Modularized intelligent detection system for detection of metering and acquisition equipment

Abstract

The invention relates to the technical field of equipment detection, and discloses a modular intelligent detection system for detection of metering and acquisition equipment. Comprising the steps of extracting task features, splitting a detection task into detection sub-tasks, marking sub-features, screening out target sub-tasks, generating a detection scene, collecting comprehensive detection parameters of equipment terminals, calculating scene performance indexes and distinguishing the equipment terminals. Compared with the prior art, the method has the advantages that the target sub-tasks can be accurately and quickly screened out, the one-to-one association effect between the tasks and the equipment is achieved, meanwhile, the virtual detection scene is established, the scene performance index is calculated by collecting the comprehensive detection parameters, numerical representation can be conducted on performance detection of the equipment terminal in analogue simulation, and the simulation efficiency is improved. The screening and matching time between the equipment terminal and the target subtask can be shortened, the intelligent detection process of the equipment terminal is also reasonably simulated, and the detection efficiency of the metering and acquisition equipment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment detection, and more specifically, to a modular intelligent detection system for measuring and collecting equipment detection. Background Art

[0002] Metering and data collection equipment testing mainly refers to the process of testing, calibrating and verifying equipment used for data measurement and data collection. These devices may include temperature sensors, pressure sensors, flow meters, electricity meters, gas analyzers, etc. Through reasonable intelligent detection means and methods, the accuracy and reliability of metering and data collection equipment in daily work can be improved, and the effect of accurate identification and screening of equipment terminals with different performance strengths can be achieved, thereby effectively reducing the probability of negative problems.

[0003] The patent application with reference publication number CN111240986A discloses a smart home lighting control method and device, including a partition detection submodule, a configuration submodule, a test file generation submodule, a test writing submodule, a test reading submodule, a system startup judgment submodule and a test result reporting submodule, wherein: the partition detection submodule is used to detect the number of system partitions and the name, total capacity, available size, mount point, partition format and read and write permissions of each partition during the first run; the configuration submodule is used to specify the test mode and configure the number of test rounds; the test file generation submodule is used to automatically generate a test file for testing the partition according to the detection results detected by the partition detection submodule and the test mode configured by the configuration submodule; the test writing submodule is used to detect the partitions that need to be tested to the limit The writable partition of the artificial intelligence device is written to perform a write operation of the corresponding test mode, and the result of the write is judged during the writing process. If successful, the test continues. If unsuccessful, the test is stopped and the result is reported to the test result reporting submodule; the limit test refers to the test writing submodule performing write operations and multiple rounds of repeated tests based on the percentage of the available size of the tested partition of the artificial intelligence device as the standard; the test reading submodule is used to perform data read operations on the tested partition with only read permission or the specified partition configured as read permission, read the specified content and judge the reading result. If normal, the test continues. If abnormal, the test is terminated and the test result is reported; the system startup judgment submodule judges whether the system is started normally according to the keywords or identification bits of the normal system startup, and returns the corresponding startup result; the test result reporting submodule, the whole process of testing l og and the number of normal startup rounds are uploaded to the designated server for relevant personnel to view and analyze; by applying extreme pressure to the artificial intelligence equipment, during the startup process of the artificial intelligence equipment, the purpose of simulating the entire design life cycle of the tested product can be achieved in a short time, and the design quality of the tested product can be effectively evaluated. In addition, the anti-interference test function is introduced to test the startup ability of the artificial intelligence equipment system under the condition of simulating electromagnetic interference interference to each sub-module of the artificial intelligence equipment, and improve the ability to intercept probabilistic system startup problems;

[0004] The prior art has the following deficiencies:

[0005] When performing performance testing, the existing equipment intelligent detection system needs to identify and match the best target task one by one from a large number of detection tasks based on the actual type of the device terminal to be tested and the performance to be tested. When the number of optional detection tasks is large and the types are complex, the one-by-one identification and matching method will increase the screening time of the target task, thereby increasing the time required for equipment intelligent detection. At the same time, there is a lack of detection operations for simulating equipment performance testing, which makes the environment and process of equipment intelligent detection less realistic, thereby reducing the accuracy of the equipment intelligent detection results.

[0006] In view of this, the present invention proposes a modular intelligent detection system for measuring and collecting equipment detection to solve the above problems. Summary of the invention

[0007] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solutions: a modular intelligent detection system for measuring and collecting equipment detection, applied to a detection server, comprising:

[0008] A task feature extraction module is used to receive the detection task distributed by the detection system and extract the task features of the detection task, wherein the task features include the task type and task attributes;

[0009] A subtask splitting module is used to mark the task splitting line in the detection task, split the detection task into detection subtasks based on the task splitting line, and mark the sub-features in the detection subtasks;

[0010] The subtask screening module is used to extract the device features of the device terminal, calculate the task overlap based on the device features and subfeatures, and screen the target subtask from the detection subtask. The device features include device type and device attributes;

[0011] A detection scene construction module is used to construct an initial scene with a scene position, and import the target subtask and the device terminal into the scene position of the initial scene to generate a detection scene;

[0012] The performance index calculation module is used to collect the comprehensive detection parameters of the device terminal in the detection scene, and calculate the scene performance index of the device terminal based on the comprehensive detection parameters. The comprehensive detection parameters include task processing rate, detection pass rate and effective feedback time;

[0013] The test result analysis module is used to compare the scene performance index with the preset scene performance threshold, distinguish the performance levels of the device terminals, and analyze the performance test results.

[0014] Furthermore, the method for extracting the task type and task attributes includes:

[0015] The first target semantics and the second target semantics in the detection task are respectively identified by natural language processing technology;

[0016] The electric energy meter, controller, concentrator and special transformer terminal are recorded as type standard semantics;

[0017] Record the first target semantics containing any type of standard semantics as the task type, obtain i task types, and number the i task types in ascending order;

[0018] Record power consumption, performance, loading, consistency, interchangeability and accuracy as standard semantics of attributes;

[0019] The second target semantics containing any attribute standard semantics is recorded as the task attribute, i task attributes are obtained, and the i task attributes are numbered in ascending order.

[0020] Furthermore, the splitting method of the detection subtask includes:

[0021] The task types and task attributes with the same number are aggregated to form a feature group, i feature groups are obtained, and the i feature groups are numbered in ascending order;

[0022] Identify the first and second values ​​associated with the task type and the task attribute in the feature group by natural language processing technology, and aggregate the first and second values ​​one by one to form a value group, thereby obtaining i value groups;

[0023] Arrange the i feature groups in order from small to large, and mark the midpoints between two adjacent feature groups one by one to obtain i-1 midpoints;

[0024] Draw straight lines passing through the midpoints of i-1 positions in the vertical direction to obtain i-1 task splitting lines;

[0025] Taking the position of the i-1 task splitting line as the splitting position, split the detection task into i split tasks;

[0026] A remark box is constructed at the end of each of the i split tasks, and the i value groups are imported one by one into the remark boxes of the i split tasks with the same number, so as to obtain i detection subtasks.

[0027] Furthermore, the sub-feature marking method includes:

[0028] Count the number of characters of the task type and task attribute in i detection subtasks one by one, and obtain i first character values ​​and i second character values;

[0029] Constructing i first blank workstation groups and i second blank workstation groups, respectively, whose numbers of workstations are equal to the i first character values ​​and the i second character values;

[0030] Import i task types and i task attributes into i first blank workstation groups and i second blank workstation groups one by one, and obtain i first characteristic workstation groups and i second characteristic workstation groups;

[0031] Separate the first and second characters in the i first feature station groups to obtain i front features;

[0032] Separate the penultimate and penultimate characters in the i second feature station groups to obtain i post-position features;

[0033] Mark the numbers of i detection subtasks one by one to obtain i number features;

[0034] i feature sets having a prefix set, a suffix set and a number set are constructed, and the prefix features, suffix features and number features having the same number are sequentially imported into the prefix set, suffix set and number set of the i feature sets to generate i sub-features.

[0035] Furthermore, the calculation method of task overlap includes:

[0036] Mark the preceding features in the i sub-features one by one, and record the characters of the same preceding features and the character of the device type as the first coincident characters;

[0037] Count the number of first coincident characters in i detection subtasks one by one to obtain i first coincidence values;

[0038] Mark the post-features in the i sub-features one by one, and record the characters of the same post-features and the characters of the device attributes as the second coincident characters;

[0039] Count the number of second coincident characters in i detection subtasks one by one to obtain i second coincidence values;

[0040] Based on i first coincidence values ​​and corresponding i second coincidence values, obtaining i task coincidence degrees;

[0041] The expression of task overlap is:

[0042] R chi =σ 1 *CH d1i +σ 2 *CHd 2i ;

[0043] Where RW chi is the task overlap of the i-th detection subtask, CH d1i is the first coincidence value of the i-th detection subtask, CH d2i is the second coincidence value of the i-th detection subtask, σ 1 , σ 2 is a weight factor greater than 0.

[0044] Furthermore, the target subtask screening method includes:

[0045] Arrange the overlap of i tasks in descending order from large to small;

[0046] When the number of task overlaps ranked first is 1, the detection subtask corresponding to the task overlap ranked first is recorded as the target subtask;

[0047] When the number of task overlaps ranked first is greater than 1, the detection subtask corresponding to the maximum value of the second overlap value is recorded as the target subtask;

[0048] When the sizes of the second coincidence values ​​are all the same, the detection subtask corresponding to the minimum value of the numbered feature is recorded as the target subtask.

[0049] Furthermore, the scenario bit includes a task scenario bit and a device scenario bit;

[0050] The generation method of the detection scene includes:

[0051] Query the test items of the target subtask through the task attribute table;

[0052] Query the detection parameters of the device terminal through the technical parameter table to obtain the scene condition configuration parameters;

[0053] Under the scenario condition configuration parameters, a blank scenario matching the test item is constructed by interacting with the device terminal;

[0054] Mark the equipment scene position and task scene position in the blank scene respectively to obtain the initial scene;

[0055] Receiving points are established at the device scene position and the task scene position respectively, and the sub-features of the target subtask and the device features of the device terminal are imported into the receiving points respectively to obtain the detection scene.

[0056] Furthermore, the expression of task processing rate is:

[0057] Split the target subtask into X task items, and distribute the X task items one by one on the task points in the detection scene to generate X task detection points;

[0058] At time T1, the number of task item data existing in X task detection points is queried one by one through the traffic monitoring tool to obtain X initial data volumes;

[0059] At time T2, the number of remaining task item data in X task detection points is queried one by one through the traffic monitoring tool to obtain X end data volumes;

[0060] Subtract the X initial data volumes from the X final data volumes one by one to obtain X total processing volumes;

[0061] The total processing volume is expressed as:

[0062] CL zx =CS lx -JS lx ;

[0063] In the formula, CL zxis the total processing capacity of the xth task detection point, CS lx is the initial data volume of the Xth task detection point, JS lx The ending data volume of the x-th task detection point;

[0064] The duration between time T1 and time T2 is recorded as the total duration, and X total processing amounts are compared with the total duration in sequence to obtain X sub-processing rates;

[0065] The expression of sub-processing rate is:

[0066]

[0067] In the formula, SL zx is the sub-processing rate of the x-th task detection point, SC 12 is the total duration;

[0068] Remove the maximum and minimum values ​​of the sub-processing rates, accumulate the remaining x-2 sub-processing rates and calculate the average to obtain the task processing rate;

[0069] The expression of task processing rate is:

[0070]

[0071] Where RW sl is the task processing rate, SL za is the sub-processing rate of the a-th task detection point.

[0072] Furthermore, the method for obtaining the effective feedback duration includes:

[0073] Number the X task detection points in ascending order one by one;

[0074] Using natural language processing technology, keywords of task items in X task detection points are identified one by one to obtain X keywords;

[0075] Using X keywords as request content and the number of the task detection point as the request sequence number, generate X task item detection requests;

[0076] Query the time when X task item detection requests are sent to the task detection point one by one through the timestamp to obtain the time when X requests are sent;

[0077] Query the time when X task detection points start to execute the task item detection request one by one through the timestamp to obtain the X request execution time;

[0078] The time between the time when X requests are sent and the time when X requests are executed is recorded as the feedback time, and X feedback time is obtained;

[0079] Subtract the X feedback durations from the fixed delay duration one by one, add them up and calculate the average to obtain the effective feedback duration;

[0080] The expression of effective feedback duration is:

[0081]

[0082] In the formula, YX sc SC is the effective feedback duration. zb The sub-duration of the detection request for the b-th task item, YC sc The delay time is fixed.

[0083] Furthermore, the expression of the scene performance index is:

[0084]

[0085] In the formula, XN zs is the scene performance index, JC hg is the qualified rate of detection, σ 3 , σ 4 , σ 5 is a weight factor greater than 0;

[0086] The performance levels include the first performance level, the second performance level and the third performance level;

[0087] The methods for distinguishing performance levels 1, 2 and 3 include:

[0088] Set the scene performance index XN zs With the preset first scenario performance threshold XN yz1 and the second scenario performance threshold XN yz2 Compare, XN yz1 Less than XN yz2 ;

[0089] When XN zs Less than or equal to XN yz1 When , a first-level performance level is generated;

[0090] When XN zs Greater than XN yz1 , and XN zs Less than or equal to XN yz2 When , a secondary performance level is generated;

[0091] When XN zs Greater than XN yz2 When , three performance levels are generated.

[0092] The technical effects and advantages of the modular intelligent detection system for measuring and collecting equipment detection of the present invention are as follows:

[0093] The present invention receives the detection task distributed by the detection system, extracts the task feature of the detection task, marks the task splitting line in the detection task, splits the detection task into detection subtasks based on the task splitting line, marks the subfeatures in the detection subtasks, extracts the device features of the device terminal, calculates the task overlap based on the device features and the subfeatures, screens out the target subtask from the detection subtasks, constructs an initial scene with a scene position, imports the target subtask and the device terminal into the scene position of the initial scene, generates a detection scene, collects the comprehensive detection parameters of the device terminal in the detection scene, calculates the scene performance index of the device terminal based on the comprehensive detection parameters, compares the scene performance index with a preset scene performance threshold, distinguishes the performance level of the device terminal, and analyzes the performance detection result; compared with the prior art, the present invention can extract the task features to detect the device terminal. The large number of complex detection tasks can be subdivided to achieve the independence of each detection subtask, and based on the equipment characteristics, the target subtask can be accurately and quickly screened out to achieve a one-to-one association effect between tasks and equipment. At the same time, a virtual detection scene can be established to simulate the detection effect between the target subtask and the equipment terminal, and the scene performance index can be calculated by collecting comprehensive detection parameters. The performance detection performance of the equipment terminal in the simulation can be accurately represented by numerical value, so as to accurately calculate the performance strength of the equipment terminal, which can shorten the screening and matching time between the equipment terminal and the corresponding target subtask and improve the screening accuracy of the target subtask. At the same time, the intelligent detection process of the equipment terminal is simulated rationally, thereby effectively improving the intelligent detection accuracy of the metering and acquisition equipment, and facilitating the accurate screening and distinction of the metering and acquisition equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Figure 1 A schematic diagram of a modular intelligent detection system for measuring and collecting equipment detection provided in Example 1 of the present invention;

[0095] Figure 2 A schematic flow chart of a modular intelligent detection method for measuring and collecting equipment detection provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0096] 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.

[0097] Example 1: Please refer to Figure 1As shown, the modular intelligent detection system for measuring and collecting equipment detection described in this embodiment is applied to the detection server, including:

[0098] A task feature extraction module is used to receive the detection task distributed by the detection system and extract the task features of the detection task;

[0099] The detection task refers to the sum of the overall and standardized detection measures formulated by the detection system for the performance detection requirements of various types of measurement and acquisition equipment, so that the detection task can provide appropriate and accurate detection measures for all equipment terminals that meet the performance detection requirements. After receiving the detection task, since the task measures contained in the detection task are diverse, in order to accurately identify each task measure, it is necessary to mark the true meaning and representation type of each task measure, so as to obtain the task characteristics;

[0100] Task characteristics include task type and task attributes; task type refers to the type of device terminal corresponding to each task measure in the detection task, which can meet the identification requirements of different types of device terminals; task attribute refers to the specific meaning of the detection item corresponding to each task measure in the detection task, so as to facilitate the distinction of detection measures;

[0101] The methods for extracting task types and task attributes include:

[0102] The first target semantics and the second target semantics in the detection task are respectively identified by natural language processing technology; the first target semantics is used to represent the real text corresponding to the type of task measures in the detection task, and the second target semantics is used to represent the real text corresponding to the attribute of the task measures in the detection task, so that the type and attribute of the task measures in the detection task can be effectively and concisely distinguished;

[0103] The electric energy meter, controller, concentrator and special transformer terminal are recorded as type standard semantics;

[0104] Record the first target semantics containing any type of standard semantics as the task type, obtain i task types, and number the i task types in ascending order;

[0105] Record power consumption, performance, loading, consistency, interchangeability and accuracy as standard semantics of attributes;

[0106] Record the second target semantics containing any attribute standard semantics as the task attribute, obtain i task attributes, and number the i task attributes in ascending order;

[0107] It should be noted that after obtaining the task type and task attributes, the equipment and attributes corresponding to the detection in the detection task can be comprehensively represented, so that in the detection task with a large amount of messy data, the detection measures in the detection task can be directly represented concisely and accurately, which is convenient for the subsequent splitting and processing of the detection task.

[0108] A subtask splitting module is used to mark the task splitting line in the detection task, split the detection task into detection subtasks based on the task splitting line, and mark the sub-features in the detection subtasks;

[0109] A detection subtask refers to a detection measure with a unique task type and task attributes. It is also the smallest unit that constitutes a detection task. Different types of detection measures in a detection task can be represented separately to meet the detection requirements of different equipment terminals and different detection items. The task splitting line is the corresponding splitting position when the detection task is split into detection subtasks to ensure that each detection subtask can be accurately and reasonably split.

[0110] The splitting methods of detection subtasks include:

[0111] The task types and task attributes with the same number are aggregated to form a feature group, i feature groups are obtained, and the i feature groups are numbered in ascending order;

[0112] A first value and a second value associated with a task type and a task attribute in a feature group are identified by natural language processing technology, and the first value and the second value are summarized one by one to form a value group, and i value groups are obtained; the first value refers to a value located behind and closely adjacent to the character of the task type, and is used to numerically represent the task type; the second value refers to a value located behind and closely adjacent to the character of the task attribute, and is used to numerically represent the task attribute;

[0113] Arrange the i feature groups in order from small to large, and mark the midpoints between two adjacent feature groups one by one to obtain i-1 midpoints. This allows the midpoints to effectively divide the two adjacent feature groups, ensuring that the two adjacent feature groups are independent of each other.

[0114] Draw straight lines passing through the midpoints of i-1 positions in the vertical direction to obtain i-1 task splitting lines;

[0115] Taking the position of the i-1 task splitting line as the splitting position, split the detection task into i split tasks;

[0116] At the end of each of the i split tasks, a remark box is constructed, and the i value groups are imported into the remark boxes of the i split tasks with the same number one by one to obtain i detection subtasks. The remark box is a text box used to represent and import the serial number of the detection subtask, ensuring that each detection subtask has a unique serial number to facilitate the subsequent rapid identification of the detection subtask.

[0117] When the detection task is split into detection subtasks, it is necessary to refer to the meaning and detection items of each detection subtask, so as to facilitate the subsequent intuitive representation of the specific type and detection object of each detection subtask. The sub-feature is the word used to represent the specific meaning of the detection subtask and serves as the basis for subsequent matching of device terminals.

[0118] Sub-feature marking methods include:

[0119] Count the number of characters of the task type and task attribute in i detection subtasks one by one, and obtain i first character values ​​and i second character values;

[0120] i first blank workstation groups and i second blank workstation groups are constructed respectively, the number of workstations being equal to the i first character values ​​and the i second character values; a blank workstation group refers to a combination of workstations without any features imported, so that the blank workstation group can meet the import requirements of task features, and a workstation is the smallest unit constituting a blank workstation group, providing a location for importing task features;

[0121] Import i task types and i task attributes into i first blank workstation groups and i second blank workstation groups one by one, and obtain i first characteristic workstation groups and i second characteristic workstation groups;

[0122] Separate the first and second characters in the i first feature station groups to obtain i front features;

[0123] Separate the penultimate and penultimate characters in the i second feature station groups to obtain i post-position features;

[0124] Mark the numbers of i detection subtasks one by one to obtain i number features;

[0125] Construct i feature sets with a pre-set, a post-set and a numbered set, and import the pre-features, post-features and numbered features with the same number into the pre-set, post-set and numbered set of the i feature sets in order to generate i sub-features. The pre-set, post-set and numbered set are respectively used to provide locations for the pre-feature, post-feature and numbered features to be imported, so that the pre-feature, post-feature and numbered features can be composed of sub-features as needed to ensure that the sub-features can accurately represent the detection subtask;

[0126] When i sub-features are obtained, each sub-feature can represent the corresponding detection sub-task, so that the sub-feature can be used as the basis for subsequent device terminal matching and improve the accuracy of the detection sub-task and device terminal identification matching.

[0127] The subtask screening module is used to extract the device features of the device terminal, calculate the task overlap based on the device features and subfeatures, and screen the target subtask from the detection subtask;

[0128] Device features refer to the true representation of the type of device terminal to be tested and the meaning of the test items, that is, they can refer to the specific test content of the device terminal. At the same time, device features can also serve as the basis for subsequent matching with sub-features, so as to quickly and accurately select the best test sub-task from a large number of test sub-tasks to meet the test needs of the device terminal;

[0129] Device characteristics include device type and device attributes; device type refers to the type of device terminal, which can represent different types of device terminals; device attributes refer to the attributes to be detected by the device terminal, which can represent different detection attributes; both device type and device attributes are obtained by querying the technical parameter table of the device terminal;

[0130] Task overlap refers to the matching overlap between the sub-features of the detection sub-task and the device features of the device terminal. The matching degree between the detection sub-task and the device terminal can be numerically expressed and used as the basis for subsequent target sub-task screening.

[0131] The calculation method of task overlap includes:

[0132] Mark the preceding features in the i sub-features one by one, and record the characters of the same preceding features and the character of the device type as the first coincident characters;

[0133] Count the number of first coincident characters in i detection subtasks one by one to obtain i first coincidence values;

[0134] Mark the post-features in the i sub-features one by one, and record the characters of the same post-features and the characters of the device attributes as the second coincident characters;

[0135] Count the number of second coincident characters in i detection subtasks one by one to obtain i second coincidence values;

[0136] Based on i first coincidence values ​​and corresponding i second coincidence values, obtaining i task coincidence degrees;

[0137] The expression of task overlap is:

[0138] R chi =σ 1 *CH d1i +σ 2 *CH d2i ;

[0139] Where RW chi is the task overlap of the i-th detection subtask, CH d1i is the first coincidence value of the i-th detection subtask, CH d2i is the second coincidence value of the i-th detection subtask, σ 1 , σ 2 is a weight factor greater than 0;

[0140] Among them, σ 1 +σ 2 =1, exemplary, σ 1 is 0.532, σ 2 is 0.468; it should be noted that σ 1 , σ 2 The purpose is to adjust the proportion of the first overlap value and the second overlap value in the task overlap so that the first overlap value and the second overlap value can maintain a relatively balanced influence on the size of the task overlap, thereby ensuring the accuracy of the task overlap.

[0141] After calculating the task overlap, the matching overlap between the detection subtasks and the device terminal can be ranked according to the task overlap of each detection subtask, and the detection subtask with the largest task overlap can be selected as the target subtask, so that the target subtask can be used as the object of intelligent detection by the subsequent device terminal;

[0142] The screening methods for target subtasks include:

[0143] Arrange the overlap of i tasks in descending order from large to small;

[0144] When the number of task overlaps ranked first is 1, it means that the matching overlaps of the i detection subtasks and the device terminal are not consistent, and the detection subtask corresponding to the task overlap ranked first is recorded as the target subtask;

[0145] When the number of task overlaps ranked first is greater than 1, it means that there are s detection subtasks and the matching overlap of the device terminal is consistent. At this time, the second overlap values ​​of the s sub-features are compared, and the detection subtask corresponding to the maximum value of the second overlap value is recorded as the target subtask;

[0146] When the sizes of the s second coincidence values ​​are all the same, the sizes of the numbered features in the s sub-features are compared, and the detection subtask corresponding to the minimum value of the numbered feature is recorded as the target subtask.

[0147] After the target subtask is obtained, the target subtask can be sent to the device terminal for matching the task with the device, so that the device terminal can accurately obtain the detection subtask, thereby meeting the needs of the device terminal for intelligent detection.

[0148] A detection scene construction module is used to construct an initial scene with a scene position, and import the target subtask and the device terminal into the scene position of the initial scene to generate a detection scene;

[0149] The initial scene refers to a blank scene that can provide a stable environment for the intelligent detection operation of the device terminal and the target subtask, and serves as the basis for the subsequent construction of the detection scene, so as to ensure that the target subtask and the device terminal can perform safe and stable intelligent detection in the detection scene, and facilitate the subsequent collection of relevant detection data. The scene position is the location in the initial scene that is provided to the target subtask and the device terminal for detection, and serves as the location where the target subtask and the device terminal are imported;

[0150] The scene bit includes the task scene bit and the device scene bit; the task scene bit is used to provide the import location for the target subtask, and the device scene bit is used to provide the import location for the device terminal;

[0151] The generation method of the detection scene includes:

[0152] Query the test items of the target subtask through the task attribute table; the test items refer to the specific items that need to be tested in the target subtask, which can accurately represent the test object of the target subtask;

[0153] Query the detection parameters of the device terminal through the technical parameter table to obtain the scene condition configuration parameters;

[0154] Under the scenario condition configuration parameters, a blank scene matching the test item is constructed by interacting with the device terminal; the blank scene is the basis for constructing the initial scene and provides a basic outline for the subsequent construction of the initial scene;

[0155] Mark the equipment scene position and task scene position in the blank scene to obtain the initial scene; the equipment scene position and task scene position are used to meet the import requirements of equipment and tasks, so as to construct a detection scene that meets the requirements;

[0156] Receiving points are established at the device scene position and the task scene position respectively, and the sub-features of the target subtask and the device features of the device terminal are imported into the receiving points respectively to obtain the detection scene.

[0157] It should be noted that after the detection scene is generated, a one-to-one correspondence will be established between the device terminal and the target subtask in the detection scene, so that the target subtask can only perform intelligent detection on the device terminal, thereby ensuring the independent correspondence between the target subtask and the device terminal, and effectively isolating the interference that other detection subtasks may bring to the device terminal detection.

[0158] The performance index calculation module is used to collect comprehensive detection parameters of the device terminal in the detection scene, and calculate the scene performance index of the device terminal based on the comprehensive detection parameters;

[0159] Comprehensive detection parameter values ​​are various detection parameters generated by the device terminal when performing intelligent detection of target subtasks in the detection scene. By collecting comprehensive detection parameters, they can serve as the basis for judging the intelligent detection results of the device terminal. The scene performance index is a numerical representation of the high and low intelligent detection results, so that the performance of the device terminal can be intuitively represented.

[0160] Comprehensive detection parameters include task processing rate, detection pass rate and effective feedback time;

[0161] The task processing rate refers to the speed at which the device terminal processes multiple task items in the target subtask in the detection scenario within a unit time. When the task processing rate is higher, it means that the device terminal can process more task items within a unit time, and the performance of the device terminal in intelligent detection is better, the performance is stronger, and the scenario performance index of the device terminal is larger;

[0162] The expression of task processing rate is:

[0163] Split the target subtask into X task items, and distribute the X task items one by one on the task points in the detection scene to generate X task detection points;

[0164] At time T1, the number of task item data existing in X task detection points is queried one by one through the traffic monitoring tool to obtain X initial data volumes;

[0165] At time T2, the number of remaining task item data in X task detection points is queried one by one through the traffic monitoring tool to obtain X end data volume; time T2 is the next time of time T1, and the time length between time T2 and time T1 can meet the time length required for all types of task item data to be processed at least once;

[0166] Subtract the X initial data volumes from the X final data volumes one by one to obtain X total processing volumes;

[0167] The total processing volume is expressed as:

[0168] CL zx =CS lx -JS lx ;

[0169] In the formula, CL zx is the total processing capacity of the xth task detection point, CS lx is the initial data volume of the Xth task detection point, JS lx The ending data volume of the Xth task detection point;

[0170] The duration between time T1 and time T2 is recorded as the total duration, and X total processing amounts are compared with the total duration in sequence to obtain X sub-processing rates;

[0171] The expression of sub-processing rate is:

[0172]

[0173] In the formula, SL zx is the sub-processing rate of the x-th task detection point, SC 12 is the total duration;

[0174] Remove the maximum and minimum values ​​of the sub-processing rates, accumulate the remaining x-2 sub-processing rates and calculate the average to obtain the task processing rate;

[0175] The expression of task processing rate is:

[0176]

[0177] Where RW sl is the task processing rate, SL za is the sub-processing rate of the a-th task detection point.

[0178] The detection pass rate refers to the proportion of qualified detection results when the device terminal performs intelligent detection on the task items in the target subtask in the detection scenario. The larger the detection pass rate, the larger the proportion of qualified detection results of the device terminal in intelligent detection. The better the performance of the device terminal in intelligent detection, the stronger the performance, and the larger the scene performance index of the device terminal. The detection pass rate is obtained by querying the value of the detection pass rate in the detection system of the device terminal.

[0179] The effective feedback time refers to the time between the device terminal sending the task item detection request and executing the task item detection request in the detection scenario. The longer the effective feedback time is, the longer the time between the device terminal sending the task item detection request and executing the task item detection request in the detection scenario is, the worse the performance of the device terminal in intelligent detection is, the weaker the performance is, and the smaller the scene performance index of the device terminal is;

[0180] Methods for obtaining effective feedback duration include:

[0181] Number the X task detection points in ascending order one by one;

[0182] Using natural language processing technology, keywords of task items in X task detection points are identified one by one to obtain X keywords;

[0183] Using X keywords as request content and the number of the task detection point as the request sequence number, generate X task item detection requests;

[0184] Query the time when X task item detection requests are sent to the task detection point one by one through the timestamp to obtain the time when X requests are sent;

[0185] Query the time when X task detection points start to execute the task item detection request one by one through the timestamp to obtain the X request execution time;

[0186] The time between the time when X requests are sent and the time when X requests are executed is recorded as the feedback time, and x feedback times are obtained;

[0187] Subtract the x feedback durations from the fixed delay duration one by one, and then add them up and calculate the average to obtain the effective feedback duration; the fixed delay duration refers to the processing time that the task detection point itself needs to spend when receiving the task item detection request. Since the fixed delay duration does not belong to the actual time spent between receiving and executing the task item detection request, the fixed delay duration needs to be excluded to improve the authenticity and accuracy of the effective feedback duration;

[0188] The expression of effective feedback duration is:

[0189]

[0190] In the formula, YX sc SC is the effective feedback duration. zb The sub-duration of the detection request for the b-th task item, YC sc The delay time is fixed.

[0191] The scene performance index is used to numerically represent the detection result of the device terminal when performing intelligent detection in the detection scene, and is used to represent the performance of the device terminal. When the scene performance index is larger, it means that the intelligent detection result of the device terminal is higher and the performance of the device terminal is stronger;

[0192] The expression of the scene performance index is:

[0193]

[0194] In the formula, XN zs is the scene performance index, JC hg is the qualified rate of detection, σ 3 , σ 4 , σ 5 is a weight factor greater than 0;

[0195] In the formula, σ 3 , σ 4 , σ 5 The setting logic is the same as the above σ 1 , σ 2 The setting logic is consistent.

[0196] The test result analysis module is used to compare the scene performance index with the preset scene performance threshold, distinguish the performance levels of the device terminals, and analyze the performance test results;

[0197] After calculating the scene performance index, the performance level of the device terminal for the target subtask in the detection scene can be accurately obtained, and the calculated scene performance index is compared with the preset scene performance threshold. According to the comparison result, the intelligent detection result of the device terminal is analyzed and the performance level is distinguished;

[0198] The performance levels include level 1, level 2 and level 3. Level 3 performance level corresponds to strong performance of the equipment terminal, and the test results are excellent; level 2 performance level corresponds to medium performance of the equipment terminal, and the test results are average; level 1 performance level corresponds to weak performance of the equipment terminal, and the test results are poor;

[0199] The method of distinguishing the first-level performance level, the second-level performance level and the third-level performance level includes: the performance intensity corresponding to the first-level performance level is lower than the performance intensity corresponding to the second-level performance level, and the performance intensity corresponding to the second-level performance level is lower than the performance intensity corresponding to the third-level performance level;

[0200] Set the scene performance index XN zs With the preset first scenario performance threshold XN yz1 and the second scenario performance threshold XN yz2 Compare, XN yz1 Less than XN yz2 ; The first scene performance threshold and the second scene performance threshold refer to the maximum value and minimum value of the scene performance index when the intelligent detection result of the device terminal is in a good state and a bad state, respectively, so that the detection result of the intelligent detection result of the device terminal can be distinguished by numerical value. The first scene performance threshold and the second scene performance threshold are respectively obtained by collecting the maximum value and the minimum value of the scene performance index when the intelligent detection results of a large number of device terminals in history are in a good state and a bad state, and then calculating their average values;

[0201] When XN zs Less than or equal to XN yz1 When , it means that the scene performance index is less than or equal to the first scene performance threshold. At this time, the intelligent detection result of the device terminal is poor and the performance is weak, so a first-level performance level is generated;

[0202] When XN zs Greater than XN yz1 , and XN zs Less than or equal to XN yz2 When , it means that the scenario performance index is greater than the first scenario performance threshold and less than or equal to the second scenario performance threshold. At this time, the intelligent detection result of the device terminal is medium and the performance is medium, then the second-level performance level is generated;

[0203] When XN zs Greater than XN yz2 When , it means that the scene performance index is greater than the second scene performance threshold. At this time, the intelligent detection result of the device terminal is good and the performance is strong, and the third level performance level is generated;

[0204] After distinguishing the first-level performance level, the second-level performance level and the third-level performance level, the intelligent detection results of the device terminal can be analyzed and evaluated. If the device terminal is at the first-level performance level, it means that the performance of the device terminal is poor and it needs to be discarded or repaired before use. When the device terminal is at the second-level performance level, it means that the performance of the device terminal is average and can be used in occasions with lower performance requirements. When the device terminal is at the third-level performance level, it means that the performance of the device terminal is excellent and can be used in all standardized occasions and can maximize the performance of the device terminal.

[0205] In this embodiment, by receiving the detection task distributed by the detection system and extracting the task features of the detection task, marking the task splitting line in the detection task, splitting the detection task into detection subtasks based on the task splitting line, marking the subfeatures in the detection subtasks, extracting the device features of the device terminal, calculating the task overlap based on the device features and the subfeatures, screening the target subtask from the detection subtasks, constructing an initial scene with a scene position, importing the target subtask and the device terminal into the scene position of the initial scene, generating a detection scene, collecting the comprehensive detection parameters of the device terminal in the detection scene, and calculating the scene performance index of the device terminal based on the comprehensive detection parameters, comparing the scene performance index with a preset scene performance threshold, distinguishing the performance levels of the device terminals, and analyzing the performance detection results; compared with the prior art, by extracting task features, The massive and complex detection tasks are subdivided to achieve the independence of each detection subtask. Based on the equipment characteristics, the target subtasks can be accurately and quickly screened out to achieve a one-to-one association effect between tasks and equipment. At the same time, a virtual detection scene is established to simulate the detection effect between the target subtask and the equipment terminal. By collecting comprehensive detection parameters to calculate the scene performance index, the performance detection performance of the equipment terminal in the simulation can be accurately represented numerically, thereby accurately calculating the performance strength of the equipment terminal. It can shorten the screening and matching time between the equipment terminal and the corresponding target subtask and improve the screening accuracy of the target subtask. At the same time, it also rationalizes the simulation of the intelligent detection process of the equipment terminal, thereby effectively improving the intelligent detection accuracy of the metering and acquisition equipment, and facilitating the accurate screening and distinction of the metering and acquisition equipment.

[0206] Example 2: Please refer to Figure 2 As shown, the part not described in detail in this embodiment is described in Example 1, and a modular intelligent detection method for measuring and collecting equipment detection is provided, which is applied to a detection server and is implemented based on a modular intelligent detection system for measuring and collecting equipment detection, including:

[0207] S1: Receive the detection task distributed by the detection system and extract the task features of the detection task, which include task type and task attributes;

[0208] S2: Mark the task splitting line in the detection task, split the detection task into detection subtasks based on the task splitting line, and mark the sub-features in the detection subtasks;

[0209] S3: Extract the device features of the device terminal, calculate the task overlap based on the device features and sub-features, and select the target sub-task from the detection sub-tasks. The device features include device type and device attributes.

[0210] S4: construct an initial scene with scene positions, and import the target subtask and the device terminal into the scene positions of the initial scene to generate a detection scene;

[0211] S5: Collect comprehensive detection parameters of the device terminal in the detection scene, and calculate the scene performance index of the device terminal based on the comprehensive detection parameters. The comprehensive detection parameters include task processing rate, detection pass rate and effective feedback time;

[0212] S6: Compare the scenario performance index with the preset scenario performance threshold, differentiate the performance levels of the device terminals, and analyze the performance test results.

[0213] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A modular intelligent detection system for measuring and collecting equipment detection, applied to the detection server, characterized in that: include: A task feature extraction module is used to receive the detection task distributed by the detection system and extract the task features of the detection task, wherein the task features include the task type and task attributes; A subtask splitting module is used to mark the task splitting line in the detection task, split the detection task into detection subtasks based on the task splitting line, and mark the sub-features in the detection subtasks; The subtask screening module is used to extract the device features of the device terminal, calculate the task overlap based on the device features and subfeatures, and screen the target subtask from the detection subtask. The device features include device type and device attributes; A detection scene construction module is used to construct an initial scene with a scene position, and import the target subtask and the device terminal into the scene position of the initial scene to generate a detection scene; The performance index calculation module is used to collect the comprehensive detection parameters of the device terminal in the detection scene, and calculate the scene performance index of the device terminal based on the comprehensive detection parameters. The comprehensive detection parameters include task processing rate, detection pass rate and effective feedback time; The test result analysis module is used to compare the scene performance index with the preset scene performance threshold, distinguish the performance levels of the device terminals, and analyze the performance test results.

2. A modular intelligent detection system for measuring and collecting equipment detection according to claim 1, characterized in that: The method for extracting the task type and task attributes includes: The first target semantics and the second target semantics in the detection task are respectively identified by natural language processing technology; The electric energy meter, controller, concentrator and special transformer terminal are recorded as type standard semantics; Record the first target semantics containing any type of standard semantics as the task type, obtain i task types, and number the i task types in ascending order; Record power consumption, performance, loading, consistency, interchangeability and accuracy as standard semantics of attributes; The second target semantics containing any attribute standard semantics is recorded as the task attribute, i task attributes are obtained, and the i task attributes are numbered in ascending order.

3. A modular intelligent detection system for measuring and collecting equipment detection according to claim 2, characterized in that: The method for splitting the detection subtasks includes: The task types and task attributes with the same number are aggregated to form a feature group, i feature groups are obtained, and the i feature groups are numbered in ascending order; Identify the first and second values ​​associated with the task type and the task attribute in the feature group by natural language processing technology, and aggregate the first and second values ​​one by one to form a value group, thereby obtaining i value groups; Arrange the i feature groups in order from small to large, and mark the midpoints between two adjacent feature groups one by one to obtain i-1 midpoints; Draw straight lines passing through the midpoints of i-1 positions in the vertical direction to obtain i-1 task splitting lines; Taking the position of the i-1 task splitting line as the splitting position, split the detection task into i split tasks; A remark box is constructed at the end of each of the i split tasks, and the i value groups are imported one by one into the remark boxes of the i split tasks with the same number, so as to obtain i detection subtasks.

4. A modular intelligent detection system for measuring and collecting equipment detection according to claim 3, characterized in that: The sub-feature marking method includes: Count the number of characters of the task type and task attribute in i detection subtasks one by one, and obtain i first character values ​​and i second character values; Constructing i first blank workstation groups and i second blank workstation groups, respectively, whose numbers of workstations are equal to the i first character values ​​and the i second character values; Import i task types and i task attributes into i first blank workstation groups and i second blank workstation groups one by one, and obtain i first characteristic workstation groups and i second characteristic workstation groups; Separate the first and second characters in the i first feature station groups to obtain i front features; Separate the penultimate and penultimate characters in the i second feature station groups to obtain i post-position features; Mark the numbers of i detection subtasks one by one to obtain i number features; i feature sets having a prefix set, a suffix set and a number set are constructed, and the prefix features, suffix features and number features having the same number are sequentially imported into the prefix set, suffix set and number set of the i feature sets to generate i sub-features.

5. A modular intelligent detection system for measuring and collecting equipment detection according to claim 4, characterized in that: The calculation method of the task overlap includes: Mark the preceding features in the i sub-features one by one, and record the characters of the same preceding features and the character of the device type as the first coincident characters; Count the number of first coincident characters in i detection subtasks one by one to obtain i first coincidence values; Mark the post-features in the i sub-features one by one, and record the characters of the same post-features and the characters of the device attributes as the second coincident characters; Count the number of second coincident characters in i detection subtasks one by one to obtain i second coincidence values; Based on i first coincidence values ​​and corresponding i second coincidence values, obtaining i task coincidence degrees; The expression of task overlap is: RW chi =σ1*CH d1i +σ2*CH d2i ; Where RW chi is the task overlap of the i-th detection subtask, CH d1i is the first coincidence value of the i-th detection subtask, CH d2i is the second coincidence value of the i-th detection subtask, σ1 and σ2 are weight factors greater than 0.

6. A modular intelligent detection system for measuring and collecting equipment detection according to claim 5, characterized in that: The method for screening the target subtasks includes: Arrange the overlap of i tasks in descending order from large to small; When the number of task overlaps ranked first is 1, the detection subtask corresponding to the task overlap ranked first is recorded as the target subtask; When the number of task overlaps ranked first is greater than 1, the detection subtask corresponding to the maximum value of the second overlap value is recorded as the target subtask; When the sizes of the second coincidence values ​​are all the same, the detection subtask corresponding to the minimum value of the numbered feature is recorded as the target subtask.

7. A modular intelligent detection system for measuring and collecting equipment detection according to claim 6, characterized in that: The scene bit includes a task scene bit and a device scene bit; The generation method of the detection scene includes: Query the test items of the target subtask through the task attribute table; Query the detection parameters of the device terminal through the technical parameter table to obtain the scene condition configuration parameters; Under the scenario condition configuration parameters, a blank scenario matching the test item is constructed by interacting with the device terminal; Mark the equipment scene position and task scene position in the blank scene respectively to obtain the initial scene; Receiving points are established at the device scene position and the task scene position respectively, and the sub-features of the target subtask and the device features of the device terminal are imported into the receiving points respectively to obtain the detection scene.

8. A modular intelligent detection system for measuring and collecting equipment detection according to claim 7, characterized in that: The expression of the task processing rate is: Split the target subtask into X task items, and distribute the X task items one by one on the task points in the detection scene to generate X task detection points; At time T1, the number of task item data existing in X task detection points is queried one by one through the traffic monitoring tool to obtain X initial data volumes; At time T2, the number of remaining task item data in X task detection points is queried one by one through the traffic monitoring tool to obtain X end data volumes; Subtract the X initial data volumes from the X final data volumes one by one to obtain the x total processing volumes; The total processing volume is expressed as: CL zx =CS lx -JS lx ; In the formula, CL zx is the total processing capacity of the xth task detection point, CS lx is the initial data volume of the xth task detection point, JS lx The end data volume of the Xth task detection point; The duration between time T1 and time T2 is recorded as the total duration, and X total processing amounts are compared with the total duration in sequence to obtain X sub-processing rates; The expression of sub-processing rate is: In the formula, SL zx is the sub-processing rate of the Xth task detection point, SC 12 is the total duration; Remove the maximum and minimum values ​​of the sub-processing rates, accumulate the remaining x-2 sub-processing rates and calculate the average to obtain the task processing rate; The expression of task processing rate is: Where RW sl is the task processing rate, SL za is the sub-processing rate of the a-th task detection point.

9. A modular intelligent detection system for measuring and collecting equipment detection according to claim 8, characterized in that: The method for obtaining the effective feedback duration includes: Number the X task detection points in ascending order one by one; Using natural language processing technology, keywords of task items in X task detection points are identified one by one to obtain x keywords; With x keywords as the request content and the number of the task detection point as the request sequence number, generate X task item detection requests; Query the time when X task item detection requests are sent to the task detection point one by one through the timestamp to obtain the time when X requests are sent; Query the time when X task detection points start to execute the task item detection request one by one through the timestamp to obtain the X request execution time; The time between the time when X requests are sent and the time when X requests are executed is recorded as the feedback time, and X feedback time is obtained; Subtract the X feedback durations from the fixed delay duration one by one, add them up and calculate the average to obtain the effective feedback duration; The expression of effective feedback duration is: In the formula, YX sc SC is the effective feedback duration. zb The sub-duration of the detection request for the b-th task item, YC sc The delay time is fixed.

10. A modular intelligent detection system for measuring and collecting equipment detection according to claim 9, characterized in that: The expression of the scene performance index is: In the formula, XN zs is the scene performance index, JC hg is the detection pass rate, σ3, σ4, σ5 are weight factors greater than 0; The performance levels include the first performance level, the second performance level and the third performance level; The methods for distinguishing performance levels 1, 2 and 3 include: Set the scene performance index XN zs With the preset first scenario performance threshold XN yz1 and the second scenario performance threshold XN yz2 Compare, XN yz1 Less than XN yz2 ; When XN zs Less than or equal to XN yz1 When , a first-level performance level is generated; When XN zs Greater than XN yz1 , and XN zs Less than or equal to XN yz2 When , a secondary performance level is generated; When XN zs Greater than XN yz2 When , three performance levels are generated.

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