Double random extraction method and device, electronic equipment and storage medium

Abstract

Embodiments of the present application provide a double random extraction method and device, electronic equipment and storage medium. The method comprises: obtaining all label categories of the object to be checked, and determining a first weight coefficient of the label category of the object to be checked based on all label categories of the object to be checked and a type weight coefficient of the object to be checked; sorting all objects to be checked in the type of the object to be checked in the object to be checked library in descending order of the first weight coefficient of the label category to obtain a sorted object to be checked library; obtaining all label categories of the inspector, and determining a second weight coefficient of the label category of the inspector based on all label categories of the inspector and a type weight coefficient of the inspector; and sorting all inspectors in the type of the inspector in the inspector library in descending order of the second weight coefficient of the label category to obtain a sorted inspector library. The present application improves the balance and accuracy of the random check.

Description

Technical Field

[0001] This invention belongs to the field of information processing technology, and in particular relates to a double random sampling method, apparatus, electronic device and storage medium. Background Technology

[0002] To reduce violations by businesses and ensure fairness and impartiality as much as possible, a "double random" approach is typically used for business supervision. The double random approach mainly refers to: randomly selecting the entity to be inspected or the inspectors.

[0003] Currently, the "double random" approach mainly involves establishing a database of objects to be inspected and a database of inspectors. Then, objects to be inspected are randomly selected from the database of objects to be inspected, and inspectors are randomly selected from the database of inspectors.

[0004] However, in the process of selecting objects to be inspected from the object database, there are problems of imbalance and extreme selection. Similar problems exist in the selection of inspectors from the inspector database. Summary of the Invention

[0005] This invention provides a method, apparatus, electronic device, and storage medium for double random sampling, aiming to solve the problems of uneven and extreme sampling of objects to be inspected and of inspectors to be selected.

[0006] In a first aspect, the present invention provides a double random sampling method, the method comprising:

[0007] Obtain the type of the object to be searched in the object database, and determine the type weight coefficient of the object to be searched based on the type of the object to be searched;

[0008] Obtain all tag categories of the object to be searched, and determine the first weight coefficient of the tag category of the object to be searched based on all tag categories of the object to be searched and the type weight coefficient of the object to be searched;

[0009] In the object database, all objects of the type of the object to be searched are sorted in descending order according to the first weight coefficient of the tag category to obtain the sorted object database; the sorted object database is used to extract the object to be searched.

[0010] Obtain the types of inspectors in the inspector database, and determine the type weight coefficient of the inspectors based on their types;

[0011] Obtain all the label categories of the inspector, and determine the second weight coefficient of the label category of the inspector based on all the label categories of the inspector and the type weight coefficient of the inspector;

[0012] All inspectors in the inspector database, categorized by type, are sorted in descending order according to the second weight coefficient of the tag category to obtain a sorted inspector database; the sorted inspector database is used to extract inspectors.

[0013] In a second aspect, the present invention provides a dual random sampling device, the device comprising:

[0014] The first acquisition module is used to acquire the type of the object to be searched in the object database, and determine the type weight coefficient of the object to be searched based on the type of the object to be searched;

[0015] The second acquisition module is used to acquire all the tag categories of the object to be searched, and determine the first weight coefficient of the tag category of the object to be searched based on all the tag categories of the object to be searched and the type weight coefficient of the object to be searched.

[0016] The first sorting module is used to sort all the objects to be searched in the object to be searched database according to the first weight coefficient of the tag category from largest to smallest, so as to obtain a sorted object to be searched database; the sorted object to be searched database is used to extract objects to be searched.

[0017] The third acquisition module is used to acquire the types of inspectors in the inspector database and determine the type weight coefficient of the inspectors based on the types of the inspectors.

[0018] The fourth acquisition module is used to acquire all the label categories of the inspector, and determine the second weight coefficient of the label category of the inspector based on all the label categories of the inspector and the type weight coefficient of the inspector;

[0019] The second sorting module is used to sort all the inspectors in the inspector database according to the second weight coefficient of the label category from largest to smallest, so as to obtain a sorted inspector database; the sorted inspector database is used to extract inspectors.

[0020] Thirdly, the present invention provides an electronic device comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described double random sampling method.

[0021] Fourthly, the present invention provides a readable storage medium that, when the instructions in the storage medium are executed by the processor of an electronic device, enables the electronic device to perform the above-described double random sampling method.

[0022] In this embodiment of the invention, the type weight coefficient of the objects to be searched in the object database is determined, and based on the type weight coefficient of the objects to be searched and all the tag categories of the objects to be searched, the first weight coefficient of the tag category of the objects to be searched is determined. All the objects to be searched in the object database of ... The type weight coefficient of inspectors in the inspector database is determined. Based on the type weight coefficient and all label categories of inspectors, the second weight coefficient of label categories of inspectors is determined. All inspectors of the inspector types in the inspector database are sorted in descending order of the second weight coefficient of label categories to obtain the sorted inspector database. The sorted inspector database reflects at least both the type weight and the label category weight of inspectors, providing accurate information support for inspector extraction. Extracting inspectors based on the sorted inspector database can improve balance and accuracy, and reduce the probability of imbalance and extremes. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of the steps of a double random sampling method provided in an embodiment of the present invention;

[0025] Figure 2 This is a flowchart of the steps for processing a database of objects to be queried, provided by an embodiment of the present invention;

[0026] Figure 3 This is a flowchart illustrating the steps involved in processing an inspection personnel database, as provided in an embodiment of the present invention.

[0027] Figure 4 This is a flowchart of another double random sampling method provided in an embodiment of the present invention;

[0028] Figure 5 This is a structural diagram of a dual random sampling device provided in an embodiment of the present invention;

[0029] Figure 6This is a structural diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] The inventors discovered that the main reason for the uneven and extreme selection of objects from the object database in the existing technology is that the existing object database basically only reflects information such as the name of the object and does not involve information such as the inspection dimensions of the object. The selection of objects from this object database is largely random, resulting in uneven and extreme selection. Similar problems exist when selecting inspectors from the inspector database.

[0032] To address the aforementioned issues, in this embodiment of the invention, the type weight coefficient of the objects to be searched in the object database is determined. Based on the type weight coefficient of the objects to be searched and all tag categories of the objects to be searched, a first weight coefficient of the tag category of the objects to be searched is determined. All objects to be searched in the object database of ... The type weight coefficient of inspectors in the inspector database is determined. Based on the type weight coefficient and all label categories of inspectors, the second weight coefficient of label categories of inspectors is determined. All inspectors of the inspector types in the inspector database are sorted in descending order of the second weight coefficient of label categories to obtain the sorted inspector database. The sorted inspector database reflects at least both the type weight and the label category weight of inspectors, providing accurate information support for inspector extraction. Extracting inspectors based on the sorted inspector database can improve balance and accuracy, and reduce the probability of imbalance and extremes.

[0033] Figure 1 This is a flowchart of a double random sampling method provided in an embodiment of the present invention, as follows: Figure 1 As shown, the method may include:

[0034] Step 101: Obtain the type of the object to be searched in the object database, and determine the type weight coefficient of the object to be searched based on the type of the object.

[0035] The entities to be inspected refer to all enterprises and institutions that can be included in the inspection. For example, entities to be inspected may include: industrial and commercial enterprises, social organizations, construction projects, etc. Industrial and commercial enterprises may include sole proprietorships, social organizations may include: schools, government units, etc., and construction projects may include: construction sites, etc. Specifically, the database of entities to be inspected may include all enterprises and institutions within a geographical area that can be included in the inspection. For example, the database of entities to be inspected could be all 100 enterprises and institutions within geographical area A that can be included in the inspection.

[0036] The type of the object to be inspected can be categorized based on the characteristics or dimensions to be inspected. There are no specific limitations on which types of objects are included in the inspection. An object to be inspected can have one or more types.

[0037] Figure 2 This is a flowchart illustrating the steps involved in processing a database of objects to be searched, as provided in an embodiment of the present invention. For example, refer to... Figure 2 As shown, based on the potential pollution level of the objects to be investigated to the environment, all 100 objects in the object database within geographical area A can be divided into three types: key type, general type, and special type. Among them, there are 20 objects in the key type, 56 objects in the general type, and 24 objects in the special type.

[0038] For example, based on whether the object to be investigated has a record of illegal or irregular activities, all 100 objects in the object database within geographical area A can be divided into two categories: illegal / irregular activities and law-abiding activities. Among them, there are 8 objects in the illegal / irregular activities category and 92 objects in the law-abiding activities category.

[0039] The type weight coefficient of the object to be checked can reflect the proportion of the object to be checked in the type indicator.

[0040] Step 102: Obtain all tag categories of the object to be searched, and determine the first weight coefficient of the tag category of the object to be searched based on all tag categories of the object to be searched and the type weight coefficient of the object to be searched.

[0041] The label category of the object to be searched can be an inspection dimension that is different from the aforementioned object type. Alternatively, the label category of the object to be searched can be a further subdivision of the aforementioned object type; there is no specific limitation on this. An object to be searched can have one or more label categories. There is no specific limitation on how many label categories an object to be searched can have.

[0042] The "all tag categories" of the object to be searched specifically refers to which tag categories the object to be searched has. For example, the all tag categories of object 1 in the object database for geographic area A are: tag category 1, tag category 2, tag category 3, tag category 4, and tag category 5.

[0043] The first weight of the tag category of the object to be searched can reflect the proportion of the object under the tag category.

[0044] Step 103: Sort all the objects to be queried in the object to be queried category in the object to be queried library in descending order according to the first weight coefficient of the tag category to obtain the sorted object to be queried library; the sorted object to be queried library is used to extract the objects to be queried.

[0045] This step involves sorting all objects in each type of the object database according to the first weight coefficient of each object's tag category from largest to smallest. The sorted object database reflects at least both the type weight and the tag category weight of the objects, providing accurate information support for the extraction of objects. Extracting objects based on the sorted object database can improve balance and accuracy, and reduce the probability of imbalance and extremes.

[0046] For example, among all objects to be inspected within the same category, the higher the first weight of the object's label category, the higher its ranking among all objects in that category. This indicates a relatively higher potential for illegal or non-compliant activities, or a relatively higher potential risk of environmental pollution. Conversely, the lower the first weight of the object's label category, the lower its ranking among all objects in that category. This indicates a relatively lower potential for illegal or non-compliant activities, or a relatively lower potential risk of environmental pollution. Therefore, during the random selection of objects from the ranked object database, for all objects in the same category, selecting more of the higher-ranked objects ensures that no illegal activities are missed, while reducing the number of lower-ranked objects minimizes disruption to the normal production and operation of legitimate businesses.

[0047] Step 104: Obtain the types of inspectors in the inspector database, and determine the type weight coefficient of the inspectors based on their types.

[0048] Inspection personnel refers to all personnel who can participate in inspections. For example, inspection personnel can include personnel from inspection and law enforcement departments. Specifically, an inspection personnel database can include all personnel within a geographical area who can participate in inspections. For instance, the inspection personnel database could be all 30 inspection and law enforcement personnel within geographical area A who can participate in inspections.

[0049] Inspectors can be categorized based on their professional competence, etc. There is no specific limitation on which types of inspectors are included. An inspector can be classified into one or more types.

[0050] Figure 3 This is a flowchart illustrating the steps involved in processing an inspection personnel database, as provided in an embodiment of the present invention. For example, refer to... Figure 3 As shown, based on the professional competence of the inspectors, all 30 inspectors in the inspector pool of geographical area A can be divided into three types: primary level, intermediate level, and advanced level. Among them, there are 8 inspectors of primary level, 16 inspectors of intermediate level, and 6 inspectors of advanced level.

[0051] The type weighting coefficient of the inspector can reflect the proportion of the inspector in the type indicator to be inspected.

[0052] Step 105: Obtain all the label categories of the inspector, and determine the second weight coefficient of the label category of the inspector based on all the label categories of the inspector and the type weight coefficient of the inspector.

[0053] The label category for an inspector can be an inspection dimension different from the aforementioned inspector type. Alternatively, the label category for an inspector can be a further subdivision of the aforementioned inspector type; there is no specific limitation on this. An inspector can have one or more label categories. There is no specific limitation on how many label categories an inspector can have.

[0054] The "All Tag Categories" of an inspector specifically refers to the number of tag categories that inspector has. For example, the "All Tag Categories" of inspector number 1 in the inspector database for geographic area A are: Tag Category 1, Tag Category 2, Tag Category 3, and Tag Category 6.

[0055] The second weight of the inspector's label category can reflect the proportion of that inspector under that label category.

[0056] Step 106: Sort all inspectors in the inspector database by type according to the second weight coefficient of the tag category from largest to smallest to obtain the sorted inspector database; the sorted inspector database is used to extract inspectors.

[0057] This step involves sorting all inspectors in each type of the inspector database according to the second weight coefficient of each inspector's label category from largest to smallest. The sorted inspector database reflects at least both the type weight and the label category weight of the inspectors, providing accurate information support for inspector extraction. Extracting inspectors based on the sorted inspector database can improve balance and accuracy, and reduce the probability of imbalance and extremes.

[0058] For example, among all inspectors of the same type, the higher the second weight of an inspector's label category, the higher their ranking among all inspectors of that type. This indicates that the inspector has a relatively broad knowledge base and relatively high professional competence. Conversely, the lower the second weight of an inspector's label category, the lower their ranking among all inspectors of that type. This indicates that the inspector has a relatively narrow knowledge base and relatively low professional competence. Therefore, in the process of randomly selecting targets from the ranked inspector pool, for targets with many inspection items or high risk of violations, inspectors with higher rankings can be selected. This allows one inspector to inspect multiple items simultaneously with high accuracy, thereby reducing disruption to businesses and ensuring high inspection accuracy. For targets with fewer inspection items, inspectors with lower rankings can be selected.

[0059] In this embodiment of the invention, the type weight coefficient of inspectors in the inspector database is determined, and based on the type weight coefficient of inspectors and all label categories of inspectors, the second weight coefficient of label categories of inspectors is determined. All inspectors of the inspector types in the inspector database are sorted in descending order according to the first weight coefficient of label categories to obtain the sorted inspector database. The sorted inspector database reflects at least both the type weight and the label category weight of inspectors, providing accurate information support for the extraction of inspectors. Extracting the target object based on the sorted inspector database can improve balance and accuracy, and reduce the probability of imbalance and extremes. The type weight coefficient of inspectors in the inspector database is determined. Based on the type weight coefficient and all label categories of inspectors, the second weight coefficient of label categories of inspectors is determined. All inspectors of the inspector types in the inspector database are sorted in descending order of the second weight coefficient of label categories to obtain the sorted inspector database. The sorted inspector database reflects at least both the type weight and the label category weight of inspectors, providing accurate information support for inspector extraction. Extracting inspectors based on the sorted inspector database can improve balance and accuracy, and reduce the probability of imbalance and extremes.

[0060] This application can be applied in scenarios including, but not limited to: inspectors checking the compliance of entities under inspection. For example, inspectors may check the wastewater discharge of entities under inspection; food processing and sales entities may be inspected for food safety; grain operators may be inspected for grain quality and storage safety; processing and production market entities may be inspected for safe production; and pharmaceutical-related market entities may be inspected for drug quality and safety.

[0061] Figure 4 This is a flowchart of another double random sampling method provided in an embodiment of the present invention, as follows: Figure 4 As shown, the method may include:

[0062] Step 201: Obtain the type of the object to be searched in the object database, the first quantity of all objects to be searched in the object database, the second quantity of all types of all objects to be searched, and the third quantity of all objects to be searched in each type of the object to be searched.

[0063] The types of objects to be searched in the object database are similar to those in step 101 above, and will not be repeated here. The first quantity of all objects to be searched in the object database specifically refers to the total number of objects in the database. For example, in the previous example, if the object database for geographical region A contains 100 objects, then the first quantity of all objects to be searched in the object database for geographical region A is 100.

[0064] The second number of all types of objects to be investigated specifically refers to the total number of types into which all objects in the object database are divided. For example, in the previous example, if the 100 objects in the object database for geographical area A are divided into three types: key type, general type, and special type, then the second number of all types of objects to be investigated is 3. As another example, if the 100 objects in the object database for geographical area A are divided into two types: illegal / irregular type and law-abiding type, then the second number of all types of objects to be investigated is 2.

[0065] The "third quantity" of each category of the objects to be searched specifically refers to the number of objects to be searched within each category of the object to be searched. For example, if object number 1 in the object database for geographical area A is of the "key" category, and there are 20 objects of the "key" category, then the "third quantity" of each category of the objects to be searched within the "key" category of object number 1 in the object database for geographical area A is 20. As another example, if object number 1 in the object database for geographical area A is of the "law-abiding" category, and there are 92 objects of the "law-abiding" category, then the "third quantity" of each category of the objects to be searched within the "law-abiding" category of object number 1 in the object database for geographical area A is 92.

[0066] Step 202: Determine the type weight coefficient of the object to be searched based on the type of the object to be searched, the first quantity, the second quantity, and each of the third quantities.

[0067] The type weight coefficient of the object to be searched is determined by the type of the object to be searched, the first number of all objects to be searched in the object database, the second number of all types of objects to be searched, and the third number of all objects to be searched in each type of the object to be searched. The above factors can accurately reflect the distribution of the object to be searched in its type. Therefore, the type weight coefficient of the object to be searched determined by this factor accurately reflects the actual distribution of the object to be searched in its type.

[0068] Optionally, step 202 may include the following sub-steps.

[0069] Sub-step 2021: Sum all natural numbers from 1 to the first quantity to obtain the first sum value.

[0070] Sub-step 2022: Divide the third quantity of all objects in a type of the object to be searched by the second quantity to obtain the factor of the type of the object to be searched.

[0071] Sub-step 2023: Divide the first quantity by the first quotient of the first sum, multiply by the factor of the type of the object to be searched, and obtain the sub-type weight coefficient of the type of the object to be searched.

[0072] Sub-step 2024: Sum the weight coefficients of each subtype of all types of the object to be checked to obtain the type weight coefficient of the object to be checked.

[0073] Specifically, the subtype weight coefficient of the i-th type of the object to be searched is V. i , Where m is the first number of all objects to be searched in the object database, and d i is the third number of all objects in the i-th type of the object to be searched, and n is the second number of all types of all objects in the object database where the object to be searched is located. The first sum, Let V be the factor of the i-th type of the object to be searched. Then, sum the subtype weight coefficients of the p types of the object to be searched to obtain the type weight coefficient of the object to be searched, where p ≤ n. That is, V = ∑V i When p = 1, i = 1; or when p > 1, i = natural numbers from 1 to p.

[0074] For example, in the aforementioned case, the first quantity m of all objects to be searched in the object database for geographical area A is 100. These 100 objects are categorized into three types: key type, general type, and special type. Therefore, the second quantity n of all types of objects to be searched is 3. Object number 1 in the object database for geographical area A is of the key type only. There are 20 objects of the key type. Therefore, the third quantity d of all objects of the key type within object number 1 in the object database for geographical area A is... i That is, 20. Therefore, the subtype weight coefficient of the key type of the first object in the object database within geographical region A is...

[0075] Since the type of the first object in the database of objects to be searched within geographical scope A is only the key type, i.e., p = 1, the sum of the weight coefficients of each subtype of all types of the first object in the database of objects to be searched within geographical scope A is the subtype weight coefficient V of the key type of the first object in the database of objects to be searched within geographical scope A. i That is, the type weight coefficient V of the first object in the object database of the geographic range A is 0.1320.

[0076] For example, in the previous example, the type of the second object in the object database of geographical area A is only general. There are a total of 56 general objects in this object database. Then, the third number d of all objects of the general type in the object database of the second object in geographical area A is... i That is, 56. Therefore, the subtype weight coefficient of the general type of the second object in the object database within geographical region A is... Since the type of the second object in the object database within geographical region A is only the general type (i.e., p = 1), the sum of the weight coefficients of each subtype of all types of the second object in the object database within geographical region A is the subtype weight coefficient V of the general type of the second object in the object database within geographical region A. i That is, the type weight coefficient V of the second object in the object database of the geographic scope A is 0.3696.

[0077] For example, in the previous example, the type of the third object in the object database of the geographic area A is only a special type. There are a total of 24 special type objects in this object database. Then, the third number d of all objects of the special type in the object database of the third object in the object database of the geographic area A is... i That is, 24. Therefore, the subtype weight coefficient of the special type of the third object in the object database within geographical region A is... Since the type of the third object in the object database within geographical region A is only a special type (i.e., p = 1), the sum of the weight coefficients of each subtype of all types of the third object in the object database within geographical region A is the subtype weight coefficient V of the special type of the third object in the object database within geographical region A. i That is, the type weight coefficient V of the third object in the object database of the geographic range A is 0.1584.

[0078] Step 203: Obtain all tag categories of the object to be searched, the fourth number of all tag categories of the object to be searched, the fifth number of all objects to be searched in each tag category of the object to be searched in the object to be searched library, and the sixth number of all objects to be searched corresponding to all tag categories of the object to be searched in the object to be searched library.

[0079] Obtaining all tag categories for the object to be searched is similar to step 102 above, and will not be repeated here to avoid repetition. The fourth number of all tag categories for the object to be searched refers to the total number of tags assigned to the object. For example, in the previous example, the tag categories for object 1 in the object database for geographic range A are: tag category 1, tag category 2, tag category 3, tag category 4, and tag category 5, a total of 5 tag categories. Therefore, the fourth number of all tag categories for object 1 in the object database for geographic range A is 5.

[0080] In the database of objects to be searched, the "number of fifths" for each tag category of all objects within that tag category refers to the number of objects within each tag category of that object. For example, in the above scenario, if the database for object #1 in region A contains five tag categories: Tag Category 1, Tag Category 2, Tag Category 3, Tag Category 4, and Tag Category 5, and five objects in region A are tagged with Tag Category 1, then the number of fifths for all objects in Tag Category 1 is 5. If ten objects in region A are tagged with Tag Category 2, then the number of fifths for all objects in Tag Category 2 is 10. If twelve objects in region A are tagged with Tag Category 3, then the number of fifths for all objects in Tag Category 3 is 12. If 4 objects in the database of objects to be searched within geographical region A are tagged with category 4, then the total number of fifths for all objects in category 4 is 4. If 10 objects in the database of objects to be searched within geographical region A are tagged with category 5, then the total number of fifths for all objects in category 5 is 10.

[0081] In the object database, the sixth number of all objects corresponding to all tag categories of the object to be searched is specifically the total number of objects in the database whose tag categories have been assigned to that object. For example, in the above example, if the tag categories of object 1 in the object database for geographical region A are: tag category 1, tag category 2, tag category 3, tag category 4, and tag category 5 (a total of 5 tag categories), then 5 objects in the object database for geographical region A are tagged with tag category 1, 10 objects in the object database for geographical region A are tagged with tag category 2, 12 objects in the object database for geographical region A are tagged with tag category 3, 4 objects in the object database for geographical region A are tagged with tag category 4, and 10 objects in the object database for geographical region A are tagged with tag category 5. If, in the database of objects to be searched within geographical area A, there are no duplicate objects corresponding to all objects to be searched for under tag category 1, all objects to be searched for under tag category 2, all objects to be searched for under tag category 3, all objects to be searched for under tag category 4, and all objects to be searched for under tag category 5, then the sixth number of all objects to be searched for under tag category 1 in the database of objects to be searched within geographical area A is: 5 + 10 + 12 + 4 + 10 = 41.

[0082] Optionally, in step 203, obtaining the sixth quantity of all objects corresponding to all tag categories of the object to be searched in the object database can include: obtaining the first original quantity of all objects corresponding to all tag categories of the object to be searched in the object database. This first original quantity is the sum of the fifth quantities of all objects in each tag category of the object to be searched in the object database. If there are duplicate objects among all objects corresponding to all tag categories of the object to be searched, only one of the duplicate objects is retained from the first original quantity to obtain the sixth quantity of all objects corresponding to all tag categories of the object to be searched. If there are no duplicate objects among all objects corresponding to all tag categories of the object to be searched, the first original quantity of all objects corresponding to all tag categories of the object to be searched is determined as the sixth quantity of all objects corresponding to all tag categories of the object to be searched.

[0083] For example, in the above case, if the tag categories of the first object in the database of objects to be searched within geographical region A are: tag category 1, tag category 2, tag category 3, tag category 4, and tag category 5 (a total of 5 tag categories), where 5 objects in the database of objects to be searched within geographical region A are tagged with tag category 1, 10 objects are tagged with tag category 2, 12 objects are tagged with tag category 3, 4 objects are tagged with tag category 4, and 10 objects are tagged with tag category 5, then the first original number of all objects corresponding to all tag categories of the first object in the database of objects to be searched within geographical region A is: 5 + 10 + 12 + 4 + 10 = 41. If all the target objects corresponding to all the tag categories of target object 1 in the target object database of geographical area A include target object 5, and target object 5 is tagged with three tag categories: tag category 1, tag category 2, and tag category 4, then target object 5 is a duplicate target object among all the target objects corresponding to all the tag categories of target object 1. Target object 5 appears 3 times in the first original quantity 41, meaning the quantity of target object 5 in 41 is 3. Therefore, we retain only 1 instance of target object 5 in the first original quantity 41, 41-2=39, which is the sixth quantity of all the target objects corresponding to all the tag categories of target object 1 in the target object database of geographical area A. If, in the object database for geographic area A, there are no duplicate objects for all objects corresponding to tag category 1, tag category 2, tag category 3, tag category 4, and tag category 5, that is, for the five tag categories (tag category 1, tag category 2, tag category 3, tag category 4, and tag category 5), each object is only tagged with one of these tag categories, then there are no duplicate objects among all objects corresponding to all tag categories of the object to be queried (object number 1). In this case, the first original quantity 41 is used to determine the sixth quantity of all objects corresponding to all tag categories of the object to be queried (object number 1) in the object database for geographic area A.

[0084] Step 204: Determine the first weight coefficient of the tag category of the object to be searched based on the type weight coefficient of the object to be searched, all tag categories of the object to be searched, the fourth quantity, each of the fifth quantities, and the sixth quantity.

[0085] The first weight coefficient of the tag category of the object to be searched is determined by the type weight coefficient of the object to be searched, all tag categories of the object to be searched, the fourth number of all tag categories of the object to be searched, the fifth number of all objects to be searched in each tag category of the object to be searched in the object database, and the sixth number of all objects to be searched corresponding to all tag categories of the object to be searched in the object database. The above factors can accurately reflect the distribution of the object to be searched in its tag category. Therefore, the first weight coefficient of the tag category of the object to be searched accurately reflects the actual distribution of the object to be searched in its tag category.

[0086] Optionally, step 204 may include the following sub-steps.

[0087] Sub-step 2041: Summing all natural numbers from 1 to the sixth quantity to obtain the second sum.

[0088] Sub-step 2042: Divide the fifth number of all objects to be searched in one of the tag categories of the object to be searched by the fourth number to obtain the factor of the tag category of the object to be searched.

[0089] Sub-step 2043: Divide the sixth quantity by the second quotient of the second sum, multiply by the type weight coefficient of the object to be searched, and then multiply by the factor of the tag category of the object to be searched to obtain the sub-tag category weight coefficient of the tag category of the object to be searched.

[0090] Sub-step 2044: Sum the weight coefficients of each sub-label category of all label categories of the object to be searched to obtain the first weight coefficient of the label category of the object to be searched.

[0091] Specifically, the sub-tag category weight coefficient of the q-th tag category of the object to be searched is W. q . Where V is the type weight coefficient of the object to be searched, u is the sixth number of all objects to be searched corresponding to all tag categories of the object to be searched in the object database, and y q Let x be the fifth number of all objects in the q-th tag category among all tag categories of the object to be searched in the object database, and let x be the fourth number of all tag categories of the object to be searched. The second sum, Let W be the factor for the q-th tag category of the object to be searched. Then, sum the subtype weight coefficients of the x tag categories of the object to be searched to obtain the first weight coefficient of the tag category of the object to be searched. That is, W = ∑W q .

[0092] For example, in the aforementioned example, for the object to be searched in the object database for geographical scope A, the total number of tag categories for object 1 is: Tag Category 1, Tag Category 2, Tag Category 3, Tag Category 4, and Tag Category 5, a total of 5 tag categories. Therefore, the total number of the fourth tag categories for object 1 in the object database is 5. Since 5 objects in the object database for geographical scope A are tagged with Tag Category 1, the total number of the fifth tag categories for all objects in Tag Category 1 for object 1 in the object database for geographical scope A is 5. Since 10 objects in the object database for geographical scope A are tagged with Tag Category 2, the total number of the fifth tag categories for all objects in Tag Category 2 for object 1 in the object database for geographical scope A is 10. Since 12 objects in the object database for geographical scope A are tagged with Tag Category 3, the total number of the fifth tag categories for all objects in Tag Category 3 for object 1 in the object database for geographical scope A is 10. In the object database for object A, the number of fifth elements for all objects in tag category 3 of object A1 is 12. In the object database for geographical region A, 4 objects are tagged with tag category 4, meaning the number of fifth elements for all objects in tag category 4 of object A1 in the object database for geographical region A is 4. In the object database for geographical region A, 10 objects are tagged with tag category 5, meaning the number of fifth elements for all objects in tag category 5 of object A1 in the object database for geographical region A is 10. In the object database for geographical region A, there are no duplicate objects corresponding to tag category 1, tag category 2, tag category 3, tag category 4, or tag category 5. Therefore, the total number of sixth elements for all objects corresponding to all tag categories in the object database is 5 + 10 + 12 + 4 + 10 = 41. In the database of objects to be searched within geographical scope A, the tag category 1 of object 1 has the first weight coefficient. In the database of objects to be searched within geographical area A, the tag category 2 of object 1 has the first weight coefficient. In the database of objects to be searched within geographical area A, the tag category 3 of object 1 has the first weight coefficient. In the database of objects to be searched within geographical area A, the tag category 4 of object 1 has the first weight coefficient. In the database of objects to be searched within geographical area A, the tag category 5 of object number 1 has the first weight coefficient. Sum the subtype weight coefficients of the five tag categories for object 1 in the object database within geographical scope A. The first weight coefficient W for the tag category of object 1 in the object database within geographical scope A is: W = ∑W q＝ 0.006286+0.012571+0.015086+0.005029+0.012571=0.051543.

[0093] Step 205: Sort all objects in the object to be queried category in the object to be queried library according to the first weight coefficient of the tag category from largest to smallest to obtain the sorted object to be queried library; the sorted object to be queried library is used to extract objects to be queried.

[0094] Step 205 can be referred to the aforementioned step 103. To avoid repetition, it will not be repeated here.

[0095] Step 206: Obtain the type of inspector in the inspector database, the seventh number of all inspectors in the inspector database, the eighth number of all types of all inspectors, and the ninth number of all inspectors in each type of inspector.

[0096] The types of inspectors in the inspector database are similar to those in step 104 above, and will not be repeated here. The seventh number of all inspectors in the inspector database specifically refers to the total number of inspectors in the database. For example, in the previous example, if there are 30 inspectors in the inspector database for geographical area A, then the seventh number of all inspectors in the inspector database for geographical area A is 30.

[0097] The eighth number of all types of inspectors specifically refers to the total number of types into which all inspectors in the inspector pool are divided. For example, in the aforementioned case, if all 30 inspectors in the inspector pool for geographical area A are divided into three types: beginner level, intermediate level, and advanced level, then the eighth number of all types of inspectors is 3.

[0098] The number of ninth inspectors in each type of inspector specifically refers to the number of inspectors in each type. For example, if inspector number 1 in the inspector pool for geographic area A is of the advanced level type, and there are a total of 6 inspectors of the advanced level type, then the number of ninth inspectors in the advanced level type of inspector number 1 in the inspector pool for geographic area A is 6.

[0099] Step 207: Determine the type weighting coefficient of the inspectors based on the type of the inspectors, the seventh quantity, the eighth quantity, and each of the ninth quantities.

[0100] The type weight coefficient of the inspector is jointly determined by the inspector's type, the seventh number of all inspectors in the inspector database, the eighth number of all inspector types, and the ninth number of all inspectors in each type. The above factors can accurately reflect the distribution of the inspector in its type. Therefore, the type weight coefficient of the inspector determined in this way accurately reflects the actual distribution of the inspector in its type.

[0101] Optionally, step 207 may include the following sub-steps.

[0102] Sub-step 2071: Summing all natural numbers from 1 to the seventh quantity to obtain the third sum.

[0103] Sub-step 2072: Divide the ninth number of all inspectors in one type of inspector by the eighth number to obtain the factor of the type of inspector.

[0104] Sub-step 2073: Divide the seventh quantity by the third quotient of the third sum, multiply by the factor of the inspector's type, and obtain the sub-type weight coefficient of the inspector's type.

[0105] Sub-step 2074: Sum the weight coefficients of each sub-type of all types of the inspector to obtain the type weight coefficient of the inspector.

[0106] Specifically, the subtype weight coefficient for the f-th type of inspector is B. f , Where g is the seventh number of all inspectors in the inspector pool, h f Let k be the ninth number of all inspectors in the f-th type of inspector, and k be the eighth number of all types of inspectors in the inspector database to which this inspector belongs. The third sum, Let f be the factor of the inspector's type. Then, sum the subtype weight coefficients of the inspector's r types to obtain the inspector's type weight coefficient, where r ≤ k. That is... When r = 1, f = 1; or when r > 1, f = natural numbers from 1 to r.

[0107] For example, in the aforementioned case, the seventh quantity g of all inspectors in the inspector pool for geographical area A is 30. All 30 inspectors in the inspector pool for geographical area A are divided into three types: beginner, intermediate, and advanced. Therefore, the eighth quantity k of all types of inspectors is 3. Inspector number 1 in the inspector pool for geographical area A is only of the advanced level. There are 6 inspectors of the advanced level. Therefore, the ninth quantity h of all inspectors of the advanced level type in the inspector pool for inspector number 1 in geographical area A is... f That is, 6. Therefore, the subtype weight coefficient of the advanced level type for inspector number 1 in the inspector database within geographical region A is...

[0108] Since inspector number 1 in the inspector database for geographical area A is only of the advanced level type (r = 1), the sum of the weight coefficients of all subtypes of all types for inspector number 1 in the inspector database for geographical area A is the subtype weight coefficient B of the advanced level type for inspector number 1 in the inspector database for geographical area A. f That is, the type weight coefficient B of inspector No. 1 in the inspector database of geographical area A is 0.1290.

[0109] Step 208: Obtain all label categories of the inspectors, the tenth number of all label categories of the inspectors, the eleventh number of all inspectors in each label category of the inspectors in the inspector database, and the twelfth number of all inspectors corresponding to all label categories of the inspectors in the inspector database.

[0110] Obtaining all label categories for inspectors is similar to step 105 above, and will not be repeated here to avoid repetition. The tenth number of all label categories for an inspector refers to the total number of tags assigned to that inspector. For example, in the previous example, if inspector number 1 in the inspector database for geographical area A has four label categories: label category 1, label category 2, label category 3, and label category 6, then the tenth number of all label categories for inspector number 1 in the inspector database for geographical area A is 4.

[0111] The eleventh number for each label category of an inspector refers to the number of inspectors in each label category for that inspector. For example, in the above scenario, if inspector number 1 in the inspector database for region A has four label categories: Category 1, Category 2, Category 3, and Category 6, and five inspectors in region A are labeled with Category 1, then the eleventh number for all inspectors in Category 1 is 5. If three inspectors in region A are labeled with Category 2, then the eleventh number for all inspectors in Category 2 is 3. If two inspectors in region A are labeled with Category 3, then the eleventh number for all inspectors in Category 3 is 2. If four inspectors in region A are labeled with Category 6, then the eleventh number for all inspectors in Category 6 is 4.

[0112] In the inspector database, the eleventh number of all inspectors corresponding to all label categories is specifically the total number of inspectors in the database across all label categories assigned to that inspector. For example, in the above scenario, if inspector number 1 in the inspector database for region A has four label categories: Category 1, Category 2, Category 3, and Category 6, and five inspectors in region A are labeled with Category 1, then the eleventh number of all inspectors in Category 1 is 5. If three inspectors in region A are labeled with Category 2, then the eleventh number of all inspectors in Category 2 is 3. If two inspectors in region A are labeled with Category 3, then the eleventh number of all inspectors in Category 3 is 2. If four inspectors in region A are labeled with Category 6, then the eleventh number of all inspectors in Category 6 is 4. If, in the inspector database for geographical area A, there are no duplicate inspectors corresponding to label category 1, label category 2, label category 3, and label category 6, then the twelfth number of inspector number 1 in the inspector database for geographical area A is 5 + 3 + 2 + 4 = 14.

[0113] Optionally, in step 207, obtaining the twelfth number of all inspectors corresponding to all tag categories of the inspector in the inspector database may include: obtaining the second original number of all inspectors corresponding to all tag categories of the inspector in the inspector database. This second original number is the sum of the eleventh numbers of all inspectors in each tag category of the inspector in the inspector database. If there are duplicate inspectors among all inspectors corresponding to all tag categories of the inspector, only one of these duplicate inspectors is retained in the second original number, resulting in the twelfth number of all inspectors corresponding to all tag categories of the inspector. If there are no duplicate inspectors among all inspectors corresponding to all tag categories of the inspector, the second original number of all inspectors corresponding to all tag categories of the inspector is determined as the twelfth number of all inspectors corresponding to all tag categories of the inspector.

[0114] For example, in the above case, if the inspector database for inspector number 1 in region A has four tag categories: Tag Category 1, Tag Category 2, Tag Category 3, and Tag Category 6, and five inspectors in region A are tagged with Tag Category 1, then the eleventh number of all inspectors in Tag Category 1 is 5. If three inspectors in region A are tagged with Tag Category 2, then the eleventh number of all inspectors in Tag Category 2 is 3. If two inspectors in region A are tagged with Tag Category 3, then the eleventh number of all inspectors in Tag Category 3 is 2. If four inspectors in region A are tagged with Tag Category 6, then the eleventh number of all inspectors in Tag Category 6 is 4. Therefore, the second original number of all inspectors corresponding to all tag categories for inspector number 1 in region A is: 5 + 3 + 2 + 4 = 14. If, in the inspector database of region A, all inspectors corresponding to all tag categories of inspector No. 1 include inspector No. 5, and inspector No. 5 is labeled with two tag categories, label category 1 and label category 2, then among all inspectors corresponding to all tag categories of inspector No. 1, inspector No. 5 is a duplicate inspector. In the second original quantity 14, inspector No. 5 appears twice, meaning that the number of inspectors No. 5 in 41 is 2. Therefore, we retain only 1 instance of inspector No. 5 in the second original quantity 14, 14-1=13, which is the twelfth number of all inspectors corresponding to all tag categories of inspector No. 1 in the inspector database of region A. If, in the inspector database for geographical area A, there are no duplicate inspectors for the inspectors corresponding to label category 1, label category 2, label category 3, and label category 6, that is, for the four label categories (label category 1, label category 2, label category 3, and label category 6), each inspector is only assigned one label category, then there are no duplicate inspectors among all inspectors corresponding to all label categories of inspector number 1. In this case, the second original quantity 14 is used to determine the twelfth quantity of all inspectors corresponding to all label categories of inspector number 1 in the inspector database for geographical area A.

[0115] Step 209: Determine the second weight coefficient of the label category of the inspector based on the type weight coefficient of the inspector, all label categories of the inspector, the tenth quantity, each of the eleventh quantities, and the twelfth quantities.

[0116] The second weighting coefficient of the inspector's label category is determined by the inspector's type weighting coefficient, all of the inspector's label categories, the tenth number of all of the inspector's label categories, the eleventh number of all inspectors in each of the inspector's label categories in the inspector database, and the twelfth number of all inspectors corresponding to all of the inspector's label categories in the inspector database. The above factors can accurately reflect the distribution of the inspector in its label category. Therefore, the second weighting coefficient of the inspector's label category determined in this way accurately reflects the actual distribution of the inspector in its label category.

[0117] Optionally, step 209 may include the following sub-steps.

[0118] Sub-step 2091: Summate all natural numbers from 1 to the twelfth quantity to obtain the fourth sum.

[0119] Sub-step 2092: Divide the eleventh number of all inspectors in one of the inspector's label categories by the tenth number to obtain the factor of the inspector's label category.

[0120] Sub-step 2093: Divide the twelfth quantity by the fourth quotient of the fourth sum, multiply by the type weight coefficient of the inspector, and then multiply by the factor of the label category of the inspector to obtain the sub-label category weight coefficient of the label category of the inspector.

[0121] Sub-step 2094: Sum the weight coefficients of each sub-label category of all label categories of the inspector to obtain the second weight coefficient of the label category of the inspector.

[0122] Specifically, the sub-label category weight coefficient of the inspector's t-th label category is D. t . Where B is the type weight coefficient of the inspector, s is the twelfth number of all inspectors corresponding to all tag categories of the inspector in the inspector database, vt is the eleventh number of all inspectors in the t-th tag category of the inspector in the inspector database, and c is the tenth number of all tag categories of the inspector. The fourth sum, Let D be the factor for the t-th label category of the inspector. Then, sum the subtype weight coefficients of the inspector's c label categories to obtain the second weight coefficient of the inspector's label categories. That is, D = ∑D t .

[0123] For example, in the aforementioned case, the total number of tag categories for Inspector No. 1 in the inspector database for geographical area A is 4: Tag Category 1, Tag Category 2, Tag Category 3, and Tag Category 6. Therefore, the tenth number of all tag categories for Inspector No. 1 in the inspector database is 4. Specifically, if 5 inspectors in the inspector database for geographical area A are tagged with Tag Category 1, then the eleventh number of all inspectors in Tag Category 1 is 5. If 3 inspectors in the inspector database for geographical area A are tagged with Tag Category 2, then the eleventh number of all inspectors in Tag Category 2 is 3. If 2 inspectors in the inspector database for geographical area A are tagged with Tag Category 3, then the eleventh number of all inspectors in Tag Category 3 is 2. If 4 inspectors in the inspector database for geographical area A are tagged with Tag Category 6, then the eleventh number of all inspectors in Tag Category 6 is 4. If, in the inspector database for geographical area A, there are no duplicate inspectors corresponding to label category 1, label category 2, label category 3, and label category 6, then the twelfth number of inspector number 1 in the inspector database for geographical area A is 5 + 3 + 2 + 4 = 14. The second weight coefficient for label category 1 of inspector number 1 in the inspector database for geographical area A is... Inspector No. 1 in the inspector database within geographic area A, the second weighting coefficient of label category 2. Inspector No. 1 in the inspector database within geographic area A has a tag category 3 and a second weighting coefficient. Inspector No. 1 in the inspector database within geographic area A has a tag category of 6 and a second weighting coefficient. Sum the subtype weight coefficients of the four tag categories for inspector No. 1 in the inspector database for geographical scope A. The second weight coefficient D for the tag category of inspector No. 1 in the inspector database for geographical scope A is: D = ∑D t =0.0215+0.0129+0.0086+0.00172=0.0602.

[0124] Step 210: Sort all inspectors in the inspector database by type according to the second weight coefficient of the tag category from largest to smallest to obtain a sorted inspector database; the sorted inspector database is used to extract inspectors.

[0125] Step 210 can be referred to the aforementioned step 106. To avoid repetition, it will not be repeated here.

[0126] It should be noted that the determination of the type weight coefficient of inspectors in the inspector database is similar to the determination of the type weight coefficient of the objects to be inspected in the object database. Similarly, the determination of the second weight coefficient of the label category of inspectors in the inspector database is similar to the determination of the first weight coefficient of the label category of the objects to be inspected in the object database. The relevant parts can be referred to each other.

[0127] If the type or tag category of an object in the object database changes, this method can still be used. The first weight coefficient of the changed tag category is determined based on the changed parameters, and then the objects are re-sorted based on this first weight coefficient. Similarly, if the type or tag category of an inspector in the inspector database changes, this method can still be used. The second weight coefficient of the changed tag category is determined based on the changed parameters, and then the objects are re-sorted based on this second weight coefficient.

[0128] Figure 5 This is a structural diagram of a dual random selection device provided in an embodiment of the present invention. The device may include:

[0129] The first acquisition module 501 is used to acquire the type of the object to be searched in the object database, and determine the type weight coefficient of the object to be searched based on the type of the object to be searched.

[0130] The second acquisition module 501 is used to acquire all the tag categories of the object to be searched, and determine the first weight coefficient of the tag category of the object to be searched based on all the tag categories of the object to be searched and the type weight coefficient of the object to be searched;

[0131] The first sorting module 503 is used to sort all the objects to be searched in the object to be searched database according to the first weight coefficient of the tag category from largest to smallest, so as to obtain a sorted object to be searched database; the sorted object to be searched database is used to extract objects to be searched.

[0132] The third acquisition module 504 is used to acquire the types of inspectors in the inspector database and determine the type weight coefficient of the inspectors based on the types of the inspectors.

[0133] The fourth acquisition module 505 is used to acquire all the label categories of the inspector and determine the second weight coefficient of the label category of the inspector based on all the label categories of the inspector and the type weight coefficient of the inspector.

[0134] The second sorting module 506 is used to sort all the inspectors in the inspector type in the inspector database according to the second weight coefficient of the tag category from largest to smallest, to obtain a sorted inspector database; the sorted inspector database is used to extract inspectors.

[0135] Optionally, the first acquisition module 501 includes:

[0136] The first acquisition submodule is used to acquire the type of the object to be searched in the object to be searched library, the first quantity of all objects to be searched in the object to be searched library, the second quantity of all types of all objects to be searched, and the third quantity of all objects to be searched in each type of the object to be searched.

[0137] The first determining submodule is used to determine the type weight coefficient of the object to be searched based on the type of the object to be searched, the first quantity, the second quantity, and each of the third quantities.

[0138] The second acquisition module 502 includes:

[0139] The second acquisition submodule is used to acquire all tag categories of the object to be searched, the fourth number of all tag categories of the object to be searched, the fifth number of all objects to be searched in each tag category of the object to be searched in the object to be searched library, and the sixth number of all objects to be searched corresponding to all tag categories of the object to be searched in the object to be searched library.

[0140] The second determining submodule is used to determine the first weight coefficient of the tag category of the object to be searched based on the type weight coefficient of the object to be searched, all tag categories of the object to be searched, the fourth quantity, each of the fifth quantities, and the sixth quantity.

[0141] Optionally, the first determining submodule includes:

[0142] The first sum determination unit is used to sum all natural numbers from 1 to the first quantity to obtain the first sum;

[0143] The first factor determination unit is configured to divide the third quantity of all objects in a type of the object to be searched by the second quantity to obtain the factor of the type of the object to be searched;

[0144] The first subtype weight coefficient determination unit is used to divide the first quantity by the first quotient of the first sum and multiply it by the factor of the type of the object to be searched to obtain the subtype weight coefficient of the type of the object to be searched.

[0145] The first summation unit is used to sum the weight coefficients of each subtype of all types of the object to be searched, so as to obtain the type weight coefficient of the object to be searched.

[0146] Optionally, the second determining submodule includes:

[0147] The second sum determination unit is used to sum all natural numbers from 1 to the sixth quantity to obtain the second sum.

[0148] The second factor determination unit is used to divide the fifth number of all objects to be searched in one of the tag categories of the object to be searched by the fourth number to obtain the factor of the tag category of the object to be searched;

[0149] The first sub-label category weight coefficient determination unit is used to divide the sixth quantity by the second sum value, multiply the second quotient by the type weight coefficient of the object to be searched, and then multiply the factor of the label category of the object to be searched to obtain the sub-label category weight coefficient of the label category of the object to be searched;

[0150] The second summation unit is used to sum the weight coefficients of each sub-label category of all label categories of the object to be searched, so as to obtain the first weight coefficient of the label category of the object to be searched.

[0151] Optionally, the second acquisition submodule includes:

[0152] The second acquisition unit is used to acquire all tag categories of the object to be searched, the fourth quantity of all tag categories of the object to be searched, the fifth quantity of all objects to be searched in each tag category of the object to be searched in the object to be searched library, and the first original quantity of all objects to be searched corresponding to all tag categories of the object to be searched in the object to be searched library.

[0153] The sixth quantity first determination unit is used to, in the case that there are duplicate objects to be checked among all objects to be checked corresponding to all label categories of the object to be checked, retain only 1 of the duplicate objects to be checked in the first original quantity, so as to obtain the sixth quantity of all objects to be checked corresponding to all label categories of the object to be checked.

[0154] The sixth quantity second determination unit is used to determine the first original quantity of all objects to be checked corresponding to all label categories of the object to be checked as the sixth quantity of all objects to be checked corresponding to all label categories of the object to be checked, in the case that there are no duplicate objects to be checked among all objects to be checked corresponding to all label categories of the object to be checked.

[0155] Optionally, the third acquisition module 504 includes:

[0156] The third acquisition submodule is used to acquire the type of the inspector in the inspector database, the seventh number of all inspectors in the inspector database, the eighth number of all types of all inspectors, and the ninth number of all inspectors in each type of inspector.

[0157] The third determining submodule is used to determine the type weighting coefficient of the inspectors based on the type of the inspectors, the seventh quantity, the eighth quantity, and each of the ninth quantities;

[0158] The fourth acquisition module 505 includes:

[0159] The fourth acquisition submodule is used to acquire all the label categories of the inspectors, the tenth number of all the label categories of the inspectors, the eleventh number of all inspectors in each of the label categories of the inspectors in the inspector database, and the twelfth number of all inspectors corresponding to all the label categories of the inspectors in the inspector database.

[0160] The fourth determination submodule is used to determine the second weight coefficient of the label category of the inspector based on the type weight coefficient of the inspector, all label categories of the inspector, the tenth quantity, each of the eleventh quantities, and the twelfth quantity.

[0161] Optionally, the third determining submodule includes:

[0162] The third sum determination unit is used to sum all natural numbers from 1 to the seventh quantity to obtain the third sum.

[0163] The third factor determination unit is used to divide the ninth number of all inspectors in one type of inspector by the eighth number to obtain the factor of the inspector's type;

[0164] The second subtype weight coefficient determination unit is used to divide the seventh quantity by the third quotient of the third sum, and multiply it by the factor of the type of the inspector to obtain the subtype weight coefficient of the type of the inspector.

[0165] The third summation unit is used to sum the weight coefficients of each subtype of all types of the inspector to obtain the type weight coefficient of the inspector.

[0166] Optionally, the fourth determining submodule includes:

[0167] The fourth sum determination unit is used to sum all natural numbers from 1 to the twelfth quantity to obtain the fourth sum;

[0168] The fourth factor determination unit is used to obtain the factor of the label category of the inspector by dividing the eleventh number of all inspectors in one of the label categories of the inspector by the tenth number;

[0169] The second sub-label category weight coefficient determination unit is used to divide the twelfth quantity by the fourth quotient of the fourth sum, multiply it by the type weight coefficient of the inspector, and then multiply it by the factor of the label category of the inspector to obtain the sub-label category weight coefficient of the label category of the inspector.

[0170] The fourth summation unit is used to sum the weight coefficients of each sub-label category of all label categories of the inspector to obtain the second weight coefficient of the label category of the inspector.

[0171] Optionally, the fourth acquisition submodule includes:

[0172] The fourth acquisition unit is used to acquire all the label categories of the inspectors, the tenth number of all the label categories of the inspectors, the eleventh number of all inspectors in each of the label categories of the inspectors in the inspector database, and the second original number of all inspectors corresponding to all the label categories of the inspectors in the inspector database.

[0173] The twelfth quantity first determination unit is used to, in the case that there are duplicate inspectors among all inspectors corresponding to all label categories of the inspectors, retain only 1 of the duplicate inspectors in the second original quantity, so as to obtain the twelfth quantity of all inspectors corresponding to all label categories of the inspectors.

[0174] The twelfth quantity second determination unit is used to determine the second original quantity of all inspectors corresponding to all label categories of the inspector as the twelfth quantity of all inspectors corresponding to all label categories of the inspector when there are no duplicate inspectors among all inspectors corresponding to all label categories of the inspector.

[0175] Optionally, the device further includes:

[0176] The first labeling module is used to label the object to be investigated according to the environmental pollution sources involved in the object to be investigated.

[0177] The second labeling module is used to label the inspectors according to their industry knowledge.

[0178] The present invention also provides an electronic device, see [link to relevant documentation]. Figure 6It includes: a processor 901, a memory 902, and a computer program 9021 stored in the memory and executable on the processor. When the processor executes the program, it implements the double random sampling method of the foregoing embodiments.

[0179] The present invention also provides a readable storage medium that, when the instructions in the storage medium are executed by the processor of an electronic device, enables the electronic device to perform the double random sampling method of the foregoing embodiments.

[0180] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0181] It should be noted that all information and data obtained in the embodiments of the present invention were obtained with the authorization of the information / data holder.

[0182] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0183] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0184] Similarly, it should be understood that, in order to simplify the invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0185] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature serving the same, equivalent, or similar purpose.

[0186] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSRs) can be used in practice to implement some or all of the functions of some or all of the components in the dual random sampling device according to the present invention. The present invention can also be implemented as a device or apparatus program for performing part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0187] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0188] The user information (including but not limited to user device information, user personal information, etc.) and related data involved in this invention are all information authorized by the user or by the parties.

[0189] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0190] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0191] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A double random sampling method, characterized in that, The method includes: Obtain the type of the object to be searched in the object database, and determine the type weight coefficient of the object to be searched based on the type of the object to be searched; Obtain all tag categories of the object to be searched, and determine the first weight coefficient of the tag category of the object to be searched based on all tag categories of the object to be searched and the type weight coefficient of the object to be searched; In the object database, all objects of the type of the object to be searched are sorted in descending order according to the first weight coefficient of the tag category to obtain the sorted object database; the sorted object database is used to extract the object to be searched. Obtain the types of inspectors in the inspector database, and determine the type weight coefficient of the inspectors based on their types; Obtain all the label categories of the inspector, and determine the second weight coefficient of the label category of the inspector based on all the label categories of the inspector and the type weight coefficient of the inspector; All inspectors in the inspector database, categorized by inspector type, are sorted in descending order according to the second weight coefficient of the label category to obtain a sorted inspector database; the sorted inspector database is used to extract inspectors. The step of obtaining the type of the object to be searched in the object database and determining the type weight coefficient of the object to be searched based on the type of the object to be searched includes: Obtain the type of the object to be searched in the object database, the first number of all objects to be searched in the object database, the second number of all types of all objects to be searched, and the third number of all objects to be searched in each type of the object to be searched. The type weight coefficient of the object to be searched is determined based on the type of the object to be searched, the first quantity, the second quantity, and each of the third quantities; The step of obtaining all tag categories of the object to be searched, and determining the first weight coefficient of the tag category of the object to be searched based on all tag categories of the object to be searched and the type weight coefficient of the object to be searched, includes: Obtain all tag categories of the object to be searched, the fourth number of all tag categories of the object to be searched, the fifth number of all objects to be searched in each tag category of the object to be searched in the object to be searched database, and the sixth number of all objects to be searched corresponding to all tag categories of the object to be searched in the object to be searched database. The first weight coefficient of the tag category of the object to be searched is determined based on the type weight coefficient of the object to be searched, all tag categories of the object to be searched, the fourth quantity, each of the fifth quantities, and the sixth quantity.

2. The method according to claim 1, characterized in that, The step of determining the type weight coefficient of the object to be searched based on the type of the object to be searched, the first quantity, the second quantity, and each of the third quantities includes: Summing all natural numbers from 1 to the first quantity yields the first sum; Divide the third quantity of all objects in one type of the object to be searched by the second quantity to obtain the factor of the type of the object to be searched; The first quotient of the first quantity divided by the first sum is multiplied by the factor of the type of the object to be searched to obtain the subtype weight coefficient of the type of the object to be searched. The type weight coefficient of the object to be searched is obtained by summing the weight coefficients of each subtype of all types of the object to be searched.

3. The method according to claim 1 or 2, characterized in that, The step of determining the first weight coefficient of the tag category of the object to be searched based on the type weight coefficient of the object to be searched, all tag categories of the object to be searched, the fourth quantity, each of the fifth quantities, and the sixth quantity includes: Summing all natural numbers from 1 to the sixth quantity yields the second sum. The factor of the tag category of the object to be searched is obtained by dividing the fifth number of all objects in one of the tag categories of the object to be searched by the fourth number; The second quotient of the sixth quantity divided by the second sum is multiplied by the type weight coefficient of the object to be searched, and then multiplied by the factor of the tag category of the object to be searched to obtain the sub-tag category weight coefficient of the tag category of the object to be searched; The weight coefficients of each sub-label category of all label categories of the object to be searched are summed to obtain the first weight coefficient of the label category of the object to be searched.

4. The method according to claim 1, characterized in that, The step of obtaining all tag categories of the object to be searched, the fourth quantity of all tag categories of the object to be searched, the fifth quantity of all objects to be searched in each tag category of the object to be searched in the object to be searched database, and the sixth quantity of all objects to be searched corresponding to all tag categories of the object to be searched in the object to be searched database, includes: Obtain all tag categories of the object to be searched, the fourth number of all tag categories of the object to be searched, the fifth number of all objects to be searched in each tag category of the object to be searched in the object to be searched database, and the first original number of all objects to be searched corresponding to all tag categories of the object to be searched in the object to be searched database; If there are duplicate objects among all objects corresponding to all tag categories of the object to be checked, only one of the duplicate objects in the first original quantity is retained to obtain the sixth quantity of all objects corresponding to all tag categories of the object to be checked. If there are no duplicate objects among all objects corresponding to all tag categories of the object to be searched, the first original number of all objects corresponding to all tag categories of the object to be searched is determined as the sixth number of all objects corresponding to all tag categories of the object to be searched.

5. The method according to claim 1, characterized in that, The step of obtaining the types of inspectors from the inspector database and determining the type weight coefficient of the inspectors based on the types of the inspectors includes: Obtain the type of inspector in the inspector database, the seventh number of all inspectors in the inspector database, the eighth number of all types of all inspectors, and the ninth number of all inspectors in each type of inspector. The type weighting coefficient of the inspectors is determined based on the type of the inspectors, the seventh quantity, the eighth quantity, and each of the ninth quantities; The step of obtaining all the label categories of the inspector and determining the second weight coefficient of the label category of the inspector based on all the label categories of the inspector and the type weight coefficient of the inspector includes: Obtain all the label categories of the inspectors, the tenth number of all the label categories of the inspectors, the eleventh number of all inspectors in each of the label categories of the inspectors in the inspector database, and the twelfth number of all inspectors corresponding to all the label categories of the inspectors in the inspector database. The second weight coefficient for the label category of the inspector is determined based on the type weight coefficient of the inspector, all label categories of the inspector, the tenth quantity, each of the eleventh quantities, and the twelfth quantities.

6. The method according to claim 5, characterized in that, The step of determining the type weighting coefficient of the inspectors based on the type of the inspectors, the seventh quantity, the eighth quantity, and each of the ninth quantities includes: Summing all natural numbers from 1 to the seventh quantity yields the third sum. The factor of the inspector type is obtained by dividing the ninth number of all inspectors in one of the inspector types by the eighth number; The third quotient of the seventh quantity divided by the third sum is multiplied by the factor of the inspector's type to obtain the subtype weight coefficient of the inspector's type; The type weight coefficient of the inspector is obtained by summing the weight coefficients of each subtype of all types of the inspector.

7. The method according to claim 5 or 6, characterized in that, The step of determining the second weighting coefficient of the inspector's label category based on the inspector's type weighting coefficient, all label categories of the inspector, the tenth quantity, each of the eleventh quantities, and the twelfth quantities includes: Summing all natural numbers from 1 to the twelfth quantity yields the fourth sum. The factor of the label category of the inspector is obtained by dividing the eleventh number of all inspectors in one of the label categories of the inspector by the tenth number; The fourth quotient of the twelfth quantity divided by the fourth sum is multiplied by the type weight coefficient of the inspector, and then multiplied by the factor of the label category of the inspector to obtain the sub-label category weight coefficient of the label category of the inspector. The weight coefficients of each sub-label category of all label categories of the inspector are summed to obtain the second weight coefficient of the label category of the inspector.

8. The method according to claim 5, characterized in that, The step of obtaining all tag categories of the inspectors, the tenth number of all tag categories of the inspectors, the eleventh number of all inspectors in each tag category of the inspectors in the inspector database, and the twelfth number of all inspectors corresponding to all tag categories of the inspectors in the inspector database includes: Obtain all label categories of the inspectors, the tenth number of all label categories of the inspectors, the eleventh number of all inspectors in each label category of the inspectors in the inspector database, and the second original number of all inspectors corresponding to all label categories of the inspectors in the inspector database; If there are duplicate inspectors among all inspectors corresponding to all label categories of the inspectors, the number of duplicate inspectors in the second original number is kept to be only 1, and the twelfth number of all inspectors corresponding to all label categories of the inspectors is obtained. If there are no duplicate inspectors among all inspectors corresponding to all label categories of the inspector, the second original number of all inspectors corresponding to all label categories of the inspector is determined as the twelfth number of all inspectors corresponding to all label categories of the inspector.

9. The method according to any one of claims 1, 2, 4, 5, 6, and 8, characterized in that, Before obtaining all tag categories of the object to be searched, and determining the first weight coefficient of the tag category of the object to be searched based on all tag categories of the object to be searched and the type weight coefficient of the object to be searched, the method further includes: Based on the environmental pollution sources involved in the object to be investigated, the object to be investigated is categorized. Before obtaining all the label categories of the inspector and determining the second weight coefficient of the inspector's label category based on all the label categories of the inspector and the type weight coefficient of the inspector, the method further includes: The inspectors are categorized based on their industry knowledge.

10. A dual random sampling device, characterized in that, The device includes: The first acquisition module is used to acquire the type of the object to be searched in the object database, and determine the type weight coefficient of the object to be searched based on the type of the object to be searched; The second acquisition module is used to acquire all the tag categories of the object to be searched, and determine the first weight coefficient of the tag category of the object to be searched based on all the tag categories of the object to be searched and the type weight coefficient of the object to be searched. The first sorting module is used to sort all the objects to be searched in the object to be searched database according to the first weight coefficient of the tag category from largest to smallest, so as to obtain a sorted object to be searched database; the sorted object to be searched database is used to extract objects to be searched. The third acquisition module is used to acquire the types of inspectors in the inspector database and determine the type weight coefficient of the inspectors based on the types of the inspectors. The fourth acquisition module is used to acquire all the label categories of the inspector, and determine the second weight coefficient of the label category of the inspector based on all the label categories of the inspector and the type weight coefficient of the inspector; The second sorting module is used to sort all the inspectors in the inspector database according to the second weight coefficient of the label category from largest to smallest to obtain the sorted inspector database; the sorted inspector database is used to extract inspectors. The first acquisition module includes: The first acquisition submodule is used to acquire the type of the object to be searched in the object to be searched library, the first quantity of all objects to be searched in the object to be searched library, the second quantity of all types of all objects to be searched, and the third quantity of all objects to be searched in each type of the object to be searched. The first determining submodule is used to determine the type weight coefficient of the object to be searched based on the type of the object to be searched, the first quantity, the second quantity, and each of the third quantities. The second acquisition module includes: The second acquisition submodule is used to acquire all tag categories of the object to be searched, the fourth number of all tag categories of the object to be searched, the fifth number of all objects to be searched in each tag category of the object to be searched in the object to be searched library, and the sixth number of all objects to be searched corresponding to all tag categories of the object to be searched in the object to be searched library. The second determining submodule is used to determine the first weight coefficient of the tag category of the object to be searched based on the type weight coefficient of the object to be searched, all tag categories of the object to be searched, the fourth quantity, each of the fifth quantities, and the sixth quantity.

11. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as described in any one of claims 1-9.

12. A readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the method described in any one of claims 1-9.

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