CN114462925A - Inventory abnormal asset identification method and device and terminal equipment - Google Patents
Inventory abnormal asset identification method and device and terminal equipment Download PDFInfo
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Abstract
The invention provides an inventory abnormal asset identification method, an inventory abnormal asset identification device and terminal equipment, wherein the method comprises the following steps: acquiring a circulation track and attribute data of a target asset; calculating asset abnormal probability corresponding to the asset circulation track, extracting characteristic values corresponding to target asset characteristics from the attribute data, and generating characteristic vectors corresponding to target assets on the basis of the asset abnormal probability and the characteristic values corresponding to the target asset characteristics; and inputting the characteristic vector into a preset abnormal asset identification model, and determining an inventory abnormal identification result of the target asset. The method, the device and the terminal equipment for identifying the abnormal inventory assets can improve the efficiency and the accuracy of identifying the abnormal inventory assets.
Description
Technical Field
The invention belongs to the technical field of inventory abnormal asset identification, and particularly relates to an inventory abnormal asset identification method, an inventory abnormal asset identification device and terminal equipment.
Background
In the daily asset operation management of the network provincial company, the phenomenon that the measured asset account card objects are inconsistent occurs, for example, the units and the states of the inventory assets are inconsistent. The metering assets have the characteristics of large quantity, scattered positions, complex processing links and the like. At present, management personnel generally use traditional warehouse inventory methods such as field inspection and service inspection to predict inventory assets, the workload is large, and the identification efficiency of abnormal inventory assets is low.
Disclosure of Invention
The invention aims to provide an inventory abnormal asset identification method, an inventory abnormal asset identification device and terminal equipment, and aims to solve the technical problem that the identification efficiency of inventory abnormal assets is low in the prior art.
In a first aspect of the embodiments of the present invention, a method for identifying an inventory abnormal asset is provided, including:
acquiring a circulation track and attribute data of a target asset;
calculating asset abnormal probability corresponding to the asset circulation track, extracting characteristic values corresponding to target asset features from the attribute data, and generating characteristic vectors corresponding to target assets on the basis of the asset abnormal probability and the characteristic values corresponding to the target asset features;
and inputting the characteristic vector into a preset abnormal asset identification model, and determining an inventory abnormal identification result of the target asset.
In a possible implementation manner, the calculating an asset abnormal probability corresponding to the asset transition trajectory includes:
judging whether the asset transfer track is matched with a historical asset transfer track in a preset database;
if a certain historical circulation track is matched with the asset circulation track, taking the asset abnormal probability corresponding to the historical circulation track as the asset abnormal probability corresponding to the asset circulation track;
and if the historical asset transfer track matched with the asset transfer track does not exist in the preset database, inputting the asset transfer track into a preset track detection model to obtain the asset abnormal probability corresponding to the asset transfer track.
In a possible implementation manner, before the asset circulation trajectory is input into a preset trajectory detection model, the inventory abnormal asset identification method further includes:
extracting each circulation node in the asset circulation track and circulation time corresponding to each circulation node, and generating a characteristic sequence based on each circulation node and the circulation time corresponding to each circulation node;
correspondingly, inputting the asset transfer track into a preset track detection model to obtain the asset abnormal probability corresponding to the asset transfer track, including:
and inputting the characteristic sequence into a preset track detection model to obtain the asset abnormal probability corresponding to the asset circulation track.
In a possible implementation manner, the generating a feature sequence based on each streaming node and the streaming time corresponding to each streaming node includes:
representing each circulation node by different values;
acquiring a preset time sequence with an empty value, judging a circulation node of the target asset at a certain moment in the preset time sequence based on the circulation time of each circulation node, and taking the value of the circulation node of the target asset at the moment as a corresponding value of the moment;
and determining corresponding values of all moments in the preset time sequence, and taking the preset time sequence with the determined corresponding values of all the moments as a characteristic sequence.
In a possible implementation manner, the generating a feature vector corresponding to a target asset based on the asset anomaly probability and a feature value corresponding to each target asset feature includes:
generating a feature vector based on the asset abnormal probability, and recording the feature vector as a first feature vector; generating a feature vector based on the feature value corresponding to each target asset feature, and recording the feature vector as a second feature vector; and synthesizing the first feature vector and the second feature vector to obtain a feature vector corresponding to the target asset.
In one possible implementation, the method for determining the characteristics of each target asset includes:
and acquiring all asset characteristics of the target asset, and performing principal component analysis on all asset characteristics to obtain the target asset characteristics of the target asset.
In one possible implementation, the abnormal asset identification model is an XGBoost model.
In a second aspect of the embodiments of the present invention, an inventory abnormal asset identification apparatus is provided, including:
the data acquisition module is used for acquiring the circulation track and the attribute data of the target asset;
the characteristic extraction module is used for calculating asset abnormal probability corresponding to the asset circulation track, extracting characteristic values corresponding to all target asset characteristics from the attribute data, and generating characteristic vectors corresponding to target assets based on the asset abnormal probability and the characteristic values corresponding to all the target asset characteristics;
and the state identification module is used for inputting the characteristic vector into a preset abnormal asset identification model and determining an inventory abnormal identification result of the target asset.
In a third aspect of the embodiments of the present invention, there is provided a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the above-mentioned inventory abnormal asset identification method when executing the computer program.
In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the above-mentioned inventory abnormal asset identification method.
The method, the device and the terminal equipment for identifying the abnormal inventory assets have the advantages that:
different from the manual checking scheme in the prior art, the invention provides the stock abnormal asset identification method based on the big data, which can realize automatic checking and improve the efficiency of stock abnormal asset identification. In the process, the uncertainty of the asset transfer track (the track length is uncertain, and the track has randomness) is considered, the asset transfer track is determined according to the asset transfer track, namely the asset transfer track is converted into the asset transfer abnormal probability, the asset transfer track is added into the feature vector in the form of the asset transfer abnormal probability, the comprehensive consideration of the asset transfer track and the attribute data is realized, and the identification accuracy of the abnormal assets is improved on the basis of improving the identification efficiency of the abnormal assets in inventory.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating a method for identifying abnormal assets in inventory according to an embodiment of the present invention;
fig. 2 is a block diagram illustrating an exemplary embodiment of an abnormal inventory asset identification apparatus;
fig. 3 is a schematic block diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following description is made by way of specific embodiments with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for identifying abnormal inventory assets according to an embodiment of the present invention, where the method includes:
s101: and acquiring the circulation track and the attribute data of the target asset.
In this embodiment, the circulation trajectory and the attribute data of the target asset can be directly acquired from the MDS system and the marketing system of the metering center production scheduling platform in the power grid. The attribute data of the target asset includes, but is not limited to, an equipment identifier, a unit, an equipment type, a specification, a wiring mode, a voltage, a calibration current, an active accuracy level, a filing date, and the like of the target asset. The circulation track of the target asset can comprise verification dates, meter installation dates, meter disassembly dates, assembly and disassembly times, scrapping dates and the like.
S102: and calculating asset abnormal probability corresponding to the asset circulation track, extracting characteristic values corresponding to the target asset characteristics from the attribute data, and generating characteristic vectors corresponding to the target assets based on the asset abnormal probability and the characteristic values corresponding to the target asset characteristics.
The asset circulation tracks are the same, which means that the use environments of the target assets are the same, and considering that the probability that the assets in the same application batch or the same use environment have the same problems is higher when the inventory of the target assets is abnormal, the embodiment of the invention takes the asset circulation tracks as a part of the identification characteristics so as to improve the identification accuracy of the inventory abnormal assets.
Considering that the track length of the asset transfer track and the track itself have randomness, when identifying the abnormal inventory assets based on the asset transfer track, the embodiment does not directly generate the feature vector corresponding to the target asset according to the asset transfer track. Moreover, although the asset circulation trajectory has randomness, in view of the huge number of target assets and the high contact ratio of the asset circulation trajectory, on the basis, a lot of unnecessary computing resources are consumed by taking the whole asset circulation trajectory as a feature vector to operate every time. Therefore, the asset abnormal probability corresponding to the asset circulation track is firstly calculated, namely the asset abnormal probability is added into the calculation of the target asset feature vector as the characteristic quantity of the asset circulation track, so that the attribute information of the target asset and the asset circulation track are comprehensively considered. The asset abnormal probability corresponding to the asset transfer trajectory refers to an influence parameter of the target asset inventory abnormality by taking the asset transfer trajectory as the target asset inventory abnormality, and the inventory abnormality probability of the target asset is calculated on the basis of the influence parameter.
S103: and inputting the characteristic vector into a preset abnormal asset identification model, and determining an inventory abnormal identification result of the target asset.
In this embodiment, the identification result of the abnormal inventory of the target asset is the abnormal inventory of the target asset and the normal inventory of the target asset. The inventory anomaly of the target asset represents that the inventory information of the target asset has errors, such as the target asset is lost, the inventory position of the target asset is wrong, the use state of the target asset is wrong, and the like. Wherein, the target assets can be electric energy meters, circuit breakers and the like.
In a possible implementation manner of this embodiment, the abnormal asset identification model may be an XGBoost model. The XGboost model firstly trains a base learner from an initial training set, then adjusts the distribution of training samples according to the performance of the base learner, so that the previous error samples are concerned more in the subsequent process, then trains the next base learner based on the adjusted sample distribution, and gives higher weight to the base learner with higher accuracy. This is repeated until the number of basis learners reaches a value specified in advance. The combined goal is to minimize the exponential loss function by a combination of learners. Regular terms such as leaf node weight, tree depth and the like are added into the cost function by the XGboost, so that on one hand, the complexity of the model can be controlled, and on the other hand, overfitting can be prevented. Meanwhile, the cost function is approximated by using second-order Taylor expansion, so that the approximation optimization of the objective function is closer to an actual value, and the prediction precision is improved. The XGboost model is adopted to identify abnormal assets in inventory in the embodiment.
From the above, the embodiment of the invention provides a stock abnormal asset identification method based on big data, which is different from the manual inspection scheme in the prior art, and can realize automatic inspection and improve the identification efficiency of stock abnormal assets. Specifically, the asset transfer track and the attribute data of the target asset are comprehensively considered, in the process, the uncertainty (the track length is uncertain, and the track has randomness) of the asset transfer track is considered, the asset transfer track is determined according to the asset transfer track, namely the asset transfer track is converted into the asset transfer abnormal probability, the asset transfer track is added into the feature vector in the form of the asset transfer abnormal probability, the comprehensive consideration of the asset transfer track and the attribute data is realized, and the identification accuracy of the abnormal asset is improved on the basis of improving the identification efficiency of the inventory abnormal asset.
In one possible implementation manner, calculating an asset anomaly probability corresponding to an asset circulation trajectory includes:
and judging whether the asset transfer track is matched with the historical asset transfer track in the preset database.
And if the historical circulation track is matched with the asset circulation track, taking the asset abnormal probability corresponding to the historical circulation track as the asset abnormal probability corresponding to the asset circulation track.
And if the historical asset transfer track matched with the asset transfer track does not exist in the preset database, inputting the asset transfer track into a preset track detection model to obtain the asset abnormal probability corresponding to the asset transfer track.
In this embodiment, the step of determining whether the asset transition trajectory matches the historical asset transition trajectory in the preset database is to detect whether a historical asset transition trajectory identical to the asset transition trajectory of the target asset exists in the preset database, and if the historical asset transition trajectory identical to the asset transition trajectory of the target asset exists, it indicates that the historical transition trajectory matches the asset transition trajectory. And if the historical asset circulation track which is the same as the asset circulation track of the target asset does not exist, the fact that the historical circulation track is not matched with the asset circulation track is shown.
In this embodiment, the preset trajectory detection model may be a neural network model obtained by pre-training, and the neural network model takes the asset transition trajectory as input and takes the asset anomaly probability as output.
In a possible implementation manner, before the asset circulation trajectory is input into the preset trajectory detection model, the inventory abnormal asset identification method further includes:
extracting each circulation node in the asset circulation track and circulation time corresponding to each circulation node, and generating a characteristic sequence based on each circulation node and the circulation time corresponding to each circulation node.
Correspondingly, the asset transfer track is input into a preset track detection model, and the asset abnormal probability corresponding to the asset transfer track is obtained, and the method comprises the following steps:
and inputting the characteristic sequence into a preset track detection model to obtain the asset abnormal probability corresponding to the asset circulation track.
In the embodiment, the length of the generated feature sequence is fixed, wherein when the asset stream track length is short and the feature sequence is generated, the insufficient track length part is complemented by '0'.
In a possible implementation manner, generating a feature sequence based on each streaming node and a streaming time corresponding to each streaming node includes:
each streaming node is represented by a different value.
The method comprises the steps of obtaining a preset time sequence with a null value, judging a circulation node where a target asset is located at a certain moment in the preset time sequence based on circulation time of each circulation node, and taking the value of the circulation node where the target asset is located at the certain moment as a corresponding value of the certain moment.
And determining corresponding values of all moments in the preset time sequence, and taking the preset time sequence with the determined corresponding values of all the moments as a characteristic sequence.
In this embodiment, the feature sequence represents the circulation node where the target asset is located at each preset time. For example, the circulation nodes are a, b and c, the target asset at the first time is a, the target asset at the third time is circulated from a to b, the target asset at the seventh time is circulated from b to c, the preset time sequence comprises ten times, and the corresponding characteristic sequence in this example is [ aabbbcccc ].
In a possible implementation manner, generating a feature vector corresponding to a target asset based on the asset anomaly probability and a feature value corresponding to each target asset feature includes:
and generating a feature vector based on the asset abnormal probability, and recording the feature vector as a first feature vector. And generating a feature vector based on the feature value corresponding to each target asset feature, and recording the feature vector as a second feature vector. And synthesizing the first feature vector and the second feature vector to obtain the feature vector corresponding to the target asset.
In this embodiment, the asset anomaly probability may be directly used as a feature value to generate a feature vector, the feature vector may be generated based on the feature value corresponding to each target asset feature, and finally the feature vector may be synthesized to obtain the feature vector corresponding to the target asset.
In one possible implementation, the method for determining the characteristics of each target asset includes:
and acquiring all asset characteristics of the target asset, and performing principal component analysis on all asset characteristics to obtain the target asset characteristics of the target asset.
In this embodiment, the target asset feature refers to an asset feature that has an abnormal influence on the inventory of assets to a degree greater than a preset threshold. In order to effectively select the characteristics, the embodiment of the invention adopts a principal component analysis or correlation analysis method to realize the screening of the characteristics of the target assets.
Fig. 2 is a block diagram of an inventory abnormal asset identification device according to an embodiment of the present invention, which corresponds to the inventory abnormal asset identification method according to the above embodiment. For convenience of explanation, only portions related to the embodiments of the present invention are shown. Referring to fig. 2, the inventory anomaly asset identification device 20 includes: a data acquisition module 21, a feature extraction module 22 and a state identification module 23.
The data obtaining module 21 is configured to obtain a circulation track and attribute data of a target asset.
The feature extraction module 22 is configured to calculate asset anomaly probabilities corresponding to asset transition trajectories, extract feature values corresponding to each target asset feature from the attribute data, and generate a feature vector corresponding to a target asset based on the asset anomaly probabilities and the feature values corresponding to each target asset feature.
And the state identification module 23 is configured to input the feature vector into a preset abnormal asset identification model, and determine an inventory abnormal identification result of the target asset.
In one possible implementation, the feature extraction module 22 is specifically configured to:
and judging whether the asset transfer track is matched with the historical asset transfer track in the preset database.
And if the historical circulation track is matched with the asset circulation track, taking the asset abnormal probability corresponding to the historical circulation track as the asset abnormal probability corresponding to the asset circulation track.
And if the historical asset transfer track matched with the asset transfer track does not exist in the preset database, inputting the asset transfer track into a preset track detection model to obtain the asset abnormal probability corresponding to the asset transfer track.
In a possible implementation manner, the feature extraction module 22 is further configured to extract each circulation node and circulation time corresponding to each circulation node in the asset circulation trajectory before the asset circulation trajectory is input into the preset trajectory detection model, and generate the feature sequence based on each circulation node and the circulation time corresponding to each circulation node.
Correspondingly, the feature extraction module 22 is specifically configured to:
and inputting the characteristic sequence into a preset track detection model to obtain the asset abnormal probability corresponding to the asset circulation track.
In one possible implementation, the feature extraction module 22 is specifically configured to:
each streaming node is represented by a different value.
The method comprises the steps of obtaining a preset time sequence with a null value, judging a circulation node where a target asset is located at a certain moment in the preset time sequence based on circulation time of each circulation node, and taking the value of the circulation node where the target asset is located at the certain moment as a corresponding value of the certain moment.
And determining corresponding values of all moments in the preset time sequence, and taking the preset time sequence with the determined corresponding values of all the moments as a characteristic sequence.
In one possible implementation, the feature extraction module 22 is specifically configured to:
and generating a feature vector based on the asset abnormal probability, and recording the feature vector as a first feature vector. And generating a feature vector based on the feature value corresponding to each target asset feature, and recording the feature vector as a second feature vector. And synthesizing the first feature vector and the second feature vector to obtain the feature vector corresponding to the target asset.
In one possible implementation, the feature extraction module 22 is further configured to:
and acquiring all asset characteristics of the target asset, and performing principal component analysis on all asset characteristics to obtain the target asset characteristics of the target asset.
In one possible implementation, the anomalous asset identification model is the XGBoost model.
Referring to fig. 3, fig. 3 is a schematic block diagram of a terminal device according to an embodiment of the present invention. The terminal 300 in the present embodiment as shown in fig. 3 may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processor 301, the input device 302, the output device 303, and the memory 304 are in communication with each other via a communication bus 305. The memory 304 is used to store a computer program comprising program instructions. Processor 301 is operative to execute program instructions stored in memory 304. Wherein the processor 301 is configured to call program instructions to perform the following functions of operating the modules/units in the above-described device embodiments, such as the functions of the modules 21 to 23 shown in fig. 2.
It should be understood that, in the embodiment of the present invention, the Processor 301 may be a Central Processing Unit (CPU), and the Processor may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The input device 302 may include a touch pad, a fingerprint sensor (for collecting fingerprint information of a user and direction information of the fingerprint), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.
The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.
In a specific implementation, the processor 301, the input device 302, and the output device 303 described in this embodiment of the present invention may execute the implementation manners described in the first embodiment and the second embodiment of the inventory abnormal asset identification method provided in this embodiment of the present invention, and may also execute the implementation manner of the terminal described in this embodiment of the present invention, which is not described herein again.
In another embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, where the computer program includes program instructions, and the program instructions, when executed by a processor, implement all or part of the processes in the method of the above embodiments, and may also be implemented by a computer program instructing associated hardware, and the computer program may be stored in a computer-readable storage medium, and the computer program, when executed by a processor, may implement the steps of the above methods embodiments. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. It should be noted that the computer readable medium may include any suitable increase or decrease as required by legislation and patent practice in the jurisdiction, for example, in some jurisdictions, computer readable media may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The computer readable storage medium may be an internal storage unit of the terminal of any of the foregoing embodiments, for example, a hard disk or a memory of the terminal. The computer readable storage medium may also be an external storage device of the terminal, such as a plug-in hard disk provided on the terminal, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the computer-readable storage medium may also include both an internal storage unit and an external storage device of the terminal. The computer-readable storage medium is used for storing a computer program and other programs and data required by the terminal. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
Those of ordinary skill in the art will appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the components and steps of the various examples have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working process of the terminal and the unit described above may refer to the corresponding process in the foregoing method embodiment, and details are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed terminal and method can be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces or units, and may also be an electrical, mechanical or other form of connection.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An inventory abnormal asset identification method is characterized by comprising the following steps:
acquiring a circulation track and attribute data of a target asset;
calculating asset abnormal probability corresponding to the asset circulation track, extracting characteristic values corresponding to target asset features from the attribute data, and generating characteristic vectors corresponding to target assets on the basis of the asset abnormal probability and the characteristic values corresponding to the target asset features;
and inputting the characteristic vector into a preset abnormal asset identification model, and determining an inventory abnormal identification result of the target asset.
2. The inventory anomaly asset identification method according to claim 1, wherein said calculating an asset anomaly probability corresponding to said asset transition trajectory comprises:
judging whether the asset transfer track is matched with a historical asset transfer track in a preset database;
if a certain historical circulation track is matched with the asset circulation track, taking the asset abnormal probability corresponding to the historical circulation track as the asset abnormal probability corresponding to the asset circulation track;
and if the historical asset transfer track matched with the asset transfer track does not exist in the preset database, inputting the asset transfer track into a preset track detection model to obtain the asset abnormal probability corresponding to the asset transfer track.
3. The inventory anomaly asset identification method as recited in claim 2, wherein prior to inputting the asset circulation trajectory into a preset trajectory detection model, the inventory anomaly asset identification method further comprises:
extracting each circulation node in the asset circulation track and circulation time corresponding to each circulation node, and generating a characteristic sequence based on each circulation node and the circulation time corresponding to each circulation node;
correspondingly, inputting the asset transfer track into a preset track detection model to obtain the asset abnormal probability corresponding to the asset transfer track, including:
and inputting the characteristic sequence into a preset track detection model to obtain the asset abnormal probability corresponding to the asset circulation track.
4. The inventory anomaly asset identification method according to claim 3, wherein said generating a feature sequence based on each circulation node and the circulation time corresponding to each circulation node comprises:
representing each circulation node by different values;
acquiring a preset time sequence with an empty value, judging a circulation node of the target asset at a certain moment in the preset time sequence based on the circulation time of each circulation node, and taking the value of the circulation node of the target asset at the moment as a corresponding value of the moment;
and determining corresponding values of all moments in the preset time sequence, and taking the preset time sequence with the determined corresponding values of all the moments as a characteristic sequence.
5. The method for identifying abnormal inventory assets of claim 1, wherein the generating a feature vector corresponding to a target asset based on the asset anomaly probability and the feature value corresponding to each target asset feature comprises:
generating a feature vector based on the asset abnormal probability, and recording the feature vector as a first feature vector; generating a feature vector based on the feature value corresponding to each target asset feature, and recording the feature vector as a second feature vector; and synthesizing the first feature vector and the second feature vector to obtain a feature vector corresponding to the target asset.
6. The method for identifying abnormal assets in inventory as claimed in claim 1, wherein the determination method of each target asset characteristic is:
and acquiring all asset characteristics of the target asset, and performing principal component analysis on all asset characteristics to obtain the target asset characteristics of the target asset.
7. An inventory abnormal asset identification method according to any one of claims 1 to 6, characterized in that said abnormal asset identification model is the XGboost model.
8. An inventory anomaly asset identification device, comprising:
the data acquisition module is used for acquiring the circulation track and the attribute data of the target asset;
the characteristic extraction module is used for calculating asset abnormal probability corresponding to the asset circulation track, extracting characteristic values corresponding to all target asset characteristics from the attribute data, and generating characteristic vectors corresponding to target assets based on the asset abnormal probability and the characteristic values corresponding to all the target asset characteristics;
and the state identification module is used for inputting the characteristic vector into a preset abnormal asset identification model and determining an inventory abnormal identification result of the target asset.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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