Intelligent data acquisition and analysis method, device and equipment and storage medium

Abstract

The invention relates to the technical field of data integration analysis, in particular to an intelligent data collection and analysis method and device, equipment and a storage medium, and the method comprises the steps: recognizing a data type after obtaining basic data of collection equipment, and then obtaining a corresponding monomer analysis result from historical analysis data; the method comprises the following steps: retrieving in a network information base by taking target data as a keyword to obtain original pairing information, further screening out adaptive information by determining associated information and auxiliary information and calculating an information proportion value, and then integrating the adaptive information based on a processing method of a single analysis result of the target data to form an information combination; further analyzing information components in the auxiliary information, calculating a set proportion value and the like to determine derivative data of the processing method, and finally performing real-time acquisition on the corresponding derivative data by taking the processing method of the target data as a data instruction, so as to guarantee the data timeliness and effectiveness of subsequent analysis and improve the accuracy of data processing. And the working efficiency of data processing is further improved.

Description

Technical Field

[0001] The present invention relates to the field of data integration and analysis technology, and in particular to methods, devices, equipment and storage media for intelligent data collection and analysis. Background Art

[0002] In today's digital world, data acquisition equipment has become a key tool for collecting, processing and analyzing big data.

[0003] The prior art CN107831699A discloses an intelligent data collection and analysis method and system, which includes realizing data collection and discarding invalid data when the wind direction is wrong, discarding abnormal fluctuation data, and restoring accurate data of soluble components in rainfall, eliminating the adverse effects of wind direction and rainfall on data collection.

[0004] However, during the process of data collection and analysis, it is usually combined with other data for comprehensive analysis to avoid the limitations brought about by analyzing only a single data. However, when conducting comprehensive analysis on data collected in real time, data from other fields will be involved. If historical data is directly obtained, the real-time nature of the data will be reduced. If real-time collection instructions are issued to data in other fields, the efficiency of data processing will be reduced. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems in the background technology and to propose a data intelligent collection and analysis method, device, equipment and storage medium.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: The data intelligent collection and analysis method specifically includes the following steps: Step 1: Obtain the basic data collected by the collection device and identify the data type of the basic data. Then, obtain the analysis results of the data in the historical analysis data and mark them as individual analysis results. Step 2: Mark the data type of the basic data as target data, use the target data as the keyword for information retrieval, and enter it into the network information database for retrieval to obtain the original matching information; Identify the information content in the original pairing information according to the target data, determine the associated information and auxiliary information, divide the information volume of the associated information by the information volume of the original pairing information to obtain an information ratio value, and then determine the adaptation information based on the information ratio value; Step 3: Obtain the individual analysis results of the target data, identify the processing methods of the individual analysis results, and integrate the adaptation information corresponding to the same processing method into an information combination; The processing methods are sequentially used as target processing methods, sub-information in the information combination corresponding to the target processing methods is marked as target analysis information, auxiliary information in the target analysis information is extracted, and information components in the auxiliary information are identified; Obtaining the amount of auxiliary information in the information combination, identifying information components in the auxiliary information, integrating the auxiliary information based on the information components to obtain different component sets, counting the number of elements in the component sets, and then calculating the set proportion value based on the number of elements and the amount of auxiliary information, and determining the derivative data of the processing method based on the set proportion value; Step 4: Use the target data processing method as a data instruction, identify the derived data corresponding to the data instruction, and collect the derived data in real time.

[0007] As a further embodiment of the present invention, the monomer analysis result refers to the result of data analysis obtained by using only basic data as the data analysis object, including central tendency analysis, dispersion analysis and distribution morphology analysis of the basic data; Among them, central tendency analysis includes mean and mode, dispersion analysis refers to range, variance and standard deviation, and distribution shape analysis refers to skewness coefficient and kurtosis coefficient.

[0008] As a further solution of the present invention, a method for determining adaptation information includes: S1: Obtain the data type of the basic data and input it into the network information database as the keyword for information retrieval, and then mark the obtained retrieval results as original pairing information; Mark the data type of the basic data as target data, then obtain the original pairing information, identify the information content in the original pairing information, and divide the information content in the original pairing information into related information and auxiliary information; Among them, the associated information refers to the information content related to the target data in the original pairing information, and the auxiliary information refers to the information content obtained by subtracting the associated information from the original pairing information; S2: Identify the information volume of the original pairing information and the information volume of the corresponding associated information respectively, then divide the information volume of the associated information by the information volume of the original pairing information, and mark the result as the information ratio value Bx; The information ratio value Bx is compared with the ratio threshold A1. If Bx ≥ A1, the corresponding original pairing information is directly marked as adapted information.

[0009] As a further solution of the present invention, if Bx<A1, the associated information is identified. If the associated information belongs to the basic data of the enterprise, that is, the original pairing information references the historical basic data as data support, then the corresponding original pairing information is marked as adapted information. If Bx<A1 and the associated information does not belong to the basic data of the enterprise, the corresponding original pairing information is deleted.

[0010] As a further solution of the present invention, a method for obtaining an information combination includes: SS1: Obtain the individual analysis results of basic data in the enterprise comprehensive database and identify the processing methods in the individual analysis results. The processing methods include mean processing and mode processing in central tendency analysis, range processing, variance processing and standard deviation processing in dispersion analysis, and skewness coefficient processing and kurtosis coefficient processing in distribution shape analysis. SS2: Acquire the adaptation information, extract the associated information in the adaptation information, identify the processing methods in the associated information, and classify them according to the processing methods, that is, organize the adaptation information corresponding to the processing methods of the same associated information into an information combination.

[0011] As a further aspect of the present invention, a method for determining derivative data of a treatment method includes: Randomly select a processing method and mark it as the target processing method. At the same time, obtain the information combination corresponding to the target processing method and mark the sub-information in the information combination as the target analysis information. Extracting auxiliary information from the target analysis information and identifying information components in the auxiliary information, wherein the information components refer to other data components, that is, data other than the associated information; Obtain all auxiliary information in the target analysis information, count the number of auxiliary information, and mark it as Fs, where the number of auxiliary information is the number of adaptation information in the information combination corresponding to the target processing method; Then extract the information components in each auxiliary information and mark the information components in the auxiliary information as , where i represents different auxiliary information and j represents different information components; Then the auxiliary information is sorted and summarized according to the information component j. The information components of j=j1 in are integrated into a set, the information components of j=j2 are integrated into a set, and so on, to obtain several component sets; Get the number of elements in each component set and mark them as Dn, where n represents a different component set. Then, calculate Dn ÷ Fs = Pn to get the set proportion Pn. Then compare the set proportion value Pn with the proportion threshold Py. If Pn≥Py, obtain the component set of this set proportion value Pn, identify the information component of the component set, and mark this information component as derived data. Conversely, if Pn<Py, do not process this component set.

[0012] As a further solution of the present invention, a method for real-time collection of derivative data includes: The processing method of the target data is used as the data instruction. When the system recognizes the data instruction of the target data, it obtains the derivative data of the corresponding processing method, identifies the data collection address of the derivative data, and generates a real-time collection instruction at the same time. The system transmits the real-time collection instruction to the data collection address of the derivative data and collects the derivative data in real time.

[0013] An intelligent data collection and analysis device, comprising: The data acquisition module is used to collect basic data and transmit it to the data retrieval module and information matching module respectively; The data retrieval module is used to retrieve the analysis results of the basic data in the historical analysis data, obtain the monomer analysis results, and transmit them to the integrated processing module; The information matching module is used to identify the data type of the basic data and mark the data type as the target data. The target data is used as the keyword for information retrieval to search in the network database to obtain the original matching information. The information matching module then transmits the original matching information to the information identification module. The information recognition module is used to identify the information content in the original pairing information according to the target data, determine the associated information and auxiliary information, and select the adaptation information according to the information volume of the associated information and the information volume of the original pairing information. The information recognition module then transmits the adaptation information to the integrated processing module; The integrated processing module is used to identify the processing method of the monomer analysis results and integrate the adaptation information corresponding to the same processing method into an information combination; The processing methods are sequentially used as target processing methods, sub-information in the information combination corresponding to the target processing methods is marked as target analysis information, auxiliary information in the target analysis information is extracted, and information components in the auxiliary information are identified; Identify the information components in the auxiliary information and integrate them according to the information components to obtain a component set. Count the number of elements in the component set. Then, calculate the set proportion value based on the number of elements and the amount of auxiliary information. Determine the derivative data of the processing method based on the set proportion value. Then, the integrated processing module transmits the processing method and its corresponding derivative data to the collaborative acquisition module. The collaborative acquisition module is used to use the processing method of the target data as a data instruction. When the data instruction of the target data is detected, the collaborative acquisition module identifies the derived data corresponding to the data instruction and collects the derived data in real time.

[0014] A data intelligent collection and analysis device includes: a memory and at least one processor, wherein the memory stores instructions; At least one processor calls instructions in the memory to enable the data intelligent collection and analysis device to execute the above-mentioned data intelligent collection and analysis method.

[0015] A computer storage medium, wherein instructions are stored on the computer-readable storage medium, and when the instructions are executed by a processor, the above-mentioned data intelligent collection and analysis method is implemented.

[0016] Compared with the existing technology, the advantages of the present invention are: The present invention obtains the basic data of the acquisition device and then identifies the data type, then obtains the corresponding single analysis results from the historical analysis data, takes the data type of the basic data as the target data, and uses the target data as the keyword to retrieve the original matching information in the network information database, and further screens out the adaptation information by determining the associated information, auxiliary information, and calculating the information ratio value. Among them, the screening of the adaptation information improves the pertinence and value of information acquisition, and then integrates the adaptation information based on the processing method of the single analysis result of the target data to form an information combination, and further determines the derivative data of the processing method by analyzing the information components in the auxiliary information, calculating the set ratio value, etc., which helps to integrate various information to mine more valuable data associations and derive related data, and finally uses the processing method of the target data as a data instruction to perform real-time collection on the corresponding derivative data, which solves the problem of accurately and targetedly carrying out real-time data collection according to specific needs and association relationships, ensures the timeliness and effectiveness of the data for subsequent analysis, and further improves the work efficiency of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the method flow structure of the present invention; Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0019] Example 1, refer to Figure 1 , a data intelligent collection and analysis method, the analysis method specifically includes the following steps: Step 1: Based on the data collection instructions of the device, the data collected by the device is marked as basic data, and the collection device recognizes the data collection instructions and then collects the basic data in real time; It should be further explained that after the basic data is collected in real time, it is necessary to clean the basic data to improve the data quality of the basic data. Data cleaning includes checking the integrity of the data, identifying erroneous data, duplicate data, abnormal data, and unifying the data format. Identify the data type of the basic data and use the data type of the basic data as the information subject. At the same time, obtain the analysis results of the information subject in the historical analysis data and mark them as single analysis results. Among them, the single analysis results refer to the results of data analysis obtained by using only basic data as the data analysis object, including the central tendency analysis, dispersion analysis and distribution shape analysis of the basic data. Furthermore, the central tendency analysis includes the mean and mode, the dispersion analysis refers to the range, variance and standard deviation, and the distribution shape analysis refers to the skewness coefficient and kurtosis coefficient; Step 2: Obtain the data type of the basic data again and input it into the network information database as the keyword for information retrieval. Then, mark the retrieval results as original matching information. Then, process the original matching information again to determine the adaptation information of the basic data. The specific method for determining the adaptation information includes: S1: Mark the data type of the basic data as target data, then obtain the original pairing information, identify the information content in the original pairing information, and divide the information content in the original pairing information into related information and auxiliary information; Among them, the associated information refers to the information content related to the target data in the original pairing information, and the auxiliary information refers to the information content obtained by subtracting the associated information from the original pairing information; It should be further explained that natural language processing technology is used to identify the original pairing information in this embodiment, and natural language processing technology is an existing technology and will not be described in detail here. S2: Identify the information volume of the original pairing information and the information volume of the corresponding associated information respectively, then divide the information volume of the associated information by the information volume of the original pairing information, and mark the result as the information ratio value Bx; Compare the information ratio value Bx with the ratio threshold A1. If Bx ≥ A1, the corresponding original pairing information is directly marked as adapted information. Conversely, if Bx < A1, the associated information is identified. If the associated information belongs to the basic data of the enterprise, that is, the original pairing information references the historical basic data as data support, then the corresponding original pairing information is marked as adapted information. If Bx < A1 and the associated information does not belong to the basic data of the enterprise, the corresponding original pairing information is deleted. The specific value of the ratio threshold A1 is obtained by those skilled in the art after big data calculation; Step 3: Obtain the individual analysis results of the target data from the enterprise's comprehensive database, combine the adaptation information with the individual analysis results, and perform secondary analysis to determine the derivative data of the basic data. The specific methods for determining the derivative data include: SS1: Obtain the individual analysis results of basic data in the enterprise comprehensive database and identify the processing methods in the individual analysis results. The processing methods include mean processing and mode processing in central tendency analysis, range processing, variance processing and standard deviation processing in dispersion analysis, and skewness coefficient processing and kurtosis coefficient processing in distribution shape analysis. SS2: Acquire the adaptation information, extract the associated information in the adaptation information, identify the processing methods in the associated information, and classify them according to the processing methods. That is, the adaptation information corresponding to the processing methods of the same associated information is organized into an information combination; SS3: arbitrarily select a processing method and mark it as the target processing method. At the same time, obtain the information combination corresponding to the target processing method and mark the sub-information in the information combination as the target analysis information. It should be further explained that the sub-information in the information combination is the adaptation information corresponding to the target processing method; Extract auxiliary information from the target analysis information and identify information components in the auxiliary information, where information components refer to other data components, that is, data other than associated information. For example, when analyzing user electricity consumption data and adjusting the power distribution parameters for each time period based on the data analysis results, the data information involved includes the user's total electricity consumption, the individual equipment electricity consumption, and the real-time electricity consumption time. If the user's total electricity consumption is used as associated information when analyzing the user's electricity consumption, the information content corresponding to the individual equipment electricity consumption and the real-time electricity consumption time is the auxiliary information, and the individual equipment electricity consumption and the real-time electricity consumption time are data components in the auxiliary information; SS4: Obtain all auxiliary information in the target analysis information, count the number of auxiliary information, and mark it as Fs, where the number of auxiliary information is the number of adaptation information in the information combination corresponding to the target processing method; Then extract the information components in each auxiliary information and mark the information components in the auxiliary information as , where i represents different auxiliary information and j represents different information components; Then the auxiliary information is sorted and summarized according to the information component j. The information components of j=j1 in are integrated into a set, the information components of j=j2 are integrated into a set, and so on, to obtain several component sets; It should be further explained that in this implementation, information component refers to a general term for a data type, rather than a specific string of numbers. For example, for a string of numbers {5.0, 3.5, 4.1, 3.2, 4.7}, a convolutional neural network is used to identify the local features before and after this string of numbers. After confirming that this string of numbers belongs to the individual power consumption of the device, the individual power consumption of the device is ultimately used as the information component of this auxiliary information. SS5: Get the number of elements in each component set and mark them as Dn, where n represents a different component set. Then, Dn ÷ Fs = Pn to get the set proportion Pn. Then, the set proportion value Pn is compared with the proportion threshold Py. If Pn≥Py, the component set of this set proportion value Pn is obtained, and the information component of the component set is identified, and this information component is marked as derived data. Conversely, if Pn<Py, this component set is not processed. The specific value of the proportion threshold Py is obtained by those skilled in the art after big data calculation. All the processing methods are sequentially used as target processing methods, and are processed according to the methods in steps SS3 to SS5 above, thereby obtaining derivative data corresponding to each processing method of the target data; Step 4: Use the processing method of the target data as the data instruction. When the system recognizes the data instruction of the target data, it obtains the derivative data of the corresponding processing method, identifies the data collection address of the derivative data, and generates a real-time collection instruction. The system transmits the real-time collection instruction to the data collection address of the derivative data, and then collects the derivative data in real time. The present invention improves the efficiency of data processing, ensures the synchronization between data, and further ensures the accuracy of data analysis.

[0020] Example 2, refer to Figure 2 The present invention also provides a data intelligent collection and analysis device, which specifically includes: The data acquisition module is used to collect basic data and transmit it to the data retrieval module and information matching module respectively; The data retrieval module is used to retrieve the analysis results of the basic data in the historical analysis data, obtain the monomer analysis results, and transmit them to the integrated processing module; The information matching module is used to identify the data type of the basic data and mark the data type as the target data. The target data is used as the keyword for information retrieval to search in the network database to obtain the original matching information. The information matching module then transmits the original matching information to the information identification module. The information recognition module is used to identify the information content in the original pairing information according to the target data, determine the associated information and auxiliary information, and select the adaptation information according to the information volume of the associated information and the information volume of the original pairing information. The information recognition module then transmits the adaptation information to the integrated processing module; The integrated processing module is used to identify the processing method of the monomer analysis results and integrate the adaptation information corresponding to the same processing method into an information combination; The processing methods are sequentially used as target processing methods, sub-information in the information combination corresponding to the target processing methods is marked as target analysis information, auxiliary information in the target analysis information is extracted, and information components in the auxiliary information are identified; Obtaining the amount of auxiliary information in the information combination, identifying the information components in the auxiliary information, integrating the auxiliary information based on the information components to obtain different component sets, counting the number of elements in the component sets, and then calculating the set proportion value based on the number of elements and the amount of auxiliary information. Derivative data of the processing method is determined based on the set proportion value, and then the integrated processing module transmits the processing method and its corresponding derivative data to the collaborative acquisition module; The collaborative acquisition module is used to use the processing method of the target data as a data instruction. When the data instruction of the target data is detected, the collaborative acquisition module identifies the derived data corresponding to the data instruction and collects the derived data in real time.

[0021] Embodiment 3: The present invention also provides a data intelligent collection and analysis device, which includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor executes the steps of the data intelligent collection and analysis method in the above embodiment.

[0022] Embodiment 4: The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the data intelligent collection and analysis method.

[0023] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. The method for intelligent data collection and analysis is characterized by: The analysis method specifically includes the following steps: Step 1: Obtain the basic data collected by the collection device and identify the data type of the basic data. Then, obtain the analysis results of the data in the historical analysis data and mark them as individual analysis results. Step 2: Mark the data type of the basic data as target data, use the target data as the keyword for information retrieval, and enter it into the network information database for retrieval to obtain the original matching information; Identify the information content in the original pairing information according to the target data, determine the associated information and auxiliary information, divide the information volume of the associated information by the information volume of the original pairing information to obtain an information ratio value, and then determine the adaptation information based on the information ratio value; Step 3: Obtain the individual analysis results of the target data, identify the processing methods of the individual analysis results, and integrate the adaptation information corresponding to the same processing method into an information combination; The processing methods are sequentially used as target processing methods, sub-information in the information combination corresponding to the target processing methods is marked as target analysis information, auxiliary information in the target analysis information is extracted, and information components in the auxiliary information are identified; Obtaining the amount of auxiliary information in the information combination, identifying information components in the auxiliary information, integrating the auxiliary information based on the information components to obtain different component sets, counting the number of elements in the component sets, and then calculating the set proportion value based on the number of elements and the amount of auxiliary information, and determining the derivative data of the processing method based on the set proportion value; Step 4: Use the target data processing method as a data instruction, identify the derived data corresponding to the data instruction, and collect the derived data in real time.

2. The data intelligent collection and analysis method according to claim 1, characterized in that: Single analysis results refer to the results of data analysis obtained by using only basic data as the data analysis object, including the central tendency analysis, dispersion analysis and distribution pattern analysis of the basic data; Among them, central tendency analysis includes mean and mode, dispersion analysis refers to range, variance and standard deviation, and distribution shape analysis refers to skewness coefficient and kurtosis coefficient.

3. The data intelligent collection and analysis method according to claim 1, characterized in that: Methods for determining adaptation information include: S1: Obtain the data type of the basic data and input it into the network information database as the keyword for information retrieval, and then mark the obtained retrieval results as original pairing information; Mark the data type of the basic data as target data, then obtain the original pairing information, identify the information content in the original pairing information, and divide the information content in the original pairing information into related information and auxiliary information; Among them, the associated information refers to the information content related to the target data in the original pairing information, and the auxiliary information refers to the information content obtained by subtracting the associated information from the original pairing information; S2: Identify the information volume of the original pairing information and the information volume of the corresponding associated information respectively, then divide the information volume of the associated information by the information volume of the original pairing information, and mark the result as the information ratio value Bx; The information ratio value Bx is compared with the ratio threshold A1. If Bx ≥ A1, the corresponding original pairing information is directly marked as adapted information.

4. The data intelligent collection and analysis method according to claim 3, characterized in that: If Bx<A1, the associated information is identified. If the associated information belongs to the basic data of the enterprise, that is, the original pairing information refers to the historical basic data as data support, then the corresponding original pairing information is marked as adapted information. If Bx<A1 and the associated information does not belong to the basic data of the enterprise, the corresponding original pairing information is deleted.

5. The data intelligent collection and analysis method according to claim 1, characterized in that: Methods for obtaining information combinations include: SS1: Obtain the individual analysis results of basic data in the enterprise comprehensive database and identify the processing methods in the individual analysis results. The processing methods include mean processing and mode processing in central tendency analysis, range processing, variance processing and standard deviation processing in dispersion analysis, and skewness coefficient processing and kurtosis coefficient processing in distribution shape analysis. SS2: Acquire the adaptation information, extract the associated information in the adaptation information, identify the processing methods in the associated information, and classify them according to the processing methods, that is, organize the adaptation information corresponding to the processing methods of the same associated information into an information combination.

6. The data intelligent collection and analysis method according to claim 5, characterized in that: Methods for determining the derived data for a treatment include: Randomly select a processing method and mark it as the target processing method. At the same time, obtain the information combination corresponding to the target processing method and mark the sub-information in the information combination as the target analysis information. Extracting auxiliary information from the target analysis information and identifying information components in the auxiliary information, wherein the information components refer to other data components, that is, data other than the associated information; Obtain all auxiliary information in the target analysis information, count the number of auxiliary information, and mark it as Fs, where the number of auxiliary information is the number of adaptation information in the information combination corresponding to the target processing method; Then extract the information components in each auxiliary information and mark the information components in the auxiliary information as , where i represents different auxiliary information and j represents different information components; Then the auxiliary information is sorted and summarized according to the information component j. The information components of j=j1 in are integrated into a set, the information components of j=j2 are integrated into a set, and so on, to obtain several component sets; Get the number of elements in each component set and mark them as Dn, where n represents a different component set. Then, calculate Dn ÷ Fs = Pn to get the set proportion Pn. Then compare the set proportion value Pn with the proportion threshold Py. If Pn≥Py, obtain the component set of this set proportion value Pn, identify the information component of the component set, and mark this information component as derived data. Conversely, if Pn<Py, do not process this component set.

7. The data intelligent collection and analysis method according to claim 1, characterized in that: Methods for real-time collection of derived data include: The processing method of the target data is used as the data instruction. When the system recognizes the data instruction of the target data, it obtains the derivative data of the corresponding processing method, identifies the data collection address of the derivative data, and generates a real-time collection instruction at the same time. The system transmits the real-time collection instruction to the data collection address of the derivative data and collects the derivative data in real time.

8. The intelligent data collection and analysis device is characterized by: The device includes: The data acquisition module is used to collect basic data and transmit it to the data retrieval module and information matching module respectively; The data retrieval module is used to retrieve the analysis results of the basic data in the historical analysis data, obtain the monomer analysis results, and transmit them to the integrated processing module; The information matching module is used to identify the data type of the basic data and mark the data type as the target data. The target data is used as the keyword for information retrieval to search in the network database to obtain the original matching information. The information matching module then transmits the original matching information to the information identification module. The information recognition module is used to identify the information content in the original pairing information according to the target data, determine the associated information and auxiliary information, and select the adaptation information according to the information volume of the associated information and the information volume of the original pairing information. The information recognition module then transmits the adaptation information to the integrated processing module; The integrated processing module is used to identify the processing method of the monomer analysis results and integrate the adaptation information corresponding to the same processing method into an information combination; The processing methods are sequentially used as target processing methods, sub-information in the information combination corresponding to the target processing methods is marked as target analysis information, auxiliary information in the target analysis information is extracted, and information components in the auxiliary information are identified; Identify the information components in the auxiliary information and integrate them according to the information components to obtain a component set. Count the number of elements in the component set. Then, calculate the set proportion value based on the number of elements and the amount of auxiliary information. Determine the derivative data of the processing method based on the set proportion value. Then, the integrated processing module transmits the processing method and its corresponding derivative data to the collaborative acquisition module. The collaborative acquisition module is used to use the processing method of the target data as a data instruction. When the data instruction of the target data is detected, the collaborative acquisition module identifies the derived data corresponding to the data instruction and collects the derived data in real time.

9. Intelligent data collection and analysis equipment, characterized in that: The intelligent data collection device includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory to enable the data intelligent collection and analysis device to execute the data intelligent collection and analysis method according to any one of claims 1 to 7.

10. A computer storage medium, wherein instructions are stored on the computer readable storage medium, characterized in that: When the instructions are executed by the processor, the data intelligent collection and analysis method according to any one of claims 1 to 7 is implemented.

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