CN111191891A - Evaluation accuracy calibration method and device, electronic device and storage medium - Google Patents
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Abstract
The invention discloses an evaluation accuracy calibration method, equipment, electronic equipment and a storage medium, wherein an external evaluation result of a target is obtained, and an initial weight value is set for the external evaluation result; sequentially arranging the external evaluation results, determining a median result, calculating the deviation degree of the external evaluation results and the median result, and judging whether the deviation degree meets a preset condition; if so, adjusting the initial weight value of the external evaluation result corresponding to the deviation degree to be a first weight value, and adjusting the initial weight values of other external evaluation results to be second weight values, so that the sum of the first weight value and the second weight value is the same as the sum of the initial weight values; and calculating the final evaluation of the determined target according to the first weight value, the second weight value and the corresponding external evaluation result, and outputting the final evaluation. By applying the technical scheme, the problem that the existing evaluation mode is too subjective and one-sided is effectively solved, the accuracy is greatly improved, and the evaluation error probability is smaller and is closer to the objective reality.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for calibrating evaluation accuracy, an electronic apparatus, and a storage medium.
Background
With the development of internet communication technology, modern society has slowly entered the cloud evaluation era. In the computer field, the accuracy of execution of an algorithm, the accuracy of a model output result, and the like, and in the financial field, the evaluation of an asset, for example: the evaluation of house property value, the evaluation of collateral value, etc., the evaluation of chip performance, the evaluation of chip physical performance (heat resistance, heat dissipation), etc., in the field of computer hardware, and the evaluation of material compression resistance, etc., have gradually gone into the cloud evaluation stage. The method determines the evaluation accuracy or value of the target by means of internet online evaluation through massive cloud evaluation member data of the database and transmits the evaluation accuracy or value back to the service request system in a parameter mode, so that evaluation is faster and more comprehensive. The method aims to solve the problem that the traditional evaluation is too dependent on manual operation, and improve the operation efficiency of the evaluation in an online and standardized mode.
However, in the prior art, the demander lacks a confirmation and identification mechanism for receiving the evaluation, the data quantity and quality of each manufacturer are different, and the data of the manufacturer is estimated to be one-sided by a single cloud. The whole evaluation is too subjective and unilateral, and the accuracy of the whole evaluation is seriously influenced.
Disclosure of Invention
In view of the above, the present invention provides a method, a device, an electronic device, and a storage medium for calibrating evaluation accuracy, so as to solve the problem that the conventional evaluation method is too subjective and has too low accuracy.
In view of the above object, the present invention provides, in a first aspect, an evaluation accuracy calibration method, including:
obtaining at least one external evaluation result of a target, and setting the same initial weight value for each external evaluation result;
sequentially arranging the external evaluation results, determining a median result in the external evaluation results, calculating the deviation degrees of all the external evaluation results and the median result, and judging whether any deviation degree meets a preset condition;
if so, adjusting the initial weight value of the external evaluation result corresponding to the deviation degree to be a first weight value, and adjusting the initial weight values of other external evaluation results to be second weight values, so that the sum of the first weight value and the second weight value is the same as the sum of the initial weight values;
and calculating and determining a final evaluation of the target according to the first weight value and the corresponding external evaluation result, and the second weight value and the corresponding external evaluation result, and outputting the final evaluation.
In some embodiments, the determining a median result in the external evaluation results specifically includes:
acquiring the number of the external evaluation results;
if the number of the external evaluation results is odd, taking the external evaluation result which is ranked at the middle as the middle result;
and if the number of the external evaluation results is even, calculating the simple arithmetic mean of the two sorted most middle external evaluation results, and taking the simple arithmetic mean as the middle result.
In some embodiments, the first weight value is less than the initial weight value, and the second weight value is greater than the initial weight value.
In some embodiments, before obtaining at least one external evaluation result of the target, the method further includes:
generating a target list according to the target, and sending the target list to an external data provider;
receiving an initial evaluation result fed back by the external data provider;
acquiring basic information of a target, setting a reasonable result range interval according to the basic information, and judging whether the initial evaluation result falls into the reasonable result range interval;
and if so, setting the initial evaluation result as the external evaluation result.
In some embodiments, the determining whether the initial evaluation result falls within the reasonable result range further includes:
if not, returning the initial evaluation result to the external data provider, and indicating the external data provider to perform manual verification on the initial evaluation result;
receiving a secondary evaluation result manually verified by the external data provider, and judging whether the secondary evaluation result is the same as the initial evaluation result;
and if so, setting the initial evaluation result as the external evaluation result.
In some embodiments, the determining whether the secondary evaluation result is the same as the initial evaluation result further comprises:
if not, replacing the initial evaluation result with the secondary evaluation result, and judging whether the initial evaluation result falls into the reasonable result range again.
In some embodiments, after said outputting said final evaluation, further comprises:
and recording all intermediate data corresponding to each external evaluation result, generating a quality report according to the intermediate data, and outputting the quality report.
In a second aspect, the present application also provides an evaluated accuracy calibration device, comprising:
the acquisition module is used for acquiring at least one external evaluation result of a target and setting the same initial weight value for each external evaluation result;
the judging module is used for sequentially arranging the external evaluation results, determining a median result in the external evaluation results, calculating the deviation degrees of all the external evaluation results and the median result, and judging whether any deviation degree meets a preset condition;
if so, adjusting the initial weight value of the external evaluation result corresponding to the deviation degree to be a first weight value, and adjusting the initial weight values of other external evaluation results to be second weight values, so that the sum of the first weight value and the second weight value is the same as the sum of the initial weight values;
and the output module calculates and determines the final evaluation of the target according to the first weight value and the corresponding external evaluation result, and the second weight value and the corresponding external evaluation result, and outputs the final evaluation.
In a third aspect, the present application further provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for evaluating accuracy calibration as described above when executing the program.
In a fourth aspect, the present application further provides a computer-readable storage medium having stored therein instructions that, when run on a terminal device, cause the terminal device to perform the evaluated accuracy calibration method as described above.
As can be seen from the foregoing, the method, the device, the electronic device, and the storage medium for calibrating accuracy of evaluation provided by the present invention obtain an external evaluation result of a target, and set an initial weight value for the external evaluation result; sequentially arranging the external evaluation results, determining a median result, calculating the deviation degree of the external evaluation results and the median result, and judging whether the deviation degree meets a preset condition; if so, adjusting the initial weight value of the external evaluation result corresponding to the deviation degree to be a first weight value, and adjusting the initial weight values of other external evaluation results to be second weight values, so that the sum of the first weight value and the second weight value is the same as the sum of the initial weight values; and calculating the final evaluation of the determined target according to the first weight value, the second weight value and the corresponding external evaluation result, and outputting the final evaluation. By applying the technical scheme, the problem that the existing evaluation mode is too subjective and one-sided is effectively solved, the accuracy is greatly improved, and the evaluation error probability is smaller and is closer to the objective reality.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of an evaluation accuracy calibration method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating an exemplary method for calibrating accuracy for evaluating mortgages in the financial field according to the present invention;
fig. 3 is a schematic structural diagram of an evaluation accuracy calibration apparatus according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
It is to be noted that technical terms or scientific terms used in the embodiments of the present invention should have the ordinary meanings as understood by those having ordinary skill in the art to which the present disclosure belongs, unless otherwise defined. The use of "first," "second," and similar terms in this disclosure is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that a element, article, or method step that precedes the word, and includes the element, article, or method step that follows the word, and equivalents thereof, does not exclude other elements, articles, or method steps.
As background art, in the prior art, in order to obtain an evaluation of an object, the general procedure is: a demand side firstly generates a target list to be evaluated; the method comprises the steps that an external data provider server pushes an evaluation task to a demand side; the method comprises the steps that an external data provider server receives an evaluation task, a bottom database is called according to the condition of specific attributes of a target (for example, the specific model type of a target chip is obtained if the heat dissipation new energy of a certain model of the chip needs to be obtained), the evaluation of the target is indexed (for example, the target is evaluated based on a sample), cloud evaluation is determined through weighted combination, and a target cloud evaluation result is generated; the external data providing direction pushes an evaluation result to a demand side server; and the demander receives the evaluation result pushed by the external data provider, and uses the target cloud evaluation as a final result for application.
By adopting the technical scheme, the demander lacks a confirmation and identification mechanism for receiving the evaluation, the data quantity and quality of each manufacturer are different, and the data of the manufacturer is estimated to be one-sided by a single cloud. The whole evaluation is too subjective and unilateral, and the accuracy of the whole evaluation is seriously influenced.
The method comprises the steps of obtaining at least one evaluation result, setting weights for the evaluation results, evaluating and calculating the deviation degree through the evaluation result and a median result in the evaluation results, adjusting the weights of external evaluation results according to the deviation degree, and calculating an accurate and stable final evaluation based on the external evaluation results and the corresponding adjusted weight values. The strategy of the target can be accurately adjusted by the final evaluation demander. The problem that the existing evaluation mode is too subjective and unilateral is effectively solved, the accuracy is greatly improved, and the evaluation error probability is smaller and is closer to the objective reality.
The technical solutions provided by the embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
As shown in fig. 1, a schematic flow chart of an evaluation accuracy calibration method in this embodiment is shown, and the method specifically includes the following steps:
This step is intended to obtain at least one external evaluation result, which is set to the same weight value. The external evaluation result of the target refers to a specific result value of the target in a certain direction, which is required by the demander, for example: computer domain, stability value of model output data, financial domain, price value of house or mortgage, hardware domain, heat resistance value of chip, etc. The external evaluation result is generally directly provided by the external evaluation means, and the external evaluation means may generally be provided in many places.
The acquisition process can be carried out in many ways, for example: the single target data can be directly sent to an external evaluation mechanism for a demand side, and the external evaluation mechanism only calls or obtains the evaluation of the target and sends the evaluation back; the external evaluation mechanism calls or obtains the evaluation of the corresponding target according to the list and sends back the evaluation; the external evaluation result can be directly and manually input by a worker of the demand party; the system can automatically acquire target data required today from an external evaluation mechanism according to a task schedule.
After that, an initial weight value is set for each external evaluation result. The weight values set here are equal, i.e., the initial weight of each external evaluation result is the same.
And 102, sequentially arranging the external evaluation results, determining a median result in the external evaluation results, calculating the deviation degrees of all the external evaluation results and the median result, and judging whether any deviation degree meets a preset condition.
The step aims to select a median result in the external evaluation results, calculate the deviation degree of the external evaluation results and the median result, and judge whether the deviation degree reaches a preset condition. The external evaluation results obtained from different external evaluation mechanisms may be different, and there is a greater or lesser difference between different external evaluation results, and these external evaluation results need to be sorted according to the numerical value. Then, a median result is selected from the external evaluation results, and the selection process is various, for example: selecting the most middle result as a median result (odd number, determining the most middle one as the median result; even number, determining the most middle two and taking the simple arithmetic mean) from the sorted results; the sorted results are averaged, squared, etc.
Then, each external evaluation result is compared with the median result and the degree of deviation is found. The calculation process of the deviation degree comprises the steps of determining the difference between the two numbers and calculating the proportion of the difference to the median result. This ratio is the degree of deviation.
Finally, whether the deviation degree meets the specific condition is judged, namely a deviation degree threshold value is set, and the relationship between the calculated deviation degree and the threshold value is judged, wherein the relationship can be larger than the threshold value or smaller than the threshold value or equal to the threshold value, and the like. As an example, a threshold value of 25% is set, and it is determined whether the degree of deviation is greater than 25%.
The step aims to adjust the weight value of the external evaluation result corresponding to the deviation degree meeting the condition to be a first weight value, and adjust the weight value of the external evaluation result corresponding to the deviation degree not meeting the condition to be a second weight value. The first weight value and the second weight value are not necessarily the same as the initial weight value, but may be slightly larger or slightly smaller than the initial weight value, but it is ensured that the sum of all the first weight values and all the second weight values is the same as the sum of all the initial weight values, for example: 10 initial weighted values are all 10%, according to the scheme, five of the initial weighted values are adjusted to be first weighted values of 5%, and the other five initial weighted values are adjusted to be second weighted values of 15%, so that the sum of the 5 first weighted values and the 5 second weighted values is the same as the sum of the 10 initial weighted values.
The first weight value and the second weight value may be fixed, for example, if the first weight value and the second weight value are greater than the threshold, the first weight value and the second weight value are uniformly adjusted to 20%, and the like; it may also be non-fixed, and may be adjusted according to a degree of meeting the condition, and assuming that the preset condition is greater than the threshold of 30%, the magnitude of the first weight value may be adjusted according to a degree of deviation from the threshold of 30%, for example: the first weight value corresponding to a deviation of 40% is 20%, and the first weight value corresponding to a deviation of 50% is 15%.
And 104, calculating and determining a final evaluation of the target according to the first weight value and the corresponding external evaluation result, and the second weight value and the corresponding external evaluation result, and outputting the final evaluation.
The step aims to calculate to obtain a final evaluation according to an external evaluation result and a corresponding adjusted weight value, and output the final evaluation.
The calculation process can be that products of all weighted values and external evaluation results are obtained and then are summed; it is also possible to take the average, variance, confidence interval, etc. of the results after taking the product of all the weight values and the external evaluation result.
And finally, outputting the final evaluation. According to different application scenarios and implementation requirements, the specific output mode for final evaluation can be flexibly selected.
For example, for an application scenario in which the method of the present embodiment is executed on a single device, the final evaluation may be directly output in a display manner on a display section (display, projector, etc.) of the current device, so that the operator of the current device can directly see the content of the final evaluation from the display section.
For another example, for an application scenario executed on a system composed of multiple devices by the method of this embodiment, the final evaluation may be sent to other preset devices serving as receivers in the system through any data communication manner (e.g., wired connection, NFC, bluetooth, wifi, cellular mobile network, etc.), so that the preset devices receiving the final evaluation may perform subsequent processing on the preset devices. Optionally, the preset device may be a preset server, and the server is generally arranged at a cloud end and used as a data processing and storage center, which can store and distribute the final evaluation; the receiver of the distribution is a terminal device, and the holder or operator of the terminal device may be a user, a person of a target object, a manager of a tax authority, a manager of a banking authority, and the like.
For another example, for an application scenario executed on a system composed of multiple devices, the method of this embodiment may directly send the final evaluation to a preset terminal device through any data communication manner, where the terminal device may be one or more of the foregoing paragraphs.
In a specific application scenario, for example: and acquiring the maximum heat resistance degree of the target chip. 5 data were acquired from the outside: 195 degrees, 210 degrees, 208 degrees, 212 degrees, 220 degrees. An initial weight of 20% was then set for each data. Determining the median as 210 degrees, and then respectively calculating the deviation degree between each datum and the median as: 7.14%, 0%, 0.95%, 4.76%. If the preset threshold is 4%, the weight values of 195 degrees and 220 degrees are adjusted to 10%, and the weight values of 210 degrees, 208 degrees and 212 degrees are adjusted to 26.67%. Then, the final evaluation was calculated to be 195 × 10% +210 × 26.67% +208 × 26.67% +212 × 26.67% +220 × 10% — 209.5, and the final evaluation of the output was 209.5 degrees.
By applying the technical scheme, the external evaluation result of the target is obtained, and the initial weight value is set for the external evaluation result; sequentially arranging the external evaluation results, determining a median result, calculating the deviation degree of the external evaluation results and the median result, and judging whether the deviation degree meets a preset condition; if so, adjusting the initial weight value of the external evaluation result corresponding to the deviation degree to be a first weight value, and adjusting the initial weight values of other external evaluation results to be second weight values, so that the sum of the first weight value and the second weight value is the same as the sum of the initial weight values; and calculating the final evaluation of the determined target according to the first weight value, the second weight value and the corresponding external evaluation result, and outputting the final evaluation. By applying the technical scheme, the problem that the existing evaluation mode is too subjective and one-sided is effectively solved, the accuracy is greatly improved, and the evaluation error probability is smaller and is closer to the objective reality.
In an alternative embodiment of the present application, the accuracy of the median result is not affected by the determined median result due to the excessive abnormal result. For example: the median result of 5 data 1, 10 is found to be 7 if the median result is an average. The determining a median result in the external evaluation result specifically includes:
acquiring the number of the external evaluation results;
if the number of the external evaluation results is odd, taking the external evaluation result which is ranked at the middle as the middle result;
and if the number of the external evaluation results is even, calculating the simple arithmetic mean of the two sorted most middle external evaluation results, and taking the simple arithmetic mean as the middle result.
In an alternative embodiment of the present application, the specific process of adjustment is further defined. The first weight value is less than the initial weight value, and the second weight value is greater than the initial weight value.
In an alternative embodiment of the present application, the external evaluation results are not deviated too much in order to be ensured. Before the obtaining of the at least one external evaluation result of the target, the method further includes:
generating a target list according to the target, and sending the target list to an external data provider;
receiving an initial evaluation result fed back by the external data provider;
acquiring basic information of a target, setting a reasonable result range interval according to the basic information, and judging whether the initial evaluation result falls into the reasonable result range interval;
and if so, setting the initial evaluation result as the external evaluation result.
The basic information is various related information capable of reflecting the target state. For example: in the field of computer hardware, the model, the service time, the material and the like of a chip; in the financial field, a reasonable result range interval is generated according to the basic information, the preset calculation rules (such as chip heat resistance, reference heat resistance in factory parameters determined according to the chip model, upward and downward adjustment of 30% and the like; house price, calculation according to the information of city grade of the location, average price of sections around the location and the like) according to the belonged area of mortgages such as a house and the like, the type of the mortgages, the market average price and the like.
In an optional embodiment of the application, in order to specifically discriminate the initial evaluation result which cannot fall into the reasonable result range interval, whether the initial evaluation result can be used as an external evaluation result is judged, so that the final external evaluation result covers all data provided by the external data provider as much as possible. The judging whether the initial evaluation result falls into the reasonable result range interval further comprises:
if not, returning the initial evaluation result to the external data provider, and indicating the external data provider to perform manual verification on the initial evaluation result;
receiving a secondary evaluation result manually verified by the external data provider, and judging whether the secondary evaluation result is the same as the initial evaluation result;
and if so, setting the initial evaluation result as the external evaluation result.
Of course, the initial evaluation result which does not fall into the reasonable result range can also be directly discarded in the scheme.
In an optional embodiment of the present application, the determining whether the secondary evaluation result is the same as the initial evaluation result further includes:
if not, replacing the initial evaluation result with the secondary evaluation result, and judging whether the initial evaluation result falls into the reasonable result range again.
Of course, the present embodiment may directly use the secondary evaluation result as the external evaluation result.
In an alternative embodiment of the present application, in order to record the situation that each external data provider provides data, the user is allowed to review the quality of the data provided by the external data provider afterwards. After the outputting the final evaluation, further comprising:
and recording all intermediate data corresponding to each external evaluation result, generating a quality report according to the intermediate data, and outputting the quality report.
Wherein, all the intermediate data refers to data related to numerical values generated by recording all the value taking processes, and includes but is not limited to: initial evaluation results, secondary evaluation results, external evaluation results, mean values, median values, peak values, valley values, and the like. Recording into quality report and outputting.
In a specific application scenario, as shown in fig. 2, the mortgage evaluation in the financial field is taken as an example. The method comprises the following specific steps:
1) the demander first generates a list of mortgages to be evaluated.
2) And the demand side pushes an evaluation task to an external data provider server.
3) And the external data provider server receives the evaluation task, calls the bottom database according to the sitting address condition of the mortgage and generates an initial evaluation result of the mortgage.
4) And the external data provider pushes an initial evaluation result to the demander server.
5) And the demander receives the evaluation task result pushed by the external data provider, executes a pre-detection model on the initial evaluation result, confirms abnormal data, and sends the external data provider to call a manual confirmation mode to confirm a secondary evaluation result for the doubtful data.
The prior detection model specifically comprises the following steps: the working goal of this model is to confirm whether the data is "abnormal" or "reasonable". The working mechanism is that reasonable result range sections of different mortgages in different areas are set according to the area to which the mortgage belongs, the type of the mortgage, the market average price and the like, if the price returned by the external data provider accords with the identification of the section, the detection is passed, and if the price does not accord with the identification of the section, the detection is abnormal, the external data provider needs to be sent for secondary manual confirmation. Then, if the secondary evaluation result of the external data provider is inconsistent with the initial evaluation result, replacing the original initial evaluation result with the secondary evaluation result, and performing step 5) again; and if the secondary evaluation result of the external data provider is consistent with the initial evaluation result, executing the subsequent step 6).
6) And the demander continues to execute the in-flight value model and outputs final evaluation through model decision.
The sampling value model specifically comprises the following steps: the method comprises the steps of simultaneously adopting cloud estimation services of a plurality of different external data providers, requesting evaluation for the same mortgage from the plurality of external data providers, carrying out cross comparison on external evaluation result data of the plurality of external data providers received by a system, marking data with high deviation among the plurality of data as abnormal values, giving differentiated weights, weakening the effect of the data in model decision, enabling the finally output evaluation result to be less influenced by extreme values (the highest value or the lowest value), enabling the evaluation result to tend to the middle position as far as possible in all returned evaluation results, and enabling the numerical value to be more reliable.
As an example: simultaneously with the cloud estimation service of 3 external data providers (assuming that M is 3), mortgage a is simultaneously requested for evaluation from the 3 external data providers. And 3, respectively returning the external evaluation results of each plant manufacturer to the mortgage, and respectively recording the external evaluation results as a, b and c (namely a is more than or equal to b and more than or equal to c) from high to low by the system. The initial weights of the above values are all the same (i.e. the weights x ═ y ═ z), and then the values with higher deviation are weighted and revised (the sum of multiple weighting coefficients must be equal to 100%) by cross-comparing the deviation between the data based on the median.
Final evaluation ═ x × a) + (y × b) + (z × c)
Wherein, a, b and c are external evaluation results provided by 3 external data providers, and x, y and z are corresponding weighted values.
The specific deviation degree and the weight revision value can be flexibly adjusted according to the business risk preference, for the convenience of understanding, it is assumed that a, b and c are respectively 20000, 15000 and 11000 at the moment, the deviation degree is uniformly set to be 25%, and the initial weight is 100% ÷ 3 ≈ 33.33%. The median is then b
Due to the degree of deviation of a from b
33.3%, exceeding the preset condition by 25%, considering that the evaluation of a is inaccurate, adjusting and weakening the weight x of a, and assuming that the adjusted weight x is 25%;
due to the degree of deviation of c from b
26.7%, exceeding the preset condition by 25%, considering that the evaluation of c is not accurate, adjusting the weight z of c to be weak, and assuming that the weight z is 30% after adjustment;
the final rating was (25% 20000) + [ (1-25% -30%) 15000] + (30% 11000) ═ 15050.
7) The demander system records the original data and the secondary confirmation data of one or more external data providers and the output data of the value model of the system one by one, continuously executes the post-monitoring, generates a quality report, and is used for the demander to evaluate the external data providers and adjust the value model of the demander. The stability, deviation, rationality and the like of the external evaluation results provided by the external data providers are convenient to find, and for the external data providers with poor numerical quality, the weights of the returned external evaluation results are considered to be removed, replaced or directly adjusted.
Based on the same inventive concept, an embodiment of the present invention further provides an evaluation accuracy calibration apparatus, as shown in fig. 3, including:
an obtaining module 301, configured to obtain at least one external evaluation result of a target, and set the same initial weight value for each external evaluation result;
the judging module 302 is used for sequentially arranging the external evaluation results, determining a median result in the external evaluation results, calculating the deviation degrees of all the external evaluation results and the median result, and judging whether any deviation degree meets a preset condition;
if so, the executing module 303 adjusts the initial weight value of the external evaluation result corresponding to the deviation degree to be a first weight value, and adjusts the initial weight values of the other external evaluation results to be second weight values, so that the sum of the first weight value and the second weight value is the same as the sum of the initial weight values;
an output module 304, calculating a final evaluation of the target according to the first weight value and the corresponding external evaluation result, and the second weight value and the corresponding external evaluation result, and outputting the final evaluation.
In an optional embodiment, the determining module 302 determines a median result in the external evaluation result, which specifically includes:
acquiring the number of the external evaluation results;
if the number of the external evaluation results is odd, taking the external evaluation result which is ranked at the middle as the middle result;
and if the number of the external evaluation results is even, calculating the simple arithmetic mean of the two sorted most middle external evaluation results, and taking the simple arithmetic mean as the middle result.
In an optional embodiment, the first weight value is less than the initial weight value, and the second weight value is greater than the initial weight value.
In an optional embodiment, before the obtaining module 301 obtains at least one external evaluation result of the target, the method further includes:
generating a target list according to the target, and sending the target list to an external data provider;
receiving an initial evaluation result fed back by the external data provider;
acquiring basic information of a target, setting a reasonable result range interval according to the basic information, and judging whether the initial evaluation result falls into the reasonable result range interval;
and if so, setting the initial evaluation result as the external evaluation result.
In an optional embodiment, the obtaining module 301 determines whether the initial evaluation result falls within the reasonable result range interval, further including:
if not, returning the initial evaluation result to the external data provider, and indicating the external data provider to perform manual verification on the initial evaluation result;
receiving a secondary evaluation result manually verified by the external data provider, and judging whether the secondary evaluation result is the same as the initial evaluation result;
and if so, setting the initial evaluation result as the external evaluation result.
In an optional embodiment, the obtaining module 301 determines whether the secondary evaluation result is the same as the initial evaluation result, and further includes:
if not, replacing the initial evaluation result with the secondary evaluation result, and judging whether the initial evaluation result falls into the reasonable result range again.
In an optional embodiment, after the outputting module 304 outputs the final evaluation, the method further includes:
and recording all intermediate data corresponding to each external evaluation result, generating a quality report according to the intermediate data, and outputting the quality report.
The device of the foregoing embodiment is used to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Based on the same application concept, embodiments of the present application further provide an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method according to any of the above embodiments is implemented.
Fig. 4 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the electronic device may include: a processor 410, a memory 420, an input/output interface 430, a communication interface 440, and a bus 450. Wherein processor 410, memory 420, input/output interface 430, and communication interface 440 are communicatively coupled to each other within the device via bus 450.
The processor 410 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present specification.
The Memory 420 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random access Memory), a static storage device, a dynamic storage device, or the like. The memory 420 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 420 and called to be executed by the processor 410.
The input/output interface 430 is used for connecting an input/output module to realize information input and output. The input/output module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 440 is used for connecting a communication module (not shown in the figure) to realize communication interaction between the device and other devices. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
It should be noted that although the above-mentioned device only shows the processor 410, the memory 420, the input/output interface 430, the communication interface 440 and the bus 450, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
The electronic device of the foregoing embodiment is used to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Based on the same application concept, embodiments of the present application further provide a computer-readable storage medium, where instructions are stored, and when the instructions are executed on a terminal device, the terminal device is caused to perform the method according to any of the above embodiments.
Computer-readable storage media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
The storage medium of the foregoing embodiment is used to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the idea of the invention, also features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
In addition, well known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures for simplicity of illustration and discussion, and so as not to obscure the invention. Furthermore, devices may be shown in block diagram form in order to avoid obscuring the invention, and also in view of the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the present invention is to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the invention, it should be apparent to one skilled in the art that the invention can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
The embodiments of the invention are intended to embrace all such alternatives, modifications and variances that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements and the like that may be made without departing from the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. An accuracy calibration method for evaluation, comprising:
obtaining at least one external evaluation result of a target, and setting the same initial weight value for each external evaluation result;
sequentially arranging the external evaluation results, determining a median result in the external evaluation results, calculating the deviation degrees of all the external evaluation results and the median result, and judging whether any deviation degree meets a preset condition;
if so, adjusting the initial weight value of the external evaluation result corresponding to the deviation degree to be a first weight value, and adjusting the initial weight values of other external evaluation results to be second weight values, so that the sum of the first weight value and the second weight value is the same as the sum of the initial weight values;
and calculating and determining a final evaluation of the target according to the first weight value and the corresponding external evaluation result, and the second weight value and the corresponding external evaluation result, and outputting the final evaluation.
2. The method according to claim 1, wherein the determining a median result of the external evaluation results specifically comprises:
acquiring the number of the external evaluation results;
if the number of the external evaluation results is odd, taking the external evaluation result which is ranked at the middle as the middle result;
and if the number of the external evaluation results is even, calculating the simple arithmetic mean of the two sorted most middle external evaluation results, and taking the simple arithmetic mean as the middle result.
3. The method of claim 1, wherein the first weight value is less than the initial weight value, and wherein the second weight value is greater than the initial weight value.
4. The method of claim 1, wherein before obtaining at least one external evaluation of the target, further comprising:
generating a target list according to the target, and sending the target list to an external data provider;
receiving an initial evaluation result fed back by the external data provider;
acquiring basic information of a target, setting a reasonable result range interval according to the basic information, and judging whether the initial evaluation result falls into the reasonable result range interval;
and if so, setting the initial evaluation result as the external evaluation result.
5. The method of claim 4, wherein said determining whether the initial evaluation result falls within the reasonable result range interval further comprises:
if not, returning the initial evaluation result to the external data provider, and indicating the external data provider to perform manual verification on the initial evaluation result;
receiving a secondary evaluation result manually verified by the external data provider, and judging whether the secondary evaluation result is the same as the initial evaluation result;
and if so, setting the initial evaluation result as the external evaluation result.
6. The method of claim 5, wherein said determining whether the secondary evaluation result is the same as the initial evaluation result further comprises:
if not, replacing the initial evaluation result with the secondary evaluation result, and judging whether the initial evaluation result falls into the reasonable result range again.
7. The method according to any one of claims 1-6, wherein after said outputting said final evaluation, further comprising:
and recording all intermediate data corresponding to each external evaluation result, generating a quality report according to the intermediate data, and outputting the quality report.
8. An accuracy calibration device for evaluation, comprising:
the acquisition module is used for acquiring at least one external evaluation result of a target and setting the same initial weight value for each external evaluation result;
the judging module is used for sequentially arranging the external evaluation results, determining a median result in the external evaluation results, calculating the deviation degrees of all the external evaluation results and the median result, and judging whether any deviation degree meets a preset condition;
if so, adjusting the initial weight value of the external evaluation result corresponding to the deviation degree to be a first weight value, and adjusting the initial weight values of other external evaluation results to be second weight values, so that the sum of the first weight value and the second weight value is the same as the sum of the initial weight values;
and the output module calculates and determines the final evaluation of the target according to the first weight value and the corresponding external evaluation result, and the second weight value and the corresponding external evaluation result, and outputs the final evaluation.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the program, implements the method of accuracy calibration of an evaluation according to any one of claims 1 to 7.
10. A computer-readable storage medium having stored therein instructions that, when run on a terminal device, cause the terminal device to perform the evaluated accuracy calibration method of any one of claims 1-7.
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