Intelligent test equipment data verification management system and method based on internet of things

Abstract

The application discloses an intelligent test equipment data checking management system and method based on an internet of things, relates to the technical field of equipment test management, and classifies power protection equipment according to the functions of the power protection equipment, establishes a unified detection report template for power protection equipment of different manufacturers and realizing the same function, matches test methods in a test method library, takes any one test item to be tested in a target equipment as a target item, divides electrical parameter values between the current electrical parameters of the target item and the judgment standards of abnormal conditions into a plurality of incremental intervals, adjusts test signals input into the target equipment, judges whether the state of the target equipment is normal according to test results, collects test results of the target equipment, fills the test results in the detection report template according to a corresponding relationship of test items, obtains a detection report, and saves the corresponding relationship.

Description

Technical Field

[0001] This invention relates to the field of equipment testing management technology, specifically to an intelligent testing equipment data verification management system and method based on the Internet of Things. Background Technology

[0002] Power protection equipment promptly disconnects circuits when power system faults occur, protecting equipment from damage. The correct operation of this equipment directly impacts the safe operation of the power system. Currently, power protection equipment inspections rely primarily on traditional, manual methods. However, with the increasing sophistication of condition-based maintenance for primary equipment and shorter downtime, traditional inspection methods have become a major obstacle to improving intelligent operation and maintenance, achieving unmanned or minimally staffed operation, and reducing manpower while increasing efficiency.

[0003] In addition, within the same power plant, power protection equipment with the same function may be purchased from different manufacturers. The test items for power equipment from different manufacturers have different test reports, which makes it difficult to manage the test data across the entire power plant and causes inconvenience to the relevant business departments responsible for data management. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent testing equipment data verification management system and method based on the Internet of Things, so as to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a data verification and management method for intelligent testing equipment based on the Internet of Things (IoT):

[0006] Step S100: Obtain the equipment number of each power protection device in the power system substation, obtain the historical test records of all power protection devices, conduct tests on the power protection devices, and compile test procedures for different types of power protection devices to establish a test method library;

[0007] Step S200: Classify the power protection equipment according to its function to obtain several first category sets of power protection equipment. Collect the historical records of test reports of power protection equipment in each first category set and establish a unified test report template for each first category set.

[0008] Step S300: Set any power protection device to be tested in the power system substation as the target device, obtain the device number of the target device, and match the test method of the target device in the test method library;

[0009] Step S400: Mark the test items in the test template of the target device according to the frequency of abnormal situations in the test items in the target device;

[0010] Step S500: Take any test item to be tested in the target device as the target item, divide the electrical parameter values ​​between the current electrical parameters of the target item and the judgment criteria for abnormal conditions into several incremental intervals, adjust the test signal input to the target device, and judge whether the status of the target device is normal based on the test results;

[0011] Step S600: Collect the test results of the target equipment, fill in the test results in the test report template according to the corresponding relationship of the test items, obtain the test report, save the corresponding relationship, collect it into the corresponding relationship database, and feed the test report back to the relevant management personnel.

[0012] Furthermore, step S100 includes:

[0013] Step S101: Obtain the equipment model and historical test records of each power protection device. Each equipment model corresponds to a type of power protection device. Obtain the test process of each power protection device from the historical test records of the power protection devices. The test process contains several test items. The test items in the test process of the same type of power protection device are arranged into a test item sequence according to the order in which the tests are carried out.

[0014] Step S102: Obtain the test port and test target corresponding to each test item, obtain the signal strength of several test signals input to the test port and the feedback signal corresponding to each test signal, the signal strength of the test signal is increased from the initial value to the test target several times, and by detecting the feedback signal, determine whether there is an abnormality in the circuit protection device;

[0015] Step S103: Obtain the test objectives and judgment criteria for each test item, where the test objective represents the maximum intensity of the input test signal and the judgment criteria represent the abnormal situation judgment thresholds corresponding to each test item;

[0016] To ensure the safety of power protection equipment, this solution adopts a method of gradually increasing the strength of the test signal. Once the feedback signal reaches the threshold, the power protection equipment is flagged as malfunctioning. This method protects electrical equipment in the power system from sudden accidents caused by excessively strong test signals during testing, which could pose a threat to safe production. On the other hand, this method improves testing accuracy. If a fault exists in the power protection equipment, relevant personnel can further determine the fault status of the power protection based on the abnormal state under different feedback strength conditions.

[0017] By progressively increasing the strength of the test signal, the work experience of relevant personnel is transformed into automated logic that can be iteratively applied, thereby achieving unattended automated testing.

[0018] Furthermore, step S200 includes:

[0019] Step S201: Obtain the test report of each recorded test result for each type of power protection device, and obtain the test items in each test report;

[0020] Step S202: The i-th first category set contains power protection equipment manufactured by k manufacturers, where the set of test item names corresponding to the power protection equipment manufactured by the j-th manufacturer is r. j Take the union R of all item names in the i-th first category set. i R i =r1∪r2∪r3∪……∪r k Among them, r1, r2, r3, ... and r k Let each represent a set consisting of the test item names corresponding to the power protection equipment produced by the 1st, 2nd, 3rd, ..., and kth manufacturers in the i-th first category set;

[0021] Step S203: For R i Each test item is numbered to obtain the test template number for the test item, and then applied to R. i The test items in the dataset are aggregated to obtain the detection report template for the i-th first category set;

[0022] Power protection equipment is categorized according to its functions. Test items for all power protection equipment in the same functional category are compiled to maximize the protection of power protection equipment in the same functional category. This serves as the basis for establishing test report templates for the same functional category. Each test item in the test report template is fixed in position by number, providing a basis for the corresponding data entry position in subsequent steps.

[0023] Furthermore, step S400 includes:

[0024] Step S401: Obtain the historical records of test reports within the time range T0 of the target device, and obtain the number of times abnormal situations were recorded for each test item in the test report. The number of abnormal situation records for the x-th test item is denoted as h. x Calculate the anomaly occurrence rate α of the x-th test item. x α x =h x / T0;

[0025] Step S402: Record the ongoing test for the target device as the current test. q The start time of the current test is denoted as t. q The previous test for the target device is recorded as test. q-1 test q-1 The end time is denoted as t. q Calculate the time difference T between two adjacent tests. dif T dif =t q -t q-1 ;

[0026] Step S403: Calculate the predicted value f of the abnormal situation corresponding to the x-th test item. x f x =α x ×T dif Set an abnormal situation frequency management threshold w, and mark all test items whose abnormal situation prediction value is greater than or equal to w;

[0027] Test items that are likely to experience abnormalities over a certain period of time are marked and reported to relevant managers for focused review of the test results, thereby improving the efficiency of staff inspections.

[0028] Furthermore, step S500 includes:

[0029] Step S501: Obtain historical detection records where the target item's test results are normal for y tests. In the historical detection records where the u-th result is normal, the number of times the test signal strength increases from its initial value to the test target is check. u Next, calculate the average number of segments, sub.

[0030] Step S502: Obtain the current value of the electrical parameter in the corresponding test port of the target project, denoted as par. a Let par be the test objective corresponding to the target project. b Calculate the initial intensity increment p0, p0 = (par b -par a ) / sub;

[0031] Step S503: Input the first test signal into the test port corresponding to the target item. The strength value of the first test signal is p1, p1 = par a +η1, where η1 represents the intensity increment of the first test signal, η1 = p0, and the first feedback signal corresponding to the first test signal is obtained, the value of the intensity of the first feedback signal is fb1;

[0032] Step S504: Input the (e+1)th test signal into the test port corresponding to the target item, wherein the strength value of the (e+1)th test signal is p. e+1 p e+1 =p e +η e+1 η e+1 Let fb represent the intensity increment of the (e+1)th test signal. Obtain the (e+1)th feedback signal corresponding to the (e+1)th test signal. The intensity value of the (e+1)th feedback signal is fb. e+1 ;

[0033] Step S505: Obtain the anomaly judgment threshold b corresponding to the target project. When (b-fb) e+1 ) / (b-fb e When )≤1, calculate the increment value η of the (e+2)th test signal. e+2 , The strength value of the signal in the (e+2)th test is p. e+2 p e+2 =p e+1 +η e+2 , among which, fb e The numerical value representing the strength of the e-th feedback signal;

[0034] When (b-fb) e+1 ) / (b-fb e When )>1, the increment value η of the (e+1)th test signal e+2 =η e+1 , where η e+1 This represents the increment value of the (e+1)th test signal;

[0035] When (b-fb) e+1 ) / (b-fb e When )≤1, it means that the value of the feedback signal strength of the (e+1)th time is closer to the abnormal situation judgment threshold than the value of the feedback signal strength of the eth time. In order to obtain the test signal of the target device when an abnormality is judged more accurately, it is necessary to reduce the increment of the next test signal.

[0036] The increment of the test signal is related to the change in the strength of the feedback signal. When the strength of the subsequent feedback signal is closer to the threshold for judging abnormal conditions than the previous one, the increment of the test signal will be smaller, making the test more accurate.

[0037] Furthermore, step S600 includes:

[0038] Step S601: When the strength of the test signal is equal to the test target corresponding to the target item, the target item is marked as having passed the test; when the strength of the test signal is less than the test target corresponding to the target item, and the strength of the feedback signal is greater than or equal to the abnormal situation judgment threshold corresponding to the target item, the target item is marked as having failed the test.

[0039] Step S602: Collect the test results of each test item of the target device to obtain the test result sequence of the target device, and label each test item in the test result sequence. Each test item corresponds to a result number.

[0040] Step S603: Obtain the test template number and the result number in the test result sequence for the same test item name in the test report template, establish the correspondence between the test template number and the result number, and collect the correspondence into the correspondence database;

[0041] Based on the testing sequence of the same equipment, the order of test items is fixed, and the correspondence between test template number and result number is stored so that they can be quickly matched in subsequent inspections, thereby improving the efficiency of issuing test reports.

[0042] To better implement the above methods, an intelligent test equipment data verification management system based on the Internet of Things is also proposed. The system includes:

[0043] The system includes a test method library management module, a test report template management module, a target device selection module, a test item marking module, a test management module, and an information feedback module. The test method library management module manages the test method library, the test report template management module manages the test report templates, the target device selection module selects the target device, the test item marking module marks the relevant test items, the test management module tests the target device, and the information feedback module provides feedback on the relevant test results to the administrators.

[0044] Furthermore, the test report template management module includes: a project name management unit, a project aggregation unit, and a test template number management unit. The project name management unit is used to obtain the project name of each test project, the project aggregation unit is used to aggregate the project names of the test projects, and the test template number management unit is used to manage the test template number in the test template.

[0045] Furthermore, the test item marking module includes: an occurrence rate calculation unit, a time difference calculation unit, an anomaly prediction value calculation unit, and a judgment marking unit. The occurrence rate calculation unit is used to calculate the occurrence rate of anomalies in the test items, the time difference calculation unit is used to calculate the time difference between two tests, the anomaly prediction value calculation unit is used to calculate the anomaly prediction value, and the judgment marking unit is used to mark all test items whose anomaly prediction value is greater than or equal to the threshold.

[0046] Furthermore, the test management module includes: an initial increment calculation unit, a feedback detection unit, an increment judgment unit, and a test signal adjustment unit. The initial increment calculation unit is used to calculate the initial intensity increment, the feedback detection unit is used to detect the intensity of the feedback signal, the increment judgment unit is used to calculate the intensity increment of the next test signal, and the test signal adjustment unit is used to calculate the numerical value of the intensity of the test signal.

[0047] Furthermore, the information feedback module includes: a result feedback unit, a number correspondence unit, and a correspondence database management unit. The result feedback unit is used to provide feedback on the test results, the number correspondence unit is used to record the correspondence between the test template number and the result number, and the correspondence database management unit is used to manage the correspondence database.

[0048] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: The present invention replaces the manual monitoring method of power protection equipment detection by cyclically accumulating the strength of the test signal, improves the detection accuracy when abnormal situations are reported, and collects the detection results of power protection equipment with the same function to obtain the same detection template, thereby improving the efficiency of digital processing of detection results. Attached Figure Description

[0049] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0050] Figure 1 This is a schematic diagram of the data verification and management system for intelligent testing equipment based on the Internet of Things, which is the subject of this invention patent.

[0051] Figure 2 This is a flowchart illustrating the data verification and management method for intelligent testing equipment based on the Internet of Things, as per this invention patent. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Please see Figure 1 and Figure 2 The present invention provides the following technical solution:

[0054] Step S100: Obtain the equipment number of each power protection device in the power system substation, obtain the historical test records of all power protection devices, conduct tests on the power protection devices, and compile test procedures for different types of power protection devices to establish a test method library;

[0055] Step S100 includes:

[0056] Step S101: Obtain the equipment model and historical test records of each power protection device. Each equipment model corresponds to a type of power protection device. Obtain the test process of each power protection device from the historical test records of the power protection devices. The test process contains several test items. The test items in the test process of the same type of power protection device are arranged into a test item sequence according to the order in which the tests are carried out.

[0057] Step S102: Obtain the test port and test target corresponding to each test item, obtain the signal strength of several test signals input to the test port and the feedback signal corresponding to each test signal, the signal strength of the test signal is increased from the initial value to the test target several times, and by detecting the feedback signal, determine whether there is an abnormality in the circuit protection device;

[0058] Step S103: Obtain the test objectives and judgment criteria for each test item, where the test objective represents the maximum intensity of the input test signal and the judgment criteria represent the abnormal situation judgment thresholds corresponding to each test item;

[0059] Step S200: Classify the power protection equipment according to its function to obtain several first category sets of power protection equipment. Collect the historical records of test reports of power protection equipment in each first category set and establish a unified test report template for each first category set.

[0060] Step S200 includes:

[0061] Step S201: Obtain the test report of each recorded test result for each type of power protection device, and obtain the test items in each test report;

[0062] Step S202: The i-th first category set contains power protection equipment manufactured by k manufacturers, where the set of test item names corresponding to the power protection equipment manufactured by the j-th manufacturer is r. j Take the union R of all item names in the i-th first category set. i R i =r1∪r2∪r3∪……∪r k Among them, r1, r2, r3, ... and r k Let each represent a set consisting of the test item names corresponding to the power protection equipment produced by the 1st, 2nd, 3rd, ..., and kth manufacturers in the i-th first category set;

[0063] Step S203: For R i Each test item is numbered to obtain the test template number for the test item, and then applied to R. i The test items in the dataset are aggregated to obtain the detection report template for the i-th first category set;

[0064] Step S300: Set any power protection device to be tested in the power system substation as the target device, obtain the device number of the target device, and match the test method of the target device in the test method library;

[0065] Step S400: Mark the test items in the test template of the target device according to the frequency of abnormal situations in the test items in the target device;

[0066] Step S400 includes:

[0067] Step S401: Obtain the historical records of test reports within the time range T0 of the target device, and obtain the number of times abnormal situations were recorded for each test item in the test report. The number of abnormal situation records for the x-th test item is denoted as h. x Calculate the anomaly occurrence rate α of the x-th test item. x α x =h x / T0;

[0068] Step S402: Record the ongoing test for the target device as the current test. q The start time of the current test is denoted as t. q The previous test for the target device is recorded as test. q-1 test q-1 The end time is denoted as t.q Calculate the time difference T between two adjacent tests. dif T dif =t q -t q-1 ;

[0069] Step S403: Calculate the predicted value f of the abnormal situation corresponding to the x-th test item. x f x =α x ×T dif Set an abnormal situation frequency management threshold w, and mark all test items whose abnormal situation prediction value is greater than or equal to w;

[0070] In the 6τ time period, where τ represents a unit of time, the number of times the anomaly occurs in the x-th test item is recorded is 3, and α x =0.5;

[0071] When T dif When f = 10τ, x =2;

[0072] Step S500: Take any test item to be tested in the target device as the target item, divide the electrical parameter values ​​between the current electrical parameters of the target item and the judgment criteria for abnormal conditions into several incremental intervals, adjust the test signal input to the target device, and judge whether the status of the target device is normal based on the test results;

[0073] Step S500 includes:

[0074] Step S501: Obtain historical detection records where the target item's test results are normal for y tests. In the historical detection records where the u-th result is normal, the number of times the test signal strength increases from its initial value to the test target is check. u Next, calculate the average number of segments, sub.

[0075] Step S502: Obtain the current value of the electrical parameter in the corresponding test port of the target project, denoted as par. a Let par be the test objective corresponding to the target project. b Calculate the initial intensity increment p0, p0 = (par b -par a ) / sub;

[0076] Step S503: Input the first test signal into the test port corresponding to the target item. The strength value of the first test signal is p1, p1 = par a+η1, where η1 represents the intensity increment of the first test signal, η1 = p0, and the first feedback signal corresponding to the first test signal is obtained, the value of the intensity of the first feedback signal is fb1;

[0077] Step S504: Input the (e+1)th test signal into the test port corresponding to the target item, wherein the strength value of the (e+1)th test signal is p. e+1 p e+1 =p e +η e+1 η e+1 Let fb represent the intensity increment of the (e+1)th test signal. Obtain the (e+1)th feedback signal corresponding to the (e+1)th test signal. The intensity value of the (e+1)th feedback signal is fb. e+1 ;

[0078] Step S505: Obtain the anomaly judgment threshold b corresponding to the target project. When (b-fb) e+1 ) / (b-fb e When )≤1, calculate the increment value η of the (e+2)th test signal. e+2 , The strength value of the signal in the (e+2)th test is p. e+2 p e+2 =p e+1 +η e+2 , among which, fb e The numerical value representing the strength of the e-th feedback signal;

[0079] When (b-fb) e+1 ) / (b-fb e When )>1, the increment value η of the (e+1)th test signal e+2 =η e+1 , where η e+1 This represents the increment value of the (e+1)th test signal;

[0080] In one embodiment, b = 3, fb e+1 =2.4, fb e =2.2, (b-fb) e+1 ) / (b-fb e ) = 0.75 ≤ 1;

[0081] In another embodiment, b = 3, fb e+1 =6,fb e =7, (b-fb) e+1 ) / (b-fb e ) = 0.75 ≤ 1;

[0082] Step S600: Collect the test results of the target equipment, fill in the test results in the test report template according to the corresponding relationship of the test items, obtain the test report, save the corresponding relationship, collect it into the corresponding relationship database, and feed the test report back to the relevant management personnel;

[0083] Step S600 includes:

[0084] Step S601: When the strength of the test signal is equal to the test target corresponding to the target item, the target item is marked as having passed the test; when the strength of the test signal is less than the test target corresponding to the target item, and the strength of the feedback signal is greater than or equal to the abnormal situation judgment threshold corresponding to the target item, the target item is marked as having failed the test.

[0085] Step S602: Collect the test results of each test item of the target device to obtain the test result sequence of the target device, and label each test item in the test result sequence. Each test item corresponds to a result number.

[0086] Step S603: Obtain the test template number and the result number in the test result sequence for the same test item name in the test report template, establish the correspondence between the test template number and the result number, and collect the correspondence into the correspondence database;

[0087] For example, for test item 1, the test template number in the test report template is code1, and the result number in the test result sequence is num1. Establish the relationship pair μ, μ: code1←num1.

[0088] The system includes:

[0089] The system includes a test method library management module, a test report template management module, a target device selection module, a test item marking module, a test management module, and an information feedback module.

[0090] The test method library management module is used to manage the test method library.

[0091] The test report template management module is used to manage test report templates. It includes a project name management unit, a project aggregation unit, and a test template number management unit. The project name management unit is used to obtain the project name of each test project, the project aggregation unit is used to aggregate the project names of the test projects, and the test template number management unit is used to manage the test template number in the test template.

[0092] The target device selection module is used to select the target device.

[0093] The test item marking module is used to mark relevant test items. The test item marking module includes: an occurrence rate calculation unit, a time difference calculation unit, an anomaly prediction value calculation unit, and an interpretation marking unit. The occurrence rate calculation unit is used to calculate the occurrence rate of anomalies in the test items, the time difference calculation unit is used to calculate the time difference between two tests, the anomaly prediction value calculation unit is used to calculate the anomaly prediction value, and the interpretation marking unit is used to mark all test items whose anomaly prediction value is greater than or equal to a threshold.

[0094] The test management module is used to test the target device. The test management module includes an initial increment calculation unit, a feedback detection unit, an increment judgment unit, and a test signal adjustment unit. The initial increment calculation unit is used to calculate the initial intensity increment, the feedback detection unit is used to detect the intensity of the feedback signal, the increment judgment unit is used to calculate the intensity increment of the next test signal, and the test signal adjustment unit is used to calculate the value of the intensity of the test signal.

[0095] The information feedback module is used to provide relevant test results to management personnel. The information feedback module includes a result feedback unit, a number correspondence unit, and a correspondence database management unit. The result feedback unit is used to provide feedback on the test results, the number correspondence unit is used to record and establish the correspondence between test template numbers and result numbers, and the correspondence database management unit is used to manage the correspondence database.

[0096] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0097] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A data verification and management method for intelligent testing equipment based on the Internet of Things, characterized in that, The method includes the following steps: Step S100: Obtain the equipment number of each power protection device in the power system substation, obtain the historical test records of all power protection devices, conduct tests on the power protection devices, and compile test procedures for different types of power protection devices to establish a test method library; Step S200: Classify the power protection equipment according to its function to obtain several first category sets of power protection equipment. Collect the historical records of test reports of power protection equipment in each first category set and establish a unified test report template for each first category set. Step S300: Set any power protection device to be tested in the power system substation as the target device, obtain the device number of the target device, and match the test method of the target device in the test method library; Step S400: Mark the test items in the test template of the target device according to the frequency of abnormal situations in the test items in the target device; Step S500: Take any test item to be tested in the target device as the target item, divide the electrical parameter values ​​between the current electrical parameters of the target item and the judgment criteria for abnormal conditions into several incremental intervals, adjust the test signal input to the target device, and judge whether the status of the target device is normal based on the test results; Step S600: Collect the test results of the target equipment, fill in the test results in the test report template according to the corresponding relationship of the test items, obtain the test report, save the corresponding relationship, collect it into the corresponding relationship database, and feed the test report back to the relevant management personnel.

2. The data verification and management method for intelligent testing equipment based on the Internet of Things according to claim 1, characterized in that: Step S100 includes: Step S101: Obtain the equipment model and historical test records of each power protection device. Each equipment model corresponds to a type of power protection device. Obtain the test process of each power protection device from the historical test records of the power protection devices. The test process contains several test items. The test items in the test process of the same type of power protection device are arranged into a test item sequence according to the order in which the tests are carried out. Step S102: Obtain the test port and test target corresponding to each test item, obtain the signal strength of several test signals input to the test port and the feedback signal corresponding to each test signal, the signal strength of the test signal is increased from the initial value to the test target several times, and by detecting the feedback signal, determine whether there is an abnormality in the circuit protection device; Step S103: Obtain the test objectives and judgment criteria for each test item, where the test objective represents the maximum intensity of the input test signal, and the judgment criteria represent the abnormal situation judgment threshold corresponding to each test item.

3. The data verification and management method for intelligent testing equipment based on the Internet of Things according to claim 2, characterized in that: Step S200 includes: Step S201: Obtain the test report of each recorded test result for each type of power protection device, and obtain the test items in each test report; Step S202: The i-th first category set contains power protection equipment manufactured by k manufacturers, where the set of test item names corresponding to the power protection equipment manufactured by the j-th manufacturer is r. j Take the union R of all item names in the i-th first category set. i R i =r1∪r2∪r3∪……∪r k Among them, r1, r2, r3, ... and r k Let each represent a set consisting of the test item names corresponding to the power protection equipment produced by the 1st, 2nd, 3rd, ..., and kth manufacturers in the i-th first category set; Step S203: For R i Each test item is numbered to obtain the test template number for the test item, and then applied to R. i The test items in the dataset are aggregated to obtain the detection report template for the i-th first category set.

4. The data verification and management method for intelligent testing equipment based on the Internet of Things according to claim 3, characterized in that: Step S400 includes: Step S401: Obtain the historical records of test reports within the time range T0 of the target device, and obtain the number of times abnormal situations were recorded for each test item in the test report. The number of abnormal situation records for the x-th test item is denoted as h. x Calculate the anomaly occurrence rate α of the x-th test item. x α x =h x / T0; Step S402: Record the ongoing test for the target device as the current test. q The start time of the current test is denoted as t. q The previous test for the target device is recorded as test. q-1 test q-1 The end time is denoted as t. q Calculate the time difference T between two adjacent tests. dif T dif =t q -t q-1 ; Step S403: Calculate the predicted value f of the abnormal situation corresponding to the x-th test item. x f x =α x ×T dif Set an abnormal situation frequency management threshold w, and mark all test items whose abnormal situation prediction value is greater than or equal to w.

5. The data verification and management method for intelligent testing equipment based on the Internet of Things according to claim 4, characterized in that: Step S500 includes: Step S501: Obtain historical detection records where the target item's test results are normal for y tests. In the historical detection records where the u-th result is normal, the number of times the test signal strength increases from its initial value to the test target is check. u Next, calculate the average number of segments, sub. Step S502: Obtain the current value of the electrical parameter in the corresponding test port of the target project, denoted as par. a Let par be the test objective corresponding to the target project. b Calculate the initial intensity increment p0, p0 = (par b -par a ) / sub; Step S503: Input the first test signal into the test port corresponding to the target item. The strength value of the first test signal is p1, p1 = par a +η1, where η1 represents the intensity increment of the first test signal, η1 = p0, and the first feedback signal corresponding to the first test signal is obtained, the value of the intensity of the first feedback signal is fb1; Step S504: Input the (e+1)th test signal into the test port corresponding to the target item, wherein the strength value of the (e+1)th test signal is p. e+1 p e+1 =p e +η e+1 η e+1 Let fb represent the intensity increment of the (e+1)th test signal. Obtain the (e+1)th feedback signal corresponding to the (e+1)th test signal. The intensity value of the (e+1)th feedback signal is fb. e+1 ; Step S505: Obtain the anomaly judgment threshold b corresponding to the target project. When (b-fb) e+1 ) / (b-fb e When )≤1, calculate the increment value η of the (e+2)th test signal. e+2 , The strength value of the signal in the (e+2)th test is p. e+2 p e+2 =p e+1 +η e+2 , among which, fb e The numerical value representing the strength of the e-th feedback signal; When (b-fb) e+1 ) / (b-fb e When )>1, the increment value η of the (e+1)th test signal e+2 =η e+1 , where η e+1 This represents the increment value of the test signal in the (e+1)th test.

6. The data verification and management method for intelligent testing equipment based on the Internet of Things according to claim 5, characterized in that: Step S600 includes: Step S601: When the strength of the test signal is equal to the test target corresponding to the target item, the target item is marked as having passed the test; when the strength of the test signal is less than the test target corresponding to the target item, and the strength of the feedback signal is greater than or equal to the abnormal situation judgment threshold corresponding to the target item, the target item is marked as having failed the test. Step S602: Collect the test results of each test item of the target device to obtain the test result sequence of the target device, and label each test item in the test result sequence. Each test item corresponds to a result number. Step S603: Obtain the test template number and the result number in the test result sequence for the same test item name in the test report template, establish the correspondence between the test template number and the result number, and collect the correspondence into the correspondence database.

7. An intelligent test equipment data verification management system applied to the IoT-based intelligent test equipment data verification management method according to any one of claims 1-6, characterized in that, The system includes the following modules: a test method library management module, a test report template management module, a target device selection module, a test item marking module, a test management module, and an information feedback module. The test method library management module is used to manage the test method library, the test report template management module is used to manage the test report templates, the target device selection module is used to select the target device, the test item marking module is used to mark the relevant test items, the test management module is used to test the target device, and the information feedback module is used to provide relevant test results to the management personnel.

8. The intelligent testing equipment data verification management system according to claim 7, characterized in that: The test report template management module includes: a project name management unit, a project aggregation unit, and a test template number management unit. The project name management unit is used to obtain the project name of each test project, the project aggregation unit is used to aggregate the project names of test projects, and the test template number management unit is used to manage the test template number in the test template.

9. The intelligent testing equipment data verification management system according to claim 7, characterized in that: The test item marking module includes: an occurrence rate calculation unit, a time difference calculation unit, an anomaly prediction value calculation unit, and a judgment marking unit. The occurrence rate calculation unit is used to calculate the occurrence rate of anomalies in the test items, the time difference calculation unit is used to calculate the time difference between two tests, the anomaly prediction value calculation unit is used to calculate the anomaly prediction value, and the judgment marking unit is used to mark all test items whose anomaly prediction value is greater than or equal to the threshold.

10. The intelligent testing equipment data verification management system according to claim 7, characterized in that: The test management module includes: an initial increment calculation unit, a feedback detection unit, an increment judgment unit, and a test signal adjustment unit. The initial increment calculation unit is used to calculate the initial intensity increment, the feedback detection unit is used to detect the intensity of the feedback signal, the increment judgment unit is used to calculate the intensity increment of the next test signal, and the test signal adjustment unit is used to calculate the numerical value of the intensity of the test signal. The information feedback module includes: a result feedback unit, a number correspondence unit, and a correspondence database management unit. The result feedback unit is used to provide feedback on the test results, the number correspondence unit is used to record the correspondence between the test template number and the result number, and the correspondence database management unit is used to manage the correspondence database.

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