A measurement and acquisition equipment detection method based on modular design

Abstract

The invention relates to the technical field of equipment detection, and discloses a method for detecting metering and acquisition equipment based on modular design; the method comprises splitting a detection project into atomic tasks, generating a task detection model, collecting comprehensive sequence parameters of the atomic tasks in the task detection model, calculating an execution sequence value, identifying a target subtask, adjusting and executing the target subtask according to dependencies, and continuously updating the target subtask; compared with the prior art, the invention splits the detection project into atomic tasks, and achieves an accurate adjustment effect of the execution sequence of the target subtask by analyzing and judging the dependencies of the atomic tasks, and can execute atomic tasks that do not depend on execution environment equipment in parallel, achieving an execution concept of atomic tasks that takes resource exclusivity and release as the execution basis and obtains and occupies resources as the execution means, effectively avoiding the waiting dependency phenomenon of the detection project during serial detection, thereby shortening the detection time of the detection project.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment detection, and more specifically, to a measurement and acquisition equipment detection method based on modular design. Background Art

[0002] Electric energy metering and collection equipment refers to equipment that collects, processes, stores and transmits electric energy data measured by electric energy metering and collection devices. Electric energy metering and collection equipment is generally composed of collection terminals, communication modules, storage devices and related software, so that electric energy metering and collection equipment can measure and collect various types of electricity consumption information during the electricity consumption process. In order to accurately obtain the performance of electric energy collection equipment and avoid errors in electric energy metering and collection, it is necessary to detect and process the electric energy collection equipment.

[0003] The patent application with reference publication number CN108508396A discloses a multi-meter integrated acquisition device detection system and method, which can effectively realize the comprehensive detection of the smart meter and the communication interface converter in the multi-meter integrated acquisition device, including the communication interface converter load capacity, interchangeability and power consumption test, the communication module interchangeability, power consumption and meter reading test, and the interface performance test of the smart meter, etc., with a wide detection range, high detection accuracy and high detection efficiency, and can well meet the detection requirements of the multi-meter integrated acquisition device;

[0004] The prior art has the following deficiencies:

[0005] When the existing measurement and acquisition equipment detection method detects the acquisition equipment, the execution of each detection item and test case of the object under test usually depends on the detection unit and the detection environment, so that each detection item and test case can only be detected serially. When the various detection items and test cases of the object under test arrive and are detected at different times, the serial detection method that depends on the detection unit and the detection environment is adopted, which will cause detection delays or even detection stoppage between two dependent detection items, thereby causing the detection time to become longer and the detection efficiency to decrease.

[0006] In view of this, the present invention proposes a measurement and acquisition equipment detection method based on modular design 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 solution: a measurement and acquisition equipment detection method based on modular design, applied to a detection management platform, comprising:

[0008] S1: Construct a basic domain model and mark the detection units and detection epitopes in the basic domain model based on the principle of parallel execution;

[0009] S2: Filter out the test items of the equipment to be tested from the task database and split the test items into atomic tasks;

[0010] S3: Allocate the device to be tested and the atomic task to the detection unit and the detection table respectively, generate a task detection model, and determine whether to trigger the parallel execution mode; if it is determined that the parallel execution mode is triggered, execute S4-S5; if it is determined that the parallel execution mode is not triggered, repeat S2-23;

[0011] S4: Collect the comprehensive sequence parameters of the atomic tasks in the task detection model, calculate the execution sequence value, and identify the target subtasks according to the execution sequence value. The comprehensive sequence parameters include task importance and task online rate;

[0012] S5: Obtain the dependencies of the target subtask, adjust and execute the target subtask according to the dependencies, and continuously update the target subtask until all atomic tasks are executed. The dependencies include the predecessor dependent tasks and predecessor dependent devices.

[0013] Furthermore, the parallel execution principle is: the execution level of all detection units is the same, and the execution level of a detection unit is higher than the execution level of the detection epitope within the detection unit;

[0014] Methods for labeling detection units and detection epitopes include:

[0015] A blank model with three levels is selected from the model database, and different levels are assigned to the three levels respectively;

[0016] From low to high levels, the three layers are labeled as input layer, detection layer, and application layer to form a basic domain model;

[0017] Mark i independent units in the detection layer, and assign the same first execution level to the i units one by one, so as to generate i detection units;

[0018] P independent epitopes are marked in i detection units, and the same second execution level is assigned to the p epitopes one by one to generate p detection epitopes.

[0019] Furthermore, the screening method of the test items includes:

[0020] Query the performance parameters corresponding to the performance to be tested of the device to be tested through the technical parameter table to obtain the performance to be tested;

[0021] Mark the phrases in the remark boxes of the device to be tested one by one, and mark the remark boxes with the same phrases and the performance to be tested as target remark boxes, and obtain the number of the target remark boxes;

[0022] Identify the set keywords of the task sets in the task database one by one, and record the task sets with the same set keywords as the performance to be tested as the target set;

[0023] Arrange the task items in the target set in order from small to large numbers to obtain a task item sequence, and mark the task item numbers;

[0024] Task items with the same number as the number of the target remark box are selected from the task item sequence to obtain the detection items.

[0025] Furthermore, the splitting methods of atomic tasks include:

[0026] Identify key semantics and key values ​​of the detection items through natural language processing technology, obtain k key semantics of the items and k key values ​​of the items, and number the k key semantics of the items and the k key values ​​of the items in sequence;

[0027] Record current, voltage, power, response time, throughput, utilization, failure rate, and processing rate as standard key semantics;

[0028] Record the project key semantics containing any standard key semantics as the target key semantics, and obtain j target key semantics;

[0029] Filter out the project key values ​​that are identical to the j target key semantic numbers from the k project key values, and obtain j target key values;

[0030] After combining j target key semantics and j target key values ​​one by one, j splitting units are obtained, and the detection project is split into j atomic tasks based on the j splitting units as the splitting criteria.

[0031] Furthermore, the method for generating the task detection model includes:

[0032] Count the number of test items in the m devices under test one by one, and record it as the test quantity value;

[0033] According to the detection quantity value from small to large, m devices to be tested are allocated to m detection units one by one to generate m task units;

[0034] Import the j atomic tasks corresponding to the m devices under test into the j detection epitopes in the m task units respectively, obtain j task epitopes, and generate a task detection layer;

[0035] Query the attribute status of the device under test through the attribute parameter table, and import the attribute status into the input layer to generate the task input layer;

[0036] Establish a data transmission channel in the application layer that communicates with the detection management platform, set encryption points on the data transmission channel, and generate a task application layer;

[0037] The task input layer, task detection layer and task application layer are combined in a hierarchical manner from low to high to generate a task detection model;

[0038] The determination method of whether to trigger the parallel execution mode includes:

[0039] Count the number of task epitopes in the task detection model and obtain the parallel trigger value;

[0040] When the parallel trigger value is 1, it is determined that the parallel execution mode is not triggered;

[0041] When the parallel trigger value is greater than 1, the parallel execution mode is triggered.

[0042] Furthermore, the method for obtaining the task importance includes:

[0043] The number of detection items with standard key semantics of current, voltage, power, response time, throughput, utilization rate, failure rate and processing rate is obtained by querying the task database respectively. The detection value, (1,2,3,4,5,6,7,8);

[0044] Count the number of all test items in the task database and record it as the total test value;

[0045] Will The detected values ​​are compared with the total detected value in turn to obtain An important percentage value;

[0046] The expression of the important proportion value is:

[0047] ;

[0048] In the formula, For the An important proportion of For the The detection value, is the total amount of detection;

[0049] Will The important proportion values ​​are multiplied by the preset unit importance value to obtain Importance of personality;

[0050] The expression of sub-importance is:

[0051] ;

[0052] In the formula, For the Importance of personality, is the preset unit importance value;

[0053] Identify the task detection model one by one The standard key semantics of atomic tasks, and The atomic tasks are assigned sub-importances corresponding to the standard key semantics, and the Importance of personality;

[0054] Will The importance of each child is respectively Compare the key values ​​of the target to obtain The importance of each task;

[0055] The expression of task importance is:

[0056] ;

[0057] In the formula, For the The task importance of each atomic task, For the The importance of each atomic task, For the The target key value of an atomic task.

[0058] Furthermore, the method for obtaining the task online rate includes:

[0059] exist A mark box is established at the standard key semantic tail of each atomic task, and two workstations are planned in the mark box, which are recorded as the online workstation and the offline workstation respectively;

[0060] The same display permissions are granted to online workstations and offline workstations respectively, and a single workstation display window is opened on the marking frame;

[0061] Using a preset display duration as an interval standard, v display points are marked, and the display results of the single-station display window are queried one by one at the v display points through the system monitoring tool;

[0062] The display points of the online workstations are recorded as valid display points, and the number of valid display points is counted. After comparing the number of valid display points with the total number of display points, the result is obtained. The online rate of each task;

[0063] The expression of task online rate is:

[0064] ;

[0065] In the formula, For the The task online rate of atomic tasks, For the The number of valid display points for an atomic task, For the The total amount of display points for an atomic task.

[0066] Furthermore, the expression of the execution order value is:

[0067] ;

[0068] In the formula, For the The execution order value of atomic tasks, , is the proportionality coefficient, and , All are greater than 0;

[0069] The identification methods of target subtasks include:

[0070] Create a blank execution set and mark the execution sets separately. An independent execution bit, and The execution positions are numbered in ascending order;

[0071] According to the execution order from large to small, Atomic tasks are imported into the execution set in sequence. Generate an execution queue in each execution position;

[0072] Select the atomic task corresponding to the smallest number from the execution queue and record it as the target subtask.

[0073] Further, the method of adjusting and executing the target subtask includes:

[0074] The dependencies of the target subtask are queried through the task database. If there are no predecessor dependent tasks and predecessor dependent devices in the dependencies, the target subtask is executed normally.

[0075] When there is a predecessor dependent task in the dependency, and the execution status of the predecessor dependent task is executed, and there is no predecessor dependent device in the dependency, the target subtask will be executed normally;

[0076] When there is a predecessor dependent task in the dependency, and the execution status of the predecessor dependent task is not executed, the atomic task corresponding to the predecessor dependent task is used as the target subtask;

[0077] When there is no predecessor dependent task in the dependency, and the occupation status of the predecessor dependent device is unoccupied, the predecessor dependent device is occupied and the target subtask is executed normally;

[0078] When there is no predecessor dependent task in the dependency and the occupation status of the predecessor dependent device is occupied, the target subtask is removed from the execution queue;

[0079] The atomic task corresponding to the smallest number is selected from the remaining execution queues as the target subtask for update until all atomic tasks in the execution queue are executed.

[0080] The technical effects and advantages of the measurement and acquisition equipment detection method based on modular design of the present invention are as follows:

[0081] The present invention constructs a basic domain model, and based on the parallel execution principle, marks the detection units and detection epitopes in the basic domain model, screens out the detection items of the device to be tested from the task database, splits the detection items into atomic tasks, allocates the device to be tested and the atomic tasks to the detection units and detection epitopes respectively, generates a task detection model, determines whether to trigger the parallel execution mode, collects the comprehensive sequence parameters of the atomic tasks in the task detection model, calculates the execution sequence value, identifies the target subtask according to the execution sequence value, obtains the dependency of the target subtask, adjusts and executes the target subtask according to the dependency, and continuously updates the target subtask until all the atomic tasks are executed; compared with the prior art, it can It is able to divide different devices under test into different detection units, and perform orderly detection operations on the devices under test and the objects under test according to the detection units. At the same time, the detection items are split into the smallest atomic task granularity, and the dependencies of the atomic tasks are analyzed and judged to achieve accurate adjustment of the execution order of the target subtasks, so that the atomic tasks that do not depend on the execution environment equipment can be executed in parallel, achieving the execution concept of atomic tasks based on resource exclusivity and release, and acquiring and occupying resources as the execution means, thereby effectively avoiding the waiting dependency phenomenon of the detection items during serial detection, thereby shortening the detection time of the detection items and meeting the rapid detection needs of different objects under test and arriving at different times. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 A schematic diagram of a flow chart of a measurement and acquisition equipment detection method based on modular design provided in the first embodiment of the present invention;

[0083] Figure 2 A schematic diagram of a process for adjusting and executing a target subtask provided in the first embodiment of the present invention;

[0084] Figure 3 A schematic diagram of a measurement and acquisition equipment detection system based on modular design provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0085] 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.

[0086] Example 1: Please refer to Figure 1 and Figure 2 As shown, the measurement and collection equipment detection method based on modular design described in this embodiment is applied to the detection management platform, including:

[0087] S1: Construct a basic domain model and mark the detection units and detection epitopes in the basic domain model based on the principle of parallel execution;

[0088] The basic domain model refers to a simulation model that can provide initial induction and allocation for the devices to be tested, and serves as the basis for subsequent model upgrades based on the different epitopes and items of the actual devices to be tested, thereby providing a basic outline for the detection of the devices to be tested;

[0089] When there are a large number of devices to be tested and a large number of test items for the devices to be tested, it is easy for a large number of test items of various types to be cross-mixed with each other. In order to ensure that the basic domain model can meet the test requirements of a large number of test items of various types, it is necessary to ensure that the basic domain model can achieve the effect of parallel testing between a large number of different test items. Therefore, it is necessary to mark the test units and test epitopes of the basic domain model under the principle of parallel execution.

[0090] The parallel execution principle is: the execution level of all detection units is consistent, and the execution level of the detection unit is higher than the execution level of the detection epitope in the detection unit; the execution level is used to indicate the priority of the detection unit and the detection epitope when executing;

[0091] The detection unit refers to an area that can provide detection position limits for different devices under test, ensuring that one detection unit can correspond to the detection of one device under test, while the detection epitope is an area provided for executing the detection items in the device under test, so that the detection epitope can satisfy the detection execution operation of the detection items of one device under test;

[0092] Methods for labeling detection units and detection epitopes include:

[0093] A blank model with three levels is selected from the model database, and different levels are assigned to the three levels; a blank model refers to a basic model without any substantial content, which can be used as the outline for the construction of other subsequent models, and a level is the smallest component structure of the model, and the model is distinguished in terms of application structure;

[0094] From low to high levels, the three layers are labeled as input layer, detection layer, and application layer to form a basic domain model;

[0095] Mark i independent units in the detection layer, and assign the same first execution level to the i units one by one, so as to generate i detection units;

[0096] Marking p independent epitopes in i detection units, and assigning the same second execution level to the p epitopes one by one, to generate p detection epitopes;

[0097] It should be noted that the first execution level and the second execution level are both used to indicate the order of execution of the detection items in the model, thereby limiting the execution order of the detection items, and the first execution level is higher than the second execution level, which can ensure that the detection items in the units and epitopes can be executed and processed in an orderly manner.

[0098] S2: Filter out the test items of the equipment to be tested from the task database and split the test items into atomic tasks;

[0099] The device under test refers to the acquisition device that needs to be tested for performance. The test items refer to the performance that needs to be tested in the device under test, and serve as the overall test object to determine the performance strength and operating status of the device under test.

[0100] Screening methods for test items include:

[0101] Query the performance parameters corresponding to the performance to be tested of the device to be tested through the technical parameter table to obtain the performance to be tested;

[0102] Mark the phrases in the remark box of the device to be tested one by one, and record the remark box with the same phrase and the performance to be tested as the target remark box, and obtain the number of the target remark box; the remark box refers to a marking structure containing the real meaning of the test items of the device to be tested, and the phrase is the real data of the remark in the remark box, which is used to identify the remark box;

[0103] Identify the set keywords of the task sets in the task database one by one, and record the task sets with the same set keywords and the performance to be tested as the target set; the task set refers to a collection of tasks corresponding to various test items pre-stored in the task database, so that each task item in the collection can correspond to a certain performance of the acquisition equipment;

[0104] Arrange the task items in the target set in order from small to large numbers to obtain a task item sequence, and mark the task item numbers;

[0105] Task items with the same number as the number of the target remark box are selected from the task item sequence to obtain the detection items.

[0106] Since the test items are used to represent the overall performance of the equipment to be tested, the test items contain a lot of specific power consumption information. In order to further subdivide the overall test items, the test items can be split according to the keywords corresponding to each test parameter, so that the test items can be split into atomic tasks, and each atomic task can represent a separate power consumption information of the test item;

[0107] The splitting methods of atomic tasks include:

[0108] The key semantics and key values ​​of the test items are identified through natural language processing technology, k key semantics and k key values ​​of the items are obtained, and the k key semantics and k key values ​​of the items are numbered in sequence; the key semantics of the item refers to a concise textual representation of the performance corresponding to the test item, and the key value of the item refers to a concise numerical representation of the performance corresponding to the test item, so that more complex test items can be represented separately in words and numbers;

[0109] Record current, voltage, power, response time, throughput, utilization, failure rate, and processing rate as standard key semantics;

[0110] Record the project key semantics containing any standard key semantics as the target key semantics, and obtain j target key semantics;

[0111] Filter out the project key values ​​that are identical to the j target key semantic numbers from the k project key values, and obtain j target key values;

[0112] After combining j target key semantics and j target key values ​​one by one, j split units are obtained, and the detection project is split into j atomic tasks based on the j split units as the splitting standard;

[0113] It should be noted that each atomic task can correspond to a specific detection process of a certain power consumption information, so that j atomic tasks can perform comprehensive detection effects of various power consumption information on the equipment under test, thereby providing a diversified data basis for the performance strength detection corresponding to the detection items of the equipment under test.

[0114] S3: Allocate the device to be tested and the atomic task to the detection unit and the detection table respectively, generate a task detection model, and determine whether to trigger the parallel execution mode;

[0115] The task detection model refers to the basic domain model that imports the device under test and the atomic tasks, so that the task detection model can be used as a model for subsequent parallel execution detection of multiple atomic tasks on the device under test, so that all atomic tasks of the device under test can be executed in parallel;

[0116] The generation method of the task detection model includes:

[0117] Count the number of test items in the m devices under test one by one, and record it as the test quantity value;

[0118] According to the detection quantity value from small to large, m devices to be tested are allocated to m detection units one by one to generate m task units;

[0119] Import the j atomic tasks corresponding to the m devices under test into the j detection epitopes in the m task units respectively, obtain j task epitopes, and generate a task detection layer;

[0120] The attribute status of the device under test is queried through the attribute parameter table, and the attribute status is imported into the input layer to generate the task input layer; the attribute status is used to represent the type of the device under test, which can be used as the basis for configuring the test condition parameters of the device under test during performance testing;

[0121] A data transmission channel that communicates with the detection management platform is established in the application layer, and encryption points are set on the data transmission channel to generate the task application layer; the data transmission channel refers to a channel that can perform two-way data transmission between the task detection model and the detection management platform to ensure that the results of the detection items of the equipment under test can be quickly identified, and the encryption point is an encryption protection measure for the data transmission channel to avoid data leakage and loss in the data transmission channel;

[0122] The task input layer, task detection layer and task application layer are combined in a hierarchical manner from low to high to generate a task detection model.

[0123] After the task detection model is generated, the atomic tasks of the device under test can be executed in the task detection model. By judging the number of atomic tasks in the task detection model, it is determined whether to trigger the parallel execution mode, so that the task detection model can execute multiple atomic tasks in parallel under the limitation of the parallel execution mode.

[0124] The determination method of whether to trigger the parallel execution mode includes:

[0125] Count the number of task epitopes in the task detection model and obtain the parallel trigger value;

[0126] When the parallel trigger value is 1, it means that there is only one atomic task that needs to be detected in the task detection model. At this time, the atomic task does not need to be executed in parallel, so it is determined that the parallel execution mode is not triggered;

[0127] When the parallel trigger value is greater than 1, it means that the number of atomic tasks that need to be detected in the task detection model exceeds 1. At this time, the atomic tasks need to be executed in parallel, and the parallel execution mode is triggered.

[0128] It should be noted that since the power consumption information corresponding to the test items in the device under test is usually not unique, the number of atomic tasks included in the test items is greater than 1 in most cases, so that the number of task tables in the task detection model exceeds 1, which makes the task detection model compatible with multiple power consumption information for performance testing of the device under test.

[0129] S4: Collect the comprehensive sequence parameters of the atomic tasks in the task detection model, calculate the execution sequence value, and identify the target subtask according to the execution sequence value;

[0130] When the task detection model is generated and the parallel execution mode is triggered, there are a large number of atomic tasks that need to be executed in parallel at the same time in the task detection model. At this time, in order to ensure that each atomic task can be accurately executed in the order of execution, the atomic tasks need to be sorted. In order to ensure the accuracy of the sorting, it is necessary to obtain the comprehensive sequence parameters of the atomic tasks so that the comprehensive sequence parameters can represent the changes that affect the order of atomic task sorting, and calculate the execution order value based on the comprehensive sequence parameters so that the execution order value can be used as the basis for the arrangement of atomic tasks;

[0131] The comprehensive order parameters include task importance and task online rate;

[0132] Task importance refers to the importance of the atomic task in the detection process in the task detection model, which can affect the order of the atomic task in the sorting process. The greater the task importance, the greater the importance of the atomic task in the detection process, and the greater the execution order value;

[0133] The methods for obtaining task importance include:

[0134] The number of detection items with standard key semantics of current, voltage, power, response time, throughput, utilization rate, failure rate and processing rate is obtained by querying the task database respectively. The detection value, (1,2,3,4,5,6,7,8);

[0135] Count the number of all test items in the task database and record it as the total test value;

[0136] Will The detected values ​​are compared with the total detected value in turn to obtain An important percentage value;

[0137] The expression of the important proportion value is:

[0138] ;

[0139] In the formula, For the An important proportion of For the The detection value, is the total amount of detection;

[0140] Will The important proportion values ​​are multiplied by the preset unit importance value to obtain The preset unit importance value refers to the value preset as the standard unit importance, so that the preset unit importance value can be calculated after multiplying with the respective importance ratio value; the size of the preset unit importance value can be freely set according to actual needs;

[0141] The expression of sub-importance is:

[0142] ;

[0143] In the formula, For the Importance of personality, is the preset unit importance value;

[0144] Identify the task detection model one by one The standard key semantics of atomic tasks, and The atomic tasks are assigned sub-importances corresponding to the standard key semantics, and the Importance of personality;

[0145] Will The importance of each child is respectively Compare the key values ​​of the target to obtain The importance of each task;

[0146] The expression of task importance is:

[0147] ;

[0148] In the formula, For the The task importance of each atomic task, For the The importance of each atomic task, For the The target key value of an atomic task.

[0149] The task online rate refers to the ratio of the number of atomic tasks in the task detection model that are online during the detection process to the total number of tasks, which can affect the order of atomic tasks in the sorting process. The larger the task online rate, the greater the importance of the atomic task in the detection process, and the greater the execution order value.

[0150] Methods for obtaining task online rate include:

[0151] exist A mark box is established at the standard key semantic tail of each atomic task, and two workstations are planned in the mark box, which are recorded as online workstation and offline workstation respectively; the mark box refers to a representation that can summarize the workstation status of the atomic task, so that the online workstation and the offline workstation can constitute two different states of the mark box;

[0152] The same display permissions are granted to online workstations and offline workstations respectively, and a single-workstation display window is opened on the mark box; the same display permissions ensure that online workstations and offline workstations have the same probability of appearing in the single-workstation display window, so that the single-workstation display window can display the same conditions for online workstations and offline workstations, thereby improving the calculation accuracy of the task online rate;

[0153] Using a preset display duration as an interval standard, v display points are marked, and the display results of the single-station display window are queried one by one at the v display points through a system monitoring tool; the preset display duration refers to the duration corresponding to at least one change of the display result of the single-station display window, thereby ensuring that the display result of the single-station display window can change and improving the accuracy of the collection of the display result; the preset display duration is obtained by collecting a large number of historical single-station display windows The corresponding duration of at least one change of the display result is obtained by calculating the average value;

[0154] The display points of the online workstations are recorded as valid display points, and the number of valid display points is counted. After comparing the number of valid display points with the total number of display points, the result is obtained. The online rate of each task;

[0155] The expression of task online rate is:

[0156] ;

[0157] In the formula, For the The task online rate of atomic tasks, For the The number of valid display points for an atomic task, For the The total number of display points for an atomic task.

[0158] After obtaining the task importance and task online rate, the ranking value of each atomic task to be executed can be calculated according to the task importance and task online rate, and the execution order value can be obtained. Then, all the atomic tasks to be executed can be sorted according to the execution order value;

[0159] The expression for the execution order value is:

[0160] ;

[0161] In the formula, For the The execution order value of atomic tasks, , is the proportionality coefficient, and , All are greater than 0;

[0162] in, , exemplary, is 0.42, is 0.58; it should be noted that , The purpose is to balance the impact of task importance and task online rate on the execution order value, thereby ensuring the accuracy of the execution order value.

[0163] The target subtask refers to the atomic task that is executed first among the atomic tasks to be executed, so that the target subtask can serve as the basis for the parallel execution of subsequent atomic tasks;

[0164] The identification methods of target subtasks include:

[0165] Create a blank execution set and mark the execution sets separately. An independent execution bit, and The execution positions are numbered in ascending order;

[0166] According to the execution order from large to small, Atomic tasks are imported into the execution set in sequence. In the execution positions, an execution queue is generated;

[0167] Select the atomic task corresponding to the smallest number from the execution queue and record it as the target subtask.

[0168] S5: Obtain the dependencies of the target subtask, adjust and execute the target subtask according to the dependencies, and continuously update the target subtask until all atomic tasks are executed;

[0169] Dependencies refer to other atomic tasks and devices to be tested that each atomic task needs to rely on when executing performance testing, so that dependencies can be used as prerequisites before the target subtask is executed, thereby limiting the execution order of the target subtask;

[0170] Dependencies include pre-dependent tasks and pre-dependent devices; pre-dependent tasks refer to atomic tasks that must be completed in advance when executing the target subtask, so that the pre-dependent tasks can pave the way for the execution of the target subtask and serve as one of the pre-conditions for the execution of the target subtask; pre-dependent devices refer to the devices to be tested that must be relied upon when executing the target subtask, so that the pre-dependent devices can provide a detection environment for the execution of the target subtask and serve as another pre-condition for the execution of the target subtask; dependent tasks and pre-dependent devices are obtained by querying the task database;

[0171] After obtaining the dependencies of the target subtask, it is necessary to analyze and judge the dependencies of the target subtask in real time, and adjust the execution order of the target subtask according to the real-time status of the dependencies, so as to continuously update the target subtask until all atomic tasks are executed, so as to achieve a fast and accurate detection effect on the detection items of the equipment to be tested;

[0172] Methods for adjusting and executing target subtasks include:

[0173] Query the dependencies of the target subtask through the task database to determine whether the dependencies contain predecessor dependent tasks;

[0174] When there is no preceding dependent task in the dependency, it means that other atomic tasks do not need to be pre-executed before executing the target subtask, then it is determined whether the dependency contains preceding dependent devices;

[0175] When there is a predecessor dependent task in the dependency, the execution status of the predecessor dependent task is identified at this time; the execution status is used to indicate whether the predecessor dependent task is currently being executed, and the execution status includes not executed and executed;

[0176] If the execution status of the predecessor dependent task is executed, it means that the predecessor dependent task has been executed, then it is determined whether the dependency item contains the predecessor dependent device;

[0177] If the execution status of the predecessor dependent task is not executed, it means that the predecessor dependent task has not been completed, then the atomic task corresponding to the predecessor dependent task is used as the target subtask;

[0178] When there is no pre-dependent device in the dependency, it means that the execution of the target subtask does not need to rely on a specific device under test, then the target subtask is executed normally, and the atomic task corresponding to the smallest number is selected from the remaining execution queue as the target subtask for update until all atomic tasks in the execution queue are executed;

[0179] When there is a pre-dependent device in the dependency, it means that the execution of the target subtask needs to rely on a specific device to be tested. At this time, the occupation status of the pre-dependent device is identified; the occupation status is used to indicate whether the pre-dependent device is currently occupied, and the occupation status includes unoccupied and occupied;

[0180] If the occupancy status of the preceding dependent device is unoccupied, the preceding dependent device is occupied and the target subtask is executed normally;

[0181] If the occupancy status of the preceding dependent device is occupied, the target subtask is removed from the execution queue, and the atomic task corresponding to the smallest number is selected from the remaining execution queue as the target subtask for update until all atomic tasks in the execution queue are executed.

[0182] It should be noted that by identifying and judging the dependencies, all atomic tasks in the execution queue can be tested and executed in an orderly and rapid manner, so that the test items of the device under test can effectively avoid the phenomenon of dependent waiting, thereby shortening the test waiting time of the device under test.

[0183] In this embodiment, a basic domain model is constructed, and based on the parallel execution principle, the detection units and detection sites in the basic domain model are marked, the detection items of the device to be tested are screened out from the task database, and the detection items are split into atomic tasks, the device to be tested and the atomic tasks are respectively assigned to the detection units and detection sites, a task detection model is generated, and it is determined whether to trigger the parallel execution mode, the comprehensive sequence parameters of the atomic tasks in the task detection model are collected, the execution sequence value is calculated, and the target subtask is identified according to the execution sequence value, the dependency of the target subtask is obtained, the target subtask is adjusted and executed according to the dependency, and the target subtask is continuously updated until all the atomic tasks are executed; compared with the prior art , it can divide different devices to be tested into different detection units, and perform orderly detection operations on the devices to be tested and the objects to be tested according to the detection units. At the same time, it adopts the method of splitting the detection items into the smallest atomic task granularity, and through analyzing and judging the dependencies of the atomic tasks, it can achieve the accurate adjustment effect of the execution order of the target subtasks, so that the atomic tasks that do not depend on the execution environment equipment can be executed in parallel, achieving the execution concept of atomic tasks based on resource exclusivity and release, and acquiring and occupying resources as the execution means, thereby effectively avoiding the waiting dependency phenomenon of the detection items during serial detection, thereby shortening the detection time of the detection items, and meeting the rapid detection needs of different objects to be tested and arriving at different times.

[0184] Example 2: Please refer to Figure 3 As shown, the part not described in detail in this embodiment is described in the first embodiment, and a measurement and acquisition equipment detection system based on modular design is provided, which is applied to a detection management platform and is used to implement a measurement and acquisition equipment detection method based on modular design, including: a model marking unit, a task splitting module, a model determination module, a task identification module and a task execution module, wherein each module is connected by a limited or wireless method;

[0185] A model marking unit is used to construct a basic domain model and mark the detection units and detection epitopes in the basic domain model based on the parallel execution principle;

[0186] The task splitting module is used to filter out the test items of the equipment to be tested from the task database and split the test items into atomic tasks;

[0187] The model determination module is used to allocate the device to be tested and the atomic task to the detection unit and the detection table, generate a task detection model, and determine whether to trigger the parallel execution mode;

[0188] The task identification module is used to collect the comprehensive sequence parameters of the atomic tasks in the task detection model, calculate the execution sequence value, and identify the target subtasks according to the execution sequence value. The comprehensive sequence parameters include task importance and task online rate;

[0189] The task execution module is used to obtain the dependencies of the target subtask, adjust and execute the target subtask according to the dependencies, and continuously update the target subtask until all atomic tasks are executed. The dependencies include the predecessor dependent tasks and predecessor dependent devices.

[0190] 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 measurement and acquisition equipment detection method based on modular design, applied to the detection management platform, characterized in that: include: S1: Construct a basic domain model, and based on the principle of parallel execution, mark the detection units and detection epitopes in the basic domain model. The detection unit refers to the area that can provide detection position limits for different devices under test, and the detection epitope is the area provided for executing the detection items in the device under test; S2: Filter out the test items of the equipment to be tested from the task database and split the test items into atomic tasks; S3: Allocate the device to be tested and the atomic task to the detection unit and the detection table respectively, generate a task detection model, and determine whether to trigger the parallel execution mode; if it is determined that the parallel execution mode is triggered, execute S4-S5; If it is determined that the parallel execution mode is not triggered, S2-23 is executed repeatedly; S4: Collect the comprehensive sequence parameters of the atomic tasks in the task detection model, calculate the execution sequence value, and identify the target subtasks according to the execution sequence value. The comprehensive sequence parameters include task importance and task online rate; The methods for obtaining task importance include: The number of detection items with standard key semantics of current, voltage, power, response time, throughput, utilization rate, failure rate and processing rate is obtained by querying the task database respectively. The detection value, (1,2,3,4,5,6,7,8); Count the number of all test items in the task database and record it as the total test value; Will The detected values ​​are compared with the total detected value in turn to obtain An important percentage value; The expression of the important proportion value is: ； In the formula, For the An important proportion of For the The detection value, is the total detection value; Will The important proportion values ​​are multiplied by the preset unit importance value to obtain Importance of personality; The expression of sub-importance is: ； In the formula, For the Importance of personality, is the preset unit importance value; Identify the task detection model one by one The standard key semantics of atomic tasks, and The atomic tasks are assigned sub-importances corresponding to the standard key semantics, and the Importance of personality; Will The importance of each child is respectively Compare the key values ​​of the target to obtain The importance of each task; The expression of task importance is: ； In the formula, For the The task importance of each atomic task, For the The importance of each atomic task, For the The target key value of each atomic task; Methods for obtaining task online rate include: exist A mark box is established at the standard key semantic tail of each atomic task, and two workstations are planned in the mark box, which are recorded as the online workstation and the offline workstation respectively; The same display permissions are granted to online workstations and offline workstations respectively, and a single workstation display window is opened on the marking frame; Using a preset display duration as an interval standard, v display points are marked, and the display results of the single-station display window are queried one by one at the v display points through the system monitoring tool; The display points of the online workstations are recorded as valid display points, and the number of valid display points is counted. After comparing the number of valid display points with the total number of display points, the result is obtained. The online rate of each task; The expression of task online rate is: ； In the formula, For the The task online rate of atomic tasks, For the The number of valid display points for an atomic task, For the The total number of display points for an atomic task; The expression for the execution order value is: ； In the formula, For the The execution order value of atomic tasks, , is the proportionality coefficient, and , are greater than 0, ; The identification methods of target subtasks include: Create a blank execution set and mark the execution sets separately. An independent execution bit, and The execution positions are numbered in ascending order; According to the execution order from large to small, Atomic tasks are imported into the execution set in sequence. In the execution positions, an execution queue is generated; Select the atomic task with the smallest number from the execution queue and record it as the target subtask; S5: Obtain the dependencies of the target subtask, adjust and execute the target subtask according to the dependencies, and continuously update the target subtask until all atomic tasks are executed. The dependencies include the predecessor dependent tasks and predecessor dependent devices. Methods for adjusting and executing target subtasks include: The dependencies of the target subtask are queried through the task database. If there are no predecessor dependent tasks and predecessor dependent devices in the dependencies, the target subtask is executed normally. When there is a predecessor dependent task in the dependency, and the execution status of the predecessor dependent task is executed, and there is no predecessor dependent device in the dependency, the target subtask will be executed normally; When there is a predecessor dependent task in the dependency, and the execution status of the predecessor dependent task is not executed, the atomic task corresponding to the predecessor dependent task is used as the target subtask; When there is no predecessor dependent task in the dependency, and the occupation status of the predecessor dependent device is unoccupied, the predecessor dependent device is occupied and the target subtask is executed normally; When there is no predecessor dependent task in the dependency and the occupation status of the predecessor dependent device is occupied, the target subtask is removed from the execution queue; The atomic task corresponding to the smallest number is selected from the remaining execution queues as the target subtask for update until all atomic tasks in the execution queue are executed.

2. According to the modular design-based measurement and acquisition equipment detection method of claim 1, it is characterized in that: The parallel execution principle is: the execution level of all detection units is consistent, and the execution level of the detection unit is higher than the execution level of the detection epitope in the detection unit; Methods for labeling detection units and detection epitopes include: A blank model with three levels is selected from the model database, and different levels are assigned to the three levels respectively; From low to high levels, the three layers are labeled as input layer, detection layer, and application layer to form a basic domain model; Mark i independent units in the detection layer, and assign the same first execution level to the i units one by one, so as to generate i detection units; P independent epitopes are marked in i detection units, and the same second execution level is assigned to the p epitopes one by one to generate p detection epitopes.

3. A method for detecting measurement and acquisition equipment based on modular design according to claim 2, characterized in that: The screening method of the detection items includes: Query the performance parameters corresponding to the performance to be tested of the device to be tested through the technical parameter table to obtain the performance to be tested; Mark the phrases in the remark boxes of the device to be tested one by one, and mark the remark boxes with the same phrases and the performance to be tested as target remark boxes, and obtain the number of the target remark boxes; Identify the set keywords of the task sets in the task database one by one, and record the task sets with the same set keywords as the performance to be tested as the target set; Arrange the task items in the target set in order from small to large numbers to obtain a task item sequence, and mark the task item numbers; Task items with the same number as the number of the target remark box are selected from the task item sequence to obtain the detection items.

4. A method for detecting measurement and acquisition equipment based on modular design according to claim 3, characterized in that: The atomic task splitting method includes: Identify key semantics and key values ​​of the detection items through natural language processing technology, obtain k key semantics of the items and k key values ​​of the items, and number the k key semantics of the items and the k key values ​​of the items in sequence; Record current, voltage, power, response time, throughput, utilization, failure rate, and processing rate as standard key semantics; Record the project key semantics containing any standard key semantics as the target key semantics, and obtain j target key semantics; Filter out the project key values ​​that are identical to the j target key semantic numbers from the k project key values, and obtain j target key values; After combining j target key semantics and j target key values ​​one by one, j splitting units are obtained, and the detection project is split into j atomic tasks based on the j splitting units as the splitting criteria.

5. A method for detecting measurement and acquisition equipment based on modular design according to claim 4, characterized in that: The method for generating the task detection model includes: Count the number of test items in the m devices under test one by one, and record it as the test quantity value; According to the detection quantity value from small to large, m devices to be tested are allocated to m detection units one by one to generate m task units; Import the j atomic tasks corresponding to the m devices under test into the j detection epitopes in the m task units respectively, obtain j task epitopes, and generate a task detection layer; Query the attribute status of the device under test through the attribute parameter table, and import the attribute status into the input layer to generate the task input layer; Establish a data transmission channel in the application layer that communicates with the detection management platform, set encryption points on the data transmission channel, and generate a task application layer; The task input layer, task detection layer and task application layer are combined in a hierarchical manner from low to high to generate a task detection model; The determination method of whether to trigger the parallel execution mode includes: Count the number of task epitopes in the task detection model and obtain the parallel trigger value; When the parallel trigger value is 1, it is determined that the parallel execution mode is not triggered; When the parallel trigger value is greater than 1, the parallel execution mode is triggered.

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