In order to improve the total utility of task allocation in spatiotemporal crowdsourcing dynamic reality, a Dynamic Threshold algorithm based on online Random Forest (DTRF) was proposed. Firstly, the online random forest was initialized based on the historical matching data of workers and tasks on the crowdsourcing platform. Then, the online random forest was used to predict the expected task return rate of each worker as the threshold, and the candidate matching set was selected for each worker according to the threshold. Finally, the matching with the highest sum of current utility was selected from the candidate match set, and the online random forest was updated based on the allocation result. The experiments show that the algorithm can improve the average income of workers while increasing the total utility. Compared with the greedy algorithm, the proposed algorithm has the task assignment rate increased by 4.1%, the total utility increased by 18.2%, and the average worker income increased by 11.2%. Compared with the random threshold algorithm, this algorithm has a better improvement in task allocation rate, total utility, average income of workers with better stability.

Focusing on the poor overall allocation effect due to the total utility of task allocation or task waiting time being considered respectively in the study of task allocation under spatial crowdsourcing environment, a dynamic threshold algorithm based on allocation time factor was proposed. Firstly, the allocation time factor of task was calculated based on the estimated waiting time and the already waiting time. Secondly, the task allocation order was obtained by comprehensively considering the return value of task and the allocation time factor. Thirdly, the dynamic adjustment item was added based on the initial value to set the threshold for each task. Finally, candidate matching set was set for each task according to the threshold condition, and the candidate matching pair with the largest matching coefficient was selected from the candidate matching set to join the result set, and the task allocation was completed. When the task allocation rate was 95.8%, compared with greedy algorithm, the proposed algorithm increased total allocation utility by 20.4%; compared with random threshold algorithm, it increased total allocation utility by 17.8% and decreased task average waiting time by 13.2%; compared with Two phase based Global Online Allocation-Greedy (TGOA-Greedy) algorithm, it increased total allocation utility by 13.9%. The experimental results show that proposed algorithm can shorten the average waiting time of task while improving the total utility of task allocation, to achieve the balance between the total allocation utility and the task waiting time.

Concerning the problem that existing software crowdsourcing worker selection mechanisms do not consider the collaboration among workers, a crowdsourcing worker selection mechanism with bidding model based on active time grouping was proposed. Firstly, crowd-sourced workers were divided into multiple collaborative working groups based on active time. Then, the weights of the working groups were calculated according to the development capabilities of the workers in the group and collaboration factors. Finally, the collaborative working group with the highest weight was selected as the optimal working group, and the most suitable worker from this group was selected for each task module according to the complexity of the module. The experimental results show that the proposed mechanism has a gap of only 0.57% in the average worker ability compared to the ability only allocation method. At the same time, it reduces the project risk by an average of 32% due to the ensurence of the cooperation between workers, which can effectively guide the selection of workers for multi-person collaborative crowdsourcing software tasks.

Aiming at the problem that the existing software crowdsourcing platforms do not consider the order of task release, a method of calculating Task Release Priority (TRP) of software crowdsourcing based on task publisher weight and task weight was proposed. Firstly, a time weight function based on semi-sinusoidal curve was used to measure the activity of the task publisher and the cumulative turnover of the task, so as to calculate the weight of the task publisher. Secondly, the task complexity was calculated according to the system architecture diagram and data flow diagram to measure module complexity, design complexity and data complexity, and the task benefit factor and task emergency factor were calculated based on task quotation and task duration. In this way, the task weight was calculated. Finally, the task publishing priority would be given according to task publisher weight and task weight. The experimental results show that the proposed algorithm not only is effective and reasonable, but also has a maximum success rate of 98%.

The existing software crowdsourcing platforms do not consider the ability of workers adequately, which leads to the low completion quality of tasks assigned to workers. In order to solve the problem, a new Ability Dynamic Measurement algorithm (ADM) for software crowdsourcing workers was proposed to achieve the dynamic measurement of the workers' ability. Firstly, the initial ability of a worker was calculated based on his static skill coverage rate. Secondly, for the single task completed by the worker in history, task complexity, task completion quality, and task development timeliness were integrated to realize the calculation of development ability, and the development ability decaying with time was calculated according to a time factor. Then, according to the time sequence of all the completed tasks in history, the dynamic update of ability measurement value was realized. Finally, the development ability of the worker for a task to be assigned was calculated based on the skill coverage rates of historical tasks. The experimental results show that, compared with the user reliability measurement algorithm, the proposed ability dynamic measurement algorithm has a better rationality and effectiveness, and the average coincidence degree of ability measurement is up to 90.5%, which can effectively guide task assignment.

Concerning that the traditional Web text clustering algorithm without considering the Web text topic information leads to a low accuracy rate of multi-topic Web text clustering, a new algorithm was proposed for Web text clustering based on the topic theme. In the method, multi-topic Web text was clustered by three steps: topic extraction, feature extraction and text clustering. Compared to the traditional Web text clustering algorithm, the proposed method fully considered the Web text topic information. The experimental results show that the accuracy rate of the proposed algorithm for multi-topic Web text clustering is higher than the text clustering method based on K-means or HowNet.