CN113778868A - Method and device for data detection based on data buried points - Google Patents
Method and device for data detection based on data buried points Download PDFInfo
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Abstract
The application provides a method and a device for data detection based on data buried points, wherein the method comprises the following steps: the method comprises the steps that first equipment obtains first data of a target type in a data transmission process based on a capture mode in a data buried point model; the first equipment compares the first information with second information in the data buried point model to obtain a comparison result; wherein the first information and the second information are used to characterize at least one of: attribute information used for representing the data acquisition process and attribute information of data; the comparison result is used for representing whether the first information is matched with the second information; the first device detects abnormality of the first data based on the comparison result. Through the method and the device, the problem that the equipment working efficiency is low due to the fact that the operation of the equipment in operation needs to be blocked in the abnormal detection method of the Internet of things equipment in the prior art is solved.
Description
Technical Field
The present application relates to the field of data embedding technology, and in particular, to a method and an apparatus for data detection based on data embedding.
Background
The application field of the Internet of things is spread over various industries, so that the intelligent development in the aspects of industry, agriculture, traffic, logistics and the like is effectively promoted, and the influence is not inconstant. As a new product, the Internet of things equipment has a complex system structure and has a security vulnerability. In the prior art, the abnormality detection method for the internet of things equipment needs to block the running operation of the equipment, so that the working efficiency of the equipment is reduced, and the safety performance of the equipment is also risked.
Disclosure of Invention
An object of the embodiment of the application is to provide a method and a device for data detection based on data embedding points, and the problem that in the prior art, the operation of equipment in operation needs to be blocked in an abnormal detection method of internet of things equipment, so that the working efficiency of the equipment is reduced is solved. The specific technical scheme is as follows:
in a first aspect of this embodiment, there is provided a method for data detection based on data burial points, where the method includes: the method comprises the steps that first equipment obtains first data of a target type in a data transmission process based on a capture mode in a data buried point model; the first equipment compares the first information with second information in the data buried point model to obtain a comparison result; wherein the first information and the second information are used to characterize at least one of: attribute information used for representing the data acquisition process and attribute information of data; the comparison result is used for representing whether the first information is matched with the second information; the first device detects abnormality of the first data based on the comparison result.
In a second aspect of this embodiment, there is also provided a method for data detection based on data burial points, including: presetting a data buried point model by second equipment; wherein, the data buried point model includes: indexing, capturing mode and second information of the target type data; the second device sends the data buried point model to the first device.
In a third aspect of the present application, there is also provided an apparatus for data detection based on data burial points, the apparatus including: the first acquisition module is used for acquiring first data of a target type in a data transmission process by first equipment based on a capture mode in a data buried point model; the first processing module is used for comparing the first information with the second information in the data buried point model at the first equipment to obtain a comparison result; wherein the first information and the second information are used to characterize at least one of: attribute information used for representing the data acquisition process and attribute information of data; the comparison result is used for representing whether the first information is matched with the second information; and the second processing module is used for detecting the abnormity of the first data according to the first equipment based on the comparison result.
In a fourth aspect of this application, there is also provided an apparatus for data detection based on data burial points, including: the preset module is used for presetting a data buried point model; wherein, the data buried point model includes: indexing, capturing mode and second information of the target type data; and the sending module is used for sending the data buried point model to the first equipment.
In a fifth aspect of the present application, there is also provided an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus; a memory for storing a computer program; a processor for implementing the method of the first or second aspect when executing a program stored in the memory.
In a sixth aspect implemented by the present application, there is also provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of the first or second aspect.
In the embodiment of the application, first data of a target type in a data transmission process are acquired through first equipment based on a capturing mode in a data buried point model; the first equipment compares the first information with second information in the data buried point model to obtain a comparison result; wherein the first information and the second information are used to characterize at least one of: attribute information used for representing the data acquisition process and attribute information of data; the comparison result is used for representing whether the first information is matched with the second information; the first device detects an abnormality of the first data based on the comparison result; that is to say, the first device acquires the first data according to a capture mode of a preset data buried point model, then compares the first information with the second information, and judges whether the first data is abnormal or not according to whether the first information is matched with the second information, so that the problem that the working efficiency of the device is reduced because the operation of the device in the running process needs to be blocked by an abnormality detection method of the internet of things device in the prior art is solved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
FIG. 1 is a flowchart illustrating a method for data detection based on data embedding points according to an embodiment of the present disclosure;
FIG. 2 is a second flowchart of a method for data detection based on data embedding points according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of a data detection system based on data embedding points according to an embodiment of the present disclosure;
FIG. 4 is a flowchart illustrating a method for pre-determining data embedding points according to an embodiment of the present disclosure;
FIG. 5 is a flowchart of a data-based embedded point detection method according to an embodiment of the present disclosure;
FIG. 6 is a schematic structural diagram of an apparatus for data detection based on data embedding points according to an embodiment of the present disclosure;
FIG. 7 is a second schematic structural diagram of a data detection apparatus based on data embedding points according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the following description, suffixes such as "module", "unit" used to denote elements are used only for facilitating the explanation of the present application and have no specific meaning by themselves. Thus, "module" and "component" may be used in a mixture.
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. The embodiment of the application provides a method for data detection based on data buried points, as shown in fig. 1, the method includes the following steps:
102, acquiring first data of a target type in a data transmission process by first equipment based on a capturing mode in a data buried point model;
it should be noted that the first device may be, in an example, an internet of things device, including but not limited to a smart home, a smart wearable device, an automatic road toll collection system, and a parking space management system. The data buried point model has different types, for example, the data buried point model a may be used to analyze temperature data, that is, when the temperature data is transmitted, the data type of the acquired first data is "temperature"; and the data buried point model B can be used for analyzing the wind power data, and the data type of the acquired first data is wind power. The "temperature" and "wind force" are merely examples to explain the type of data, and other data types that can distinguish different data buried point models are within the scope of the present application.
104, the first equipment compares the first information with second information in the data buried point model to obtain a comparison result; wherein the first information and the second information are used to characterize at least one of: attribute information used for representing the data acquisition process and attribute information of data; the comparison result is used for representing whether the first information is matched with the second information;
it should be noted that the data embedding point model includes, but is not limited to, a target data index, a data capturing method, and a data processing method. And the first equipment performs data embedding according to the target data corresponding to the target data index. The data capture method determines a data capture method of the data buried point, for example, capture at the time of data input and capture at the time of data output, and the data capture method of capturing at the time of data input is preferred in the embodiment of the present application. The second information is used for representing attribute information of the data acquisition process and attribute information of the data, that is, data processing can be performed based on the attribute information of the data acquisition process and the attribute information of the data, that is, analysis processing is performed on the captured data. In a specific example, the attribute information of the data acquisition process may be a capturing frequency of the data, or a capturing time point of the data, or the like, that is, attribute information related to the capturing process; the attribute information of the data may be information related to the attribute of the data application, such as a numerical reasonable interval of the data or a capacity occupied by the data.
The first device detects an anomaly of the first data based on the comparison result, step 106.
When the first equipment compares the first information with the second information in the data buried point model and the obtained comparison result is that the first information is not matched with the second information, the first data is abnormal; and when the first equipment compares the first information with the second information in the data buried point model and the obtained comparison result is that the first information is matched with the second information, the first data has no abnormity.
Through the steps 102 to 106 in the embodiment of the present application, the first device performs data embedding in corresponding target data according to a target data index of the data embedding point model, and then acquires first data according to a data capturing method of the data embedding point model; the first device compares the first information with the second information, since the first information and the second information may include: the method is used for representing the attribute information of the data acquisition process and the attribute information of the data, namely, the attribute information of the data acquisition process or the attribute information of the data can be compared to determine whether the first data is abnormal or not, namely, whether the first equipment is abnormal or not is detected under the condition that the normal operation of the equipment is not required to be blocked in the process, so that the working efficiency of the equipment is improved, and meanwhile, the safety of the equipment is also improved, so that the problem that the working efficiency of the equipment is reduced due to the fact that the operation of the equipment in the operation process is required to be blocked in the abnormal detection method of the equipment of the internet of things in the prior art is solved.
In an optional implementation manner of the embodiment of the present application, the manner in which the first device involved in step 106 of the present application detects the abnormality of the first data based on the comparison result further may include:
step 11: under the condition that the first information is matched with the second information, the first equipment determines that the first data is normal data;
step 12: and under the condition that the first information does not match with the second information, the first equipment determines that the first data is abnormal data.
In the above steps 11 to 12, specifically, the matching of the first information and the second information means that the first information conforms to a data processing method in the data buried point model, for example, the data type of the first data is "temperature", the first information is data capture frequency, the capture frequency is 10 seconds/time, the second information is also data capture frequency, the capture frequency is 10 seconds/time, the first information is also a numerical reasonable interval of the data, the numerical reasonable interval is-5 ℃ to 40 ℃, the second information is also a numerical reasonable interval of the data, and the numerical reasonable interval is-5 ℃ to 40 ℃, so that it can be seen that the capture frequency in the first information and the capture frequency in the second information are matched with the numerical reasonable interval, that is, the first data is normal data. If the first information is data capturing frequency which is 10 seconds/time, the second information is data capturing frequency which is 5 seconds/time, or the first information is a numerical value reasonable interval of the data which is-5 ℃ to 40 ℃, and the second information is a numerical value reasonable interval of the data which is 50 ℃ to 55 ℃, therefore, the capturing frequency and the numerical value reasonable interval in the first information and the second information are not matched, or any one of the capturing frequency and the numerical value reasonable interval is not matched, the first data is abnormal data.
In an optional implementation manner of the embodiment of the present application, the manner, related to step 12 of the present application, in which the first device determines that the first data is abnormal data when the first information does not match the second information, may further include:
step 21: under the condition that the first data are abnormal data, the first equipment reports the first data to the second equipment;
the second device may be an internet of things device, such as a server.
In an optional implementation manner of the embodiment of the present application, the method for data detection based on data burial points, provided by the embodiment of the present application, may further include:
step 22: before the first device obtains the first data of the target type in the data transmission process based on the capture mode in the data buried point model, the method for data detection based on the data buried point provided by the embodiment of the application further includes: the method comprises the steps that a first device obtains a data buried point model preset by a second device; wherein, the data buried point model includes: indexing, capturing mode and second information of the target type data;
step 23: the method for acquiring first data of a target type in a data transmission process by first equipment based on a capture mode in a data buried point model comprises the following steps: and the first equipment acquires first data in the data corresponding to the index according to the capturing mode.
In the above steps 21 to 23, specifically, after the data embedding point type is preset by the second device, the first device obtains the preset data embedding point model. The data buried point model comprises: index, capture mode, second information of the target type data. And the first equipment performs data embedding according to the target data corresponding to the target data index. The data capture method determines a data capture method of the data buried point, for example, capture at the time of data input and capture at the time of data output, and the data capture method of capturing at the time of data input is preferred in the embodiment of the present application. The second information is used for representing attribute information of the data acquisition process and attribute information of the data, that is, data processing can be performed based on the attribute information of the data acquisition process and the attribute information of the data, that is, analysis processing is performed on the captured data.
The embodiment of the present application further provides a method for data detection based on data embedding points, as shown in fig. 2, the method includes the following steps:
step 202: presetting a data buried point model by second equipment; wherein, the data buried point model includes: indexing, capturing mode and second information of the target type data;
step 204: the second equipment sends the data embedded point model to the first equipment;
in the foregoing step 202 and step 204, specifically, the second device may be an internet of things device, for example, a server, the second device presets a data embedding type, and the first device obtains a preset data embedding model. The data buried point model comprises: index, capture mode, second information of the target type data. And the first equipment performs data embedding according to the target data corresponding to the target data index. The data capture method determines a data capture method of the data buried point, for example, capture at the time of data input and capture at the time of data output, and the data capture method of capturing at the time of data input is preferred in the embodiment of the present application. The second information is used for representing attribute information of the data acquisition process and attribute information of the data, that is, data processing can be performed based on the attribute information of the data acquisition process and the attribute information of the data, that is, analysis processing is performed on the captured data.
Through the specific implementation mode, the second device can preset a data embedded point model, set the index, the capture mode and the second information of the target type data, then send the data embedded point model to the first device, and the first device performs detection on the first data according to the data embedded point model, namely, the device is subjected to abnormal detection, the operation of the device in operation is not required to be blocked, and the working efficiency of the device is ensured.
In an alternative implementation of the embodiment of the present application, the method may further include:
step 206, when the first information of the first data acquired by the first device based on the capturing mode is not matched with the second information, the second device receives the first data reported by the first device; wherein the first data is abnormal data.
Based on the foregoing step 206, when the first information of the first data is not matched with the second information, the first device determines that the first data is abnormal data, and reports the first data to the second device, and the second device receives the first data reported by the first device, so that the first data with the abnormal data detection result is transmitted from the first device to the second device, so as to notify other devices of the abnormal data.
The present application is exemplified below with reference to a specific implementation manner of an embodiment of the present application, which provides a device detection system based on data burial points, as shown in fig. 3, the system includes a device (corresponding to a first device in the above-mentioned embodiment) and a server (corresponding to a second device in the above-mentioned embodiment);
wherein the device 32 comprises: the first processing module is used for analyzing the data embedded point model and carrying out analysis processing on the data embedded points and the data after being captured; the first storage module is used for storing preset data rules and data; the first communication module is used for communicating with the server.
The server 34 includes: the second processing module is used for generating a data buried point model according to the user requirement; the second storage module is used for acquiring a storage data embedded point model and data uploaded by equipment; and the second communication module is used for communicating with the equipment.
Based on the data burial point-based device detection system in fig. 3, this embodiment further provides a method for presetting data burial points, as shown in fig. 4, the method includes the steps of:
in the specific example, the server presets the data buried point model according to the type of the data. The data buried point model comprises a target data index, a data capturing method and a data processing method. The data capture method is a data capture rule, for example, capture is performed at the time of data input or capture is performed at the time of data output. The data processing method is to analyze and process the captured data, and in addition, the data processing method in this specific embodiment further includes a preset data rule, for example, the capturing frequency of the data, the reasonable interval of the data, and the like, and if the captured data does not meet the preset data rule, it is determined that the data is abnormal data, and the abnormal data is reported to the server.
the device analyzes the data buried point model after acquiring the data buried point model, acquires a target data index, performs data buried point in corresponding target data according to a data capturing method, and executes a data processing method after data capturing.
Based on the foregoing fig. 3 and fig. 4, this embodiment further provides a data-based embedded point detection method, as shown in fig. 5, the method includes the steps of:
when the target data accords with the data capturing rule, the data embedding point triggers data capturing, and the captured data is processed by using a data processing method.
according to different data buried point models, each data buried point has a corresponding preset data rule, and after the data buried point triggers to capture data, the corresponding preset data rule is obtained according to the data buried point.
the method comprises the steps that aiming at captured data, preset data rules are used for comparing the captured data, for example, whether the frequency of the captured data meets the preset data rules or not is judged, if not, the data is judged to be abnormal data, equipment generates abnormality, and data and results are fed back to a server; and if the data accords with the preset data rule, judging the data is normal data.
Through the specific implementation manner in the embodiment, in the data transmission process, the captured data is subjected to abnormal judgment, so that the phenomenon that the equipment generates extra performance expense for detecting whether the equipment is normal or not can be avoided.
Corresponding to fig. 1, an embodiment of the present application provides an apparatus for data detection based on data burial points, as shown in fig. 6, the apparatus includes:
a first obtaining module 62, configured to obtain, by a first device, first data of a target type in a data transmission process based on a capture mode in a data embedding point model;
a comparison module 64, configured to compare, at the first device, the second information in the first information data embedding point model to obtain a comparison result; wherein the first information and the second information are used to characterize at least one of: attribute information used for representing the data acquisition process and attribute information of data; the comparison result is used for representing whether the first information is matched with the second information;
a detection module 66 for detecting an anomaly of the first data based on the comparison result according to the first device.
Through the device provided by the embodiment of the application, the first device obtains the first data according to the capture mode of the preset data buried point model, and then the first device compares the first information with the second information, and the first information and the second information can include: the method is used for representing the attribute information of the data acquisition process and the attribute information of the data, namely, the attribute information of the data acquisition process or the attribute information of the data can be compared to determine whether the first data is abnormal or not, namely, whether the first equipment is abnormal or not is detected under the condition that the normal operation of the equipment is not required to be blocked in the process, so that the working efficiency of the equipment is improved, and meanwhile, the safety of the equipment is also improved, so that the problem that the working efficiency of the equipment is reduced due to the fact that the operation of the equipment in the operation process is required to be blocked in the abnormal detection method of the equipment of the internet of things in the prior art is solved.
Optionally, the detection module 66 in this embodiment of the present application further includes: the first determining unit is used for determining the first data as normal data under the condition that the first information is matched with the second information; and a second determining unit configured to determine that the first data is abnormal data if the first information does not match the second information.
Optionally, the apparatus in this embodiment of the present application may further include: and the first sending module is used for reporting the first data to the second equipment under the condition that the first data is abnormal data.
Optionally, the apparatus in this embodiment of the present application may further include: the second acquisition module is used for acquiring a data buried point model preset by second equipment before acquiring first data of a target type in a data transmission process based on a capture mode in the data buried point model; wherein, the data buried point model includes: indexing, capturing mode and second information of the target type data;
based on this, the first obtaining module comprises: and the acquisition unit is used for acquiring first data in the data corresponding to the index according to the capture mode.
Corresponding to fig. 2, an embodiment of the present application further provides an apparatus for data detection based on data burial points, as shown in fig. 7, the apparatus includes:
a presetting module 72, configured to preset a data buried point model; wherein, the data buried point model includes: indexing, capturing mode and second information of the target type data;
and a second sending module 74, configured to send the data burial point model to the first device.
Optionally, the apparatus further comprises: the device comprises a receiving module, a sending module and a receiving module, wherein the receiving module is used for receiving first data reported by first equipment under the condition that first information of the first data acquired by the equipment based on a capturing mode is not matched with second information; wherein the first data is abnormal data.
The embodiment of the present application further provides an electronic device, as shown in fig. 8, which includes a processor 801, a communication interface 802, a memory 803, and a communication bus 804, where the processor 801, the communication interface 802, and the memory 803 complete mutual communication through the communication bus 804,
a memory 803 for storing a computer program;
the processor 801 is configured to implement the method steps in fig. 1 or fig. 2 when executing the program stored in the memory 803, and the functions of the method steps are similar to those of the method steps in fig. 1 and fig. 2, and are not described again here.
The communication bus mentioned in the above terminal may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 8, but this is not intended to represent only one bus or type of bus.
The communication interface is used for communication between the terminal and other equipment.
The Memory may include a Random Access Memory (RAM) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
In yet another embodiment provided by the present application, a computer-readable storage medium is further provided, which has instructions stored therein, and when the instructions are executed on a computer, the computer is caused to execute the method for data detection based on data burial points in any one of the above embodiments.
In yet another embodiment provided by the present application, there is also provided a computer program product containing instructions, which when run on a computer, causes the computer to execute the method for data burial point-based data detection as described in any of the above embodiments.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only for the preferred embodiment of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application are included in the protection scope of the present application.
Claims (10)
1. A method for data detection based on data embedding points is characterized by comprising the following steps:
the method comprises the steps that first equipment obtains first data of a target type in a data transmission process based on a capture mode in a data buried point model;
the first equipment compares the first information with the second information in the data buried point model to obtain a comparison result; wherein the first information and the second information are used to characterize at least one of: attribute information used for representing the data acquisition process and attribute information of data; the comparison result is used for representing whether the first information is matched with the second information;
the first device detects an abnormality of the first data based on the comparison result.
2. The method of claim 1, wherein the first device detects the anomaly of the first data based on the comparison, comprising:
under the condition that the first information is matched with the second information, the first equipment determines that the first data is normal data;
and under the condition that the first information does not match with the second information, the first equipment determines that the first data is abnormal data.
3. The method of claim 2, comprising:
and under the condition that the first data are abnormal data, the first equipment reports the first data to second equipment.
4. The method of claim 3, comprising:
before the first device acquires the first data of the target type in the data transmission process based on the capture mode in the data buried point model, the method further comprises the following steps: the first equipment acquires the data buried point model preset by the second equipment; wherein the data-embedded point model comprises: the index of the target type data, the capture mode and the second information;
the first equipment acquires first data of a target type in a data transmission process based on a capture mode in a data buried point model, and the first equipment comprises the following steps: and the first equipment acquires the first data in the data corresponding to the index according to the capturing mode.
5. A method for data detection based on data embedding points is characterized by comprising the following steps:
presetting a data buried point model by second equipment; wherein the data-embedded point model comprises: indexing, capturing mode and second information of the target type data;
and the second equipment sends the data buried point model to the first equipment.
6. The method of claim 5, comprising:
under the condition that first information of first data acquired by the first equipment based on the capturing mode is not matched with the second information, the second equipment receives the first data reported by the first equipment; wherein the first data is abnormal data.
7. An apparatus for data detection based on data embedding, comprising:
the first acquisition module is used for acquiring first data of a target type in a data transmission process based on a capture mode in the data buried point model;
the comparison module is used for comparing the first information with the second information in the data buried point model to obtain a comparison result; wherein the first information and the second information are used to characterize at least one of: attribute information used for representing the data acquisition process and attribute information of data; the comparison result is used for representing whether the first information is matched with the second information;
a detection module for detecting an abnormality of the first data based on the comparison result.
8. An apparatus for data detection based on data embedding, comprising:
the preset module is used for presetting a data buried point model; wherein the data-embedded point model comprises: indexing, capturing mode and second information of the target type data;
and the sending module is used for sending the data buried point model to the first equipment.
9. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any one of claims 1 to 4, or the method steps of any one of claims 5 to 6, when executing a program stored in a memory.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method steps of any one of claims 1 to 4 or carries out the method steps of any one of claims 5 to 6.
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