CN117391592A - Tenant mode asset inventory method and system based on edge computing - Google Patents

Tenant mode asset inventory method and system based on edge computing Download PDF

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CN117391592A
CN117391592A CN202311684678.1A CN202311684678A CN117391592A CN 117391592 A CN117391592 A CN 117391592A CN 202311684678 A CN202311684678 A CN 202311684678A CN 117391592 A CN117391592 A CN 117391592A
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asset
inventory
tenant
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profit
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CN117391592B (en
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张尔喜
先晓兵
陈凤
王加年
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Changshu Institute of Technology
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Abstract

The invention relates to the technical field of asset management, in particular to an edge calculation-based tenant mode asset inventory method and system, which are used for collecting tenant statistical data; acquiring the warehouse-in number and the warehouse-out number of each asset of the tenant in each time period in the inventory period; acquiring a profit and loss fluctuation sequence and an inventory short-term floating sequence of the asset in the inventory period according to the warehousing number and the ex-warehouse number of the asset in the time period, and further acquiring an inventory long-term association index of the asset; acquiring asset change association according to the inventory short-term floating sequence of the asset; acquiring the asset information destructibility of the tenant according to the inventory long-term association index of the asset and the asset change association degree; encrypting tenant statistical data according to the asset information destructibility of the tenant to obtain encrypted statistical data; and transmitting the encrypted statistical data to an asset inventory cloud management system to obtain an asset inventory report. The method and the device solve the problem that the leakage risk of tenant statistical data is high in the asset inventory process.

Description

Tenant mode asset inventory method and system based on edge computing
Technical Field
The invention relates to the technical field of asset management, in particular to an edge computing-based tenant mode asset inventory method and system.
Background
Asset inventory is a kind of physical statistics and verification of assets, and aims to determine the number and value of the assets and the current physical condition, and in actual operation, enterprises need to conduct physical inventory on the assets to determine the actual storage number of various properties in a certain period. The asset management of the enterprise can be enhanced through asset inventory, inventory and depletion of the enterprise are found, the reasons are found out, measures are timely taken to make up for the loopholes in the management and management, the asset management system of the enterprise is perfected, and the management level of the enterprise is improved.
The asset inventory system comprises an asset inventory cloud management system, an edge end service management system and a mobile intelligent terminal system, wherein a user sends an asset inventory request to the asset inventory cloud management system through the mobile intelligent terminal system, the asset inventory cloud management system sends the request to the edge end service management system, the edge end service management system executes an asset inventory task based on edge calculation according to the request to obtain tenant statistical data, the tenant statistical data is returned to the asset inventory cloud management system, and the asset inventory cloud management system generates an asset inventory report and returns a result to the mobile intelligent terminal.
Because the asset inventory system stores a plurality of tenant statistical data in the asset inventory cloud management system, encryption is required to be performed on the tenant statistical data in order to ensure the safety of the data. The existing AES encryption algorithm can encrypt tenant statistical data, but regularity and relativity among tenant statistical data are not considered in the encryption process, all tenant statistical data are directly and uniformly converted into a plaintext matrix to be encrypted, and accordingly leakage risk of tenant statistical data in the asset inventory process is high.
Disclosure of Invention
The invention provides a tenant mode asset checking method and system based on edge calculation, which are used for solving the problem that the leakage risk of tenant statistical data is high in the asset checking process.
In a first aspect, an embodiment of the present invention provides a tenant mode asset inventory method based on edge computing, the method including the steps of:
collecting tenant statistical data; acquiring the asset types of tenants, and the warehouse-in number and the warehouse-out number of each asset in each time period in the inventory period;
acquiring the profit and loss coefficients of the asset in the time period according to the warehouse-in number and the warehouse-out number of the asset in the time period; acquiring a profit and loss fluctuation sequence of the asset in the inventory period according to the profit and loss coefficient of the asset in the time period, and further acquiring the profit and loss fluctuation disorder degree of the asset in the inventory period; obtaining an inventory short-term floating coefficient of the asset in the inventory period according to the inventory number, the inventory number and the fluctuation turbulence degree of the profit and loss of each time period contained in the inventory period; acquiring an inventory short-term floating sequence of the asset according to the inventory short-term floating coefficient of the asset in the inventory period, and acquiring an inventory long-term association index of the asset by combining the profit and loss fluctuation sequence;
acquiring asset change association of the tenant according to the inventory short-term floating sequence of each asset of the tenant; acquiring asset information destructibility of the tenant according to inventory long-term association indexes of all assets of the tenant and asset change association degrees;
obtaining scrambling times of tenant statistical data according to the asset information hackability of the tenant, and encrypting the tenant statistical data according to the scrambling times of the tenant statistical data to obtain encrypted statistical data; and transmitting the encrypted statistical data to an asset inventory cloud management system to obtain an asset inventory report.
Further, the specific method for obtaining the time period is as follows:
taking the preset days as a checking period;
the inventory period is divided equally into a preset number of time periods.
Further, the obtaining the profit and loss coefficient of the asset in the time period according to the warehouse-in number and the warehouse-out number of the asset in the time period comprises the following specific steps:
the difference value between the ex-warehouse number of the assets in the time period and the warehouse-in number of the assets in the time period is recorded as the profit and loss degree of the assets in the time period;
when the profit and loss degree of the asset in the time period is equal to the number 0, the number 0 is recorded as the profit and loss coefficient of the asset in the time period;
when the level of the asset's profit and loss in the period is not equal to the number 0, the ratio of the level of the asset's profit and loss to the absolute value of the level of the asset's profit and loss is recorded as the profit and loss coefficient of the asset in the period.
Further, the method for obtaining the profit and loss fluctuation sequence of the asset in the inventory period according to the profit and loss coefficient of the asset in the time period, and further obtaining the profit and loss fluctuation disorder of the asset in the inventory period comprises the following specific steps:
arranging the profit and loss coefficients of the assets in all time periods contained in the inventory period according to a time sequence to obtain a short-term profit and loss time sequence of the assets in the inventory period;
recording a first-order differential sequence of a short-term profit and loss time sequence of the asset in the inventory period as a profit and loss fluctuation sequence of the asset in the inventory period;
and (5) recording the information entropy of the profit and loss fluctuation sequence of the asset in the inventory period as the profit and loss fluctuation turbulence degree of the asset in the inventory period.
Further, the method for obtaining the short-term floating coefficient of the inventory of the asset in the inventory period according to the warehousing number, the ex-warehouse number and the fluctuation turbulence degree of the profit and loss of each time period contained in the inventory period comprises the following specific steps:
recording the sum of the ex-warehouse number of the assets in the time period and the warehouse-in number of the assets in the time period as the flowing amount of the assets in the time period;
recording the sum of the flowing amount of the asset in all the time periods contained in the inventory period as the flowing total amount of the asset in the inventory period;
recording the sum of the profit and the loss of the assets in all the time periods contained in the inventory period as the profit and loss total quantity of the assets in the inventory period;
and (3) recording the product of the flowing total quantity, the profit and loss total quantity and the profit and loss fluctuation turbulence degree of the asset in the inventory period as the inventory short-term floating coefficient of the asset in the inventory period.
Further, the method for obtaining the inventory short-term floating sequence of the asset according to the inventory short-term floating coefficient of the asset in the inventory period and obtaining the inventory long-term association index of the asset by combining the profit and loss fluctuation sequence comprises the following specific steps:
arranging the short-term inventory floating coefficients of the asset in the inventory period according to the time sequence to obtain a short-term inventory floating sequence of the asset;
arranging all the counting periods according to a time sequence to obtain a counting period sequence;
respectively recording each counting period in the counting period sequence as a counting period to be analyzed;
recording the sum of Euclidean distances of the profit and loss fluctuation sequences of the asset in the inventory period to be analyzed and the profit and loss fluctuation sequences of the asset in all the inventory periods after the inventory period to be analyzed as the profit and loss association degree of the asset in the inventory period to be analyzed;
the sum of the profit and loss association of the asset in all inventory periods is recorded as the long-term profit and loss association of the asset;
the ratio of the hurst index to the long-term earning relevance of the inventory short-term float sequence of the asset is recorded as the inventory long-term relevance index of the asset.
Further, the method for obtaining the association degree of the asset change of the tenant according to the short-term floating sequence of the inventory of each asset of the tenant comprises the following specific steps:
respectively marking each asset of the tenant as an asset to be analyzed;
recording all assets of the tenant except the asset to be analyzed as related assets;
recording a correlation coefficient between the inventory short-term floating sequence of the asset to be analyzed and the inventory short-term floating sequence of the associated asset as a correlation degree of the asset to be analyzed and the associated asset;
recording the average value of the association degrees of the asset to be analyzed and all the associated assets as the asset floating association degree of the asset to be analyzed;
and (5) recording the maximum value of the asset floating association degrees of all the assets as the asset change association degree of the tenant.
Further, the method for obtaining the property information destructibility of the tenant according to the inventory long-term association index and the property change association degree of all the properties of the tenant includes the following specific steps:
recording a normalized value of the sum of the inventory long-term association indexes of all the assets of the tenant as the asset long-term association degree of the tenant;
and recording the product of the asset change association degree and the asset long-term association degree of the tenant as the asset information destructibility of the tenant.
Further, the scrambling frequency of the tenant statistical data is obtained according to the asset information destructibility of the tenant, and the tenant statistical data is encrypted according to the scrambling frequency of the tenant statistical data to obtain encrypted statistical data, comprising the following specific methods:
recording the product of the property information cracking degree of the tenant and the preset initial scrambling times as tenant statistical data scrambling times;
converting tenant statistical data into tenant statistical coding data of a preset system;
recording each row of data of tenant statistical coding data as a row to be analyzed;
performing exclusive OR operation on the scrambling times of tenant statistical data on the row to be analyzed and the next row of the row to be analyzed to obtain scrambling data of the row to be analyzed;
the scrambling data of all lines are arranged according to line numbers to obtain scrambling statistical data;
the scrambling times of the tenant statistical data are converted into scrambling times codes of a preset system, and the scrambling times codes are used as the last row of the scrambling statistical data;
the disorder statistical data is encrypted by using an encryption algorithm to obtain encrypted statistical data.
In a second aspect, an embodiment of the present invention further provides an edge computing-based tenant mode asset inventory system, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the steps of any one of the methods described above when executing the computer program.
The beneficial effects of the invention are as follows: according to the method, the short-term inventory floating coefficient of the assets in the inventory period is obtained according to the warehousing time and the ex-warehouse time in the tenant unit information, the asset change association degree of the tenants and the inventory long-term association index of the assets are calculated, the autocorrelation of the change of the assets of the tenants and the correlation of the change between the assets are synthesized, and the asset information destructibility of the tenants is obtained according to the inventory long-term association index of all the assets of the tenants and the asset change association degree of the tenants, so that the risk of leakage of the tenant asset information is measured, and the reliability of evaluating the tenant asset information destructibility is improved; according to the method, the scrambling times of tenant statistics data are obtained according to the property information cracking degree of tenants, exclusive OR operation is carried out on the tenant statistics data to obtain scrambling statistics data, an AES encryption algorithm is used for encrypting the scrambling statistics data to obtain encryption statistics data, the encryption statistics data are transmitted to an asset inventory cloud management system, different tenant statistics data scrambling times are set for different tenants to improve the encryption grade of tenant statistics data with higher leakage risk, and the problem that the existing AES encryption algorithm does not consider regularity and relativity between tenant statistics data, so that the leakage risk is high in the asset inventory process is solved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a flow chart of an embodiment of a tenant mode asset inventory method based on edge computing of the present invention;
FIG. 2 is a schematic diagram of an asset inventory system.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
An embodiment of a tenant mode asset inventory method based on edge computing of the present invention, as shown in fig. 1, includes:
s1, acquiring tenant unit information of tenants in each time period through an asset inventory cloud management system.
Sharing ofEvery inventory period, each inventory period length is +.>On the day, each inventory period is divided equally into +.>In each time period, a user uploads asset information to an asset inventory cloud management system through a mobile intelligent terminal system, sends an asset inventory request, and acquires tenant unit information of tenants in each time period according to the asset information uploaded by all users, wherein the tenant unit information comprises asset names, asset models, asset specifications, asset brands, warehouse-in time and warehouse-out time>The empirical value was taken to be 30,the empirical value was 12.
As shown in fig. 2, which is a schematic diagram of an asset counting system, compared with cloud computing, edge computing has advantages of good security, low latency, high reliability, etc., in order to ensure security of tenant asset information, an asset counting cloud management system sends a request to an edge end service management system, the edge end service management system realizes high availability through a synchronization mechanism, performs an asset counting task based on the edge computing according to the request, obtains tenant statistical data, and dynamically expands the number of edge nodes according to the data amount of tenant unit information and the change of network topology, a new node coordinates and joins into a data synchronization system through the asset counting cloud management system, performs asset counting on tenants according to the tenant unit information, obtains tenant statistical data, and performs data verification and repair with the asset counting cloud management system regularly.
So far, tenant unit information of each tenant in each time period is obtained.
S2, acquiring an inventory short-term floating coefficient of the asset in the inventory period according to the warehousing time and the ex-warehouse time in the tenant unit information, calculating the asset change association degree of the tenant, and acquiring an inventory long-term association index of the asset according to the inventory short-term floating coefficient of the asset in all the inventory periods.
The user sends an asset inventory request to the asset inventory cloud management system through the mobile intelligent terminal system, the asset inventory cloud management system sends the request to the edge end service management system, the edge end service management system executes an asset inventory task based on edge calculation according to the request, tenant statistical data are obtained, and the tenant statistical data are returned to the asset inventory cloud management system.
Because the purposes of the assets with different models and different specifications may be different, when the assets are checked, the types of the assets need to be divided according to the models and the specifications of the assets. Judging the assets with the same asset name, asset model, asset specification and asset brand as the same asset, and obtaining the asset category number of the tenant。
The tenant unit information of different tenants has different characteristics, and the tenant unit information with regularity and relativity is easier to crack, so that higher encryption strength is set when the tenant statistical data is encrypted, and the security of the tenant statistical data is ensured.
When the floating degree of the assets in a period of time is higher, the change of tenant unit information is less regular, the warehousing number and the ex-warehouse number of each asset in a period of time are counted according to the warehousing time and the ex-warehouse time in the tenant unit information, and the profit and loss coefficients of the assets in the period of time are expressed as follows:
wherein,the profit and loss coefficients of the assets in the time period are obtained; />The number of the assets which are delivered in the time period is calculated; />Is the number of warehouses of the asset in the time period.
When the ex-warehouse number of the assets is larger than the warehouse-in number, the profit and loss coefficient value is-1, which represents that the assets are in a loss state in a time period; when the ex-warehouse number of the assets is equal to the warehouse-in number, the profit and loss coefficient value is 0, which represents that the assets are in a profit and loss balance state in a time period; when the ex-warehouse number of the assets is smaller than the warehouse-in number, the profit and loss coefficient value is 1, which represents that the assets are in a profit state in a time period.
In order to measure the regularity of the fluctuation characteristics of the asset in the inventory period, the profit and loss coefficients of the asset in all time periods of the inventory period are arranged according to the acquired time sequence, a short-term profit and loss time sequence is constructed, the short-term profit and loss time sequence is subjected to first-order difference, and the profit and loss fluctuation sequence of the asset in the inventory period is acquiredInformation entropy of the profit and loss fluctuation sequence of the asset in the inventory period is recorded as the profit and loss fluctuation turbulence degree of the asset in the inventory period, and further the inventory short-term floating coefficient +_ of the asset in the inventory period is obtained>The calculation formula is as follows:
wherein,is->Seed asset at->Inventory short-term floating coefficients within a single inventory period; />Counting the number of time periods in the period; />Is->Seed asset at->Count cycle +.>Number of warehouse-ins for each time period;is->Seed asset at->Count cycle +.>Number of exits for each time period; />Is->Seed asset at->And the disturbance degree of the fluctuation of the profit and loss in each inventory period.
The sum of the warehouse-in number and the warehouse-out number of the assets in the time period represents the flowing amount of the assets in the time period, and when the warehouse-out number and the warehouse-in number of the assets in the inventory period are larger, the flowing amount of the assets is larger, and the short-term floating coefficient value of the inventory is larger; when the ex-warehouse number and the in-warehouse number of the assets in the inventory period are approximately equal, the inventory quantity of the assets is kept in a certain range, and the more stable the hold quantity of the assets is, the smaller the short-term floating coefficient value of the inventory is; when the information entropy of the fluctuation sequence of the profit and the loss of the asset in the inventory period is larger, the fluctuation of the profit and the loss coefficient of the asset in the inventory period is not regular, and the short-term floating coefficient value of the inventory is larger.
In the long term, the change of the asset is also self-correlation, the inventory short-term floating coefficients of the asset in all inventory cycles are arranged according to the acquired time sequence, and an inventory short-term floating sequence of the asset is constructedAcquiring a hurst index of an inventory short-term floating sequence, and calculating an inventory long-term association index +.>The calculation formula is as follows:
wherein,is->Inventory long-term association index of seed assets; />Is->A hurst index of an inventory short-term floating sequence of seed assets; />Is->Seed asset at->A sequence of fluctuation of the profit and loss in a single inventory period +.>And->Seed asset at->A sequence of fluctuation of the profit and loss in a single inventory period +.>Euclidean distance between them.
When the Hurst index of the inventory short-term floating sequence is larger, the inventory short-term floating sequence is indicated to have long-term correlation, the change of the inventory short-term floating coefficient of the asset in all inventory periods is more regular, and the inventory long-term correlation index value of the asset is larger; when the Euclidean distance of the profit and loss fluctuation sequences of the asset in any two inventory periods is smaller, the profit and loss fluctuation sequences of the asset in each inventory period are similar, and the inventory long-term association index value of the asset is larger.
To this end, an inventory long-term association index for the asset is obtained.
S3, acquiring asset information destructibility of the tenant according to the inventory long-term association indexes of all the assets of the tenant and the asset change association degree and the inventory long-term association indexes.
Because there may be a complementary relationship or a substitution relationship between two commodities in economics, where the complementary relationship is that two commodities together satisfy one desire, they are mutually complementary, and the substitution relationship means that two commodities can mutually substitute to satisfy the same desire. Therefore, the fluctuation of the assets can be mutually influenced and correlated, once a reviewer grasps the correlation among the assets, the difficulty of decryption is greatly reduced, and when the tenant statistical data is encrypted, higher encryption intensity is required to be set for the tenant statistical data with higher correlation degree.
To obtain the degree of association between assets, obtain the pearson correlation coefficient between the short-term floating sequences of the inventory of the assets, calculate the asset floating association of the assetsThe calculation formula is as follows:
wherein,is->Asset float association of seed asset; />Is->Inventory short-term float sequence of seed asset->And->Inventory short-term float sequence of seed asset->Pearson correlation coefficient between, wherein when +.>When the pearson correlation coefficient between two stock short-term floating sequences is 1; />The asset class number for the tenant.
When the pearson correlation coefficient between a certain asset of a tenant and the inventory short-term floating sequence of other assets is larger, the higher the correlation degree between the asset of the tenant and the other assets is, and when the asset is changed, the other assets can be changed correspondingly, and the larger the asset floating correlation value of the tenant is, the more likely a reviewer can decrypt according to the correlation between the assets.
Recording the maximum value of the asset floating association degrees of all the assets of the tenant as the asset change association degrees of the tenant. Combining the autocorrelation of the change of the assets of the tenant and the correlation of the change between the assets, and calculating the asset information destructibility of the tenant according to the inventory long-term association index of all the assets of the tenant and the asset change association degree of the tenant>The calculation formula is as follows:
wherein,asset information degradability for tenants; />Is an exponential function with a natural constant as a base;changing association for assets of the tenant; />Is the%>Inventory long-term association index of seed assets; />The asset class number for the tenant.
When the higher the association degree of the asset change of the tenant is and the longer the inventory long-term association index of various assets of the tenant is, the more relevant and regular the tenant unit information is, the more likely a decipherer decrypts according to the relevant and regular tenant unit information, and the larger the value of the asset information destructibility of the tenant is.
So far, the asset information degradability of the tenant is obtained.
S4, obtaining scrambling times of tenant statistical data according to the asset information destructibility of the tenant, performing exclusive or operation on the tenant statistical data to obtain scrambling statistical data, encrypting the scrambling statistical data by using an AES encryption algorithm to obtain encrypted statistical data, and transmitting the encrypted statistical data to an asset inventory cloud management system to inventory the tenant asset.
When the asset information of the tenant has larger destructibility value, the encryption strength of the tenant statistical data is improved, and the security of the tenant statistical data is ensured. Setting initial scrambling times of statistical dataThe experience value is 10, the scrambling times of the tenant statistical data are calculated according to the property information destructibility of the tenant, and the calculation formula is as follows:
wherein,count scrambling times for tenant, +.>To round down the function ++>Asset information destructibility for tenant, < +.>The number of scrambling is initially counted for the data.
When the asset information of the tenant has a larger value of the destructibility, in order to improve the encryption strength of the tenant statistical data, the greater the scrambling frequency of the tenant statistical data is, the greater the value of the scrambling frequency of the tenant statistical data is.
Firstly, converting tenant statistical data into decimal statistical data by using an ASCII character coding standard, then converting the decimal statistical data into binary statistical data, carrying out exclusive OR operation on each row and the next row of the binary statistical data for a plurality of times such as the scrambling times of the tenant statistical data according to scrambling times of the tenant statistical data, obtaining scrambling statistical data, taking a binary code of the scrambling times of the tenant statistical data as the last row of the scrambling statistical data, then encrypting the scrambling statistical data by using an AES encryption algorithm to obtain encrypted statistical data, transmitting the encrypted statistical data to an asset checking cloud management system based on gRPC protocol and HTTPS protocol, generating an asset checking report by the asset checking cloud management system, and returning a result to a mobile intelligent terminal to finish checking of tenant assets.
Thus, the checking of the tenant assets is completed.
Based on the same inventive concept as the above method, the embodiment of the invention further provides a tenant mode asset inventory system based on edge calculation, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor realizes the steps of any one of the tenant mode asset inventory methods based on edge calculation when executing the computer program.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.

Claims (10)

1. The tenant mode asset inventory method based on edge computing is characterized by comprising the following steps:
collecting tenant statistical data; acquiring the asset types of tenants, and the warehouse-in number and the warehouse-out number of each asset in each time period in the inventory period;
acquiring the profit and loss coefficients of the asset in the time period according to the warehouse-in number and the warehouse-out number of the asset in the time period; acquiring a profit and loss fluctuation sequence of the asset in the inventory period according to the profit and loss coefficient of the asset in the time period, and further acquiring the profit and loss fluctuation disorder degree of the asset in the inventory period; obtaining an inventory short-term floating coefficient of the asset in the inventory period according to the inventory number, the inventory number and the fluctuation turbulence degree of the profit and loss of each time period contained in the inventory period; acquiring an inventory short-term floating sequence of the asset according to the inventory short-term floating coefficient of the asset in the inventory period, and acquiring an inventory long-term association index of the asset by combining the profit and loss fluctuation sequence;
acquiring asset change association of the tenant according to the inventory short-term floating sequence of each asset of the tenant; acquiring asset information destructibility of the tenant according to inventory long-term association indexes of all assets of the tenant and asset change association degrees;
obtaining scrambling times of tenant statistical data according to the asset information hackability of the tenant, and encrypting the tenant statistical data according to the scrambling times of the tenant statistical data to obtain encrypted statistical data; and transmitting the encrypted statistical data to an asset inventory cloud management system to obtain an asset inventory report.
2. The tenant mode asset inventory method based on edge computing of claim 1, wherein the specific method for obtaining the time period is as follows:
taking the preset days as a checking period;
the inventory period is divided equally into a preset number of time periods.
3. The tenant mode asset inventory method based on edge calculation according to claim 1, wherein the obtaining the profit and loss coefficient of the asset in the time period according to the warehouse-in number and the warehouse-out number of the asset in the time period comprises the following specific steps:
the difference value between the ex-warehouse number of the assets in the time period and the warehouse-in number of the assets in the time period is recorded as the profit and loss degree of the assets in the time period;
when the profit and loss degree of the asset in the time period is equal to the number 0, the number 0 is recorded as the profit and loss coefficient of the asset in the time period;
when the level of the asset's profit and loss in the period is not equal to the number 0, the ratio of the level of the asset's profit and loss to the absolute value of the level of the asset's profit and loss is recorded as the profit and loss coefficient of the asset in the period.
4. The tenant mode asset inventory method based on edge calculation according to claim 1, wherein the obtaining the profit and loss fluctuation sequence of the asset in the inventory period according to the profit and loss coefficient of the asset in the time period, and further obtaining the profit and loss fluctuation disorder of the asset in the inventory period comprises the following specific steps:
arranging the profit and loss coefficients of the assets in all time periods contained in the inventory period according to a time sequence to obtain a short-term profit and loss time sequence of the assets in the inventory period;
recording a first-order differential sequence of a short-term profit and loss time sequence of the asset in the inventory period as a profit and loss fluctuation sequence of the asset in the inventory period;
and (5) recording the information entropy of the profit and loss fluctuation sequence of the asset in the inventory period as the profit and loss fluctuation turbulence degree of the asset in the inventory period.
5. The tenant mode asset inventory method based on edge calculation according to claim 3, wherein the method for obtaining the short-term inventory floating coefficient of the asset in the inventory period according to the number of in-warehouse, the number of out-warehouse and the fluctuation turbulence degree of the profit and loss of each time period included in the inventory period comprises the following specific steps:
recording the sum of the ex-warehouse number of the assets in the time period and the warehouse-in number of the assets in the time period as the flowing amount of the assets in the time period;
recording the sum of the flowing amount of the asset in all the time periods contained in the inventory period as the flowing total amount of the asset in the inventory period;
recording the sum of the profit and the loss of the assets in all the time periods contained in the inventory period as the profit and loss total quantity of the assets in the inventory period;
and (3) recording the product of the flowing total quantity, the profit and loss total quantity and the profit and loss fluctuation turbulence degree of the asset in the inventory period as the inventory short-term floating coefficient of the asset in the inventory period.
6. The tenant mode asset inventory method based on edge calculation according to claim 1, wherein the specific method for obtaining the inventory short-term floating sequence of the asset according to the inventory short-term floating coefficient of the asset in the inventory period and obtaining the inventory long-term association index of the asset by combining the profit and loss fluctuation sequence comprises the following steps:
arranging the short-term inventory floating coefficients of the asset in the inventory period according to the time sequence to obtain a short-term inventory floating sequence of the asset;
arranging all the counting periods according to a time sequence to obtain a counting period sequence;
respectively recording each counting period in the counting period sequence as a counting period to be analyzed;
recording the sum of Euclidean distances of the profit and loss fluctuation sequences of the asset in the inventory period to be analyzed and the profit and loss fluctuation sequences of the asset in all the inventory periods after the inventory period to be analyzed as the profit and loss association degree of the asset in the inventory period to be analyzed;
the sum of the profit and loss association of the asset in all inventory periods is recorded as the long-term profit and loss association of the asset;
the ratio of the hurst index to the long-term earning relevance of the inventory short-term float sequence of the asset is recorded as the inventory long-term relevance index of the asset.
7. The tenant mode asset inventory method based on edge computing according to claim 1, wherein the obtaining the asset change association degree of the tenant according to the short-term floating sequence of the inventory of each asset of the tenant comprises the following specific steps:
respectively marking each asset of the tenant as an asset to be analyzed;
recording all assets of the tenant except the asset to be analyzed as related assets;
recording a correlation coefficient between the inventory short-term floating sequence of the asset to be analyzed and the inventory short-term floating sequence of the associated asset as a correlation degree of the asset to be analyzed and the associated asset;
recording the average value of the association degrees of the asset to be analyzed and all the associated assets as the asset floating association degree of the asset to be analyzed;
and (5) recording the maximum value of the asset floating association degrees of all the assets as the asset change association degree of the tenant.
8. The tenant mode asset inventory method based on edge computing according to claim 1, wherein the obtaining the property information degradability of the tenant according to the inventory long-term association index and the property change association degree of all the properties of the tenant comprises the following specific steps:
recording a normalized value of the sum of the inventory long-term association indexes of all the assets of the tenant as the asset long-term association degree of the tenant;
and recording the product of the asset change association degree and the asset long-term association degree of the tenant as the asset information destructibility of the tenant.
9. The tenant mode asset inventory method based on edge computing according to claim 1, wherein the obtaining tenant statistical data scrambling times according to the property information destructibility of the tenant, and encrypting tenant statistical data according to the tenant statistical data scrambling times, to obtain encrypted statistical data, comprises the specific steps of:
recording the product of the property information cracking degree of the tenant and the preset initial scrambling times as tenant statistical data scrambling times;
converting tenant statistical data into tenant statistical coding data of a preset system;
recording each row of data of tenant statistical coding data as a row to be analyzed;
performing exclusive OR operation on the scrambling times of tenant statistical data on the row to be analyzed and the next row of the row to be analyzed to obtain scrambling data of the row to be analyzed;
the scrambling data of all lines are arranged according to line numbers to obtain scrambling statistical data;
the scrambling times of the tenant statistical data are converted into scrambling times codes of a preset system, and the scrambling times codes are used as the last row of the scrambling statistical data;
the disorder statistical data is encrypted by using an encryption algorithm to obtain encrypted statistical data.
10. A tenant-mode asset inventory system based on edge computing, comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1-9 when executing the computer program.
CN202311684678.1A 2023-12-11 2023-12-11 Tenant mode asset inventory method and system based on edge computing Active CN117391592B (en)

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CN111625802A (en) * 2019-02-27 2020-09-04 深圳光峰科技股份有限公司 Projector authorization use method
CN114240351A (en) * 2021-12-13 2022-03-25 南京科技职业学院 Distributed intelligent economic management system
CN116862409A (en) * 2023-06-14 2023-10-10 上海明奇网络科技有限公司 Asset management method based on system

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111625802A (en) * 2019-02-27 2020-09-04 深圳光峰科技股份有限公司 Projector authorization use method
CN114240351A (en) * 2021-12-13 2022-03-25 南京科技职业学院 Distributed intelligent economic management system
CN116862409A (en) * 2023-06-14 2023-10-10 上海明奇网络科技有限公司 Asset management method based on system

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