CN111538871A - Integrated retrieval method supporting different data types - Google Patents
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Abstract
The invention provides an integrated retrieval method supporting different data types, and belongs to the technical field of information retrieval. The integrated retrieval method comprises the following steps: in a retrieval system, a data retrieval model is constructed by utilizing a plurality of kinds of original data with different data types; carrying out self-adaptive updating on the data retrieval model by using the newly added data type of the original data; detecting whether a retrieval triggering operation exists in real time, and acquiring a retrieval condition corresponding to the retrieval triggering operation when the retrieval triggering operation is detected; and calling corresponding original data by using the data retrieval model and the retrieval conditions.
Description
Technical Field
The invention provides an integrated retrieval method supporting different data types, and belongs to the technical field of information retrieval.
Background
The data retrieval system is an important component of the existing industrial production monitoring platform system, and data to be searched can be called at any time through the data retrieval system. However, as the system architecture of the industrial production monitoring platform is continuously expanded, the types of data to be monitored and acquired by the platform are continuously increased, so that a large amount of original data of different data types are generated, and the situation directly causes the problems of slow retrieval response speed, low accuracy and the like caused by insufficient data updating of the data retrieval system due to excessive data types.
Disclosure of Invention
The invention provides an integrated retrieval method supporting different data types, which is used for solving the problems that the existing retrieval method only carries out retrieval aiming at a single data type, and when multi-data retrieval is carried out, the retrieval response speed is low and the retrieval accuracy is low, and adopts the following technical scheme:
an integrated retrieval method supporting different data types, the integrated retrieval method comprising:
in a retrieval system, a data retrieval model is constructed by utilizing a plurality of kinds of original data with different data types;
carrying out self-adaptive updating on the data retrieval model by using the newly added data type of the original data;
detecting whether a retrieval triggering operation exists in real time, and acquiring a retrieval condition corresponding to the retrieval triggering operation when the retrieval triggering operation is detected;
and calling corresponding original data by using the data retrieval model and the retrieval conditions.
Further, in the retrieval system, constructing a data retrieval model by using a plurality of kinds of raw data with different data types includes:
acquiring original data of each device corresponding to the retrieval system, and identifying the data type of each original data, wherein the data type comprises character data, table data, picture data and video data;
respectively establishing type databases corresponding to the data types according to the data types; respectively storing the original data of different data types in the type database;
classifying and combining the original data in the type database according to the attribute characteristic value of the source equipment of each original data to obtain a combined data set;
and extracting target information corresponding to the target data of each original data in the combined data set, and setting a retrieval condition corresponding to each target information.
Further, the data retrieval model includes: the system comprises a type database, each combined data set in the type database, a retrieval condition corresponding to each combined data set and a self-adaptive updating module.
Further, the adaptive update module comprises:
the type database building module is used for building a type database corresponding to the newly added data type by using the newly added data type and the original data corresponding to the newly added data type;
the characteristic value extraction module is used for extracting the attribute characteristic value of the source equipment corresponding to the original data aiming at the newly stored original data when the newly stored original data is detected not to belong to the current original data source equipment;
the comparison module is used for comparing the attribute characteristic value of the source equipment with the attribute characteristic value of the source equipment corresponding to each combined data set in the type database and calculating a difference value between the attribute characteristic value corresponding to the newly stored original data and the attribute characteristic value of the source equipment corresponding to each combined data set in the type database;
an attribute determining module, configured to perform attribute determination on the source device corresponding to the newly stored original data according to the difference value, determine whether the source device belongs to a source device attribute corresponding to an existing combined data set in a type database, and if the source device belongs to a source device attribute corresponding to an existing combined data set in the type database, allocate the newly stored original data to a corresponding combined data set; and if the source equipment attribute does not belong to the source equipment attribute corresponding to the existing combined data set in the type database, establishing a combined data set corresponding to the newly stored original data.
Further, the adaptively updating the data retrieval model by using the data type newly added to the original data comprises:
detecting the data types of the original data acquired from each device corresponding to the retrieval system in real time, judging whether a new data type exists, if so, sending the new data type and the original data corresponding to the new data type to a data retrieval model, and establishing a type database corresponding to the newly added data type;
detecting newly stored original data of each type database, judging whether the newly stored original data belong to current original data source equipment or not, and controlling the data retrieval model to extract attribute characteristic values of source equipment corresponding to the original data aiming at the newly stored original data if the newly stored original data do not belong to the current original data source equipment;
the data retrieval model compares the attribute characteristic value of the source equipment with the attribute characteristic value of the source equipment corresponding to each combined data set in the type database, and calculates a difference value between the attribute characteristic value corresponding to the newly stored original data and the attribute characteristic value of the source equipment corresponding to each combined data set in the type database;
the data retrieval model determines the attribute of the source equipment corresponding to the newly stored original data according to the difference value, judges whether the attribute belongs to the attribute of the source equipment corresponding to the existing combined data set in the type database, and distributes the newly stored original data to the corresponding combined data set if the attribute belongs to the attribute of the source equipment corresponding to the existing combined data set in the type database; if the source device attribute does not belong to the source device attribute corresponding to the existing combined data set in the type database, establishing a combined data set corresponding to the newly stored original data;
and extracting target information corresponding to the target data of the original data from the combined data set corresponding to the newly stored original data, and setting a retrieval condition corresponding to the target information.
Further, the attribute feature value of the source device is automatically generated through a feature value generation rule, where the feature value generation rule is:
the characteristic values are as follows according to the sequence of the digits from left to right: the first bit represents the device location, the second bit represents the device operation type, and the third bit represents the data attributes
Wherein the first number 1 represents an industrial production site location, the first number 2 represents an office location, and the first number 3 represents a warehouse storage location;
the second digit 1 represents index monitoring equipment, the second digit 2 represents office equipment, and the second digit 3 represents entrance guard monitoring equipment;
the third digit 1 represents index monitoring data, the third digit 2 represents office file data, and the third digit 3 represents access control monitoring data;
and if detecting that the source equipment corresponding to the original data belongs to the new attribute, generating an attribute characteristic value for the new attribute equipment according to the rule that the current digit corresponds to the number +1 and corresponding to different digits according to the attribute characteristics.
Further, the difference value is obtained through the following formula, and the source device attribute is determined according to the difference value:
wherein the content of the first and second substances,Q（P，T) Representing a difference value between attribute feature values;Hrepresenting an attribute judgment condition;Pattribute feature representing source device corresponding to newly stored original dataThe value of the one or more of,Trepresenting the existing attribute feature value set in each combined data set;X i indicating the number of differences in the first digit of the attribute feature value,Y j indicating the number of differences in the second digit in the attribute feature value,Z k representing the number of differences of the third digit in the attribute characteristic value;nand representing the number of attribute characteristic values corresponding to the existing combined data set in the type database.
Further, the retrieval system includes:
the retrieval model construction module is used for constructing a data retrieval model by utilizing various kinds of original data with different data types;
the retrieval interface generation module is used for generating a retrieval interface and retrieval options in the retrieval interface; the retrieval options comprise a text data retrieval option, a table data retrieval option, a picture data retrieval option and a video data retrieval option;
the updating module is used for carrying out self-adaptive updating on the data retrieval model by utilizing the newly added data types of the original data;
the detection module is used for detecting whether the retrieval triggering operation exists in real time, and acquiring a retrieval condition corresponding to the retrieval triggering operation when the retrieval triggering operation is detected;
and the calling module is used for calling corresponding original data by using the data retrieval model and the retrieval conditions.
Further, the retrieval model building module comprises:
the original data acquisition module is used for acquiring original data of each device corresponding to the retrieval system and identifying the data type of each original data, wherein the data type comprises character data, table data, picture data and video data;
the type database establishing module is used for respectively establishing type databases corresponding to the data types according to the data types; respectively storing the original data of different data types in the type database;
the classification combination module is used for classifying and combining the original data in the type database according to the attribute characteristic value of the source equipment of each original data to obtain a combined data set;
and the retrieval condition setting module is used for extracting target information corresponding to the target data of each original data in the combined data set and setting retrieval conditions corresponding to each target information.
Further, the update module comprises
The real-time detection module is used for detecting the data type of the original data acquired from each device corresponding to the retrieval system in real time, judging whether a new data type exists or not, if the new data type exists, sending the new data type and the original data corresponding to the new data type to a data retrieval model, and establishing a type database corresponding to the newly added data type;
the new data detection module is used for detecting newly stored original data of each type database, judging whether the newly stored original data belong to current original data source equipment or not, and controlling the data retrieval model to extract attribute characteristic values of source equipment corresponding to the original data aiming at the newly stored original data if the newly stored original data do not belong to the current original data source equipment;
and the retrieval condition adding module is used for extracting target information corresponding to the target data of the original data from the combined data set corresponding to the newly stored original data after a combined data set corresponding to the newly stored original data is added, and setting a retrieval condition corresponding to the target information.
The invention has the beneficial effects that:
the integrated retrieval method supporting different data types provided by the invention improves the data calling speed in the retrieval process among various data types, and meanwhile, classification and database establishment are carried out according to the data types, so that calling disorder caused by different data types can be effectively avoided. The self-adaptive updating of the data retrieval model can effectively improve the quick response of data retrieval, simultaneously avoid the problems of invalid retrieval or failure and the like caused by untimely manual updating of the data retrieval model, and improve the accuracy of retrieval. Moreover, labor cost and time cost for maintaining the retrieval system can be effectively saved.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The embodiment of the invention provides an integrated retrieval method supporting different data types, which is used for solving the problems that the existing retrieval method only carries out retrieval aiming at a single data type, and when multi-data retrieval is carried out, the retrieval response speed is low and the retrieval accuracy rate is low.
An integrated retrieval method supporting different data types, as shown in fig. 1, includes:
s1, in the retrieval system, a data retrieval model is constructed by utilizing a plurality of kinds of original data with different data types;
s2, carrying out self-adaptive updating on the data retrieval model by using the newly added data type of the original data;
s3, detecting whether a retrieval trigger operation exists in real time, and acquiring a retrieval condition corresponding to the retrieval trigger operation when the retrieval trigger operation is detected;
and S4, calling corresponding original data by using the data retrieval model and the retrieval conditions.
The working principle of the technical scheme is as follows: firstly, in a retrieval system, a data retrieval model is constructed by utilizing various kinds of original data with different data types, and then the data retrieval model is adaptively updated by utilizing the newly added data types of the original data; then, detecting whether a retrieval trigger operation exists in real time, and acquiring a retrieval condition corresponding to the retrieval trigger operation when the retrieval trigger operation is detected; and finally, calling corresponding original data by using the data retrieval model and the retrieval conditions.
The effect of the above technical scheme is as follows: the data retrieval model is constructed to effectively classify the original data according to different data types, and the database is set on the basis of the data types, so that the data calling speed of the retrieval process among various data types is improved, and meanwhile, the classification and the database establishment are carried out according to the data types, so that the calling disorder caused by different data types can be effectively avoided. On the other hand, by means of combining the data sets, the data in each data type is further divided on the basis of the source equipment corresponding to the original data, and the corresponding speed of data calling can be further increased; meanwhile, data calling errors caused by data storage disorder can be effectively avoided through strict classification.
In addition, by utilizing the self-adaptive updating of the data types, the new data types in the database can be added and classified by the data retrieval model in a self-adaptive updating mode without manually reconstructing or reconstructing the data retrieval model when a new device is added to a corresponding device system of the retrieval system, so that the quick response of data retrieval is improved, the problems of invalid or failed retrieval and the like caused by untimely manual updating of the data retrieval model are avoided, and the accuracy of retrieval is improved. Moreover, labor cost and time cost for maintaining the retrieval system can be effectively saved.
In one embodiment of the present invention, in a retrieval system, constructing a data retrieval model by using a plurality of kinds of raw data with different data types includes:
s101, acquiring original data of each device corresponding to the retrieval system, and identifying the data type of each original data, wherein the data type comprises character data, table data, picture data and video data;
s102, respectively establishing type databases corresponding to the data types according to the data types; respectively storing the original data of different data types in the type database;
s103, classifying and combining the original data in the type database according to the attribute characteristic value of the source equipment of each original data to obtain a combined data set;
and S104, extracting target information corresponding to the target data of each original data in the combined data set, and setting a retrieval condition corresponding to each target information.
The working principle of the technical scheme is as follows: firstly, acquiring original data of each device corresponding to the retrieval system, and identifying the data type of each original data, wherein the data type comprises character data, table data, picture data and video data; then, respectively establishing type databases corresponding to the data types according to the data types; respectively storing the original data of different data types in the type database; then, according to the attribute characteristic value of the source equipment of each original data, classifying and combining the original data in the type database to obtain a combined data set; and finally, extracting target information corresponding to the target data of each original data in the combined data set, and setting a retrieval condition corresponding to each target information.
The effect of the above technical scheme is as follows: the data retrieval model is constructed to effectively classify the original data according to different data types, and the database is set on the basis of the data types, so that the data calling speed of the retrieval process among various data types is improved, and meanwhile, the classification and the database establishment are carried out according to the data types, so that the calling disorder caused by different data types can be effectively avoided. On the other hand, by means of combining the data sets, the data in each data type is further divided on the basis of the source equipment corresponding to the original data, and the corresponding speed of data calling can be further increased; meanwhile, data calling errors caused by data storage disorder can be effectively avoided through strict classification.
In an embodiment of the present invention, the adaptively updating the data retrieval model by using the newly added data type of the original data includes:
s201, detecting the data types of original data acquired from each device corresponding to the retrieval system in real time, judging whether a new data type exists, if so, sending the new data type and the original data corresponding to the new data type to a data retrieval model, and establishing a type database corresponding to the newly added data type;
s202, detecting newly stored original data of each type of database, judging whether the newly stored original data belong to current original data source equipment, and controlling the data retrieval model to extract attribute characteristic values of source equipment corresponding to the original data aiming at the newly stored original data if the newly stored original data do not belong to the current original data source equipment;
s203, the data retrieval model compares the attribute characteristic value of the source equipment with the attribute characteristic value of the source equipment corresponding to each combined data set in the type database, and calculates a difference value between the attribute characteristic value corresponding to the newly stored original data and the attribute characteristic value of the source equipment corresponding to each combined data set in the type database;
s204, the data retrieval model determines the attribute of the source equipment corresponding to the newly stored original data according to the difference value, judges whether the attribute belongs to the attribute of the source equipment corresponding to the existing combined data set in the type database, and if the attribute belongs to the attribute of the source equipment corresponding to the existing combined data set in the type database, the newly stored original data is distributed to the corresponding combined data set; if the source device attribute does not belong to the source device attribute corresponding to the existing combined data set in the type database, establishing a combined data set corresponding to the newly stored original data;
s205, extracting target information corresponding to target data of the original data from the combined data set corresponding to the newly stored original data, and setting a retrieval condition corresponding to the target information.
The effect of the above technical scheme is as follows: by utilizing the self-adaptive updating of the data types, the new data types in the database can be added and classified by the data retrieval model in a self-adaptive updating mode without manually reconstructing or reconstructing the data retrieval model when a new device is added to a corresponding device system of the retrieval system, so that the quick response of data retrieval is improved, the problems of invalid or failed retrieval and the like caused by untimely manual updating of the data retrieval model are avoided, and the accuracy of retrieval is improved. Moreover, labor cost and time cost for maintaining the retrieval system can be effectively saved.
In one embodiment of the invention, the data retrieval model comprises: the system comprises a type database, each combined data set in the type database, a retrieval condition corresponding to each combined data set and a self-adaptive updating module.
Wherein the adaptive update module comprises:
the type database building module is used for building a type database corresponding to the newly added data type by using the newly added data type and the original data corresponding to the newly added data type;
the characteristic value extraction module is used for extracting the attribute characteristic value of the source equipment corresponding to the original data aiming at the newly stored original data when the newly stored original data is detected not to belong to the current original data source equipment;
the comparison module is used for comparing the attribute characteristic value of the source equipment with the attribute characteristic value of the source equipment corresponding to each combined data set in the type database and calculating a difference value between the attribute characteristic value corresponding to the newly stored original data and the attribute characteristic value of the source equipment corresponding to each combined data set in the type database;
an attribute determining module, configured to perform attribute determination on the source device corresponding to the newly stored original data according to the difference value, determine whether the source device belongs to a source device attribute corresponding to an existing combined data set in a type database, and if the source device belongs to a source device attribute corresponding to an existing combined data set in the type database, allocate the newly stored original data to a corresponding combined data set; and if the source equipment attribute does not belong to the source equipment attribute corresponding to the existing combined data set in the type database, establishing a combined data set corresponding to the newly stored original data.
The working principle of the technical scheme is as follows: establishing a type database corresponding to the newly added data type by using the newly added data type and original data corresponding to the newly added data type through a type database adding modeling block;
when detecting that newly stored original data does not belong to current original data source equipment, a characteristic value extraction module is adopted to extract attribute characteristic values of source equipment corresponding to the original data aiming at the newly stored original data;
comparing the attribute characteristic value of the source equipment with the attribute characteristic value of the source equipment corresponding to each combined data set in the type database by using a comparison module, and calculating a difference value between the attribute characteristic value corresponding to the newly stored original data and the attribute characteristic value of the source equipment corresponding to each combined data set in the type database;
determining the attribute of the source equipment corresponding to the newly stored original data according to the difference value through an attribute determining module, judging whether the source equipment belongs to the attribute of the source equipment corresponding to the existing combined data set in the type database, and if the source equipment belongs to the attribute of the source equipment corresponding to the existing combined data set in the type database, distributing the newly stored original data to the corresponding combined data set; and if the source equipment attribute does not belong to the source equipment attribute corresponding to the existing combined data set in the type database, establishing a combined data set corresponding to the newly stored original data.
The effect of the above technical scheme is as follows: by utilizing the self-adaptive updating of the data types, the new data types in the database can be added and classified by the data retrieval model in a self-adaptive updating mode without manually reconstructing or reconstructing the data retrieval model when a new device is added to a corresponding device system of the retrieval system, so that the quick response of data retrieval is improved, the problems of invalid or failed retrieval and the like caused by untimely manual updating of the data retrieval model are avoided, and the accuracy of retrieval is improved. Moreover, labor cost and time cost for maintaining the retrieval system can be effectively saved.
In an embodiment of the present invention, the attribute feature value of the source device is automatically generated according to a feature value generation rule, where the feature value generation rule is:
the characteristic values are as follows according to the sequence of the digits from left to right: the first bit represents the device location, the second bit represents the device operation type, and the third bit represents the data attributes
Wherein the first number 1 represents an industrial production site location, the first number 2 represents an office location, and the first number 3 represents a warehouse storage location;
the second digit 1 represents index monitoring equipment, the second digit 2 represents office equipment, and the second digit 3 represents entrance guard monitoring equipment;
the third digit 1 represents index monitoring data, the third digit 2 represents office file data, and the third digit 3 represents access control monitoring data;
and if detecting that the source equipment corresponding to the original data belongs to the new attribute, generating an attribute characteristic value for the new attribute equipment according to the rule that the current digit corresponds to the number +1 and corresponding to different digits according to the attribute characteristics. When the number of a certain digit is accumulated and increased to exceed 9, the tenth number of the digit is represented by letter A, and the attribute characteristic value is generated for the new attribute device according to the letter arrangement order of the English alphabet.
The difference value is obtained through the following formula, and the source equipment attribute is determined according to the difference value:
wherein the content of the first and second substances,Q（P，T) Representing a difference value between attribute feature values;Hrepresenting an attribute judgment condition;Prepresenting the attribute characteristic value of the source device corresponding to the newly stored original data,Trepresenting the existing attribute feature value set in each combined data set;X i indicating the number of differences in the first digit of the attribute feature value,Y j indicating the number of differences in the second digit in the attribute feature value,Z k representing the number of differences of the third digit in the attribute characteristic value;nand representing the number of attribute characteristic values corresponding to the existing combined data set in the type database.
The working principle of the technical scheme is as follows: and determining whether the attribute of the source equipment corresponding to the newly stored original data is the existing equipment attribute or not by using the attribute characteristic value formed by combining the location and the equipment type of the source equipment corresponding to the original data and the attribute of the equipment acquisition data and the attribute characteristic value difference value.
The effect of the above technical scheme is as follows: the characteristics of the source equipment can be effectively embodied by using the attribute characteristic value formed by combining the location and the equipment type of the source equipment corresponding to the original data and the attribute of the equipment acquisition data, and the classification rationality of the equipment is improved. Meanwhile, whether the attribute of the source equipment corresponding to the newly stored original data is the existing equipment attribute or not is determined through the attribute characteristic value difference value, the data attribute judgment accuracy can be effectively improved, the self-adaption updating accuracy of the data retrieval model is improved, the problem that the retrieval response speed is reduced due to the fact that the self-adaption updated data is wrongly classified is solved, and the retrieval response speed after the self-adaption updating is effectively guaranteed.
In one embodiment of the invention, the retrieval system comprises:
the retrieval model construction module is used for constructing a data retrieval model by utilizing various kinds of original data with different data types;
the retrieval interface generation module is used for generating a retrieval interface and retrieval options in the retrieval interface; the retrieval options comprise a text data retrieval option, a table data retrieval option, a picture data retrieval option and a video data retrieval option; when a newly added type database is formed, a new data retrieval option can be established by utilizing the retrieval interface generation module according to the actual addition condition.
The updating module is used for carrying out self-adaptive updating on the data retrieval model by utilizing the newly added data types of the original data;
the detection module is used for detecting whether the retrieval triggering operation exists in real time, and acquiring a retrieval condition corresponding to the retrieval triggering operation when the retrieval triggering operation is detected;
and the calling module is used for calling corresponding original data by using the data retrieval model and the retrieval conditions.
Wherein the retrieval model building module comprises:
the original data acquisition module is used for acquiring original data of each device corresponding to the retrieval system and identifying the data type of each original data, wherein the data type comprises character data, table data, picture data and video data;
the type database establishing module is used for respectively establishing type databases corresponding to the data types according to the data types; respectively storing the original data of different data types in the type database;
the classification combination module is used for classifying and combining the original data in the type database according to the attribute characteristic value of the source equipment of each original data to obtain a combined data set;
and the retrieval condition setting module is used for extracting target information corresponding to the target data of each original data in the combined data set and setting retrieval conditions corresponding to each target information.
The update module comprises
The real-time detection module is used for detecting the data type of the original data acquired from each device corresponding to the retrieval system in real time, judging whether a new data type exists or not, if the new data type exists, sending the new data type and the original data corresponding to the new data type to a data retrieval model, and establishing a type database corresponding to the newly added data type;
the new data detection module is used for detecting newly stored original data of each type database, judging whether the newly stored original data belong to current original data source equipment or not, and controlling the data retrieval model to extract attribute characteristic values of source equipment corresponding to the original data aiming at the newly stored original data if the newly stored original data do not belong to the current original data source equipment;
and the retrieval condition adding module is used for extracting target information corresponding to the target data of the original data from the combined data set corresponding to the newly stored original data after a combined data set corresponding to the newly stored original data is added, and setting a retrieval condition corresponding to the target information.
The working principle of the technical scheme is as follows: constructing a data retrieval model by utilizing various original data with different data types through a retrieval model construction module; then, a retrieval interface generating module is adopted to generate a retrieval interface and retrieval options in the retrieval interface; the retrieval options comprise a text data retrieval option, a table data retrieval option, a picture data retrieval option and a video data retrieval option; when a newly added type database is formed, a module can be generated through the retrieval interface to establish a newly added data retrieval option. The data retrieval model is adaptively updated by an updating module by utilizing the newly added data type of the original data; detecting whether a retrieval triggering operation exists or not in real time by adopting a detection module, and acquiring a retrieval condition corresponding to the retrieval triggering operation when the retrieval triggering operation is detected; and calling corresponding original data by using the data retrieval model and the retrieval conditions through a calling module.
The retrieval model construction module acquires the original data of each device corresponding to the retrieval system through an original data acquisition module and identifies the data type of each original data; respectively establishing type databases corresponding to the data types according to the data types by using a type database establishing module; respectively storing the original data of different data types in the type database; classifying and combining the original data in the type database through a classification and combination module according to the attribute characteristic value of the source equipment of each original data to obtain a combined data set; and extracting target information corresponding to the target data of each original data in the combined data set by adopting a retrieval condition setting module, and setting a retrieval condition corresponding to each target information.
The updating module detects the data type of the original data acquired from each device corresponding to the retrieval system in real time through a real-time detection module, judges whether a new data type exists or not, and sends the new data type and the original data corresponding to the new data type to a data retrieval model if the new data type exists, and establishes a type database corresponding to the newly added data type; detecting newly stored original data of each type database by using a new data detection module, judging whether the newly stored original data belong to current original data source equipment, and controlling the data retrieval model to extract attribute characteristic values of source equipment corresponding to the original data aiming at the newly stored original data if the newly stored original data do not belong to the current original data source equipment; and after a combined data set corresponding to the newly stored original data is added by adopting a retrieval condition adding module, extracting target information corresponding to target data of the original data from the combined data set corresponding to the newly stored original data, and setting a retrieval condition corresponding to the target information.
The effect of the above technical scheme is as follows: the data retrieval model is constructed to effectively classify the original data according to different data types, and the database is set on the basis of the data types, so that the data calling speed of the retrieval process among various data types is improved, and meanwhile, the classification and the database establishment are carried out according to the data types, so that the calling disorder caused by different data types can be effectively avoided. On the other hand, by means of combining the data sets, the data in each data type is further divided on the basis of the source equipment corresponding to the original data, and the corresponding speed of data calling can be further increased; meanwhile, data calling errors caused by data storage disorder can be effectively avoided through strict classification.
In addition, by utilizing the self-adaptive updating of the data types, the new data types in the database can be added and classified by the data retrieval model in a self-adaptive updating mode without manually reconstructing or reconstructing the data retrieval model when a new device is added to a corresponding device system of the retrieval system, so that the quick response of data retrieval is improved, the problems of invalid or failed retrieval and the like caused by untimely manual updating of the data retrieval model are avoided, and the accuracy of retrieval is improved. Moreover, labor cost and time cost for maintaining the retrieval system can be effectively saved.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. An integrated retrieval method supporting different data types, the integrated retrieval method comprising:
in a retrieval system, a data retrieval model is constructed by utilizing a plurality of kinds of original data with different data types;
carrying out self-adaptive updating on the data retrieval model by using the newly added data type of the original data;
detecting whether a retrieval triggering operation exists in real time, and acquiring a retrieval condition corresponding to the retrieval triggering operation when the retrieval triggering operation is detected;
and calling corresponding original data by using the data retrieval model and the retrieval conditions.
2. The integrated retrieval method of claim 1, wherein the building of the data retrieval model in the retrieval system by using a plurality of kinds of raw data with different data types comprises:
acquiring original data of each device corresponding to the retrieval system, and identifying the data type of each original data, wherein the data type comprises character data, table data, picture data and video data;
respectively establishing type databases corresponding to the data types according to the data types; respectively storing the original data of different data types in the type database;
classifying and combining the original data in the type database according to the attribute characteristic value of the source equipment of each original data to obtain a combined data set;
and extracting target information corresponding to the target data of each original data in the combined data set, and setting a retrieval condition corresponding to each target information.
3. The integrated retrieval method of claim 1, wherein the data retrieval model comprises: the system comprises a type database, each combined data set in the type database, a retrieval condition corresponding to each combined data set and a self-adaptive updating module.
4. The integrated retrieval method of claim 3, wherein the adaptive update module comprises:
the type database building module is used for building a type database corresponding to the newly added data type by using the newly added data type and the original data corresponding to the newly added data type;
the characteristic value extraction module is used for extracting the attribute characteristic value of the source equipment corresponding to the original data aiming at the newly stored original data when the newly stored original data is detected not to belong to the current original data source equipment;
the comparison module is used for comparing the attribute characteristic value of the source equipment with the attribute characteristic value of the source equipment corresponding to each combined data set in the type database and calculating a difference value between the attribute characteristic value corresponding to the newly stored original data and the attribute characteristic value of the source equipment corresponding to each combined data set in the type database;
an attribute determining module, configured to perform attribute determination on the source device corresponding to the newly stored original data according to the difference value, determine whether the source device belongs to a source device attribute corresponding to an existing combined data set in a type database, and if the source device belongs to a source device attribute corresponding to an existing combined data set in the type database, allocate the newly stored original data to a corresponding combined data set; and if the source equipment attribute does not belong to the source equipment attribute corresponding to the existing combined data set in the type database, establishing a combined data set corresponding to the newly stored original data.
5. The integrated retrieval method of claim 1, wherein the adaptively updating the data retrieval model by using the data type of the new added original data comprises:
detecting the data types of the original data acquired from each device corresponding to the retrieval system in real time, judging whether a new data type exists, if so, sending the new data type and the original data corresponding to the new data type to a data retrieval model, and establishing a type database corresponding to the newly added data type;
detecting newly stored original data of each type database, judging whether the newly stored original data belong to current original data source equipment or not, and controlling the data retrieval model to extract attribute characteristic values of source equipment corresponding to the original data aiming at the newly stored original data if the newly stored original data do not belong to the current original data source equipment;
the data retrieval model compares the attribute characteristic value of the source equipment with the attribute characteristic value of the source equipment corresponding to each combined data set in the type database, and calculates a difference value between the attribute characteristic value corresponding to the newly stored original data and the attribute characteristic value of the source equipment corresponding to each combined data set in the type database;
the data retrieval model determines the attribute of the source equipment corresponding to the newly stored original data according to the difference value, judges whether the attribute belongs to the attribute of the source equipment corresponding to the existing combined data set in the type database, and distributes the newly stored original data to the corresponding combined data set if the attribute belongs to the attribute of the source equipment corresponding to the existing combined data set in the type database; if the source device attribute does not belong to the source device attribute corresponding to the existing combined data set in the type database, establishing a combined data set corresponding to the newly stored original data;
and extracting target information corresponding to the target data of the original data from the combined data set corresponding to the newly stored original data, and setting a retrieval condition corresponding to the target information.
6. The integrated retrieval method according to claim 2 or 5, wherein the attribute feature value of the source device is automatically generated by a feature value generation rule, wherein the feature value generation rule is:
the characteristic values are as follows according to the sequence of the digits from left to right: the first bit represents the device location, the second bit represents the device operation type, and the third bit represents the data attributes
Wherein the first number 1 represents an industrial production site location, the first number 2 represents an office location, and the first number 3 represents a warehouse storage location;
the second digit 1 represents index monitoring equipment, the second digit 2 represents office equipment, and the second digit 3 represents entrance guard monitoring equipment;
the third digit 1 represents index monitoring data, the third digit 2 represents office file data, and the third digit 3 represents access control monitoring data;
and if detecting that the source equipment corresponding to the original data belongs to the new attribute, generating an attribute characteristic value for the new attribute equipment according to the rule that the current digit corresponds to the number +1 and corresponding to different digits according to the attribute characteristics.
7. The integrated retrieval method of claim 4, wherein the difference value is obtained by the following formula, and the source device attribute determination is performed according to the difference value:
wherein the content of the first and second substances,Q（P，T) Representing a difference value between attribute feature values;Hrepresenting an attribute judgment condition;Prepresenting the attribute characteristic value of the source device corresponding to the newly stored original data,Trepresenting the existing attribute feature value set in each combined data set;X i indicating the number of differences in the first digit of the attribute feature value,Y j indicating the number of differences in the second digit in the attribute feature value,Z k representing the number of differences of the third digit in the attribute characteristic value;nand representing the number of attribute characteristic values corresponding to the existing combined data set in the type database.
8. The integrated retrieval method of claim 1, wherein the retrieval system comprises:
the retrieval model construction module is used for constructing a data retrieval model by utilizing various kinds of original data with different data types;
the retrieval interface generation module is used for generating a retrieval interface and retrieval options in the retrieval interface; the retrieval options comprise a text data retrieval option, a table data retrieval option, a picture data retrieval option and a video data retrieval option;
the updating module is used for carrying out self-adaptive updating on the data retrieval model by utilizing the newly added data types of the original data;
the detection module is used for detecting whether the retrieval triggering operation exists in real time, and acquiring a retrieval condition corresponding to the retrieval triggering operation when the retrieval triggering operation is detected;
and the calling module is used for calling corresponding original data by using the data retrieval model and the retrieval conditions.
9. The integrated retrieval method of claim 8, wherein the retrieval model building module comprises:
the original data acquisition module is used for acquiring original data of each device corresponding to the retrieval system and identifying the data type of each original data, wherein the data type comprises character data, table data, picture data and video data;
the type database establishing module is used for respectively establishing type databases corresponding to the data types according to the data types; respectively storing the original data of different data types in the type database;
the classification combination module is used for classifying and combining the original data in the type database according to the attribute characteristic value of the source equipment of each original data to obtain a combined data set;
and the retrieval condition setting module is used for extracting target information corresponding to the target data of each original data in the combined data set and setting retrieval conditions corresponding to each target information.
10. The integrated retrieval method of claim 8, wherein the update module comprises
The real-time detection module is used for detecting the data type of the original data acquired from each device corresponding to the retrieval system in real time, judging whether a new data type exists or not, if the new data type exists, sending the new data type and the original data corresponding to the new data type to a data retrieval model, and establishing a type database corresponding to the newly added data type;
the new data detection module is used for detecting newly stored original data of each type database, judging whether the newly stored original data belong to current original data source equipment or not, and controlling the data retrieval model to extract attribute characteristic values of source equipment corresponding to the original data aiming at the newly stored original data if the newly stored original data do not belong to the current original data source equipment;
and the retrieval condition adding module is used for extracting target information corresponding to the target data of the original data from the combined data set corresponding to the newly stored original data after a combined data set corresponding to the newly stored original data is added, and setting a retrieval condition corresponding to the target information.
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