter in the characteristic operation data with the power adapter is as follows: Obtain the characteristic threshold value U of the hth device operating parameter in the power adapter h , calculate the hth item of equipment operation parameter in the characteristic operation data and the equipment compatibility degree V of the power adapter h =Q h / |Q h -U h |, where Q h is a preset threshold value of the h-th device operation parameter in the characteristic operation data; Step S205: When the device compatibility level V h is less than or equal to a preset device compatibility threshold, it is determined that the h-th device operation parameter in the characteristic operation data does not match the power adapter, and the characteristic threshold U h , recorded as the target threshold of the h-th equipment operating parameter; When the device meets the degree V h is greater than the device compatibility threshold, determining that the hth device operation parameter in the characteristic operation data is compatible with the power adapter, and setting the Q h , recorded as the target threshold of the h-th equipment operating parameter; Step S206: Obtain target thresholds of various device operation parameters of the power adapter, and aggregate them to obtain target device operation data of the power adapter.

4. The intelligent detection method for a power adapter according to claim 3, characterized in that: The step S300 includes: Step S301: obtaining each historical charging record of the power adapter, obtaining the historical charging priority setting record of the power adapter, extracting the priority score set by the user for the historical electronic device from the historical charging priority setting record, obtaining the historical time period to which the historical charging priority setting record belongs, and recording the historical time period as the characteristic historical time period of the power adapter; Step S302: obtaining a historical charging record of the power adapter within a characteristic historical period, and recording it as a characteristic historical charging record of the power adapter; obtaining a historical electronic device that the user has prioritized from the characteristic historical charging record, and recording it as a characteristic historical electronic device in the characteristic historical charging record; Step S303: extracting charging data of the characteristic history electronic device from the characteristic history charging record, wherein the charging data includes data corresponding to various characteristic charging indicators of the characteristic history electronic device; Step S304: obtaining several characteristic historical charging records of the power adapter, and constructing a charging priority evaluation model for the power adapter. The specific construction process includes: Obtaining average values ​​of various characteristic charging indicators in a plurality of characteristic historical electronic devices in characteristic historical charging records; The average value of each characteristic charging index of the characteristic historical electronic device in the characteristic historical charging record is used as input data of the charging priority evaluation model, and the priority score of the characteristic historical electronic device is used as target output data of the charging priority evaluation model; Step S305: Based on a preset ratio, the plurality of characteristic historical charging records are divided to obtain a model training set and a model test set. The charging priority evaluation model is trained using the model training set to obtain a characteristic activation function f(x) of the charging priority evaluation model: ， Obtain the preset weight matrix W and bias p of the charging priority evaluation model, and obtain the output S of the mth layer of the charging priority evaluation model (m) : ， Among them, W (m) is the weight matrix of the mth layer of the charging priority evaluation model; S (m-1) is the output of the m-1th layer of the charging priority evaluation model; p (m) A bias for an mth layer of the charge priority evaluation model; Obtain the loss function L of the charging priority evaluation model: ， Where n is the total number of training samples in the model training set; y i is the true value of the priority score of the i-th training sample in the model training set; y' i is the predicted value of the priority score of the i-th training sample in the training set; Get the gradient δ of the bias of the mth layer of the charge priority evaluation model （m） =∂L / ∂p (m) , calculate the gradient of the weight matrix of the mth layer of the charging priority evaluation model, the specific formula is: ， Among them, S (m-1) is the output of the m-1th layer of the charging priority evaluation model; Based on the gradients of the weight matrix and the bias, the weight matrix and the bias are updated using the model training set, and the charging priority evaluation model is tested and verified using the model test set to obtain a verified charging priority evaluation model.

5. The intelligent detection method for a power adapter according to claim 4, characterized in that: The step S400 includes: Step S401: using the charging priority evaluation model, evaluating the priorities of the power adapter and a plurality of electronic devices being charged in a current cycle, and obtaining priority scores of the plurality of electronic devices; Step S402: Monitor the device operation status of the power adapter, and perform device abnormality risk detection on the power adapter in combination with the target device operation data, and analyze the device abnormality degree of the power adapter. The specific intelligent detection process is as follows: When any one of the equipment operation parameters in the power adapter is greater than the target threshold of any one of the equipment operation parameters, it is determined that the power adapter has an equipment abnormality risk; Step S403: According to the priority scores of the several electronic devices charged by the power adapter in the current cycle, the charging powers of the several electronic devices are adjusted in turn according to the scores until there are no device operating parameters in the power adapter that are greater than the target threshold, and the charging strategy of the power adapter is adjusted.

6. An intelligent detection system for a power adapter, used to execute an intelligent detection method for a power adapter as claimed in any one of claims 1 to 5, characterized in that: The system includes an abnormality analysis module, an equipment fit assessment module, an assessment model building module, and an intelligent detection module; The abnormality analysis module is used to analyze the device abnormality degree of the power adapter in the historical device detection record to obtain the abnormal historical device detection record; The device compatibility evaluation module is used to evaluate the device operation parameters in the characteristic operation data and the device compatibility between the power adapter and obtain the target device operation data; The evaluation model building module is used to build a charging priority evaluation model for the power adapter; The intelligent detection module is used to perform intelligent detection on the power adapter, analyze the degree of device abnormality of the power adapter, and adjust the charging strategy of the power adapter.

7. The intelligent detection system for a power adapter according to claim 6, characterized in that: The abnormality analysis module includes a characteristic operation range unit and an abnormality analysis unit; The characteristic operating range unit is used to obtain the characteristic operating range of the output voltage of the power adapter; The abnormality analysis unit is used to analyze the device abnormality degree of the power adapter in the historical device detection record according to the characteristic operation range, and obtain the abnormal historical device detection record.

8. The intelligent detection system for a power adapter according to claim 6, characterized in that: The device fit assessment module includes a feature threshold unit and a device fit assessment unit; The characteristic threshold unit is used to obtain characteristic thresholds of various device operating parameters in the power adapter; The device compatibility evaluation unit is used to evaluate the device operation parameters in the characteristic operation data and the degree of device compatibility with the power adapter according to the characteristic threshold value to obtain target device operation data.

9. The intelligent detection system for a power adapter according to claim 6, characterized in that: The evaluation model construction module includes a data acquisition unit and an evaluation model construction unit; The data acquisition unit is used to acquire the historical charging records and historical charging priority setting records of the power adapter; The evaluation model building unit is used to build a charging priority evaluation model for the power adapter.

10. The intelligent detection system for power adapter according to claim 6, characterized in that: The intelligent detection module includes an intelligent detection unit; The intelligent detection unit is used to monitor the device operating status of the power adapter in the current cycle, and in combination with the target device operating data, perform device intelligent detection on the power adapter, analyze the degree of device abnormality of the power adapter, and adjust the charging strategy of the power adapter.

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