Task allocation management method based on big data

Abstract

The invention discloses a task allocation management method based on big data, and relates to the technical field of enterprise management. Comprising the following steps that tasks are automatically collected and enqueued, and a system automatically captures and arranges inspection tasks to be distributed; orderly putting the sorted tasks into a message queue, sequencing according to priorities and types, and synchronizing department and personnel information with a system personnel database in real time; according to the synchronized data, the distribution ratio calculated by the distribution ratio system of each department is calculated by using a fine algorithm, and tasks are automatically distributed to employees of the corresponding department in combination with task characteristics; performing optimal allocation according to the current task load of the employees; and the distributed tasks are pushed to employees of corresponding departments through a system automation means. According to the method, multi-dimensional factors such as the number of departments and the workload of the departments can be comprehensively considered, distribution of inspection tasks is automatically calculated and optimized, the reasonability and fairness of task distribution are maximized, and meanwhile the overall working efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise management, and in particular to a task allocation management method based on big data. Background Art

[0002] Data-driven decision-making is becoming increasingly important in modern society, and it can help companies and organizations significantly improve efficiency and accuracy. For example, in the retail industry, big data analysis models can accurately predict consumer demand, thereby optimizing inventory and supply chain management, and improving customer satisfaction and sales.

[0003] Data-driven decision-making can provide objective, real-time insights based on large amounts of data, replacing traditional experience and intuitive judgment, and significantly improving decision-making quality and efficiency.

[0004] The development of big data technology can be traced back to early data storage and processing technologies, such as relational databases and data warehouses. With the rapid development of the Internet and information technology, the scale and complexity of data are increasing. The emergence of cloud computing, machine learning, and artificial intelligence technologies has provided a solid technical foundation for big data analysis models.

[0005] Cloud computing technology provides a powerful infrastructure for the storage and computing of big data. Through distributed storage and computing clusters, large-scale data can be processed efficiently. Machine learning and artificial intelligence technologies enable big data analysis models to automatically extract valuable information and knowledge from data, further improving the depth and breadth of analysis.

[0006] In the prior art, the task allocation of enterprises still relies on manual allocation, which will lead to the problem of increasing number of task allocations, employee dissatisfaction with manual task allocation, and uneven task allocation. Summary of the invention

[0007] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a task allocation management method based on big data.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A task allocation management method based on big data comprises the following steps:

[0010] S1: Automatic collection and queuing of tasks;

[0011] S2: Department and personnel data synchronization and allocation ratio calculation;

[0012] S3: Intelligent task allocation;

[0013] S4: Evaluation and adjustment of allocation effects.

[0014] Preferably, the step S1 comprises the following steps:

[0015] S11: The system automatically captures and organizes the inspection tasks to be assigned;

[0016] S12: Put the sorted tasks into the message queue in order and sort them according to priority and type.

[0017] Preferably: in the step S12, the task classification includes the following steps:

[0018] S121a: Preset multiple keywords according to all task types, set a priority weight for each keyword, and divide all task types, each type corresponds to one or more keywords;

[0019] S122a: extracting the keywords of the task and the frequency of occurrence of the keywords;

[0020] S123a: Calculate type matching, according to Where q is the number of keywords contained in the task type, p is the number of keywords matching the task type in the task, and f i is the frequency of occurrence of the i-th matching keyword, k i is the weight of the keyword matched by the ith item;

[0021] S124a: According to the calculation result in S123a, the result with the largest value is selected as the type allocation result of the task.

[0022] Preferably: in the step S12, the priority sorting includes the following steps:

[0023] S121b: Preset multiple keywords according to all task types, set a priority weight for each keyword, and divide all task types, each type corresponds to one or more keywords;

[0024] S122b: Then according to the formula Calculate the priority of each task type, where n represents the number of keywords for the task type, and k represents the number of keywords for the task type. o Represents the weight of the oth keyword.

[0025] Preferably, the step S2 comprises the following steps:

[0026] S21: Synchronize department and personnel information with the system personnel database in real time;

[0027] S22: Based on the synchronized data, a sophisticated algorithm is used to calculate the allocation ratio for each department.

[0028] Preferably, the step S22 comprises the following steps:

[0029] S221: Collect the current vacancy workload of each person in the department It represents the current vacant workload of the oth employee in the i-th department;

[0030] S222: According to the formula Calculate the average work capacity of the i-th department, where n represents the number of employees in the i-th department;

[0031] S223: The task allocation ratio of the i-th department is T is the total number of tasks, and m represents m departments.

[0032] Preferably, in step S3, the system automatically allocates the task to the employees of the corresponding department according to the allocation ratio calculated in S2 and in combination with the task characteristics.

[0033] Preferably, the allocation logic of the step S3 is:

[0034] S311: Allocate tasks to corresponding departments according to the allocation ratio calculated in S2;

[0035] S312: After receiving the workload, the department will allocate it in proportion to the workload currently available to the department staff;

[0036] In the steps S221 and S312, the current vacant workload is the total workload that the staff can handle on the day minus the remaining workload on hand.

[0037] Preferably: in the steps S221 and S312, when the task is a high-risk operation task, according to the list of personnel and the total number of tasks in the uploaded form list, the first level is 100% selection rate, and the second level is not less than 10% selection rate. The second level cannot be assigned to only the same department, and the same department cannot be assigned to only one person.

[0038] Preferably: the step S4 comprises the following steps:

[0039] S41: The system automatically evaluates the current allocation effect and calculates the difference between the allocation ratio and the preset error range;

[0040] S42: If the allocation result exceeds the error range, the system automatically triggers the reallocation mechanism and returns to S3 to recalculate and allocate tasks;

[0041] S43: Repeat steps S3 and S4 until the distribution ratio reaches a preset satisfactory range.

[0042] The beneficial effects of the present invention are:

[0043] 1. The present invention can comprehensively consider multi-dimensional factors such as the number of department personnel and the workload of department personnel, automatically calculate and optimize the distribution of inspection tasks, maximize the rationality and fairness of task distribution, and improve overall work efficiency.

[0044] 2. The present invention builds a real-time data synchronization mechanism to ensure that the department and personnel data used in the task allocation process are the latest. Combined with the automated task processing system, the entire process from task collection, queuing, allocation to distribution is automated, reducing manual intervention and improving processing efficiency and accuracy.

[0045] 3. The present invention designs a dynamic task allocation adjustment mechanism, which can trigger the reallocation process when the real-time task exceeds the preset error range after the task allocation is completed. Through continuous iterative optimization, it ensures that the task allocation ratio is always kept within a reasonable range. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A flowchart of a task allocation management method based on big data proposed by the present invention;

[0047] Figure 2 This is a flowchart of automatic task collection and queuing of a task allocation management method based on big data proposed by the present invention;

[0048] Figure 3 A flowchart of department and personnel data synchronization and allocation ratio calculation of a task allocation management method based on big data proposed by the present invention;

[0049] Figure 4 This is a flow chart of allocation effect evaluation and adjustment of a task allocation management method based on big data proposed by the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention is further described in detail below in conjunction with specific implementation methods.

[0051] Embodiment 1:

[0052] A task allocation management method based on big data, comprising the following steps:

[0053] S1: Automatic collection and queuing of tasks;

[0054] S2: Department and personnel data synchronization and allocation ratio calculation;

[0055] S3: Intelligent task allocation;

[0056] S4: Evaluation and adjustment of allocation effects.

[0057] The step S1 comprises the following steps:

[0058] S11: The system automatically captures and organizes the inspection tasks to be assigned;

[0059] S12: Put the sorted tasks into the message queue in order and sort them according to priority and type.

[0060] The step S2 comprises the following steps:

[0061] S21: Synchronize department and personnel information with the system personnel database in real time;

[0062] S22: Based on the synchronized data, a sophisticated algorithm is used to calculate the allocation ratio for each department.

[0063] The allocation logic of the S3 step is:

[0064] S311: Allocate tasks to corresponding departments according to the allocation ratio calculated in S2;

[0065] S312: After receiving the workload, the department will allocate it in proportion to the workload currently available to the department staff;

[0066] In the steps S221 and S312, the currently vacant workload is the total workload that the staff can handle on that day minus the remaining workload currently on hand.

[0067] In the steps S221 and S312, when the task is a high-risk operation task, according to the list of personnel and the total number of tasks in the uploaded form list, the first level is 100% selection rate, and the second level is not less than 10% selection rate. The second level cannot be assigned to only the same department, and the same department cannot be assigned to only one person.

[0068] The S4 step comprises the following steps:

[0069] S41: The system automatically evaluates the current allocation effect and calculates the difference between the allocation ratio and the preset error range;

[0070] S42: If the allocation result exceeds the error range, the system automatically triggers the reallocation mechanism and returns to S3 to recalculate and allocate tasks;

[0071] S43: Repeat steps S3 and S4 until the distribution ratio reaches a preset satisfactory range.

[0072] Embodiment 2:

[0073] A task allocation management method based on big data, comprising the following steps:

[0074] S1: Automatic collection and queuing of tasks;

[0075] S2: Department and personnel data synchronization and allocation ratio calculation;

[0076] S3: Intelligent task allocation;

[0077] S4: Evaluation and adjustment of allocation effects.

[0078] The step S1 comprises the following steps:

[0079] S11: The system automatically captures and organizes the inspection tasks to be assigned;

[0080] S12: Put the sorted tasks into the message queue in order and sort them according to priority and type.

[0081] In the step S12, task classification includes the following steps:

[0082] S121a: Preset multiple keywords according to all task types, set a priority weight for each keyword, and divide all task types, each type corresponds to one or more keywords;

[0083] S122a: extracting the keywords of the task and the frequency of occurrence of the keywords;

[0084] S123a: Calculate type matching, according to Where q is the number of keywords contained in the task type, p is the number of keywords matching the task type in the task, and f i is the frequency of occurrence of the i-th matching keyword, k i is the weight of the keyword matched by the ith item;

[0085] S124a: According to the calculation result in S123a, the result with the largest value is selected as the type allocation result of the task.

[0086] The step S2 comprises the following steps:

[0087] S21: Synchronize department and personnel information with the system personnel database in real time;

[0088] S22: Based on the synchronized data, a sophisticated algorithm is used to calculate the allocation ratio for each department.

[0089] The allocation logic of the S3 step is:

[0090] S311: Allocate tasks to corresponding departments according to the allocation ratio calculated in S2;

[0091] S312: After receiving the workload, the department will allocate it in proportion to the workload currently available to the department staff;

[0092] In the steps S221 and S312, the currently vacant workload is the total workload that the staff can handle on that day minus the remaining workload currently on hand.

[0093] In the steps S221 and S312, when the task is a high-risk operation task, according to the list of personnel and the total number of tasks in the uploaded form list, the first level is 100% selection rate, and the second level is not less than 10% selection rate. The second level cannot be assigned to only the same department, and the same department cannot be assigned to only one person.

[0094] The S4 step comprises the following steps:

[0095] S41: The system automatically evaluates the current allocation effect and calculates the difference between the allocation ratio and the preset error range;

[0096] S42: If the allocation result exceeds the error range, the system automatically triggers the reallocation mechanism and returns to S3 to recalculate and allocate tasks;

[0097] S43: Repeat steps S3 and S4 until the distribution ratio reaches a preset satisfactory range.

[0098] Embodiment 3:

[0099] A task allocation management method based on big data, comprising the following steps:

[0100] S1: Automatic collection and queuing of tasks;

[0101] S2: Department and personnel data synchronization and allocation ratio calculation;

[0102] S3: Intelligent task allocation;

[0103] S4: Evaluation and adjustment of allocation effects.

[0104] The step S1 comprises the following steps:

[0105] S11: The system automatically captures and organizes the inspection tasks to be assigned;

[0106] S12: Put the sorted tasks into the message queue in order and sort them according to priority and type.

[0107] In the step S12, task classification includes the following steps:

[0108] S121a: Preset multiple keywords according to all task types, set a priority weight for each keyword, and divide all task types, each type corresponds to one or more keywords;

[0109] S122a: extracting the keywords of the task and the frequency of occurrence of the keywords;

[0110] S123a: Calculate type matching, according to Where q is the number of keywords contained in the task type, p is the number of keywords matching the task type in the task, and f i is the frequency of occurrence of the i-th matching keyword, k i is the weight of the keyword matched by the ith item;

[0111] S124a: According to the calculation result in S123a, the result with the largest value is selected as the type allocation result of the task.

[0112] In the step S12, the priority sorting includes the following steps:

[0113] S121b: Preset multiple keywords according to all task types, set a priority weight for each keyword, and divide all task types, each type corresponds to one or more keywords;

[0114] S122b: Then according to the formula Calculate the priority of each task type, where n represents the number of keywords for the task type, and k represents the number of keywords for the task type. o Represents the weight of the oth keyword.

[0115] The step S2 comprises the following steps:

[0116] S21: Synchronize department and personnel information with the system personnel database in real time;

[0117] S22: Based on the synchronized data, a sophisticated algorithm is used to calculate the allocation ratio for each department.

[0118] The allocation logic of the S3 step is:

[0119] S311: Allocate tasks to corresponding departments according to the allocation ratio calculated in S2;

[0120] S312: After receiving the workload, the department will allocate it in proportion to the workload currently available to the department staff;

[0121] In the steps S221 and S312, the currently vacant workload is the total workload that the staff can handle on that day minus the remaining workload currently on hand.

[0122] In the steps S221 and S312, when the task is a high-risk operation task, according to the list of personnel and the total number of tasks in the uploaded form list, the first level is 100% selection rate, and the second level is not less than 10% selection rate. The second level cannot be assigned to only the same department, and the same department cannot be assigned to only one person.

[0123] The S4 step comprises the following steps:

[0124] S41: The system automatically evaluates the current allocation effect and calculates the difference between the allocation ratio and the preset error range;

[0125] S42: If the allocation result exceeds the error range, the system automatically triggers the reallocation mechanism and returns to S3 to recalculate and allocate tasks;

[0126] S43: Repeat steps S3 and S4 until the distribution ratio reaches a preset satisfactory range.

[0127] Embodiment 4:

[0128] A task allocation management method based on big data, comprising the following steps:

[0129] S1: Automatic collection and queuing of tasks;

[0130] S2: Department and personnel data synchronization and allocation ratio calculation;

[0131] S3: Intelligent task allocation;

[0132] S4: Evaluation and adjustment of allocation effects.

[0133] The step S1 comprises the following steps:

[0134] S11: The system automatically captures and organizes the inspection tasks to be assigned;

[0135] S12: Put the sorted tasks into the message queue in order and sort them according to priority and type.

[0136] In the step S12, task classification includes the following steps:

[0137] S121a: Preset multiple keywords according to all task types, set a priority weight for each keyword, and divide all task types, each type corresponds to one or more keywords;

[0138] S122a: extracting the keywords of the task and the frequency of occurrence of the keywords;

[0139] S123a: Calculate type matching, according to Where q is the number of keywords contained in the task type, p is the number of keywords matching the task type in the task, and f i is the frequency of occurrence of the i-th matching keyword, k i is the weight of the keyword matched by the ith item;

[0140] S124a: According to the calculation result in S123a, the result with the largest value is selected as the type allocation result of the task.

[0141] In the step S12, the priority sorting includes the following steps:

[0142] S121b: Preset multiple keywords according to all task types, set a priority weight for each keyword, and divide all task types, each type corresponds to one or more keywords;

[0143] S122b: Then according to the formula Calculate the priority of each task type, where n represents the number of keywords for the task type, and k represents the number of keywords for the task type. o Represents the weight of the oth keyword.

[0144] The step S2 comprises the following steps:

[0145] S21: Synchronize department and personnel information with the system personnel database in real time;

[0146] S22: Based on the synchronized data, a sophisticated algorithm is used to calculate the allocation ratio for each department.

[0147] The step S22 comprises the following steps:

[0148] S221: Collect the current vacant workload M of each person in the department i o , which represents the current vacant workload of the oth employee in the i-th department;

[0149] S222: According to the formula Calculate the average work capacity of the i-th department, where n represents the number of employees in the i-th department;

[0150] S223: The task allocation ratio of the i-th department is T is the total number of tasks, and m represents m departments.

[0151] The allocation logic of the S3 step is:

[0152] S311: Allocate tasks to corresponding departments according to the allocation ratio calculated in S2;

[0153] S312: After receiving the workload, the department will allocate it in proportion to the workload currently available to the department staff;

[0154] In the steps S221 and S312, the currently vacant workload is the total workload that the staff can handle on that day minus the remaining workload currently on hand.

[0155] In the steps S221 and S312, when the task is a high-risk operation task, according to the list of personnel and the total number of tasks in the uploaded form list, the first level is 100% selection rate, and the second level is not less than 10% selection rate. The second level cannot be assigned to only the same department, and the same department cannot be assigned to only one person.

[0156] The S4 step comprises the following steps:

[0157] S41: The system automatically evaluates the current allocation effect and calculates the difference between the allocation ratio and the preset error range;

[0158] S42: If the allocation result exceeds the error range, the system automatically triggers the reallocation mechanism and returns to S3 to recalculate and allocate tasks;

[0159] S43: Repeat steps S3 and S4 until the distribution ratio reaches a preset satisfactory range.

[0160] Embodiment 5:

[0161] A task allocation management method based on big data, comprising the following steps:

[0162] S1: Automatic collection and queuing of tasks;

[0163] S2: Department and personnel data synchronization and allocation ratio calculation;

[0164] S3: Intelligent task allocation;

[0165] S4: Evaluation and adjustment of allocation effects.

[0166] The step S1 comprises the following steps:

[0167] S11: The system automatically captures and organizes the inspection tasks to be assigned;

[0168] S12: Put the sorted tasks into the message queue in order and sort them according to priority and type.

[0169] In the step S12, task classification includes the following steps:

[0170] S121a: Preset multiple keywords according to all task types, set a priority weight for each keyword, and divide all task types, each type corresponds to one or more keywords;

[0171] S122a: extracting the keywords of the task and the frequency of occurrence of the keywords;

[0172] S123a: Calculate type matching, according to Where q is the number of keywords contained in the task type, p is the number of keywords matching the task type in the task, and fi is the frequency of occurrence of the i-th matching keyword, k i is the weight of the keyword matched by the ith item;

[0173] S124a: According to the calculation result in S123a, the result with the largest value is selected as the type allocation result of the task.

[0174] In the step S12, the priority sorting includes the following steps:

[0175] S121b: Preset multiple keywords according to all task types, set a priority weight for each keyword, and divide all task types, each type corresponds to one or more keywords;

[0176] S122b: Then according to the formula Calculate the priority of each task type, where n represents the number of keywords for the task type, and k represents the number of keywords for the task type. o Represents the weight of the oth keyword.

[0177] The step S2 comprises the following steps:

[0178] S21: Synchronize department and personnel information with the system personnel database in real time;

[0179] S22: Based on the synchronized data, a sophisticated algorithm is used to calculate the allocation ratio for each department.

[0180] The step S22 comprises the following steps:

[0181] S221: Collect the current vacant workload M of each person in the department i o , which represents the current vacant workload of the oth employee in the i-th department;

[0182] S222: According to the formula Calculate the average work capacity of the i-th department, where n represents the number of employees in the i-th department;

[0183] S223: The task allocation ratio of the i-th department is Where T is the total number of tasks and m represents m departments.

[0184] The allocation logic of the S3 step is:

[0185] S311: Allocate tasks to corresponding departments according to the allocation ratio calculated in S2;

[0186] S312: After receiving the workload, the department will allocate it in proportion to the workload currently available to the department staff;

[0187] In the steps S221 and S312, the currently vacant workload is the total workload that the staff can handle on that day minus the remaining workload currently on hand.

[0188] In the steps S221 and S312, when the task is a high-risk operation task, according to the list of personnel and the total number of tasks in the uploaded form list, the first level is 100% selection rate, and the second level is not less than 10% selection rate. The second level cannot be assigned to only the same department, and the same department cannot be assigned to only one person.

[0189] The S4 step comprises the following steps:

[0190] S41: The system automatically evaluates the current allocation effect and calculates the difference between the allocation ratio and the preset error range;

[0191] S42: If the allocation result exceeds the error range, the system automatically triggers the reallocation mechanism and returns to S3 to recalculate and allocate tasks;

[0192] S43: Repeat steps S3 and S4 until the distribution ratio reaches a preset satisfactory range.

[0193] The step S31 comprises the following steps:

[0194] S311: After receiving the tasks, coordinate the remaining tasks in the hands of employees in each department;

[0195] S312: The sum of the remaining task volume and the task volume received this time is taken as the total task volume of the department, and then allocated based on the capabilities of the employees.

[0196] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A task allocation management method based on big data, characterized in that: The following steps are involved: S1: Automatic collection and queuing of tasks; S2: Department and personnel data synchronization and allocation ratio calculation; S3: Intelligent task allocation; S4: Evaluation and adjustment of allocation effects.

2. The task allocation management method based on big data according to claim 1 is characterized in that: The step S1 comprises the following steps: S11: The system automatically captures and organizes the inspection tasks to be assigned; S12: Put the sorted tasks into the message queue in order and sort them according to priority and type.

3. The task allocation management method based on big data according to claim 2 is characterized in that: In the step S12, task classification includes the following steps: S121a: Preset multiple keywords according to all task types, set a priority weight for each keyword, and divide all task types, each type corresponds to one or more keywords; S122a: extracting the keywords of the task and the frequency of occurrence of the keywords; S123a: Calculate type matching, according to Where q is the number of keywords contained in the task type, p is the number of keywords matching the task type in the task, and f i is the frequency of occurrence of the i-th matching keyword, k i is the weight of the keyword matched by the ith item; S124a: According to the calculation result in S123a, the result with the largest value is selected as the type allocation result of the task.

4. The task allocation management method based on big data according to claim 2 is characterized in that: In the step S12, the priority sorting includes the following steps: S121b: Preset multiple keywords according to all task types, set a priority weight for each keyword, and divide all task types, each type corresponds to one or more keywords; S122b: Then according to the formula Calculate the priority of each task type, where n represents the number of keywords for the task type, and k represents the number of keywords for the task type. o Represents the weight of the oth keyword.

5. The task allocation management method based on big data according to claim 1 is characterized in that: The step S2 comprises the following steps: S21: Synchronize department and personnel information with the system personnel database in real time; S22: Based on the synchronized data, a sophisticated algorithm is used to calculate the allocation ratio for each department.

6. The task allocation management method based on big data according to claim 5 is characterized in that: The step S22 comprises the following steps: S221: Collect the current vacant workload M of each person in the department i o It represents the current vacant workload of the oth employee in the i-th department; S222: According to the formula Calculate the average work capacity of the i-th department, where n represents the number of employees in the i-th department; S223: The task allocation ratio of the i-th department is Where T is the total number of tasks and m represents m departments.

7. The task allocation management method based on big data according to claim 6 is characterized in that: In step S3, the system automatically allocates tasks to employees of corresponding departments according to the allocation ratio calculated in S2 and in combination with the characteristics of the tasks.

8. The task allocation management method based on big data according to claim 7 is characterized in that: The allocation logic of the S3 step is: S311: Allocate tasks to corresponding departments according to the allocation ratio calculated in S2; S312: After receiving the workload, the department will allocate it in proportion to the workload currently available to the department staff; In the steps S221 and S312, the currently vacant workload is the total workload that the staff can handle on that day minus the remaining workload currently on hand.

9. The task allocation management method based on big data according to claim 8 is characterized in that: In the steps S221 and S312, when the task is a high-risk operation task, according to the list of personnel and the total number of tasks in the uploaded form list, the first level is 100% selection rate, and the second level is not less than 10% selection rate. The second level cannot be assigned to only the same department, and the same department cannot be assigned to only one person.

10. The task allocation management method based on big data according to claim 1, characterized in that: The S4 step comprises the following steps: S41: The system automatically evaluates the current allocation effect and calculates the difference between the allocation ratio and the preset error range; S42: If the allocation result exceeds the error range, the system automatically triggers the reallocation mechanism and returns to S3 to recalculate and allocate tasks; S43: Repeat steps S3 and S4 until the distribution ratio reaches a preset satisfactory range.

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