Charging pile operation risk assessment method and device, equipment and storage medium

Abstract

The invention relates to the technical field of risk management and control, and discloses a charging pile operation risk assessment method, device and equipment and a storage medium, and the method comprises the steps: determining a customer-charging pile-processor association relationship according to obtained machine account data; determining risk assessment index items according to the obtained charging data of the charging pile corresponding to the customer and preset charging parameters, and performing statistics on the risk assessment index items by using a preset statistical model; determining whether the charging pile hits a risk alarm or not according to the statistical result and a preset risk model, and if yes, obtaining credit information of the customer; dividing the credit rating of the customer according to the credit information and a preset credit model; if the credit rating is high, the risk alarm is cancelled; and if the credit rating is low, the risk alarm is sent to a processor corresponding to the client. The risk factors are mined from the operation indexes of the charging piles to give an alarm, so that potential risk customers are found more accurately, and the troubleshooting efficiency of alarm response is improved.

Description

Technical Field

[0001] The present invention relates to the field of risk management technology, and in particular to a charging pile operation risk assessment method, device, equipment and storage medium. Background Art

[0002] When financial enterprises carry out investment business related to charging piles, they need to manage the physical assets of charging piles under the financial business, monitor the actual operation of the physical assets of charging piles, and promptly discover abnormal operation, so as to reduce the overdue repayment rate of customers.

[0003] At present, there are two main methods for managing physical assets of charging piles:

[0004] One method only focuses on whether the customer repays on time. This method does not focus on whether the repayment funds come from the operating income of the charging station. If the operating income of the charging station cannot cover the repayment amount, but relies on its own funds for repayment in the long term, it is easy to have overdue risks;

[0005] One method is to regularly check whether the charging income flow and the charging operation platform bill are consistent while paying attention to whether the customer repays on time. Although this method supervises the account, it cannot give an effective judgment on whether the daily operation of the charging pile is stable and whether the operating capacity can support continuous and stable repayment, which is not conducive to identifying potential risks as early as possible. Summary of the invention

[0006] The present invention provides a charging pile operation risk assessment method, device, equipment and storage medium, which mine risk factors from the operating indicators of the charging piles to issue alarms, more accurately discover potential risk customers, and improve the efficiency of troubleshooting of alarm responses.

[0007] In a first aspect, a charging pile operation risk assessment method is provided, comprising:

[0008] Obtain ledger data and determine the relationship between customer, charging pile and handler based on the ledger data;

[0009] Obtain the charging data of the charging pile corresponding to the customer, determine the risk assessment index items according to the charging data and the preset charging parameters, and use the preset statistical model to perform statistics on the risk assessment index items;

[0010] Determine whether the charging pile hits the risk warning based on the statistical results and the preset risk model. If the charging pile hits the risk warning, obtain the customer's credit information;

[0011] The customer's credit level is divided according to the credit information and the preset credit model; if the credit level is high, the risk warning is cancelled; if the credit level is low, the risk warning is sent to the customer's corresponding handler.

[0012] In a second aspect, a charging pile operation risk assessment device is provided, comprising:

[0013] The asset ledger access unit is used to obtain ledger data and determine the relationship between customer, charging pile and handler based on the ledger data;

[0014] A charging data monitoring unit is used to obtain the charging data of the charging pile corresponding to the customer, determine the risk assessment index items according to the charging data and the preset charging parameters, and use the preset statistical model to perform statistics on the risk assessment index items;

[0015] A risk warning trigger unit is used to determine whether the charging pile hits the risk warning based on the statistical results and the preset risk model. If the charging pile hits the risk warning, the credit information of the customer is obtained;

[0016] The alarm screening and verification unit is used to classify the customer's credit level according to the credit information and the preset credit model; if the credit level is high, the risk alarm is revoked; if the credit level is low, the risk alarm is sent to the corresponding handler of the customer.

[0017] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned charging pile operation risk assessment method when executing the computer program.

[0018] In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned charging pile operation risk assessment method are implemented.

[0019] In the scheme implemented by the above charging pile operation risk assessment method, device, equipment and storage medium, firstly, the ledger data is obtained, and the relationship between the customer-charging pile-processor is determined according to the ledger data; then the charging data of the charging pile corresponding to the customer is obtained, and the risk assessment index items are determined according to the charging data and the preset charging parameters, and the risk assessment index items are counted using the preset statistical model; then, according to the statistical results and the preset risk model, it is determined whether the charging pile hits the risk alarm, and if the charging pile hits the risk alarm, the credit information of the customer is obtained; finally, the credit level of the customer is divided according to the credit information and the preset credit model; if the credit level is high, the risk alarm is cancelled; if the credit level is low, the risk alarm is sent to the corresponding processor of the customer. In the present invention, risk factors are mined from the operating indicators of the charging pile to issue an alarm, and potential risk customers are more accurately found. For customers who hit the risk alarm, customers with good credit ratings are further screened out through their credit information for exclusion, so as to more accurately find potential customers with repayment risks. The risk alarm is sent to the processor, so that a dedicated person can respond to the specific task, and the efficiency of the alarm response investigation is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0021] Figure 1 Schematic diagram of the application environment of the charging pile operation risk assessment method in one embodiment of the present invention;

[0022] Figure 2 It is a flow chart of a charging pile operation risk assessment method in one embodiment of the present invention;

[0023] Figure 3 is a flow chart of a charging pile operation risk assessment method in another embodiment of the present invention;

[0024] Figure 4 It is a structural schematic diagram of a charging pile operation risk assessment device in one embodiment of the present invention;

[0025] Figure 5 is a schematic diagram of the structure of a charging pile operation risk assessment device in another embodiment of the present invention;

[0026] Figure 6 It is a schematic diagram of the structure of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The charging pile operation risk assessment method provided by the embodiment of the present invention can be applied in the following aspects: Figure 1 In an application environment, the client can communicate with the server through the network, the client can communicate with the charging pile through the network, the charging pile can communicate with the server through the network, and the server can communicate with the terminal through the network.

[0029] Charging piles can be used to indicate charging equipment operated by customers that provide public charging services. For example, charging piles can include base stations that provide charging services for electric vehicles or electric bicycles.

[0030] Client can be used to indicate the client side of the charging pile. For example, the client may include a company that operates multiple charging piles or multiple charging stations consisting of several charging piles. The client can be implemented with an independent server or a service platform consisting of multiple servers.

[0031] The server side can be used to indicate the investor side that provides financing support to the customer. For example, the investor may include a financial enterprise that provides financing support for the customer to operate the charging pile. The server side can be implemented with an independent server or a service platform composed of multiple servers.

[0032] The terminal can be used to indicate the processor side of the investor side. For example, the processor may include employees of a financial enterprise that provides financing support for the customer to operate charging piles. The terminal can be, but is not limited to, personal computers, laptops, smartphones, tablet computers, portable wearable devices, and other devices with display screens and input devices.

[0033] The server can first obtain ledger data from the client and the local, and determine the relationship between the customer, charging pile, and handler based on the ledger data; then it can obtain the charging data of the charging pile corresponding to the customer from the charging pile, determine the risk assessment index items based on the charging data and the preset charging parameters, and use the preset statistical model to count the risk assessment index items; then determine whether the charging pile hits the risk alarm based on the statistical results and the preset risk model. If the charging pile hits the risk alarm, the customer's credit information is obtained; finally, the customer's credit level is divided according to the credit information and the preset credit model; if the credit level is high, the risk alarm is revoked; if the credit level is low, the risk alarm is sent to the customer's corresponding handler terminal.

[0034] The present invention is described in detail below through specific embodiments.

[0035] In one embodiment, a charging pile operation risk assessment method is provided. Figure 2 As shown, Figure 2 A flow chart of a charging pile operation risk assessment method provided by an embodiment of the present invention includes the following steps:

[0036] Step S210: Obtain ledger data, and determine the relationship between customer-charging pile-processor based on the ledger data.

[0037] In order to realize the dedicated management of charging pile operation, the present invention obtains ledger data to establish the relationship between customer-charging pile-processor through the ledger data.

[0038] When the server obtains the ledger data, it can directly read the client and local ledger data within the scope of authority by providing an interface. The ledger data can include, but is not limited to: the customer's ledger data and the investor's ledger data. The customer's ledger data generally records at least which charging piles operated by the customer receive financing support from the investor, and the investor's ledger data generally records at least which person follows up and manages a customer.

[0039] Through the obtained ledger data, an association relationship with the customer as the root node is established. For example: if a customer only operates one charging pile and is managed by one handler, an association relationship can be customer A-charging pile 1-handler a; if a customer operates multiple charging piles and is managed by multiple handlers, an association relationship can be customer B-{charging pile 2, charging pile 3, ..., charging pile n}-{handler b, handler c, ..., handler m}.

[0040] Step S220, obtaining charging data of the charging pile corresponding to the customer, determining risk assessment index items according to the charging data and preset charging parameters, and performing statistics on the risk assessment index items using a preset statistical model.

[0041] In order to evaluate the operational risk of charging piles, it is necessary to first construct risk assessment indicators, which are constructed based on the charging data of the charging piles and the preset charging parameters.

[0042] First, obtain the charging data of the charging pile corresponding to the customer. When providing financing support to customers, investors can set up a data transmission interface in the charging pile where the customer receives financing in order to supervise the charging pile operated by the customer. Therefore, the server can directly read the charging data of the charging pile through the data transmission interface to ensure the accuracy and completeness of the charging data. Usually a customer operates multiple charging piles, so the charging pile corresponding to the customer here can be used to indicate multiple charging piles operated by the customer.

[0043] Then, the preset charging parameters are obtained. The charging parameters can be set according to industry standards, technical specifications or other reliable references. The charging parameters can be pre-stored in the server database. The charging parameters can include but are not limited to: daily normal order volume, daily normal charging income, daily normal single-gun charging utilization rate, daily normal charging power, daily normal charging time, etc.

[0044] Then, calculation is performed based on the charging data and charging parameters to determine the risk assessment index item. The risk assessment index item can be used to indicate the basic data for statistical determination of whether a risk alarm is hit.

[0045] For example: the ratio of the order volume of the charging pile on a certain day to the normal daily order volume can be calculated as a risk assessment indicator; the ratio of the charging income of the charging pile on a certain day to the normal daily charging income can be calculated as another risk assessment indicator; the ratio of the average charging utilization rate of the charging pile on a certain day to the normal daily single-gun charging utilization rate can be calculated as another risk assessment indicator.

[0046] After the risk assessment index items are constructed, the risk assessment index items are statistically analyzed using a preset statistical model. The statistical model can be used to indicate a model for calculating the charging pile operation trend based on the risk assessment index items, and the statistical model can include multiple statistical rules.

[0047] For example: the statistical model may include a statistical rule, which is the statistical result of the ratio of daily order volume to daily normal order volume in the first preset time period. After the risk assessment index is input into the statistical model, the statistical result of the ratio of daily order volume to daily normal order volume within the first preset time period is calculated based on the statistical model; the statistical model may include a statistical rule, which is the statistical result of the ratio of daily charging income to daily normal charging income within the second preset time period. After the wind direction assessment index is input into the statistical model, the statistical result of the ratio of daily charging income to daily normal charging income within the second preset time period is calculated based on the statistical model; the statistical model may include a statistical rule, which is the statistical result of the ratio of daily average charging utilization rate to daily normal single-gun charging utilization rate within the third preset time period. After the wind direction assessment index is input into the statistical model, the statistical result of the ratio of daily average charging utilization rate to daily normal single-gun charging utilization rate within the third preset time period is calculated based on the statistical model.

[0048] Step S230, determining whether the charging pile hits the risk warning based on the statistical results and the preset risk model, and if the charging pile hits the risk warning, obtaining the customer's credit information.

[0049] After statistically analyzing the risk assessment indicators using the statistical model, it is determined whether the charging pile has triggered a risk warning based on the matching results between the statistical results and the preset risk model.

[0050] The risk model is a model corresponding to the statistical model, and is used to determine whether a charging pile has potential risks. In other words, the statistical model may include multiple statistical rules, and the risk model may include multiple risk rules corresponding to the multiple statistical rules. The number of risk rules may be different from the number of statistical rules, but multiple risk rules should be able to correspond to multiple statistical rules. When at least one of the statistical results matches the risk rule in the risk model, it can be determined that the charging pile has hit the risk alarm.

[0051] For example: the risk model may include a risk rule that, if the proportion of daily order volume in the first preset time period that is lower than the daily normal order volume reaches more than a first percentage, then when the statistical result is that the proportion of daily order volume in the first preset time period that is lower than the daily normal order volume is higher than the first percentage, a risk alarm is triggered; the risk model may include a risk rule that, if the proportion of daily charging income in the second preset time period that is lower than the daily normal charging income reaches more than a second percentage, then when the statistical result is that the proportion of daily charging income in the second preset time period that is lower than the daily normal charging income is higher than the second percentage, a risk alarm is triggered; the risk model may include a risk rule that, if the proportion of daily average charging utilization rate in the third preset time period that is lower than the daily normal single-gun charging utilization rate reaches more than a third percentage, then when the statistical result is that the proportion of daily average charging utilization rate in the third preset time period that is lower than the daily normal single-gun charging utilization rate is higher than the third percentage, a risk alarm is triggered.

[0052] If the charging pile hits the risk alarm, the server obtains the credit information of the customer operating the charging pile.

[0053] The server can obtain the credit information of the customer by accessing public information or internal information. Public information may include public customer credit information records, and internal information may include business records stored in the database of the investor's server. Credit information may include, but is not limited to: whether there is a repayment overdue, the average number of days overdue, etc.

[0054] Step S240, classify the customer's credit level according to the credit information and the preset credit model; if the credit level is high, cancel the risk warning; if the credit level is low, send the risk warning to the corresponding processor of the customer.

[0055] After confirming the risk alert and obtaining the customer's credit information, the customer's credit level is divided according to the preset credit model.

[0056] The credit model can be used to indicate the credit rating of users based on certain rules. For example, the credit model can determine the credit rating of users with less than a certain number of overdue payments as high, and the credit rating of users with no less than a certain number of overdue payments as low.

[0057] A high credit rating can be used to indicate that although the customer has operational risks under the financing of operating charging piles, the customer's own repayment ability and willingness are strong. Therefore, the risk warning for it can be excluded, that is, the risk warning can be revoked.

[0058] A low credit rating can be used to indicate that the customer has operational risks under the financing of operating charging piles, and the customer's own repayment ability and willingness are weak. Therefore, the risk warning is sent to the customer's corresponding processor terminal, so that the processor can actively check after receiving the risk warning through the terminal.

[0059] pass Figure 2 The method shown first obtains the ledger data, and determines the relationship between the customer, the charging pile, and the handler according to the ledger data; then obtains the charging data of the charging pile corresponding to the customer, determines the risk assessment index items according to the charging data and the preset charging parameters, and uses the preset statistical model to perform statistics on the risk assessment index items; then determines whether the charging pile hits the risk alarm according to the statistical results and the preset risk model, and if the charging pile hits the risk alarm, obtains the customer's credit information; finally, divides the customer's credit level according to the credit information and the preset credit model; if the credit level is high, cancels the risk alarm; if the credit level is low, sends the risk alarm to the handler corresponding to the customer. In the present invention, risk factors are mined from the operating indicators of the charging pile to issue an alarm, and potential risk customers are more accurately discovered. For customers who hit the risk alarm, customers with good credit ratings are further screened out through their credit information for exclusion, thereby more accurately discovering potential customers with repayment risks. The risk alarm is sent to the handler, so that a dedicated person can respond to the specific task, thereby improving the efficiency of the alarm response investigation.

[0060] See also Figure 3 As shown, Figure 3 Another flow chart of the charging pile operation risk assessment method provided by the embodiment of the present invention includes the following steps:

[0061] Step S301, obtain ledger data, perform semantic recognition on the ledger data, determine the customer name, charging pile identifier and handler list in the ledger data, and establish an association relationship based on the customer name, charging pile identifier and handler list. Go to step S302.

[0062] The server can directly read the client and local ledger data by providing an interface. The server can obtain the ledger data in the following ways, but is not limited to: the server reads the ledger data in a regular periodic acquisition manner, or triggers the server to read the updated ledger data when the client and local ledger data are updated. In actual situations, since the charging piles operated by the customer may change with the changes in the overall environment, the charging piles operated by the customer may be added, updated, revoked, etc. In order to obtain the latest and most comprehensive charging pile information in order to supervise and record the charging piles, it is preferred to obtain the ledger data in the manner described above, which triggers the server to read the updated ledger data when the client and local ledger data are updated.

[0063] By automatically performing semantic recognition on the ledger data, the customer name, charging pile identification, and handler list in the ledger data can be determined. The charging pile identification can be used to uniquely identify which charging piles the customer operates. Based on the charging pile identification, the server can filter and determine the charging pile associated with the customer from all the charging piles connected to it. The handler list can be used to uniquely identify which person / which person supervises the customer. Based on the customer name and the handler list, the server can establish a dedicated supervision association for a specific person. Therefore, an association is established based on the customer name, charging pile identification, and handler list.

[0064] Step S302, obtaining the daily order volume, daily charging income and daily average charging utilization rate of the charging pile. Go to step S303.

[0065] The server obtains the daily order volume, daily charging income and daily average charging utilization rate from the charging piles. Among them, the daily order volume and daily charging income can be obtained directly from the charging piles; the daily average charging utilization rate can be determined based on the daily charging time directly obtained from the charging piles to determine the daily average charging time, and then based on the ratio of the daily average charging time to the operating time of a day.

[0066] Step S303, obtaining the historical charging data of the charging pile in the preset time period before each day, calculating the historical charging data based on the preset time period to obtain charging parameters, updating the historical charging data based on daily changes, and synchronously updating the charging parameters; wherein the charging parameters include: daily normal order volume, daily normal charging income, and daily normal single-gun charging utilization rate. Go to step S304.

[0067] In order to evaluate the operation data, an evaluation standard (i.e., charging parameter) needs to be preset to process the charging data. The evaluation standard can be a parameter value calculated based on historical operation data, which is used as a benchmark for processing the charging data.

[0068] Specifically, the server may obtain historical charging data within a preset time period before each day from the charging pile, and then calculate the parameter value based on the length of the preset time period.

[0069] For the daily normal order volume, the daily order volume in the past seven days before the current day can be summed up, and then divided by the number of days in the preset time period to obtain the daily normal order volume. The daily normal order volume can be expressed by the following (Formula 1):

[0070]

[0071] Among them, i represents a certain day, and the certain day changes with the passage of time.

[0072] For daily normal charging income, the daily charging income of the past seven days before the current day can be summed up, and then divided by the number of days in the preset time period to obtain the daily normal charging income. The daily normal charging income can be expressed by the following (Formula 2):

[0073]

[0074] For the daily normal single-gun charging utilization rate, the daily charging time in the past seven days before the current day can be summed up, and then divided by the number of days in the preset time period, the operating time of the day, and the number of charging guns to obtain the daily normal single-gun charging utilization rate. The daily normal single-gun charging utilization rate can be expressed by the following (Formula 3):

[0075]

[0076] After obtaining various charging parameters, as they change daily while the length of the preset time period does not change, the historical operating data within the preset time period changes, and the charging parameters are continuously updated to ensure the accuracy of subsequent processing.

[0077] Step S304, calculate the first ratio of the daily order volume to the daily normal order volume, calculate the second ratio of the daily charging income to the daily normal charging income, calculate the third ratio of the daily average charging utilization rate to the daily normal single-gun charging utilization rate, and integrate the first ratio, the second ratio and the third ratio into a risk assessment indicator item. Go to step S305.

[0078] The first ratio is obtained by dividing the obtained daily order volume by the daily normal order volume. The first ratio can be expressed by the following (Formula 4):

[0079]

[0080] When the first ratio is less than 1, it means that the daily order volume is lower than the daily normal order volume; when the first ratio is greater than 1, it means that the daily order volume is higher than the daily normal order volume; when the first ratio is equal to 1, it means that the daily order volume is equal to the daily normal order volume.

[0081] The obtained daily charging income is divided by the daily normal charging income to obtain a second ratio. The second ratio can be expressed by the following (Formula 5):

[0082]

[0083] When the second ratio is less than 1, it means that the daily charging income is lower than the daily normal charging income; when the second ratio is greater than 1, it means that the daily charging income is higher than the daily normal charging income; when the second ratio is equal to 1, it means that the daily charging income is equal to the daily normal charging income.

[0084] The third ratio is obtained by dividing the obtained daily average charging utilization rate by the daily normal single-gun charging utilization rate. The third ratio can be expressed by the following (Formula 6):

[0085]

[0086] When the third ratio is less than 1, it means that the daily average charging utilization rate is lower than the daily normal single-gun charging utilization rate; when the third ratio is greater than 1, it means that the daily average charging utilization rate is higher than the daily normal single-gun charging utilization rate; when the third ratio is equal to 1, it means that the daily average charging utilization rate is equal to the daily normal single-gun charging utilization rate.

[0087] It is particularly noted that a first ratio, a second ratio and a third ratio can be calculated every day, and then the first ratio, the second ratio and the third ratio are integrated into risk assessment indicators.

[0088] Step S305, using multiple statistical rules of the statistical model, statistics are performed on the first ratio, the second ratio and the third ratio to obtain multiple statistical results. Then, the process proceeds to step S306.

[0089] The following table 1 shows a specific example of a statistical model. This statistical model includes 6 statistical rules.

[0090] Table 1:

[0091] Serial number Statistical rules 1 The proportion of daily order volume lower than the normal daily order volume within 1 month 2 The proportion of daily order volume lower than the normal daily order volume within 2 months 3 The proportion of daily charging income lower than normal daily charging income within 1 month 4 The proportion of daily charging income lower than normal daily charging income within 2 months 5 The proportion of daily average charging utilization rate within one month that is lower than the daily normal single-gun charging utilization rate 6 The proportion of daily average charging utilization rate within 2 months that is lower than the daily normal single-gun charging utilization rate

[0092] Based on the statistical model illustrated in Table 1 above, statistics are performed on the risk assessment indicator items to obtain 6 statistical results corresponding to the 6 statistical rules.

[0093] Step S306, input the statistical results into the risk model to determine whether at least one of the multiple statistical results matches the risk rule of the risk model. If yes, go to step S307; if not, go to step S302.

[0094] The following table 2 shows a specific example of a risk model. The risk model includes 9 risk rules and corresponding alarm levels.

[0095] Table 2:

[0096]

[0097] A high alert level may require the handler to strictly verify the operation status with the customer. A medium alert level may require the handler to verify the operation status with the customer, but the strictness is slightly looser for a high alert level, and a low alert level may require the handler to contact the customer to focus on the issue. At each alert level, the handler must provide feedback on whether there is indeed a risk.

[0098] The six statistical results obtained in the above steps are input into the risk model to check whether at least one of the six statistical results hits the above nine risk rules. If at least one of the six statistical results hits the above nine risk rules, the charging pile is determined to have hit the risk alarm. If none of the six statistical results hits the above nine risk rules, the operation of the charging pile continues to be monitored.

[0099] For example, if the 6 statistical results are:

[0100] Statistical result 1: The proportion of daily order volume lower than the normal daily order volume within 1 month is 95%;

[0101] Statistical result 2: The proportion of daily order volume lower than the normal daily order volume within 2 months is 75%;

[0102] Statistical result 3: The proportion of daily charging income lower than the normal daily charging income within one month is 70%;

[0103] Statistical result 4: The daily charging income within 2 months is 40% lower than the normal daily charging income;

[0104] Statistical result 5: The daily average charging utilization rate within one month is 60% lower than the daily normal single-gun charging utilization rate;

[0105] Statistical result 6: The daily average charging utilization rate within 2 months is 20% lower than the daily normal single-gun charging utilization rate.

[0106] Then statistical result 1 hits risk rule 1, and the alarm level is high; statistical result 2 hits risk rule 2, and the alarm level is medium; statistical result 4 hits risk rule 6, and the alarm level is low.

[0107] Step S307, determine whether the charging pile hits the risk alarm, determine the highest alarm level according to the alarm level corresponding to the hit risk rule, obtain the customer's historical repayment data, and determine the customer's credit information according to the historical repayment data. Go to step S308.

[0108] When it is determined that the charging pile hits the risk alarm, the highest alarm level is determined according to the alarm level corresponding to the risk rule hit. Continuing with the above example, 3 alarm rules are hit in 6 statistical results, and the highest alarm level is high.

[0109] The server obtains the customer's historical repayment data through public information or internal information. The historical repayment data may include, but is not limited to: historical repayment periods, historical repayment amounts, overdue repayment periods, overdue repayment days, etc. Based on the historical repayment data, the customer's credit information is determined. The credit information may be the result of statistical analysis of the historical repayment data.

[0110] Step S308, input the customer's credit information into the credit model, use the credit model to determine the customer's credit rating, and determine whether the credit rating is high. If yes, go to step S309; ​​if not, go to step S310.

[0111] The following table 3 shows a specific example of a credit model. In this credit model, the credit rating of a customer is divided into high and low by three credit rules.

[0112] Table 3:

[0113] Serial number Credit Rules Credit rating 1 No overdue high 2 The number of overdue days is not more than 3 days and the number of overdue times is not more than 3 times high 3 other Low

[0114] After the customer's credit information is input into the above credit model, the customer is classified as having a high credit rating or a low credit rating through the credit model. If the customer has no history of overdue repayments, it means that the customer has a strong ability to repay and a strong willingness to repay, and can be classified as having a high credit rating. If the customer has a history of overdue payments, but the number of overdue days is no more than 3 days and the number of overdue payments is no more than 3 times (that is, the overdue is within a reasonable range, such as unexpected circumstances such as forgetfulness), it means that the customer has a strong ability to repay and a strong willingness to repay, and can still be classified as having a high credit rating. If the customer does not meet the above two credit rules, it means that the customer has a weak ability to repay and a weak willingness to repay, and he or she will be classified as having a low credit rating.

[0115] Since the charging piles operated by customers can only be part of the business involved by the customers, the existence of risks in charging pile operations can only mean that the charging pile financing project has risks, and does not mean that other assets of the customers have operating risks. Therefore, if the customer's credit rating is high, there is no need to issue risk warnings to the customer, but if the customer's credit rating is low, risk warnings need to be issued to the customer.

[0116] Step S309: cancel the risk warning. Go to step S302.

[0117] If the customer has a high credit rating and the risk alert is cancelled, the operation of the charging pile still needs to be monitored.

[0118] Step S310, sending the highest alarm level to the client's corresponding handler. Go to step S311.

[0119] If the customer's credit rating is low, the highest alarm level will be sent to the customer's corresponding handler. That is, when multiple statistical results hit multiple alarm rules and the levels of multiple alarm rules are inconsistent, the highest alarm level will be sent to the handler.

[0120] Step S311, determining the alarm accuracy in response to the processing result of the processor, and if the alarm accuracy is lower than a preset accuracy threshold, modifying the risk model.

[0121] After the highest alarm level is sent to the handler, the handler will verify the risk situation with the customer based on the disposal measures corresponding to the highest alarm level, and provide feedback on whether the alarm is accurate. The alarm accuracy rate is determined based on the verification results of whether the alarm is accurate as fed back by the handler.

[0122] For example, for a customer, a high alert level is sent to the corresponding handler, but after verification, the handler believes that the risk situation is not serious. The handler can feedback the verification result that the alert is inaccurate and further feedback the risk situation. After the verification results fed back by multiple handlers are counted, the accuracy of the alert is determined based on the verification results.

[0123] The server can preset an accuracy threshold, which can be used to evaluate the performance of the risk model. For example, the accuracy threshold can be 90%. If the alarm accuracy is determined to be no less than 90% based on the feedback from the processor, it means that the risk model has excellent performance in assessing the operational risk of the charging pile and no update is required. If the alarm accuracy is determined to be less than 90% based on the feedback from the processor, it means that the risk model has poor performance in assessing the operational risk of the charging pile and the risk model needs to be revised based on the risk situation reported by the processor. For example, the risk rules included in the risk model can be adjusted.

[0124] In one embodiment, a charging pile operation risk assessment device is provided, and the charging pile operation risk assessment device corresponds one-to-one to the charging pile operation risk assessment method in the above embodiment. Figure 4 As shown, Figure 4 A schematic diagram of a charging pile operation risk assessment device provided by an embodiment of the present invention includes:

[0125] The asset ledger access unit 410 is used to obtain ledger data and determine the relationship between the customer, the charging pile and the handler according to the ledger data;

[0126] The charging data monitoring unit 420 is used to obtain the charging data of the charging pile corresponding to the customer, determine the risk assessment index items according to the charging data and the preset charging parameters, and use the preset statistical model to perform statistics on the risk assessment index items;

[0127] The risk warning triggering unit 430 is used to determine whether the charging pile hits the risk warning according to the statistical results and the preset risk model, and if the charging pile hits the risk warning, obtain the credit information of the customer;

[0128] The alarm screening and verification unit 440 is used to classify the credit level of the customer according to the credit information and the preset credit model; if the credit level is high, the risk alarm is cancelled; if the credit level is low, the risk alarm is sent to the corresponding processor of the customer.

[0129] See also Figure 5 As shown, Figure 5 Another structural schematic diagram of a charging pile operation risk assessment device provided by an embodiment of the present invention includes:

[0130] The asset ledger maintenance module 501 is used to obtain ledger data, perform semantic recognition on the ledger data, determine the customer name, charging pile identification and handler list in the ledger data, and establish an association relationship based on the customer name, charging pile identification and handler list.

[0131] The charging data access module 502 is used to obtain the daily order volume, daily charging income and daily average charging utilization rate of the charging pile.

[0132] Operation monitoring module 503, wherein the operation monitoring module 503 includes:

[0133] The order quantity monitoring submodule 5031 is used to calculate a first ratio of the daily order quantity to the daily normal order quantity;

[0134] The charging income monitoring submodule 5032 is used to calculate a second ratio of the daily charging income to the daily normal charging income;

[0135] The charging utilization rate monitoring submodule 5033 is used to calculate a third ratio of the daily average charging utilization rate to the daily normal single-gun charging utilization rate;

[0136] The indicator iteration calculation submodule 5034 is used to obtain the historical charging data of the charging pile within the preset time period before each day, calculate the historical charging data based on the preset time period, obtain the charging parameters, update the historical charging data based on the daily changes, and synchronously update the charging parameters; wherein the charging parameters include the daily normal order volume, the daily normal charging income, and the daily normal single-gun charging utilization rate;

[0137] The indicator iteration calculation submodule 5034 is also used to integrate the first ratio, the second ratio and the third ratio into risk assessment indicator items, and use multiple statistical rules of the statistical model to perform statistics on the first ratio, the second ratio and the third ratio to obtain multiple statistical results.

[0138] The alarm trigger module 504 is used to input multiple statistical results into the risk model. When at least one of the multiple statistical results hits the risk rule of the risk model, it is determined that the charging pile hits the risk alarm, and the highest alarm level is determined according to the alarm level corresponding to the hit risk rule.

[0139] The alarm screening module 505 is used to obtain the customer's historical repayment data, determine the customer's credit information based on the historical repayment data, input the credit information into the credit model, and use the credit model to determine the customer's credit level. If the credit level is high, the risk alarm is revoked; if the credit level is low, the highest alarm level is sent to the customer's corresponding handler.

[0140] The alarm verification module 506 is used to determine the alarm accuracy in response to the processing result of the processor, and if the alarm accuracy is lower than a preset accuracy threshold, the risk model is modified.

[0141] For the specific definition of the charging pile operation risk assessment device, please refer to the definition of the charging pile operation risk assessment method above, which will not be repeated here. Each module in the above-mentioned charging pile operation risk assessment device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0142] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client through a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the service side of a charging pile operation risk assessment method.

[0143] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0144] Obtain ledger data and determine the relationship between customer, charging pile and handler based on the ledger data;

[0145] Obtain the charging data of the charging pile corresponding to the customer, determine the risk assessment index items according to the charging data and the preset charging parameters, and use the preset statistical model to perform statistics on the risk assessment index items;

[0146] Determine whether the charging pile hits the risk warning based on the statistical results and the preset risk model. If the charging pile hits the risk warning, obtain the customer's credit information;

[0147] The customer's credit level is divided according to the credit information and the preset credit model; if the credit level is high, the risk warning is cancelled; if the credit level is low, the risk warning is sent to the customer's corresponding handler.

[0148] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0149] Obtain ledger data and determine the relationship between customer, charging pile and handler based on the ledger data;

[0150] Obtain the charging data of the charging pile corresponding to the customer, determine the risk assessment index items according to the charging data and the preset charging parameters, and use the preset statistical model to perform statistics on the risk assessment index items;

[0151] Determine whether the charging pile hits the risk warning based on the statistical results and the preset risk model. If the charging pile hits the risk warning, obtain the customer's credit information;

[0152] The customer's credit level is divided according to the credit information and the preset credit model; if the credit level is high, the risk warning is cancelled; if the credit level is low, the risk warning is sent to the customer's corresponding handler.

[0153] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant descriptions on the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0154] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0155] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0156] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A charging pile operation risk assessment method, characterized in that: include: Obtaining ledger data, and determining the relationship between the customer, the charging pile, and the handler according to the ledger data; Acquire charging data of the charging pile corresponding to the customer, determine risk assessment index items according to the charging data and preset charging parameters, and perform statistics on the risk assessment index items using a preset statistical model; Determine whether the charging pile hits the risk warning according to the statistical results and the preset risk model, and if the charging pile hits the risk warning, obtain the credit information of the customer; Classifying the credit level of the customer according to the credit information and a preset credit model; If the credit rating is high, canceling the risk warning; If the credit rating is low, the risk warning is sent to the processor corresponding to the customer.

2. The charging pile operation risk assessment method according to claim 1, characterized in that: The obtaining of ledger data and determining the association relationship between the charging pile, the customer and the handler according to the ledger data includes: Obtaining the ledger data; Perform semantic recognition on the ledger data to determine the customer name, charging pile identifier and list of handlers in the ledger data; The association relationship is established according to the customer name, the charging pile identifier and the list of processors.

3. The charging pile operation risk assessment method according to claim 1, characterized in that: The charging parameters include: daily normal order volume, daily normal charging income and daily normal single-gun charging utilization rate; the statistical model includes multiple statistical rules; the charging data of the charging pile corresponding to the customer is obtained, the risk assessment index items are determined according to the charging data and the preset charging parameters, and the risk assessment index items are counted using the preset statistical model, including: Obtaining the daily order volume, daily charging revenue and daily average charging utilization rate of the charging pile; Calculating a first ratio of the daily order volume to the daily normal order volume; Calculating a second ratio of the daily charging income to the daily normal charging income; Calculating a third ratio of the daily average charging utilization rate to the daily normal single-gun charging utilization rate; Integrate the first ratio, the second ratio and the third ratio into the risk assessment indicator item; The first ratio, the second ratio and the third ratio are statistically analyzed by using the plurality of statistical rules of the statistical model to obtain a plurality of statistical results.

4. The charging pile operation risk assessment method according to claim 3, characterized in that: The risk model includes a plurality of risk rules and an alarm level corresponding to each risk rule; determining whether the charging pile hits the risk alarm according to the statistical results and the preset risk model, and if the charging pile hits the risk alarm, obtaining the credit information of the customer, including: Inputting the statistical results into the risk model, and when at least one of the plurality of statistical results hits the risk rule of the risk model, determining that the charging pile hits the risk alarm; Determine the highest alarm level according to the alarm level corresponding to the risk rule that is hit; Acquire the historical repayment data of the customer, and determine the credit information of the customer based on the historical repayment data.

5. The charging pile operation risk assessment method according to claim 4, characterized in that: The credit level of the customer is classified according to the credit information and a preset credit model; if the credit level is high, the risk warning is cancelled; If the credit rating is low, the risk warning is sent to the processor corresponding to the customer, including: Inputting the credit information into the credit model, and determining the credit rating using the credit model; If the credit rating is high, canceling the risk warning; If the credit level is low, the highest warning level is sent to the processor corresponding to the customer.

6. The charging pile operation risk assessment method according to claim 5, characterized in that: After the step of sending the risk warning to the processing person corresponding to the customer if the credit rating is low, the method further includes: Determining the alarm accuracy rate in response to the processing result of the processing person; If the alarm accuracy is lower than a preset accuracy threshold, the risk model is modified.

7. The charging pile operation risk assessment method according to claim 1, characterized in that: The charging parameters are determined by the following method: Acquire historical charging data of the charging pile within a preset time period before each day, and calculate the historical charging data based on the preset time period to obtain the charging parameter; Based on daily changes, the historical charging data is updated, and the charging parameters are updated synchronously.

8. A charging pile operation risk assessment device, characterized in that: include: An asset ledger access unit, used to obtain ledger data and determine the relationship between the customer, the charging pile and the handler according to the ledger data; A charging data monitoring unit, used to obtain charging data of the charging pile corresponding to the customer, determine risk assessment index items according to the charging data and preset charging parameters, and perform statistics on the risk assessment index items using a preset statistical model; A risk warning triggering unit, used to determine whether the charging pile hits the risk warning according to the statistical results and a preset risk model, and if the charging pile hits the risk warning, obtain the credit information of the customer; An alarm screening and verification unit, used to classify the credit level of the customer according to the credit information and a preset credit model; If the credit rating is high, canceling the risk warning; If the credit rating is low, the risk warning is sent to the processor corresponding to the customer.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the charging pile operation risk assessment method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the charging pile operation risk assessment method according to any one of claims 1 to 7 are implemented.

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