Scheduling optimization method and device for parallel test, equipment, medium and product

Abstract

The invention discloses a scheduling optimization method and device for parallel testing, equipment, a medium and a product, and relates to the field of test task scheduling of electric power special equipment, and the method comprises the steps: obtaining a test task set of to-be-tested electric power special equipment; constructing a recursive tree; child nodes of the recursion tree are test tasks which need to meet preset test time sequence constraints in the test task set; performing recursive search on the recursive tree to obtain a plurality of local test schemes meeting preset test time sequence constraints of the special power equipment to be tested; determining all local test schemes meeting preset test time sequence constraints as implicit set coding; the global test scheme obtained by adopting the artificial bee colony algorithm is adjusted by adopting hidden set coding, so that the adjusted global test scheme of the to-be-tested power special equipment conforms to preset test time sequence constraints, and the optimal global test scheme of the to-be-tested power special equipment is obtained. According to the method and the device, the inherent time sequence constraints among the test tasks can be considered in the arrangement process.

Description

Technical Field

[0001] The present application relates to the field of test task scheduling of special power equipment, and in particular to a scheduling optimization method, device, equipment, medium and product for parallel testing. Background Art

[0002] Testing plays a vital role in power special equipment. The development of new power special equipment usually requires a series of test items. Generally speaking, each device consists of multiple subsystems, and each subsystem needs to pass some specific test items. The subsystem to be tested is named the unit under test (UUT, Under Unit Test), and the specific test items of the UUT are named test tasks. There may be a specific technical test sequence between different test tasks of the UUT, and it depends on different types of resources (instruments). In large-scale test projects, automatic planning and then executing parallel tests can greatly improve test efficiency. However, the limitation of the technical test sequence comes from the engineering requirements of the test tasks, that is, some test tasks must be tested only after the completion of other related tasks. At present, the scheduling optimization method of parallel testing can realize the rapid arrangement of test tasks to corresponding test resources, but the arrangement process cannot take into account the inherent timing constraints between each test task. Summary of the invention

[0003] The purpose of this application is to provide a scheduling optimization method, device, equipment, medium and product for parallel testing, which can take into account the inherent timing constraints between various test tasks during the scheduling process.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a scheduling optimization method for parallel testing, comprising:

[0006] Acquire a test task set of the special electric power equipment to be tested; the test task set includes test tasks that need to meet preset test timing constraints and test tasks that do not need to meet preset test timing constraints;

[0007] Constructing a recursive tree; the child nodes of the recursive tree are the test tasks in the test task set that need to meet the preset test timing constraints;

[0008] Recursively searching the recursive tree to obtain multiple local test schemes for the special power equipment to be tested that meet the preset test timing constraints; the local test schemes include all test tasks in the test task set that need to meet the preset test timing constraints;

[0009] Determine that all local test schemes that meet preset test timing constraints are hidden set codes;

[0010] Hidden set coding is used to adjust the global test plan of the special power equipment to be tested obtained by using the artificial bee colony algorithm, so that the adjusted global test plan of the special power equipment to be tested meets the preset test timing constraints, and the optimal global test plan of the special power equipment to be tested is obtained; the global test plan includes all test tasks in the test task set.

[0011] In a second aspect, the present application provides a scheduling optimization device for parallel testing, comprising:

[0012] An acquisition module is used to acquire a test task set of the special power equipment to be tested; the test task set includes test tasks that need to meet preset test timing constraints and test tasks that do not need to meet preset test timing constraints;

[0013] A recursive tree construction module, used to construct a recursive tree; the child nodes of the recursive tree are the test tasks in the test task set that need to meet the preset test timing constraints;

[0014] A search module, used for recursively searching the recursive tree to obtain a plurality of local test schemes for the special electric power equipment to be tested that meet the preset test timing constraints; the local test schemes include all test tasks in the test task set that need to meet the preset test timing constraints;

[0015] A hidden set coding determination module, used to determine that all local test schemes that meet preset test timing constraints are hidden set coding;

[0016] The optimal test scheme determination module is used to use hidden set coding to adjust the global test scheme of the special power equipment to be tested obtained by using the artificial bee colony algorithm, so that the adjusted global test scheme of the special power equipment to be tested meets the preset test timing constraints, and obtains the optimal global test scheme of the special power equipment to be tested; the global test scheme includes all test tasks in the test task set.

[0017] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described scheduling optimization methods for parallel testing.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described scheduling optimization methods for parallel testing.

[0019] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned scheduling optimization methods for parallel testing.

[0020] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0021] The present application provides a scheduling optimization method, device, equipment, medium and product for parallel testing, which obtains multiple local test schemes of the special power equipment to be tested that meet the preset test timing constraints by recursively searching the recursive tree; and determines that all local test schemes that meet the preset test timing constraints are hidden set codes; uses hidden set coding to adjust the test scheme of the special power equipment to be tested obtained by using an artificial bee colony algorithm, so that the adjusted global test scheme of the special power equipment to be tested meets the preset test timing constraints, focusing on solving the problem of meeting the established timing relationship between test tasks, and being able to ensure that the scheduling optimization of test tasks on resources is achieved under the premise that there is no conflict in the resources dependent on each test task, and can take into account the inherent timing constraints between each test task in the scheduling process. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 This is an application environment diagram of a scheduling optimization method for parallel testing in one embodiment of the present application;

[0024] Figure 2 A schematic diagram of a scheduling optimization method for parallel testing provided in one embodiment of the present application;

[0025] Figure 3 Gantt chart of the test plan provided for this application;

[0026] Figure 4 Schematic diagram of the recursive path search for latent code sets for sequential tasks;

[0027] Figure 5 Schematic diagram for hidden code set correction;

[0028] Figure 6 A schematic diagram of a time convergence curve obtained by processing a (100, 10, 3) scale power special equipment test scheduling problem using a parallel test scheduling optimization method provided in an embodiment of the present application;

[0029] Figure 7 A schematic diagram of the structure of a computer device provided in one embodiment of the present application;

[0030] Figure 8 A flowchart of a scheduling optimization method for parallel testing provided in one embodiment of the present application. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0032] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0033] There are inherent constraints between various test tasks and test resources. Therefore, the automatic planning of test plans must meet the following requirements: 1) Automatically manage and allocate resources based on the resource dependencies of test tasks; 2) Search for task test plans that meet the technical test sequence based on engineering requirements between test tasks; 3) Obtain the distributed timing arrangement of each test task on different execution threads while meeting the limit of the maximum number of concurrent test tasks. Usually, conventional empirical test plans are far from meeting the test efficiency requirements, especially for power special equipment test projects that include a large number of test tasks. Therefore, the development of parallel test task scheduling optimization algorithms is particularly important. Based on this, the embodiments of the present application provide a parallel test scheduling optimization method. The parallel test scheduling optimization method provided in the embodiments of the present application can be applied to the following: Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store data that the server 104 needs to process. The data storage system can be set up separately, or it can be integrated on the server 104, or it can be placed on the cloud or other servers. The terminal 102 can send a set of test tasks for the special power equipment to be tested to the server 104. After the server 104 receives the set of test tasks for the special power equipment to be tested, for the set of test tasks for the special power equipment to be tested, the server 104 obtains the set of test tasks for the special power equipment to be tested; the test task set includes test tasks that need to meet preset test timing constraints and test tasks that do not need to meet preset test timing constraints; construct a recursive tree; the child nodes of the recursive tree are the test tasks in the test task set that need to meet preset test timing constraints; recursively search the recursive tree to obtain the test tasks to be tested. Test multiple local test schemes of special power equipment that meet preset test timing constraints; the local test scheme includes all test tasks in the test task set that need to meet the preset test timing constraints; determine that all local test schemes that meet the preset test timing constraints are hidden set codes; use hidden set coding to adjust the global test scheme of the special power equipment to be tested obtained by the artificial bee colony algorithm, so that the adjusted global test scheme of the special power equipment to be tested meets the preset test timing constraints, and obtain the optimal global test scheme of the special power equipment to be tested; the global test scheme includes all test tasks in the test task set. The server 104 can feed back the obtained optimal global test scheme to the terminal 102. In addition, in some embodiments, the scheduling optimization method of parallel testing can also be implemented by the server 104 or the terminal 102 alone, such as the terminal 102 can directly perform scheduling optimization of parallel testing for the test task set of the special power equipment to be tested, or the server 104 can obtain the test task set of the special power equipment to be tested from the data storage system, and perform scheduling optimization of parallel testing for the test task set of the special power equipment to be tested.

[0034] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0035] In an exemplary embodiment, Figure 2 , Figure 3 and Figure 8 As shown, Figure 3Part (a) is the Gantt chart of the test tasks and test resources. Figure 3 Part (b) is a Gantt chart showing the arrangement of test tasks on threads, as shown in FIG. 1 , providing a method for scheduling optimization of parallel testing, which is executed by a computer device, specifically, can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 Taking the server 104 in the example as an example, the following steps are included:

[0036] Step 201, obtain the test task set T of the special power equipment to be tested = {t i :1≤i≤m}, m represents the total number of test tasks in the test task set, t i represents the i-th test task. The test task set includes test tasks that need to meet the preset test timing constraints and test tasks that do not need to meet the preset test timing constraints.

[0037] Step 202, construct a recursive tree; the child nodes of the recursive tree are the test tasks in the test task set that need to meet the preset test timing constraints.

[0038] Step 203: recursively search the recursive tree to obtain multiple local test schemes for the special power equipment to be tested that meet the preset test timing constraints. The local test schemes include all test tasks in the test task set that need to meet the preset test timing constraints.

[0039] Step 204, determining that all local test schemes that meet the preset test timing constraints are hidden set coding.

[0040] Step 205, using hidden set coding to adjust the global test plan of the special power equipment to be tested obtained by using the artificial bee colony algorithm, so that the adjusted global test plan of the special power equipment to be tested meets the preset test timing constraints, and the optimal global test plan X of the special power equipment to be tested is obtained. * =(x1,x2,...,x j ,…,x m ), x j Represents the jth test task executed sequentially; the global test plan includes all test tasks in the test task set.

[0041] In another exemplary embodiment of the present application, step 203 and step 204 are specifically as follows:

[0042] Obtain a timing matrix TS, wherein the timing matrix TS is constructed according to the preset test timing constraints of the test tasks that need to meet the preset test timing constraints in the test task set of the special power equipment to be tested. TS(i,j)=1 indicates that the test task ti The test must be in the test task t j Before.

[0043] Let LS(h,i,Z i ,U j ,Q) is represented as a process of transferring from node h to node i and recursively searching downward from node i; where the node is the test task that needs to meet the preset test timing constraints, and the sequence Z i The current recursive path contains test tasks that meet the preset test timing constraints, i.e., the timing matrix, as of node i; sequence U i As of node i, the current path contains test tasks that satisfy the timing matrix that have not been searched and verified; the sequence Q contains all test tasks that satisfy the timing matrix. LS(h,i,Z i ,U j ,Q) can be expanded according to the following formula (1) to formula (3):

[0044] j=u0∈U i ,U j =U i \{u0} (1)

[0045] Z j =Z i ∪{j}(2)

[0046]

[0047] Among them, U in formula (1) i \{u0} represents the sequence U i Remove the front-end test task u0, U j =U i \{u0} represents the removed U i Indicated as U j , formula (1) represents the i Remove the front-end test task u0 and temporarily store u0 as test task j, and then i Indicated as U j ; Formula (2) means that in sequence Z i Add test task j to the end of j ; Formula (3) represents the sequence U after completing formula (1) j The test task is used to fill in the gaps in the recursive tree to achieve horizontal jumps after deep search, such as Figure 4 Jump search from the left branch node 4 (blue) to the right branch of the root node 2 (green), q represents the test task in Q.

[0048] The basic idea is to check whether the order of the current two nodes violates the timing order in TS during the successive searches on the recursive path. If there is a violation, the current path search is terminated and the branch is jumped to. The above process can be expanded according to the following formulas (4) to (6):

[0049] LS(i,j,Z j ,U j )if[i,j]∈TS or i=0;i,j,Z j ,U j ∈LS(h,i,Z i , U i ) (4)

[0050]

[0051] Z=Z∪Z j if|Z j |=|Q|,Z j ∈LS(h,i,Z i ,U j ,Q) (6)

[0052] Among them, formula (4) represents the downward recursive process that satisfies the timing constraints, formula (5) represents the recursive process of jumping horizontally to the side branches, and formula (6) represents the storage of the local test plan that satisfies the timing constraints. In formula (4), [i, j]∈TS represents the subsequent recursive search that satisfies the timing matrix, which is passed from node i to node j; in formula (5), Indicates that when the node sequence Uj is not empty, it is passed from node h to node j, skipping the subsequent recursive search of node j (horizontal jump); in formula (6), Z is the sequence storage of the local test scheme that satisfies the timing matrix. When the condition |Z is satisfied j When |=|Q| (the lengths of the two sequences are the same), it means that the local test solution Z that satisfies the timing matrix has been searched. j The whole process described above can be demonstrated by the following example.

[0053] From the timing matrix TS = [[1,2], [1,3], [3,4]], we can get the test task set Q = (1,2,3,4). Figure 4 As shown in the figure, each node in the figure is a test task that needs to meet the preset test timing constraints. The initial node is 0, U0=(1,2,3,4) and Q=(1,2,3,4). LS starts from the initial node LS(0,0,Z0,U0,Q). Recursively from node 0 to the left branch node 1, we can get h=0, j=1, Z1=(1), U1=(2,3,4,1), Q=(1,2,3,4); recursively from node 1 to the left branch node 2 LS(0,1,Z1,U1,Q), we can get h=1, j=2, Z2=(1,2), U2=(3,4,1,2), Q=(1,2,3,4); at the same time, realize the jump LS(0,1,Z1,U2,Q) from node 1 to the right branch node 3, we can get h=1, j=3, Z2=(1,3), U3=(4,1,2,3), Q=(1,2,3,4). Figure 4 The recursive search can be pruned by whether the time series matrix TS is satisfied, so the hidden set sequences are (1,2,3,4), (1,3,2,4) and (1,3,4,2).

[0054] In another exemplary embodiment of the present application, the global test scheme of the special power equipment to be tested obtained by using the artificial bee colony algorithm is adjusted by using hidden set coding, so that the adjusted global test scheme of the special power equipment to be tested meets the preset test timing constraints, and the optimal global test scheme of the special power equipment to be tested is obtained, which specifically includes:

[0055] Get the test resource set R of the power special equipment to be tested = {r k :1≤k≤n}, test time set τ={τ i :1≤i≤m}, schedule S=(s i :1≤i≤m), task-resource dependency matrix TR and timing matrix TS, n represents the total number of test resources in the test resource set. k represents the kth test resource, τ i Indicates the time taken to test the i-th test task, s i It represents the time when the i-th test task starts testing, and the test time consumption set includes the time consumed when each test task in the test task set is tested; the task-resource dependency matrix is ​​constructed according to the correspondence between each test task in the test task set and each test resource in the test resource set; the schedule includes the time when each test task in the test task set starts testing; the timing matrix is ​​constructed according to the preset test timing constraints of the test tasks in the test task set of the special power equipment to be tested that need to meet the preset test timing constraints.

[0056] In the population under the current artificial bee colony iteration number, an individual is selected to obtain the ontology solution under the current employed bee iteration number i2, and the ontology solution under the current employed bee iteration number i2 is updated to obtain the updated individual corresponding to the ontology solution under the current employed bee iteration number i2; the individual is the global test scheme for the special power equipment to be tested. Preferably, the individual obtained by processing the population under the current artificial bee colony iteration number using binary tournament selection is the neighborhood solution, and an individual is selected in the population under the current artificial bee colony iteration number in sequence as the ontology solution. If the neighborhood solution and the ontology solution are the same, a multi-point exchange operator is executed on the ontology solution, and if they are different, a multi-point insertion operator is used to process the ontology solution and the neighborhood solution.

[0057] If the updated individual corresponding to the ontology solution under the current hired bee iteration number i2 does not conform to the time series matrix, the updated individual corresponding to the ontology solution under the current hired bee iteration number i2 is corrected by using hidden set coding to obtain the corrected individual corresponding to the ontology solution under the current hired bee iteration number i2; the corrected individual corresponding to the ontology solution under the current hired bee iteration number i2 conforms to the time series matrix. Preferably, the correction strategy of hidden set coding is used to achieve the correction.

[0058] According to the modified individuals, test time set, timing matrix, task-resource dependency matrix and schedule corresponding to the ontology solution under the current hired bee iteration number i2, the fitness corresponding to the ontology solution under the current hired bee iteration number i2 is calculated.

[0059] If the updated individual corresponding to the ontology solution under the current hired bee iteration number i2 conforms to the timing matrix, then the fitness corresponding to the ontology solution under the current hired bee iteration number i2 is calculated according to the updated individual corresponding to the ontology solution under the current hired bee iteration number i2, the test time set, the timing matrix, the task-resource dependency matrix and the schedule.

[0060] If the fitness corresponding to the ontology solution under the current hired bee iteration number i2 increases, the number corresponding to the ontology solution under the current hired bee iteration number i2 is increased by 1, and the hired bee iteration number is updated to enter the next hired bee iteration until the set hired bee iteration number (EN) is reached and the follower bee stage is entered.

[0061] If the fitness corresponding to the ontology solution under the current hired bee iteration number i2 decreases, the number corresponding to the ontology solution under the current hired bee iteration number i2 is set to 0, and the hired bee iteration number is updated to enter the next hired bee iteration until the set hired bee iteration number is reached and the follower bee stage is entered.

[0062] The following bee stage is:

[0063] In the population under the current artificial bee colony iteration number, an individual is selected to obtain the neighborhood solution under the current follower bee iteration number i3, and the neighborhood solution under the current follower bee iteration number i3 is updated to obtain the updated individual corresponding to the neighborhood solution under the current follower bee iteration number i3. Preferably, a binary tournament selection is used to process the population under the current artificial bee colony iteration number to obtain a neighborhood solution, and a local search operator is executed on the neighborhood solution to obtain an updated individual, and the quality of the solution is improved by rotating the codes between high-quality individuals.

[0064] If the updated individual corresponding to the neighborhood solution under the current number of follow-bee iterations i3 does not conform to the time series matrix, the updated individual corresponding to the neighborhood solution under the current number of follow-bee iterations i3 is corrected using hidden set coding to obtain the corrected individual corresponding to the neighborhood solution under the current number of follow-bee iterations i3; the corrected individual corresponding to the neighborhood solution under the current number of follow-bee iterations i3 conforms to the time series matrix. Preferably, the correction strategy of hidden set coding is used to achieve the correction.

[0065] According to the modified individual, test time set, timing matrix, task-resource dependency matrix and schedule corresponding to the neighborhood solution under the current follower bee iteration number i3, the fitness corresponding to the neighborhood solution under the current follower bee iteration number i3 is calculated.

[0066] If the update individual corresponding to the neighborhood solution under the current follower bee iteration number i3 conforms to the timing matrix, then the fitness corresponding to the neighborhood solution under the current follower bee iteration number i3 is calculated according to the update individual corresponding to the neighborhood solution under the current follower bee iteration number i3, the test time set, the timing matrix, the task-resource dependency matrix and the schedule.

[0067] If the fitness corresponding to the neighborhood solution under the current follow-bee iteration number i3 increases, the number corresponding to the neighborhood solution under the current follow-bee iteration number i3 is increased by 1, and the follow-bee iteration number is updated to enter the next follow-bee iteration until the set follow-bee iteration number ON is reached and the scout bee stage is entered.

[0068] If the fitness corresponding to the neighborhood solution under the current follow-bee iteration number i3 decreases, the number corresponding to the neighborhood solution under the current follow-bee iteration number i3 is set to 0, and the follow-bee iteration number is updated to enter the next follow-bee iteration until the set follow-bee iteration number is reached and the scout bee stage is entered.

[0069] The scout bee stage is:

[0070] Set all the populations under the current artificial bee colony iteration times that are greater than the preset number L SN (Usually L SN= 10) individuals are regenerated to obtain the population under the next artificial bee colony iteration number, and the artificial bee colony iteration number is updated to enter the next artificial bee colony iteration until the set artificial bee colony iteration number L is reached. max , determine the individual with the best fitness in the population under the last artificial bee colony iteration number as the optimal global test solution.

[0071] Figure 6 A schematic diagram of the time convergence curve obtained by using the parallel test scheduling optimization method provided in this application to process the test scheduling problem of special power equipment with a scale of (100, 10, 3), i.e., the number of test tasks is 100, the number of test resources is 10, and the number of test threads is 3. The timing constraint matrix TS is 25 pairs of constraint tasks. Figure 6 (a) is a convergence diagram of the scheduling optimization method for parallel testing provided in this application, from Figure 6 It can be seen from (a) that the algorithm of the present invention has strong convergence, the first 40 iterations converge quickly, and the optimal global test solution can be obtained after 100 iterations; Figure 6 (b) is the time-consuming graph of the scheduling optimization method for parallel testing provided in the application, from Figure 6 From (b), we can see that the running time of this method is linearly related to the number of iterations, and the efficiency is not significantly reduced due to space occupation.

[0072] In practical applications, before obtaining the test resource set, test time set, schedule, task-resource dependency matrix and timing matrix of the power special equipment to be tested, the following steps are also required:

[0073] In the population initialization phase, the real number codes of m tasks are randomly initialized to generate N p Individual X t =(x1,x2,…,…,x m ), 1≤t≤N p .

[0074] In another exemplary embodiment of the present application, the fitness corresponding to the ontology solution under the current hired bee iteration number i2 is calculated according to the modified individual, test time set, timing matrix, task-resource dependency matrix and schedule corresponding to the ontology solution under the current hired bee iteration number i2, specifically including:

[0075] Convert the task-resource dependency matrix into MAP data structure format.

[0076] The schedule is updated according to the modified individuals, test time-consuming set, task-resource dependency matrix and timing matrix in MAP data structure format corresponding to the ontology solution under the current hired bee iteration number i2 to obtain the first updated schedule.

[0077] For any test task in the revised individual corresponding to the ontology solution under the current employed bee iteration number i2, when the number of parallel executions in all test tasks before the test task exceeds the limited number of threads, the start time of the target test task in the first update schedule is updated according to the time consumed when the target test task is tested, to obtain the first final schedule; the target test task is a test task whose number of parallel executions exceeds the limited number of threads.

[0078] The fitness corresponding to the ontology solution under the current employed bee iteration number i2 is obtained according to the first final schedule and the test time-consuming set.

[0079] In another embodiment of the present application, the task-resource dependency matrix is ​​converted into a MAP data structure format as follows:

[0080] For example: A standard TR matrix is Among them, TR(1,1)=1 and TR(1,2)=1 indicate that task t1 requires test resources r1 and r2 to cooperate in execution. The TR matrix is ​​used to convert the row and column numbers of the matrix, which can be adjusted to the following MAP data structure format: ρ={1:[1,2],2:[3],3:[4],4:[3,4],5:[1],6:[2]}, t i :[r p ,r q ] indicates task t i The execution must rely on resources r p With r q ρ can be used to perform efficient search in the subsequent algorithm process.

[0081] In another exemplary embodiment of the present application, the schedule is updated according to the modified individual corresponding to the ontology solution under the current hired bee iteration number i2, the test time-consuming set, the task-resource dependency matrix in the MAP data structure format, and the timing matrix, and the first updated schedule is obtained, which specifically includes:

[0082] Step 1: Consider that there is no time overlap between the resources of each test task and implement the resource-task schedule conversion.

[0083] According to formula (7), the start time of all test tasks in the modified individual corresponding to the ontology solution under the current hired bee iteration number i2 is calculated

[0084]

[0085] in, represents the test task x executed sequentially in the i-th corrected individual corresponding to the ontology solution under the current hired bee iteration number i2 i The time to start the test, Represents x i The maximum completion time of each resource is given by the set C = (c k :1≤k≤n) means, c k is the maximum completion time for resource k.

[0086] Step 2: Consider the order of the established test tasks and make resource-task schedule corrections.

[0087] Based on the above step 1, x i Start time Formula (8) to Formula (10) can be adjusted to meet the given test order constraints in the decoding stage:

[0088]

[0089] Among them, TS(x k ,x i )=1 indicates test task x k The test must be in the test task x i Before, Represents the corrected test task x i The start time of the first update schedule, Is the deadline for test task x i , X t The test tasks arranged in C′ max For The maximum completion time of all related tasks; if any Then Update to C′ max .

[0090] In another exemplary embodiment of the present application, for any test task in the modified individual corresponding to the ontology solution under the current employed bee iteration number i2, when the number of parallel executions in all test tasks before the test task exceeds the limited number of threads, the start time of the target test task in the first update schedule is updated according to the time consumed when the target test task is tested, and a first final schedule is obtained; the target test task is a test task whose number of parallel executions exceeds the limited number of threads, specifically including:

[0091] Based on the operations in steps 1 and 2 above, the number of parallel executions of the test task may exceed the number of threads in the schedule, so it is necessary to adjust some parallel execution tasks to meet the thread number constraint. t =(x1,x2,…,…,x m ), for X tThe internal test task executes the following process (1) to process (3) to obtain the final start time of each test task to form the first final schedule S′=(s i ″:1≤i≤m):

[0092] (1) Test x one by one i Previous position code x k (1≤k≤i-1) Is there a situation where the number of parallel executions exceeds the limited number of threads d? If so, go to process (2); if not, go to process (3).

[0093] (2) Determine the code x that exceeds the number of threads d q (1≤q≤k), update x i Time is Transfer process (3).

[0094] (3) Determine whether i=m? If yes, output the final adjusted schedule S′=(s i ″:1≤i≤m); otherwise, set i=i+1 and go to process (1) and repeat.

[0095] Through the steps of the above three stages, a timetable arrangement S can be obtained that satisfies the constraints of non-overlapping time of test resources, test sequence constraints, and test thread quantity constraints.

[0096] In another exemplary embodiment of the present application, obtaining the fitness corresponding to the ontology solution under the current employed bee iteration number i2 according to the first final schedule and the test time set specifically includes:

[0097] For any test task in the modified individual corresponding to the ontology solution under the current employed bee iteration number i2, the time corresponding to the test task is obtained by calculating the sum of the start time of the test task in the first final timetable and the time consumed when the test task is tested in the test time set.

[0098] The time corresponding to all test tasks in the modified individual corresponding to the ontology solution under the current hired bee iteration number i2 is added up to obtain the fitness of the modified individual corresponding to the ontology solution under the current hired bee iteration number i2.

[0099] Determine the fitness of the modified individual corresponding to the ontology solution under the current hired bee iteration number i2 as the fitness corresponding to the ontology solution under the current hired bee iteration number i2.

[0100] In another exemplary embodiment of the present application, the time corresponding to all test tasks in the modified individual corresponding to the ontology solution under the current hired bee iteration number i2 is added to obtain the fitness of the modified individual corresponding to the ontology solution under the current hired bee iteration number i2. The specific formula is: F(X t,TS,ρ)=max 1≤i≤m {s i ”+τ i}, where F(X t ,TS,ρ) represents the fitness function.

[0101] In another exemplary embodiment of the present application, the fitness corresponding to the ontology solution under the current hired bee iteration number i2 is calculated according to the updated individual, test time set, timing matrix, task-resource dependency matrix and schedule corresponding to the ontology solution under the current hired bee iteration number i2, specifically including:

[0102] Convert the task-resource dependency matrix into MAP data structure format.

[0103] The timetable is updated according to the updated individuals, test time set, task-resource dependency matrix and timing matrix corresponding to the ontology solution under the current hired bee iteration number i2, to obtain the second updated timetable. The process of obtaining the first updated timetable is the same as above, and will not be repeated here.

[0104] For any test task in the updated individual corresponding to the ontology solution under the current hired bee iteration number i2, when the number of parallel executions of all test tasks before the test task exceeds the limited number of threads, the start time of the target test task in the second updated schedule is updated according to the time taken to test the target test task, and the second final schedule is obtained; the target test task is a test task whose number of parallel executions exceeds the limited number of threads. The process of obtaining the first final schedule is the same as the above, and will not be repeated here.

[0105] The fitness corresponding to the ontology solution under the current employed bee iteration number i2 is obtained according to the second final schedule and the test time-consuming set.

[0106] In another exemplary embodiment of the present application, obtaining the fitness corresponding to the ontology solution under the current employed bee iteration number i2 according to the second final schedule and the test time set specifically includes:

[0107] For any test task in the updated individual corresponding to the ontology solution under the current employed bee iteration number i2, the time corresponding to the test task is obtained by calculating the sum of the start time of the test task in the second final timetable and the time consumed when the test task is tested in the test time set.

[0108] The time corresponding to all test tasks in the updated individual corresponding to the ontology solution under the current hired bee iteration number i2 is added up to obtain the fitness of the updated individual corresponding to the ontology solution under the current hired bee iteration number i2.

[0109] Determine the fitness of the updated individual corresponding to the ontology solution under the current hired bee iteration number i2 as the fitness corresponding to the ontology solution under the current hired bee iteration number i2.

[0110] In practical applications,

[0111] The specific operation process of the multi-point insertion operator can be described as follows:

[0112] Assume X t X is the individual of the ontology solution; f is an individual in the neighborhood solution, that is, an individual selected by binary selection. nrb is the number of nodes exchanged. For example, X t and X f can be represented as (1,5,3,2,9,8,10,7,4,6) and (2,6,5,9,3,1,7,8,4,10) respectively. t Randomly select nrb (usually nrb = 3) reserved points from the t It can be expressed as (x,5,x,2,9,x,x,x,x,x).

[0113] Where x represents X t The blank space can be obtained from X f Extract non-X t The other values ​​of the points are retained and filled into X in sequence t Finally, X t Can be regenerated into (6,5,3,2,9,1,7,8,4,10).

[0114] The specific operation process of the multi-point exchange operator can be described as follows:

[0115] Code X for a given individual t , by exchanging the positions of multiple points, a new individual code is generated. The multi-point exchange operator generates a new code based on a randomly generated number of exchange points (the number of codes to be exchanged) nrb, and then randomly exchanges the positions of nrb nodes. This operator aims to avoid the algorithm from falling into the local optimum through multi-point exchange.

[0116] The specific operation process of the local search operator can be described as follows:

[0117] The local search operator maximizes the quality of individuals through a weak enumeration cycle. t =(x1,x2,…,…,x m ), swap the first task number x1 with the adjacent position; if x1 cannot be improved after swapping tquality (fitness value increases), repeat the above process; if x1 exchange can improve X t If the quality (fitness value decreases), the loop is terminated and the new individual currently generated is returned.

[0118] The binary tournament selection strategy mainly selects individuals by sampling without replacement. The specific operation process can be described as follows:

[0119] Two different individuals are randomly selected from the population continuously, and then the best individual (with a higher fitness value) is used as the neighborhood solution output by the binary tournament selection.

[0120] Hidden set coding correction strategy R(X t ) can be seen in the key steps Figure 5 The specific operation process is shown in the following example:

[0121] Figure 5 The hidden set encoding is Z = {(1,2,3,4),(1,3,2,4),(1,3,4,2)}. t The test tasks that do not conform to the timing matrix are deleted, and any one of the hidden set codes is inserted into the deleted vacant position in sequence. By adopting the hidden set coding, the local task ordering adjustment is realized; (7,1,4,3,5,6,2), (6,4,5,1,2,3,7) and (5,3,1,4,6,2,7) are respectively corrected to (7,1,2,3,5,6,4), (6,1,5,3,2,4,7) and (5,1,3,4,6,2,7), and the coding sequence can be corrected to meet the given timing constraints.

[0122] Aiming at the parallel test scheduling problem with timing constraints (i.e., constraint satisfaction problem), this application introduces a recursive search function and proposes a recursive search based artificial bee colony algorithm (RS-ABC). The algorithm flow can be summarized as: recursive search phase, population initialization phase, employed bee phase, follower bee phase and scout bee phase. Among them, the recursive search phase is to search for a series of hidden set codes that do not violate the established test order; the population initialization phase is to randomly generate a population, and the population consists of multiple individuals with randomly arranged real numbers; the employed bee phase sequentially executes multi-point insertion operators and multi-point exchange operators to improve the quality of the overall solution of the population based on hidden set coding; the follower bee phase executes local search operators, focusing on local improvement of high-quality solutions; the scout bee phase executes the regeneration of individual codes, eliminates poor individuals and regenerates. The individuals of RS-ABC are all represented by real number codes. In order to effectively improve the performance of the algorithm, different optimization strategies are implemented in the above stages. The algorithm can search for the global optimal solution through multiple iterations.

[0123] The present application formulates the test tasks, test resources, task time sets, task-resource dependencies, and timing matrices between tasks required for the testing of special power equipment; first, a task code is attached to each task in the test task set, and then a test task code sequence structure is randomly generated; secondly, a timing matrix between tasks is introduced, and a recursive search function is used to search for an implicit sequence of a series of test tasks, that is, a sequence of related tasks that does not violate a predetermined test order; according to the test resources, task time, and number of threads that can be executed in parallel corresponding to each test task, an artificial bee colony algorithm based on recursive search is used, and on the basis of continuously using implicit sequences to correct test sequences, scheduling optimization from test tasks to resources is achieved, and an integrated test solution for special power equipment is generated.

[0124] The present application obtains a set of all test tasks for special power equipment; then gives each test task a unique code, and generates a corresponding real number sequence of the test task; that is, establishes a heuristic solution coding structure for the test task; obtains the temporal relationship between any two test tasks, and uses a recursive search function to generate an implicit sequence of a series of test tasks, that is, obtains a related task sequence that does not violate the established test order; based on data such as the resource set corresponding to each test task, the test task time consumption, and the number of tasks that can be executed in parallel, a hybrid coding artificial bee colony algorithm is adopted on the basis of correcting the solution coding structure based on the implicit sequence, with the goal of maximizing test efficiency.

[0125] Based on the same inventive concept, the embodiment of the present application also provides a parallel test scheduling optimization device for implementing the above-mentioned parallel test scheduling optimization method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the one or more parallel test scheduling optimization device embodiments provided below can refer to the limitations of the parallel test scheduling optimization method above, and will not be repeated here.

[0126] In an exemplary embodiment, a scheduling optimization device for parallel testing is provided, comprising:

[0127] The acquisition module is used to acquire a test task set of the special power equipment to be tested; the test task set includes test tasks that need to meet preset test timing constraints and test tasks that do not need to meet preset test timing constraints.

[0128] The recursive tree construction module is used to construct a recursive tree; the child nodes of the recursive tree are the test tasks in the test task set that need to meet the preset test timing constraints.

[0129] The search module is used to recursively search the recursive tree to obtain multiple local test schemes for the special power equipment to be tested that meet the preset test timing constraints; the local test schemes include all test tasks in the test task set that need to meet the preset test timing constraints.

[0130] The hidden set coding determination module is used to determine that all local test schemes that meet the preset test timing constraints are hidden set coding.

[0131] The optimal test scheme determination module is used to use hidden set coding to adjust the global test scheme of the special power equipment to be tested obtained by using the artificial bee colony algorithm, so that the adjusted global test scheme of the special power equipment to be tested meets the preset test timing constraints, and obtains the optimal global test scheme of the special power equipment to be tested; the global test scheme includes all test tasks in the test task set.

[0132] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store scheduling optimization data for parallel testing. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a scheduling optimization method for parallel testing is implemented.

[0133] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above-mentioned method embodiments when executing the computer program.

[0134] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the above-mentioned method embodiments are implemented.

[0135] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the above-mentioned method embodiments are implemented.

[0136] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0137] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0138] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0139] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0140] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A scheduling optimization method for parallel testing, characterized in that: The scheduling optimization method for parallel testing includes: Acquire a test task set of the special electric power equipment to be tested; the test task set includes test tasks that need to meet preset test timing constraints and test tasks that do not need to meet preset test timing constraints; Constructing a recursive tree; the child nodes of the recursive tree are the test tasks in the test task set that need to meet the preset test timing constraints; Recursively searching the recursive tree to obtain multiple local test schemes for the special power equipment to be tested that meet the preset test timing constraints; the local test schemes include all test tasks in the test task set that need to meet the preset test timing constraints; Determine that all local test schemes that meet preset test timing constraints are hidden set codes; Hidden set coding is used to adjust the global test plan of the special power equipment to be tested obtained by using the artificial bee colony algorithm, so that the adjusted global test plan of the special power equipment to be tested meets the preset test timing constraints, and the optimal global test plan of the special power equipment to be tested is obtained; the global test plan includes all test tasks in the test task set.

2. The scheduling optimization method for parallel testing according to claim 1, characterized in that: The global test scheme of the special power equipment to be tested obtained by using the artificial bee colony algorithm is adjusted by using hidden set coding, so that the adjusted global test scheme of the special power equipment to be tested meets the preset test timing constraints, and the optimal global test scheme of the special power equipment to be tested is obtained, which specifically includes: Obtain a test resource set, a test time set, a schedule, a task-resource dependency matrix and a timing matrix of the special electric power equipment to be tested; the test time set includes the time consumed when each test task in the test task set is tested; the task-resource dependency matrix is ​​constructed according to the correspondence between each test task in the test task set and each test resource in the test resource set; the schedule includes the time when each test task in the test task set starts testing; the timing matrix is ​​constructed according to the preset test timing constraints of the test tasks that need to meet the preset test timing constraints in the test task set of the special electric power equipment to be tested; In the population under the current artificial bee colony iteration number, an individual is selected to obtain the ontology solution under the current employed bee iteration number i2, and the ontology solution under the current employed bee iteration number i2 is updated to obtain the updated individual corresponding to the ontology solution under the current employed bee iteration number i2; the individual is the global test plan of the special power equipment to be tested; If the updated individual corresponding to the ontology solution under the current hired bee iteration number i2 does not conform to the time series matrix, then the updated individual corresponding to the ontology solution under the current hired bee iteration number i2 is corrected by using hidden set coding to obtain the corrected individual corresponding to the ontology solution under the current hired bee iteration number i2; the corrected individual corresponding to the ontology solution under the current hired bee iteration number i2 conforms to the time series matrix; According to the modified individuals, test time set, timing matrix, task-resource dependency matrix and schedule corresponding to the ontology solution under the current hired bee iteration number i2, the fitness corresponding to the ontology solution under the current hired bee iteration number i2 is calculated; If the updated individual corresponding to the ontology solution under the current hired bee iteration number i2 conforms to the timing matrix, then the fitness corresponding to the ontology solution under the current hired bee iteration number i2 is calculated according to the updated individual corresponding to the ontology solution under the current hired bee iteration number i2, the test time set, the timing matrix, the task-resource dependency matrix and the schedule; If the fitness corresponding to the ontology solution under the current hired bee iteration number i2 increases, then the number corresponding to the ontology solution under the current hired bee iteration number i2 is increased by 1, and the hired bee iteration number is updated to enter the next hired bee iteration until the set hired bee iteration number is reached and the follower bee stage is entered; If the fitness corresponding to the ontology solution under the current hired bee iteration number i2 decreases, then the number corresponding to the ontology solution under the current hired bee iteration number i2 is set to 0, and the hired bee iteration number is updated to enter the next hired bee iteration until the set hired bee iteration number is reached and the follower bee stage is entered; The following bee stage is: In the population under the current artificial bee colony iteration number, an individual is selected to obtain the neighborhood solution under the current follower bee iteration number i3, and the neighborhood solution under the current follower bee iteration number i3 is updated to obtain the updated individual corresponding to the neighborhood solution under the current follower bee iteration number i3; If the updated individual corresponding to the neighborhood solution under the current number of follow-bee iterations i3 does not conform to the time series matrix, the updated individual corresponding to the neighborhood solution under the current number of follow-bee iterations i3 is corrected by using hidden set coding to obtain the corrected individual corresponding to the neighborhood solution under the current number of follow-bee iterations i3; the corrected individual corresponding to the neighborhood solution under the current number of follow-bee iterations i3 conforms to the time series matrix; Calculate the fitness of the neighborhood solution under the current follower bee iteration number i3 according to the modified individual, test time set, timing matrix, task-resource dependency matrix and schedule corresponding to the neighborhood solution under the current follower bee iteration number i3; If the update individual corresponding to the neighborhood solution under the current follower bee iteration number i3 meets the timing matrix, then the fitness corresponding to the neighborhood solution under the current follower bee iteration number i3 is calculated according to the update individual corresponding to the neighborhood solution under the current follower bee iteration number i3, the test time set, the timing matrix, the task-resource dependency matrix and the schedule; If the fitness corresponding to the neighborhood solution under the current follow-bee iteration number i3 increases, then the number corresponding to the neighborhood solution under the current follow-bee iteration number i3 is increased by 1, and the follow-bee iteration number is updated to enter the next follow-bee iteration until the set follow-bee iteration number is reached and the scout bee stage is entered; If the fitness corresponding to the neighborhood solution under the current follow-bee iteration number i3 decreases, then the number corresponding to the neighborhood solution under the current follow-bee iteration number i3 is set to 0, and the follow-bee iteration number is updated to enter the next follow-bee iteration until the set follow-bee iteration number is reached and the scout bee stage is entered; The scout bee stage is: All individuals in the population under the current artificial bee colony iteration number whose number is greater than the preset number are regenerated to obtain the population under the next artificial bee colony iteration number, and the artificial bee colony iteration number is updated to enter the next artificial bee colony iteration until the set artificial bee colony iteration number is reached, and the individual with the best fitness in the population under the last artificial bee colony iteration number is determined as the optimal global test solution.

3. The scheduling optimization method for parallel testing according to claim 2, characterized in that: The method calculates the fitness corresponding to the ontology solution under the current hired bee iteration number i2 according to the modified individual, test time set, timing matrix, task-resource dependency matrix and schedule corresponding to the ontology solution under the current hired bee iteration number i2, specifically including: Convert the task-resource dependency matrix into MAP data structure format; The schedule is updated according to the modified individuals, test time-consuming set, task-resource dependency matrix and timing matrix in MAP data structure format corresponding to the ontology solution under the current hired bee iteration number i2, to obtain a first updated schedule; For any test task in the modified individual corresponding to the ontology solution under the current employed bee iteration number i2, when the number of parallel executions of all test tasks before the test task exceeds the limited number of threads, the start time of the target test task in the first update schedule is updated according to the time consumed when the target test task is tested, to obtain the first final schedule; the target test task is a test task whose number of parallel executions exceeds the limited number of threads; The fitness corresponding to the ontology solution under the current employed bee iteration number i2 is obtained according to the first final schedule and the test time-consuming set.

4. The scheduling optimization method for parallel testing according to claim 3 is characterized in that: The step of obtaining the fitness corresponding to the ontology solution under the current employed bee iteration number i2 according to the first final schedule and the test time set specifically includes: For any test task in the modified individual corresponding to the ontology solution under the current hired bee iteration number i2, calculate the sum of the start time of the test task in the first final timetable and the time consumed when the test task is tested in the test time set to obtain the time corresponding to the test task; Add up the time corresponding to all test tasks in the modified individual corresponding to the ontology solution under the current hired bee iteration number i2, and obtain the fitness of the modified individual corresponding to the ontology solution under the current hired bee iteration number i2; Determine the fitness of the modified individual corresponding to the ontology solution under the current hired bee iteration number i2 as the fitness corresponding to the ontology solution under the current hired bee iteration number i2.

5. The scheduling optimization method for parallel testing according to claim 2, characterized in that: According to the updated individuals, test time set, timing matrix, task-resource dependency matrix and schedule corresponding to the ontology solution under the current hired bee iteration number i2, the fitness corresponding to the ontology solution under the current hired bee iteration number i2 is calculated, including: Convert the task-resource dependency matrix into MAP data structure format; The timetable is updated according to the updated individuals, test time-consuming set, task-resource dependency matrix and timing matrix in MAP data structure format corresponding to the ontology solution under the current hired bee iteration number i2, to obtain a second updated timetable; For any test task in the updated individual corresponding to the ontology solution under the current employed bee iteration number i2, when the number of parallel executions of all test tasks before the test task exceeds the limited number of threads, the start time of the test of the target test task in the second updated schedule is updated according to the time consumed when the target test task is tested, to obtain the second final schedule; the target test task is a test task whose number of parallel executions exceeds the limited number of threads; The fitness corresponding to the ontology solution under the current employed bee iteration number i2 is obtained according to the second final schedule and the test time-consuming set.

6. The scheduling optimization method for parallel testing according to claim 5, characterized in that: The step of obtaining the fitness corresponding to the ontology solution under the current employed bee iteration number i2 according to the second final schedule and the test time set specifically includes: For any test task in the updated individual corresponding to the ontology solution under the current hired bee iteration number i2, calculate the sum of the start time of the test task in the second final timetable and the time consumed when the test task is tested in the test time set to obtain the time corresponding to the test task; Add up the time corresponding to all test tasks in the updated individual corresponding to the ontology solution under the current hired bee iteration number i2, and obtain the fitness of the updated individual corresponding to the ontology solution under the current hired bee iteration number i2; Determine the fitness of the updated individual corresponding to the ontology solution under the current hired bee iteration number i2 as the fitness corresponding to the ontology solution under the current hired bee iteration number i2.

7. A parallel test scheduling optimization device, characterized in that: The scheduling optimization device for parallel testing includes: An acquisition module is used to acquire a test task set of the special power equipment to be tested; the test task set includes test tasks that need to meet preset test timing constraints and test tasks that do not need to meet preset test timing constraints; A recursive tree construction module, used to construct a recursive tree; the child nodes of the recursive tree are the test tasks in the test task set that need to meet the preset test timing constraints; A search module, used for recursively searching the recursive tree to obtain a plurality of local test schemes for the special electric power equipment to be tested that meet the preset test timing constraints; the local test schemes include all test tasks in the test task set that need to meet the preset test timing constraints; A hidden set coding determination module, used to determine that all local test schemes that meet preset test timing constraints are hidden set coding; The optimal test scheme determination module is used to use hidden set coding to adjust the global test scheme of the special power equipment to be tested obtained by using the artificial bee colony algorithm, so that the adjusted global test scheme of the special power equipment to be tested meets the preset test timing constraints, and obtains the optimal global test scheme of the special power equipment to be tested; the global test scheme includes all test tasks in the test task set.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the scheduling optimization method for parallel testing described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the scheduling optimization method for parallel testing described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the scheduling optimization method for parallel testing described in any one of claims 1 to 6 is implemented.

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