Method and device for avoiding repeated supervision of same supervision task

Abstract

The invention discloses a method and device for avoiding repeated supervision of the same supervision task, and relates to the technical field of smart office. When a new supervision task is created, a newly generated task fingerprint and an existing task fingerprint in a database are subjected to quantitative similarity comparison calculation; and if the new task fingerprint is completely the same as a certain existing task fingerprint in the supervised task database, determining that the task is a repeated task. If the possibility of the associated task exists, feedback reminding information comprises relevant information of a suspected associated supervised task, and a supervised task creator can judge whether the supervised task needs to be combined or not according to the feedback information; after the supervisory task creator selects whether to merge or not, the intelligent office system pushes supervisory task information to a plurality of associated department computers based on related departments in the key features of the supervisory task to complete creation and pushing of a new supervisory task; the problems of multiple times of supervision of the same supervision task, multiple times of signing of an undertaking unit and multi-task feedback are effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of smart office technology, and in particular to a method and device for avoiding multiple supervision of the same supervision task. Background Art

[0002] In the daily work of government departments, some important tasks, such as leadership instructions or meeting minutes, will be handed over to the supervision department for supervision. The supervision department will enter these tasks into the electronic office system, set the task requirements, and send them to different government functional departments for processing. The functional departments need to sign for them within the specified time and provide feedback according to the task setting requirements.

[0003] Some of these tasks or work are continuously advanced over a long period of time. During the advancement of the tasks, the same task may be agreed upon in different meetings or leaders may have different instructions at different times. In this way, the same task will generate many similar supervision tasks at different time points. This causes the task undertaking department to sign for multiple times and provide feedback on different supervision tasks, which increases the workload of the undertaking department. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method and device for avoiding multiple supervision of the same supervision task. The following technical solutions are adopted:

[0005] A method for avoiding multiple supervision of the same supervision task includes the following steps:

[0006] Step 1: Set up a supervision task database in the smart office system. When the supervision department creates a supervision task, the smart office system uses a semantic recognition algorithm to identify the registered supervision task to obtain the key features of the supervision task, and uses a hash function to encode the key features of the supervision task to form a task fingerprint based on multiple key feature hash codes;

[0007] Step 2: When a new supervision task is created, repeat step 1 to generate a new task fingerprint, and compare the new task fingerprint with all existing task fingerprints in the supervision task database for similarity;

[0008] If a new task fingerprint has all the same key feature hash codes as the existing task fingerprint in the supervision task data, it is considered a duplicate task;

[0009] If all key feature hash codes do not exist, calculate the maximum similarity between the new task fingerprint and the existing task fingerprint in the supervision task data, record it as Smax, set the first similarity threshold Sa and the second similarity threshold Sb, if Smax≥Sa, it is determined to be a repeated task;

[0010] If Sb≤Smax<Sa, it is determined to be an associated task;

[0011] If Smax<Sb, it is determined to be a new task;

[0012] Step 3: If duplicate tasks or related tasks are detected, the smart office system will feedback reminder information to the supervision department. The reminder information will display the supervision task information corresponding to the duplicate tasks or related tasks, and remind the creator of the supervision task whether to merge the supervision tasks;

[0013] Step 4: The smart office system pushes the supervision task information to multiple related department computers based on the departments involved in the key features of the supervision task.

[0014] By adopting the above technical solution, the key features of each supervision task are extracted. These key features of the supervision task may include task title, task description, involved departments, key personnel, task type, task level, expected completion time, etc. The extracted key features of the supervision task are encoded using a hash function to generate a unique task fingerprint. The hash function can be MD5, SHA-256, etc. The task fingerprint is stored for each task in the supervision task database of the smart office system, and other relevant information of the task is recorded at the same time.

[0015] When a new supervision task is created, the fingerprint of the new task is generated using the same method. The newly generated task fingerprint is compared with the existing task fingerprints in the database, and the comparison is calculated using quantitative similarity comparison. If the new task fingerprint is exactly the same as an existing task fingerprint in the supervision task database, it is determined to be a duplicate task.

[0016] If the task fingerprints are not exactly the same, the relationship between the similarity of the task fingerprints (such as the Hamming distance) and the set threshold is used to determine whether the tasks are similar enough, thereby determining whether duplicate supervision may occur.

[0017] If there is a duplication of supervision, the smart office system will feedback reminder information to the supervision department. When the task fingerprints are exactly the same, the feedback reminder information will directly prompt that the supervision task is repeated or refuse to create the supervision task. If it is judged that there is a possibility of related tasks, the feedback reminder information will include relevant information of the suspected related supervision tasks. The creator of the supervision task can judge whether the supervision tasks need to be merged based on the feedback information;

[0018] After the creator of the supervision task chooses whether to merge, the smart office system pushes the supervision task information to multiple related department computers based on the departments involved in the key features of the supervision task, completing the creation and push of the new supervision task;

[0019] It effectively solves the problems of multiple supervision of the same inspection task, multiple signatures by the hosting units, and multiple task feedback.

[0020] Optionally, the key features of the supervision task include task title, content description, involved departments, key personnel, task type, task level and expected completion time;

[0021] The steps of encoding the key features using a hash function in step 1 include:

[0022] Step 11: Feature extraction and formatting: extract the key features of the supervision task and format them into a unified string;

[0023] Step 12: Use a hash function to generate a hash value, and use the hash function to encode the formatted feature string.

[0024] Optionally, the hash function is SHA-256 or MD5.

[0025] By adopting the above technical solution, a quantized hash value is used to represent the key features of the supervision task, which can provide data for the subsequent calculation of similarity.

[0026] Optionally, in step 2, the method for performing similarity comparison between the new task fingerprint and all existing task fingerprints in the supervision task database includes the following steps:

[0027] Step 21, calculate the hash value of the new task fingerprint;

[0028] Step 22, respectively calculating the Hamming distance between the hash value of the new task fingerprint and the hash values ​​of all existing task fingerprints in the supervision task database;

[0029] Step 23, calculating similarity based on the Hamming distance.

[0030] By adopting the above technical solution, the Hamming distance reflects the number of different bits between two hash values ​​in binary form. The smaller the Hamming distance between the hash values, the more similar the corresponding key feature descriptions of the two supervision tasks are. The key features of the supervision tasks are combined into a string, and then the SHA-256 hash function is used to generate a unique hash value, which is the fingerprint of the task. In this way, each supervision task can be ensured to have a unique identifier to facilitate subsequent duplicate task detection.

[0031] Optionally, in step 23, the Hamming distance is standardized to be between 0 and 1, where 0 indicates that they are completely identical and 1 indicates that they are completely different; the similarity is defined as 1 minus the standardized Hamming distance, so that when two fingerprints are completely identical, the similarity is 1, and when they are completely different, the similarity is 0.

[0032] Optionally, divide the Hamming distance by 64 to get a normalized distance.

[0033] By adopting the above technical solution, since the length of the SHA-256 hash value is fixed at 64 characters, we can divide the Hamming distance by 64 to get the standardized distance.

[0034] Optionally, in step 23, the method for calculating the similarity is:

[0035] For each key feature of the supervision task, set the weight in the similarity calculation, calculate its similarity in the two tasks, multiply the similarity of each feature by its weight, sum up the weighted similarities of all features, and get the final similarity. The similarity calculation formula is:

[0036] S(task a , task b )=∑W[i]·FS(task a [i], task b [i]);

[0037] Among them, S(task a , task b ) represents the similarity between the new task fingerprint and one of the task fingerprints in the supervision task database, W[i] represents the weight of the key feature of the i-th supervision task, FS(task a [i], task b [i]) represents the similarity of the key features of the first supervision task in the two task fingerprints.

[0038] By adopting the above technical solution, since SHA-256 hash values ​​are evenly distributed, even a small change will result in a completely different hash value. Considering that different supervision task characteristics may have different effects on the overall similarity of supervision tasks. Different weights can be given to different key features of supervision tasks, and then these weights can be considered when calculating similarity, so as to more accurately reflect the similarity of actual content.

[0039] Optionally, in the process of calculating the similarity, the weights of the key features of the supervision task are set as follows: the group weight of the task title is 0.5, the weight of the content description is 0.3, the weight of the department involved is 0.1, the weight of the key personnel is 0.05, the weight of the task type is 0.05, the weight of the task level is 0.05 and the weight of the expected completion time is 0.05.

[0040] A device for avoiding multiple supervision of the same supervision task, used to implement a method for avoiding multiple supervision of the same supervision task, the device includes a smart office system, multiple supervision department clients and a network communication module, the smart office system includes a storage unit and a central control computer, the storage unit stores a supervision task database, the central control computer is communicatively connected to the storage unit, the central control computer is installed with a task duplication detection program designed using a method for avoiding multiple supervision of the same supervision task, the multiple supervision department clients are terminal computers, the multiple supervision department clients are respectively installed with smart office terminal software, and new supervision tasks are entered through the smart office terminal software, the multiple supervision department clients respectively exchange supervision task data with the central control computer through the network communication module, the central control computer runs the task duplication detection program to check the new supervision task, and feeds back the inspection results to the supervision department clients.

[0041] Optionally, the smart office system further includes a plurality of associated department computers, and the central control computer is communicatively connected to the plurality of associated department computers respectively through a network communication module.

[0042] In summary, the present invention includes at least one of the following beneficial technical effects:

[0043] The present invention can provide a method and device for avoiding multiple supervision of the same supervision task, and extract the key features of each supervision task to generate a unique task fingerprint. When a new supervision task is created, the fingerprint of the new task is generated by the same method. The newly generated task fingerprint is compared and calculated with the existing task fingerprints in the database using quantitative similarity. If the new task fingerprint is exactly the same as an existing task fingerprint in the supervision task database, it is determined to be a repeated task.

[0044] If the task fingerprints are not exactly the same, the relationship between the similarity of the task fingerprints and the set threshold is used to determine whether the tasks are similar enough, thereby determining whether it may constitute duplicate supervision.

[0045] If there is a duplication of supervision, the smart office system will feedback reminder information to the supervision department. When the task fingerprints are exactly the same, the feedback reminder information will directly prompt that the supervision task is repeated or refuse to create the supervision task. If it is judged that there is a possibility of related tasks, the feedback reminder information will include relevant information of the suspected related supervision tasks. The creator of the supervision task can judge whether the supervision tasks need to be merged based on the feedback information;

[0046] After the creator of the supervision task chooses whether to merge, the smart office system pushes the supervision task information to multiple related department computers based on the departments involved in the key features of the supervision task, completing the creation and push of the new supervision task;

[0047] It effectively solves the problems of multiple supervision of the same inspection task, multiple signatures by the hosting units, and multiple task feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of a method for avoiding multiple supervision of the same supervision task according to the present invention;

[0049] Figure 2 The present invention is a schematic diagram of the connection principle of various components of a device for avoiding repeated supervision of the same supervision task.

[0050] Explanation of the accompanying reference numerals: 100, smart office system; 101, supervision department client; 1, network communication module; 1001, storage unit; 1002, central control computer; 1003, related department computer. DETAILED DESCRIPTION

[0051] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0052] The embodiment of the present invention discloses a method and a device for avoiding multiple supervision of the same supervision task.

[0053] Reference Figure 1 and Figure 2 , Embodiment 1, a method for avoiding multiple supervision of the same supervision task, comprising the following steps:

[0054] Step 1: Set up a supervision task database in the smart office system 100. When the supervision department creates a supervision task, the smart office system 100 uses a semantic recognition algorithm to identify the registered supervision task to obtain the key features of the supervision task, and uses a hash function to encode the key features of the supervision task to form a task fingerprint based on multiple key feature hash codes;

[0055] Step 2: When a new supervision task is created, repeat step 1 to generate a new task fingerprint, and compare the new task fingerprint with all existing task fingerprints in the supervision task database for similarity;

[0056] If a new task fingerprint has all the same key feature hash codes as the existing task fingerprint in the supervision task data, it is considered a duplicate task;

[0057] If all key feature hash codes do not exist, calculate the maximum similarity between the new task fingerprint and the existing task fingerprint in the supervision task data, record it as Smax, set the first similarity threshold Sa and the second similarity threshold Sb, if Smax≥Sa, it is determined to be a repeated task;

[0058] If Sb≤Smax<Sa, it is determined to be an associated task;

[0059] If Smax<Sb, it is determined to be a new task;

[0060] Step 3: If duplicate tasks or related tasks are detected, the smart office system 100 will feedback reminder information to the supervision department, and the reminder information content will display the supervision task information corresponding to the duplicate tasks or related tasks, and remind the creator of the supervision task whether to merge the supervision tasks;

[0061] In step 4, the smart office system 100 pushes the supervision task information to multiple related department computers 1003 based on the departments involved in the key features of the supervision task.

[0062] Extract key features of each supervision task, which may include task title, task description, involved departments, key personnel, task type, task level, expected completion time, etc. Use a hash function to encode the extracted key features of the supervision task to generate a unique task fingerprint. The hash function can be MD5, SHA-256, etc. The task fingerprint is stored for each task in the supervision task database of the smart office system 100, and other relevant information of the task is recorded.

[0063] When a new supervision task is created, the fingerprint of the new task is generated using the same method. The newly generated task fingerprint is compared with the existing task fingerprints in the database, and the comparison is calculated using quantitative similarity comparison. If the new task fingerprint is exactly the same as an existing task fingerprint in the supervision task database, it is determined to be a duplicate task.

[0064] If the task fingerprints are not exactly the same, the relationship between the similarity of the task fingerprints (such as the Hamming distance) and the set threshold is used to determine whether the tasks are similar enough, thereby determining whether duplicate supervision may occur.

[0065] If a task is repeated, the smart office system 100 will feedback a reminder message to the supervision department. When the task fingerprints are exactly the same, the feedback reminder message directly prompts that the supervision task is repeated or refuses to create the supervision task. If it is determined that there is a possibility of related tasks, the feedback reminder message includes relevant information of the suspected related supervision tasks. The creator of the supervision task can determine whether to merge the supervision tasks based on the feedback information.

[0066] After the creator of the supervision task chooses whether to merge, the smart office system 100 pushes the supervision task information to multiple related department computers 1003 based on the departments involved in the key features of the supervision task, completing the creation and push of the new supervision task;

[0067] It effectively solves the problems of multiple supervision of the same inspection task, multiple signatures by the hosting units, and multiple task feedback.

[0068] In embodiment 2, the key features of the supervision task include task title, content description, involved departments, key personnel, task type, task level and expected completion time;

[0069] The steps of encoding the key features using a hash function in step 1 include:

[0070] Step 11: Feature extraction and formatting: extract the key features of the supervision task and format them into a unified string;

[0071] Step 12: Use a hash function to generate a hash value, and use the hash function to encode the formatted feature string.

[0072] In embodiment 3, the hash function is SHA-256 or MD5.

[0073] Using quantized hash values ​​to represent the key features of the supervision task can provide data for subsequent similarity calculations.

[0074] In Example 4, in step 2, the method for performing similarity comparison between the new task fingerprint and all existing task fingerprints in the supervision task database includes the following steps:

[0075] Step 21, calculate the hash value of the new task fingerprint;

[0076] The following is a code example for generating a hash value for a task:

[0077] import hashlib

[0078] #Define task data

[0079] task={'title':'Annual Report Preparation','description':'Prepare andsubmit the annual report draft','departments':'Finance,Marketing','key_personnel':'Alice,Bob','type':'Report','level':'High','due_date':'2024-12-31'}

[0080] #Feature formatting function

[0081] Def format_features(task):feature_string=f"{task['title']}|{task['description']}|{task['depa rtments']}|{task['key_personnel']}|{task['type']}|{task['level']}|{task['due_date']}"

[0082] return feature_string

[0083] # Generate hash value function

[0084] def generate_hash(feature_string):

[0085] sha256 = hashlib.sha256()

[0086] sha256.update(feature_string.encode('utf-8'))

[0087] return sha256.hexdigest()

[0088] #Generate supervision task fingerprint

[0089] def generate_task_fingerprint(task):

[0090] feature_string=format_features(task)

[0091] task_fingerprint=generate_hash(feature_string)

[0092] return task_fingerprint

[0093] #Generate and print supervision task fingerprint

[0094] fingerprint=generate_task_fingerprint(task)

[0095] print(f"Task fingerprint: {fingerprint}").

[0096] Step 22, respectively calculating the Hamming distance between the hash value of the new task fingerprint and the hash values ​​of all existing task fingerprints in the supervision task database;

[0097] Step 23, calculating similarity based on the Hamming distance.

[0098] The Hamming distance reflects the number of different bits between two hash values ​​in binary form. The smaller the Hamming distance between hash values, the more similar the corresponding key feature descriptions of the two supervision tasks are. The key features of the supervision tasks are combined into a string, and then the SHA-256 hash function is used to generate a unique hash value, which is the fingerprint of the task. In this way, each supervision task can be ensured to have a unique identifier to facilitate subsequent duplicate task detection.

[0099] In Example 5, in step 23, the Hamming distance is standardized to be between 0 and 1, where 0 indicates that they are exactly the same and 1 indicates that they are completely different; the similarity is defined as 1 minus the standardized Hamming distance, so that when two fingerprints are exactly the same, the similarity is 1, and when they are completely different, the similarity is 0.

[0100] In Example 6, the Hamming distance is divided by 64 to obtain the standardized distance.

[0101] Since the length of a SHA-256 hash is fixed at 64 characters, we can divide the Hamming distance by 64 to get the normalized distance.

[0102] In Example 7, in step 23, the method for calculating the similarity is:

[0103] For each key feature of the supervision task, set the weight in the similarity calculation, calculate its similarity in the two tasks, multiply the similarity of each feature by its weight, sum up the weighted similarities of all features, and get the final similarity. The similarity calculation formula is:

[0104] S(task a , task b )=∑W[i]·FS(task a [i], task b [i]);

[0105] Among them, S(task a , task b ) represents the similarity between the new task fingerprint and one of the task fingerprints in the supervision task database, W[i] represents the weight of the key feature of the i-th supervision task, FS(task a [i], task b [i]) represents the similarity of the key features of the first supervision task in the two task fingerprints.

[0106] Since SHA-256 hash values ​​are uniformly distributed, even small changes can result in completely different hash values, and considering different supervision task features may have different effects on the overall similarity of supervision tasks. Different weights can be given to different key features of supervision tasks, and then these weights can be considered when calculating similarity, so as to more accurately reflect the similarity of the actual content.

[0107] In Example 8, in the process of calculating the similarity, the weights of the key features of the supervision task are set as follows: the group weight of the task title is 0.5, the weight of the content description is 0.3, the weight of the department involved is 0.1, the weight of the key personnel is 0.05, the weight of the task type is 0.05, the weight of the task level is 0.05 and the weight of the expected completion time is 0.05.

[0108] Embodiment 9, a device for avoiding multiple supervision of the same supervision task, used to implement a method for avoiding multiple supervision of the same supervision task, the device includes a smart office system 100, multiple supervision department clients 101 and a network communication module 1, the smart office system 100 includes a storage unit 1001 and a central control computer 1002, the storage unit 1001 stores a supervision task database, the central control computer 1002 is communicated with the storage unit 1001, the central control computer 1002 is installed with a task duplication detection program designed using a method for avoiding multiple supervision of the same supervision task according to claim 9, the multiple supervision department clients 101 are terminal computers, the multiple supervision department clients 101 are respectively installed with smart office terminal software, and new supervision tasks are entered through the smart office terminal software, the multiple supervision department clients 101 respectively exchange supervision task data with the central control computer 1002 through the network communication module 1, the central control computer 1002 runs the task duplication detection program to check the new supervision task, and feeds back the inspection results to the supervision department clients 101.

[0109] In Example 10, the smart office system 100 further includes a plurality of related department computers 1003 , and the central control computer 1002 is respectively connected to the plurality of related department computers 1003 via the network communication module 1 .

[0110] Specifically, when applied, the first similarity threshold Sa is set to 0.9, and the second similarity threshold Sb is set to 0.8. If a specific new supervision task: let the three departments A, B, and C report the implementation of the task at the end of the month, the central control computer 1002 runs the task duplication detection program to detect that the similarity between it and an existing supervision task in the supervision task database is 0.85, then it is judged that the new supervision task is suspected of being a related task with the existing supervision task, and the central control computer 1002 reports the information of the existing supervision task to the creator's computer as follows: let the two departments A and B report the implementation of the task at the end of the month, after the creator chooses to merge, the smart office system 100 notifies the user terminals of departments A and B of the supervision task information based on the departments involved in the key features of the supervision task, and pushes the new supervision task to department C. The creation and push of the new supervision task is completed, avoiding the repeated push of supervision tasks to departments A and B.

[0111] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for avoiding multiple supervision of the same supervision task, characterized in that: The following steps are involved: Step 1, setting up a supervision task database in the smart office system (100), when the supervision department creates a supervision task, the smart office system (100) uses a semantic recognition algorithm to identify the registered supervision task to obtain key features of the supervision task, and uses a hash function to encode the key features of the supervision task to form a task fingerprint based on the hash code of multiple key features; Step 2: When a new supervision task is created, repeat step 1 to generate a new task fingerprint, and compare the new task fingerprint with all existing task fingerprints in the supervision task database for similarity; If a new task fingerprint has all the same key feature hash codes as the existing task fingerprint in the supervision task data, it is considered a duplicate task; If all key feature hash codes do not exist, calculate the maximum similarity between the new task fingerprint and the existing task fingerprint in the supervision task data, record it as Smax, set the first similarity threshold Sa and the second similarity threshold Sb, if Smax≥Sa, it is determined to be a repeated task; If Sb≤Smax<Sa, it is determined to be an associated task; If Smax<Sb, it is determined to be a new task; Step 3, if duplicate tasks or related tasks are detected, the smart office system (100) feeds back reminder information to the supervision department, the reminder information content displays the supervision task information corresponding to the duplicate tasks or related tasks, and reminds the creator of the supervision task whether to merge the supervision tasks; Step 4, the smart office system (100) pushes the supervision task information to multiple related department computers (1003) based on the departments involved in the key characteristics of the supervision task.

2. A method for avoiding multiple supervision of the same supervision task according to claim 1, characterized in that: The key features of the supervision task include task title, content description, departments involved, key personnel, task type, task level and expected completion time; The steps of encoding the key features using a hash function in step 1 include: Step 11: Feature extraction and formatting: extract the key features of the supervision task and format them into a unified string; Step 12: Use a hash function to generate a hash value, and use the hash function to encode the formatted feature string.

3. A method for avoiding multiple supervision of the same supervision task according to claim 2, characterized in that: The hash function is either SHA-256 or MD5.

4. A method for avoiding multiple supervision of the same supervision task according to claim 3, characterized in that: In step 2, the method for performing similarity comparison between the new task fingerprint and all existing task fingerprints in the supervision task database includes the following steps: Step 21, calculate the hash value of the new task fingerprint; Step 22, respectively calculating the Hamming distance between the hash value of the new task fingerprint and the hash values ​​of all existing task fingerprints in the supervision task database; Step 23, calculating the similarity based on the Hamming distance.

5. A method for avoiding multiple supervision of the same supervision task according to claim 4, characterized in that: In step 23, the Hamming distance is standardized to be between 0 and 1, where 0 indicates that the two fingerprints are completely identical and 1 indicates that the two fingerprints are completely different. The similarity is defined as 1 minus the standardized Hamming distance, so that when the two fingerprints are completely identical, the similarity is 1, and when they are completely different, the similarity is 0.

6. A method for avoiding multiple supervision of the same supervision task according to claim 5, characterized in that: Divide the Hamming distance by 64 to get the normalized distance.

7. A method for avoiding multiple supervision of the same supervision task according to claim 6, characterized in that: In step 23, the method for calculating the similarity is: For each key feature of the supervision task, set the weight in the similarity calculation, calculate its similarity in the two tasks, multiply the similarity of each feature by its weight, sum up the weighted similarities of all features, and get the final similarity. The similarity calculation formula is: S(task a ,task b )=∑W[i]·FS(task a [i],task b [i]); Among them, S(task a , task b ) represents the similarity between the new task fingerprint and one of the task fingerprints in the supervision task database, W[i] represents the weight of the key feature of the i-th supervision task, FS(task a [i], task b [i]) represents the similarity of the key features of the first supervision task in the two task fingerprints.

8. A method for avoiding multiple supervision of the same supervision task according to claim 7, characterized in that: In the process of calculating the similarity, the weights of the key features of the supervision tasks are set as follows: the group weight of the task title is 0.5, the weight of the content description is 0.3, the weight of the involved departments is 0.1, the weight of the key personnel is 0.05, the weight of the task type is 0.05, the weight of the task level is 0.05 and the weight of the expected completion time is 0.

05.

9. A device for avoiding multiple inspections of the same inspection task, characterized in that: A method for avoiding multiple supervision of the same supervision task as described in claim 8, the device includes a smart office system (100), multiple supervision department clients (101) and a network communication module (1), the smart office system (100) includes a storage unit (1001) and a central control computer (1002), the storage unit (1001) stores a supervision task database, the central control computer (1002) is connected to the storage unit (1001), and the central control computer (1002) is installed with a same supervision task as described in claim 9 A task duplication detection program is designed to avoid multiple supervision tasks. The multiple supervision department clients (101) are terminal computers. The multiple supervision department clients (101) are respectively installed with smart office terminal software, and new supervision tasks are entered through the smart office terminal software. The multiple supervision department clients (101) exchange supervision task data with the central control computer (1002) through the network communication module (1). The central control computer (1002) runs the task duplication detection program to check the new supervision tasks and feeds back the inspection results to the supervision department clients (101).

10. A device for avoiding multiple supervision of the same supervision task according to claim 9, characterized in that: The smart office system (100) further comprises a plurality of associated department computers (1003), and the central control computer (1002) is respectively connected to the plurality of associated department computers (1003) via the network communication module (1).

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