Data processing method and device

Abstract

The invention relates to a data processing method and device. The method comprises the following steps: acquiring a plurality of equipment health parameters of equipment to be detected; respectively processing the plurality of equipment health parameters to obtain parameter scores corresponding to the equipment health parameters; respectively acquiring weights corresponding to the plurality of equipment health parameters; and based on the parameter scores and weights of the plurality of equipment health parameters, calculating to obtain an equipment health score of the to-be-detected equipment. The equipment health score obtained by the method can be used for comprehensively detecting and evaluating the health state of the to-be-detected equipment, and a user can manage the to-be-detected equipment according to the equipment health score, so that the to-be-detected equipment is monitored.

Description

[0001] The present disclosure is a divisional application of a Chinese patent application filed on December 28, 2018, with application number 2018116225164 and invention name “Data processing method and device”. Technical Field

[0002] The present disclosure relates to the field of medical devices, and in particular to a data processing method and device. Background Art

[0003] With the continuous development of technology, more and more medical institutions are using advanced medical equipment to assist medical treatment. As the types and number of medical equipment are growing explosively, the detection of medical equipment is becoming more and more important. In related technologies, data cleaning, classification, and aggregation are usually used to detect medical equipment, so as to judge the status of medical equipment. However, related technologies have many disadvantages, such as cumbersome operation procedures, single judgment basis, and inability to conduct comprehensive detection of medical equipment.

[0004] Therefore, it is urgent to propose a new detection method to detect medical equipment in order to simplify the operation process and conduct comprehensive detection of medical equipment. Summary of the invention

[0005] In view of this, the present disclosure proposes a data processing method and apparatus to detect medical equipment, so as to simplify the operation process and perform comprehensive detection of the medical equipment.

[0006] According to one aspect of the present disclosure, a data processing method is provided, the method comprising:

[0007] Obtain multiple device health parameters of the device to be tested;

[0008] Processing the multiple device health parameters respectively to obtain parameter scores corresponding to the device health parameters;

[0009] Obtaining weights corresponding to the plurality of device health parameters respectively;

[0010] Based on the parameter scores and weights of the multiple device health parameters, the device health score of the device to be detected is calculated.

[0011] In a possible implementation, the equipment health parameters include one or more of equipment abnormality information, cargo usage, quality control inspection data, calibration data, equipment warning information, cross-cargo information, positioning information, light source information, maintenance record information, and maintenance record information, wherein the equipment abnormality information includes the number of times the equipment to be inspected has abnormalities, the cargo usage includes the usage data of the cargo stored in the equipment to be inspected, the quality control inspection data includes the quality control measurement values ​​of the equipment to be inspected on the object to be inspected, the calibration data includes the corresponding values ​​of the object to be inspected at multiple calibration points, the cross-cargo information includes the first identification information and the second identification information of the cargo in the equipment to be inspected, the light source information includes the usage time information of the light-emitting device of the equipment to be inspected, the maintenance record information includes the number of maintenance times and maintenance time information of the equipment to be inspected, and the maintenance record information includes the number of maintenance times and maintenance time information of the equipment to be inspected.

[0012] In a possible implementation manner, the parameter score includes a detection score, and obtaining the parameter score corresponding to the device health parameter includes:

[0013] Obtaining the number of times the device to be detected has abnormalities from the device abnormality information;

[0014] Performing a quadrature process on the number of abnormalities and the preset detection base value to obtain a quadrature result;

[0015] The preset detection score value and the product result are subjected to difference processing to obtain the detection score.

[0016] In a possible implementation manner, the parameter score includes a first usage score and a second usage score, and the processing of the device health parameter to obtain the parameter score includes:

[0017] When the usage data of the goods on the current day in the goods usage is less than the usage data of the goods on the previous day, a preset usage score value is subtracted from a preset usage base value to obtain the first usage score;

[0018] When the usage data of the goods on the current day in the goods usage is greater than or equal to the usage data of the goods on the previous day, a preset usage score value is used as the second usage score.

[0019] In a possible implementation manner, the parameter score includes a quality control test score, and the processing of the device health parameter to obtain the parameter score includes:

[0020] Obtaining a first number of quality control measurement values ​​outside 2 times the standard deviation of the quality control mean value, a second number outside 3 times the standard deviation, and a third number that exceeds 2 times the standard deviation twice in a row in the quality control test data;

[0021] Obtaining a first product of the first data and the first basic score, a second product of the second number and the second basic score, and a third product of the third number and the third basic score;

[0022] performing a summation process on the first product, the second product and the third product to obtain a summation result;

[0023] The preset quality control test score value is subtracted from the sum result to obtain the quality control test score.

[0024] In a possible implementation, the parameter score includes a calibration detection score, and the processing of the device health parameter to obtain the parameter score includes:

[0025] When the calibration data does not satisfy the calibration mode, a difference is calculated between the preset calibration detection score value and the calibration basic score to obtain the calibration detection score.

[0026] In a possible implementation manner, the parameter score includes an alarm score, and the processing of the device health parameter to obtain the parameter score includes:

[0027] Obtain the number of serious warnings according to the device warning information, wherein the serious warnings include two consecutive ordinary warnings and two ordinary warnings in a preset number of detections;

[0028] Performing a product processing on the number of serious warnings and the serious warning basic score to obtain a warning product result;

[0029] A difference process is performed between a preset alarm score value and the warning product result to obtain the alarm score.

[0030] In a possible implementation manner, the parameter score includes a cross-selling score, and the processing of the device health parameter to obtain the parameter score includes:

[0031] Obtaining the number of goods in the mixed goods information whose first identification information and second identification information do not correspond to each other;

[0032] Performing product processing on the number of goods in confluence and the basic score of goods in confluence to obtain a result of product in confluence;

[0033] The preset cross-selling score value and the cross-selling product result are subtracted to obtain the cross-selling score.

[0034] In a possible implementation, the parameter score includes a positioning score, and the processing of the device health parameter to obtain the parameter score includes:

[0035] Obtaining the distance between the location address of the device to be detected and the preset address in the location information;

[0036] When the distance is greater than a preset distance value, a difference processing is performed on the preset positioning score value and the positioning basic score to obtain the positioning score.

[0037] In a possible implementation, the parameter score includes a light source score, and the processing of the device health parameter to obtain the parameter score includes:

[0038] Obtain the accumulated usage time of the light emitting device of the device to be detected in the light source information;

[0039] When the accumulated usage time meets a preset condition, a difference processing is performed on the preset light source score value and the light source basic score to obtain the light source score.

[0040] In a possible implementation manner, the parameter score includes a maintenance score, and the processing of the equipment health parameter to obtain the parameter score includes:

[0041] Obtaining the number of maintenance times of the equipment to be detected in the maintenance record information within a preset time;

[0042] Performing a product processing on the maintenance times and the maintenance basic score to obtain a maintenance product result;

[0043] A difference process is performed on a preset maintenance score value and the maintenance integral result to obtain the maintenance score.

[0044] In a possible implementation manner, the parameter score includes a first maintenance score and a second maintenance score, and the processing of the equipment health parameter to obtain the parameter score includes:

[0045] Determine whether the equipment to be tested has been maintained within a preset time according to the maintenance record information;

[0046] When the maintenance record information shows that the equipment to be detected has not been maintained, performing a difference processing on a preset maintenance score value and a maintenance basic score to obtain the first maintenance score;

[0047] When the maintenance record information shows that the equipment to be detected has been maintained, a preset maintenance score is used as the second maintenance score.

[0048] In a possible implementation, processing the plurality of parameter scores and the weights corresponding to the plurality of parameter scores to obtain a device health score of the device to be detected includes:

[0049] Multiplying each parameter score by the corresponding weight value to obtain multiple intermediate results;

[0050] The multiple intermediate results are summed to obtain the device health score.

[0051] In a possible embodiment, the device to be detected includes a protein detection analyzer.

[0052] According to another aspect of the present disclosure, a data processing device is provided, the device comprising:

[0053] A first acquisition module is used to acquire multiple device health parameters of the device to be detected;

[0054] a processing module, connected to the first acquisition module, configured to process the plurality of device health parameters respectively to obtain parameter scores corresponding to the device health parameters;

[0055] A second acquisition module, connected to the processing module, for respectively acquiring weights corresponding to the plurality of device health parameters;

[0056] A calculation module is connected to the second acquisition module and is used to calculate the device health score of the device to be detected based on the parameter scores and weights of the multiple device health parameters.

[0057] In a possible implementation, the equipment health parameters include one or more of equipment abnormality information, cargo usage, quality control inspection data, calibration data, equipment warning information, cross-cargo information, positioning information, light source information, maintenance record information, and maintenance record information, wherein the equipment abnormality information includes the number of times the equipment to be inspected has abnormalities, the cargo usage includes the usage data of the cargo stored in the equipment to be inspected, the quality control inspection data includes the quality control measurement values ​​of the equipment to be inspected on the object to be inspected, the calibration data includes the corresponding values ​​of the object to be inspected at multiple calibration points, the cross-cargo information includes the first identification information and the second identification information of the cargo in the equipment to be inspected, the light source information includes the usage time information of the light-emitting device of the equipment to be inspected, the maintenance record information includes the number of maintenance times and maintenance time information of the equipment to be inspected, and the maintenance record information includes the number of maintenance times and maintenance time information of the equipment to be inspected.

[0058] In a possible implementation manner, the parameter score includes a detection score, and obtaining the parameter score corresponding to the device health parameter includes:

[0059] Obtaining the number of times the device to be detected has abnormalities from the device abnormality information;

[0060] Performing a quadrature process on the number of abnormalities and the preset detection base value to obtain a quadrature result;

[0061] The preset detection score value and the product result are subjected to difference processing to obtain the detection score.

[0062] In a possible implementation manner, the parameter score includes a first usage score and a second usage score, and the processing of the device health parameter to obtain the parameter score includes:

[0063] When the usage data of the goods on the current day in the goods usage is less than the usage data of the goods on the previous day, a preset usage score value is subtracted from a preset usage base value to obtain the first usage score;

[0064] When the usage data of the goods on the current day in the goods usage is greater than or equal to the usage data of the goods on the previous day, a preset usage score value is used as the second usage score.

[0065] In a possible implementation manner, the parameter score includes a quality control test score, and the processing of the device health parameter to obtain the parameter score includes:

[0066] Obtaining a first number of quality control measurement values ​​outside 2 times the standard deviation of the quality control mean value, a second number outside 3 times the standard deviation, and a third number that exceeds 2 times the standard deviation twice in a row in the quality control test data;

[0067] Obtaining a first product of the first data and the first basic score, a second product of the second number and the second basic score, and a third product of the third number and the third basic score;

[0068] performing a summation process on the first product, the second product and the third product to obtain a summation result;

[0069] The preset quality control test score value is subtracted from the sum result to obtain the quality control test score.

[0070] In a possible implementation, the parameter score includes a calibration detection score, and the processing of the device health parameter to obtain the parameter score includes:

[0071] When the calibration data does not satisfy the calibration mode, a difference is calculated between the preset calibration detection score value and the calibration basic score to obtain the calibration detection score.

[0072] In a possible implementation manner, the parameter score includes an alarm score, and the processing of the device health parameter to obtain the parameter score includes:

[0073] Obtain the number of serious warnings according to the device warning information, wherein the serious warnings include two consecutive ordinary warnings and two ordinary warnings in a preset number of detections;

[0074] Performing a product processing on the number of serious warnings and the serious warning basic score to obtain a warning product result;

[0075] A difference process is performed between a preset alarm score value and the warning product result to obtain the alarm score.

[0076] In a possible implementation manner, the parameter score includes a cross-selling score, and the processing of the device health parameter to obtain the parameter score includes:

[0077] Obtaining the number of goods in the mixed goods information whose first identification information and second identification information do not correspond to each other;

[0078] Performing product processing on the number of goods in confluence and the basic score of goods in confluence to obtain a result of product in confluence;

[0079] The preset cross-selling score value and the cross-selling product result are subtracted to obtain the cross-selling score.

[0080] In a possible implementation, the parameter score includes a positioning score, and the processing of the device health parameter to obtain the parameter score includes:

[0081] Obtaining the distance between the location address of the device to be detected and the preset address in the location information;

[0082] When the distance is greater than a preset distance value, a difference processing is performed on the preset positioning score value and the positioning basic score to obtain the positioning score.

[0083] In a possible implementation, the parameter score includes a light source score, and the processing of the device health parameter to obtain the parameter score includes:

[0084] Obtain the accumulated usage time of the light emitting device of the device to be detected in the light source information;

[0085] When the accumulated usage time meets a preset condition, a difference processing is performed on the preset light source score value and the light source basic score to obtain the light source score.

[0086] In a possible implementation manner, the parameter score includes a maintenance score, and the processing of the equipment health parameter to obtain the parameter score includes:

[0087] Obtaining the number of maintenance times of the equipment to be detected in the maintenance record information within a preset time;

[0088] Performing a product processing on the maintenance times and the maintenance basic score to obtain a maintenance product result;

[0089] A difference process is performed on a preset maintenance score value and the maintenance integral result to obtain the maintenance score.

[0090] In a possible implementation manner, the parameter score includes a first maintenance score and a second maintenance score, and the processing of the equipment health parameter to obtain the parameter score includes:

[0091] Determine whether the equipment to be tested has been maintained within a preset time according to the maintenance record information;

[0092] When the maintenance record information shows that the equipment to be detected has not been maintained, performing a difference processing on a preset maintenance score value and a maintenance basic score to obtain the first maintenance score;

[0093] When the maintenance record information shows that the equipment to be detected has been maintained, a preset maintenance score is used as the second maintenance score.

[0094] In a possible implementation, processing the plurality of parameter scores and the weights corresponding to the plurality of parameter scores to obtain a device health score of the device to be detected includes:

[0095] Multiplying each parameter score by the corresponding weight value to obtain multiple intermediate results;

[0096] The multiple intermediate results are summed to obtain the device health score.

[0097] In a possible embodiment, the device to be detected includes a protein detection analyzer.

[0098] According to another aspect of the present disclosure, a data processing device is provided, comprising:

[0099] processor;

[0100] a memory for storing processor-executable instructions;

[0101] Wherein, the processor is configured to:

[0102] Execute the data processing method.

[0103] According to another aspect of the present disclosure, a non-volatile computer-readable storage medium is provided, wherein the computer program instructions implement the method when executed by a processor.

[0104] Through the above method, the present disclosure can process different device health parameters separately after obtaining multiple device health parameters of the device to be detected, obtain parameter scores corresponding to each device health parameter, and use the obtained multiple parameter scores and corresponding weights to obtain the device health score of the device to be detected. The present disclosure uses multiple device health parameters to obtain device health scores, and these device health parameters belong to different categories. According to the device health score, it can be used to comprehensively detect and evaluate the health status of the device to be detected. Users can manage the device to be detected according to the device health score, thereby realizing the monitoring of the device to be detected.

[0105] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0106] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0107] Figure 1 A flow chart of a data processing method according to an embodiment of the present disclosure is shown.

[0108] Figure 2 A block diagram of a data processing device according to an embodiment of the present disclosure is shown.

[0109] Figure 3 is a block diagram showing a data processing device according to an embodiment of the present disclosure.

[0110] Figure 4 is a block diagram showing a device for data processing according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0111] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0112] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0113] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0114] See also Figure 1 , Figure 1 A flow chart of a data processing method according to an embodiment of the present disclosure is shown.

[0115] The method can be applied to a server or a terminal, such as Figure 1 As shown, the method includes:

[0116] Step S110, obtaining multiple device health parameters of the device to be detected;

[0117] Step S120, processing the multiple device health parameters respectively to obtain parameter scores corresponding to the device health parameters;

[0118] Step S130, respectively obtaining weights corresponding to the plurality of device health parameters;

[0119] Step S140: Calculate the device health score of the device to be detected based on the parameter scores and weights of the multiple device health parameters.

[0120] Through the above method, the present disclosure can process different device health parameters separately after obtaining multiple device health parameters of the device to be detected, obtain parameter scores corresponding to each device health parameter, and use the obtained multiple parameter scores and corresponding weights to obtain the device health score of the device to be detected. The present disclosure uses multiple device health parameters to obtain device health scores, and these device health parameters belong to different categories. According to the device health score, it can be used to comprehensively detect and evaluate the health status of the device to be detected. Users can manage the device to be detected according to the device health score, thereby realizing the monitoring of the device to be detected.

[0121] In a possible implementation, the equipment health parameters include one or more of equipment abnormality information, cargo usage, quality control inspection data, calibration data, equipment warning information, cross-cargo information, positioning information, light source information, maintenance record information, and maintenance record information, wherein the equipment abnormality information includes the number of times the equipment to be inspected has abnormalities, the cargo usage includes the usage data of the cargo stored in the equipment to be inspected, the quality control inspection data includes the quality control measurement values ​​of the equipment to be inspected on the object to be inspected, the calibration data includes the corresponding values ​​of the object to be inspected at multiple calibration points, the cross-cargo information includes the first identification information and the second identification information of the cargo in the equipment to be inspected, the light source information includes the usage time information of the light-emitting device of the equipment to be inspected, the maintenance record information includes the number of maintenance times and maintenance time information of the equipment to be inspected, and the maintenance record information includes the number of maintenance times and maintenance time information of the equipment to be inspected.

[0122] In one possible implementation, the equipment health parameters can be obtained by a sensing device (including multiple sensors) of the equipment to be detected and stored in a data storage device (such as a memory) of the equipment to be detected, and the equipment health parameters detected by the sensing device can be uploaded to the network for storage in a data server.

[0123] For example, for positioning information, the device to be detected can be positioned by using the global positioning system GPS to obtain positioning information of the device to be detected, and the positioning information is stored in a data storage device or sent to a network to be stored in a data server.

[0124] In a possible implementation manner, the device health parameter may also be information input by a user, or information transmitted from other devices or equipment.

[0125] For example, the maintenance record information may be information input into a data storage device or a data server by maintenance personnel after repairing the equipment to be detected.

[0126] It should be understood that the above examples, explanations and sources of the types of equipment health parameters are exemplary descriptions and cannot be regarded as limitations of the present disclosure. Ordinary technicians in this field can increase the types of equipment health parameters as needed. The sources of equipment health parameters are also diverse and can be selected according to actual conditions.

[0127] In a possible implementation manner, the parameter score includes a detection score, and obtaining the parameter score corresponding to the device health parameter includes:

[0128] Obtaining the number of times the device to be detected has abnormalities from the device abnormality information;

[0129] Performing a quadrature process on the number of abnormalities and the preset detection base value to obtain a quadrature result;

[0130] The preset detection score value and the product result are subjected to difference processing to obtain the detection score.

[0131] The preset detection score value and the preset detection base value can be set as needed. In an example, the preset detection score value can be set to 100 points and the preset detection base value can be set to 1 point. In this way, after obtaining the number of abnormal times of the device to be detected, the corresponding score can be subtracted from the preset detection score value to obtain the detection score.

[0132] In a possible implementation, when an abnormality occurs in the device to be detected, the abnormality may be recorded. The recorded abnormality information may include, for example, the cause of the abnormality, the abnormality identifier and the number.

[0133] In this embodiment, an exception information table can be pre-set, and the above-mentioned exception cause, exception identifier and number can be included in the exception information table. In this way, when the device to be detected detects the abnormality represented by the exception cause in the exception information table, the exception identifier can be recorded. In this way, the cause of the abnormality can be obtained based on the recorded exception identifier and the exception information table.

[0134] In an example, the exception information table may be as shown in Table 1.

[0135] Table 1

[0136] serial number Abnormal identification Abnormal cause 1 NR1 Reagent 1 not received 2 NR2 Reagent 2 not received 3 NS No signal from liquid level sensor, 578 wavelength signal value is low 4 HOOK Antigen concentration is too high 5 HAT Test tube not removed 6 SE There is clot in the reaction cup or the sample turbidity is too high before reaction

[0137] In a possible implementation, when the sensor of the device to be detected detects the abnormality represented by the abnormality cause in the abnormality information table, the abnormality identifier may be stored or uploaded to the network.

[0138] It should be understood that Table 1 is exemplary, and those skilled in the art can add, delete, or change the information in the abnormal information table as needed, and the present disclosure does not limit this.

[0139] In a possible implementation, the parameter score may include a first usage score and a second usage score, and the processing of the device health parameter to obtain the parameter score includes:

[0140] When the usage data of the goods on the current day in the goods usage is less than the usage data of the goods on the previous day, a preset usage score value is subtracted from a preset usage base value to obtain the first usage score;

[0141] When the usage data of the goods on the current day in the goods usage is greater than or equal to the usage data of the goods on the previous day, a preset usage score value is used as the second usage score.

[0142] In a possible implementation, the cargo types in the devices to be detected may be different according to the types of the devices to be detected. For example, in various medical analyzers, the cargo may be a reagent.

[0143] In one possible implementation, the preset usage score value may be, for example, 100 points, and the preset usage base value may be, for example, 10 points. Of course, in other implementations, the preset usage score value and the preset usage base value may be set according to actual conditions, and the present disclosure does not limit this.

[0144] It should be understood that due to the different environments in which the devices to be tested are located, different devices to be tested have different usage data. For example, a provincial-level tertiary children's hospital may have a usage of 1,000 people / day, while a general secondary hospital or even a grassroots hospital may only have a few to a dozen. Therefore, the comparison standard selected in the present disclosure is to compare the usage data of the device to be tested on the same day with the usage data of the previous day. In other embodiments, those skilled in the art can select the comparison standard according to the different situations of the device to be tested. For example, in the same hospital, the usage data of other devices to be tested on the same day can also be used to calculate the first usage score or the second usage score, and the comparison is not limited by the present disclosure.

[0145] Through the above method, the present disclosure can monitor and evaluate the usage of the goods of the equipment to be tested, and can obtain the usage of the equipment to be tested based on the usage score.

[0146] In a possible implementation, the parameter score may include a quality control test score, and the processing of the device health parameter to obtain the parameter score includes:

[0147] Obtaining a first number of quality control measurement values ​​outside 2 times the standard deviation of the quality control mean value, a second number outside 3 times the standard deviation, and a third number that exceeds 2 times the standard deviation twice in a row in the quality control test data;

[0148] Obtaining a first product of the first data and the first basic score, a second product of the second number and the second basic score, and a third product of the third number and the third basic score;

[0149] performing a summation process on the first product, the second product and the third product to obtain a summation result;

[0150] The preset quality control test score value is subtracted from the sum result to obtain the quality control test score.

[0151] In one example, the first basic score, the second basic score and the third basic score can be 1, 2, and 2 respectively, and the preset quality control detection score can be 100. Of course, in other embodiments, the first basic score, the second basic score, the third basic score and the preset quality control detection score can be set to other scores, which is not limited in the present disclosure.

[0152] In a possible implementation, the quality control mean value and standard deviation may be determined according to actual conditions. It should be understood that, in different devices to be tested, since the uses of the devices to be tested are different, the present disclosure does not limit the values ​​of the quality control mean value and standard deviation.

[0153] In a possible implementation, a Levey-Jennings quality control chart can be prepared in advance. For example, after selecting a quality control product, the quality control average value (x) can be calculated based on 20 measurement results of the quality control product, and a quality control chart can be drawn with x±2s as the warning limit and x±3s as the out-of-control limit, where s is the standard deviation. The selection of quality control products can be based on the different adaptability of the equipment to be tested, and this disclosure does not limit this.

[0154] Through the above method, the present disclosure can obtain quality control test data in the device to be tested, and obtain a quality control test score based on the quality control test data to evaluate the quality control test of the device to be tested.

[0155] In a possible implementation, the parameter score may include a calibration detection score, and the processing of the device health parameter to obtain the parameter score includes:

[0156] When the calibration data does not satisfy the calibration mode, a difference is calculated between the preset calibration detection score value and the calibration basic score to obtain the calibration detection score.

[0157] In a possible implementation, since the calibration test of the device to be tested is an important factor in the health evaluation of the device to be tested, the preset calibration test score value can be 100, and the calibration basic score value can be 100. In this way, when the calibration data does not meet the calibration mode, the calibration test score can be set to 0, thereby highlighting the severity of the problem. Of course, in other implementations, the preset calibration test score value and the calibration basic score value can be set to other scores, which is not limited by the present disclosure.

[0158] In a possible implementation, the calibration data may be a quadratic spline curve produced based on AD values ​​detected by the selected calibration product on the device to be detected.

[0159] In a possible implementation, the calibration modes may include multiple types, and different calibration modes may be used when the device to be detected detects different items.

[0160] In a possible implementation, when the device to be tested needs to detect the C-reactive protein (CRP) item, the P9-P5 mode needs to be met:

[0161] 10mg / L (the third calibration point) - 0mg / L (the first calibration point) > 900;

[0162] 200mg / L (7th calibration point)-150mg / L (6th calibration point)>500.

[0163] When the device to be tested needs to detect serum amyloid A (SAA), it needs to meet the P13-P5 mode:

[0164] 12mg / L (the third calibration point) - 0mg / L (the first calibration point) > 1200;

[0165] 200mg / L (7th calibration point)-100mg / L (6th calibration point)>3000.

[0166] When the device to be tested needs to detect glycosylated hemoglobin (HbA1c), it needs to meet the following requirements:

[0167] The absorbance of the next calibration point must be greater than the absorbance of the previous point;

[0168] 6.8% (3rd calibration point) - 0% (1st calibration point) > 1200;

[0169] 14% (5th calibration point)-9.8% (4th calibration point)>1800.

[0170] It should be understood that the above description of the calibration mode is exemplary and does not limit the present disclosure.

[0171] Through the above method, the present disclosure can obtain calibration data and obtain calibration test scores under different calibration modes, thereby using the calibration test scores to monitor and evaluate the calibration test of the equipment to be tested.

[0172] In a possible implementation, the parameter score may include an alarm score, and the processing of the device health parameter to obtain the parameter score includes:

[0173] Obtain the number of serious warnings according to the device warning information, wherein the serious warnings include two consecutive ordinary warnings and two ordinary warnings in a preset number of detections;

[0174] Performing a product processing on the number of serious warnings and the serious warning basic score to obtain a warning product result;

[0175] A difference process is performed between a preset alarm score value and the warning product result to obtain the alarm score.

[0176] In a possible implementation, all samples (1-10 samples) on the test tube rack that have completed instrument testing can be counted as one test.

[0177] In a possible implementation manner, the preset number of times may be set to 5 times or other times.

[0178] In a possible implementation, it may be configured that when a severe warning condition is met, the device to be detected sends an alarm message to a user or a server. The alarm message may be, for example, a text message, an APP text message, or the like.

[0179] In a possible implementation, after a normal warning or a serious warning occurs, device warning information may be generated and recorded in the device to be detected or in a server.

[0180] In one possible implementation, after a normal warning or a serious warning appears, a method for canceling the warning can be set. For example, you can choose to cancel it automatically or by the user after a certain period of time. A serious warning can be canceled by the user (the reason for cancellation can be written or not), and the calendar can be selected to continue counting after a certain time. An alarm message is sent every time a serious warning condition is met. After the user cancels it on the same day, there will be no more prompts on the same day.

[0181] In a possible implementation, the preset alarm score value may be 100, and the severe warning basic score value may be 5. Of course, the preset alarm score value and the severe warning basic score value may be set to other scores, which are not limited in the present disclosure.

[0182] Through the above method, the present disclosure can obtain the device warning information of the device to be detected, and obtain the alarm score according to the device warning information, so as to monitor and evaluate the operating status of the device to be detected.

[0183] In a possible implementation, the parameter score may include a cross-selling score, and the processing of the device health parameter to obtain the parameter score includes:

[0184] Obtaining the number of goods in the mixed goods information whose first identification information and second identification information do not correspond to each other;

[0185] Performing product processing on the number of goods in confluence and the basic score of goods in confluence to obtain a result of product in confluence;

[0186] The preset cross-selling score value and the cross-selling product result are subtracted to obtain the cross-selling score.

[0187] In a possible implementation, the first identification information may be, for example, an identification code (such as a barcode, a QR code, etc.) of the goods in the device to be detected, and the second identification information may be, for example, user information. An identification information comparison table may be set in advance, and the device to be detected may detect the first identification information of the goods and obtain the second identification information of the user, and make a judgment based on the identification information comparison table. When the first identification information does not correspond to the second identification information, it is judged that the goods are mixed.

[0188] For example, when the goods are shipped out of the warehouse, the goods (a box of reagents) should be sent to the first hospital, so the first identification information of the box of reagents and the second identification information of the first hospital are bound and recorded in the identification information comparison table. When the goods are placed in the device to be detected, the device to be detected detects the box of reagents (for example, scans a QR code) to obtain the first identification information, and obtains the second identification information of the location of the device to be detected (for example, the first hospital), and makes a judgment based on the obtained first identification information and second identification information. It should be understood that the identification information comparison table can be in other forms and can be broadcast over the network after being set up to facilitate the device to be detected to judge whether there is mixed goods.

[0189] In one possible implementation, the preset score value for cross-selling may be 100, and the basic score value for cross-selling may be 10 points. Of course, in other implementations, the preset score value for cross-selling and the basic score value for cross-selling may be other scores, which is not limited in the present disclosure.

[0190] Through the above method, the present disclosure can obtain the cross-selling information of the equipment to be detected, and monitor and evaluate the cross-selling situation of the equipment to be detected based on the cross-selling information or the cross-selling score of the equipment to be detected.

[0191] In a possible implementation, the parameter score includes a positioning score, and the processing of the device health parameter to obtain the parameter score includes:

[0192] Obtaining the distance between the location address of the device to be detected and the preset address in the location information;

[0193] When the distance is greater than a preset distance value, a difference processing is performed on the preset positioning score value and the positioning basic score to obtain the positioning score.

[0194] In a possible implementation manner, the location address of the device to be detected may be obtained through GPS or other means, and may be obtained when the device to be detected performs a self-test or at other times.

[0195] In a possible implementation, the preset distance value can be set according to actual needs. For example, some devices to be detected need to be at a fixed location to operate well or play an important role. When the position deviates from the fixed location by more than the preset distance value, the working state and working value of the devices to be detected will be greatly affected.

[0196] In one example, the preset distance value may be set to 2 kilometers, or other values.

[0197] In a possible implementation, the preset positioning score value may be set to 100, and the positioning base score may be set to 100. Therefore, when the position deviation value of the device to be detected is greater than the preset distance value, the positioning score will be set to the lowest value of 0.

[0198] In a possible implementation, the parameter score may include a light source score, and the processing of the device health parameter to obtain the parameter score includes:

[0199] Obtain the accumulated usage time of the light emitting device of the device to be detected in the light source information;

[0200] When the accumulated usage time meets a preset condition, a difference processing is performed on a preset light source score value and a light source basic score to obtain the light source score.

[0201] In a possible implementation, the light emitting device may be a halogen lamp, an LED lamp, or the like.

[0202] In a possible implementation, the device to be detected can time the light-emitting device when it is in normal use, and save the accumulated usage time of the light-emitting device in the device to be detected or in a server.

[0203] In a possible implementation, the preset condition may be:

[0204] The accumulated usage time of the lighting device is between 1800 hours and 2000 hours; or

[0205] The cumulative usage time of the light-emitting device is more than 2000 hours.

[0206] When the accumulated usage time meets different conditions, the basic score of the light source can be set to different values, which is not limited in the present disclosure.

[0207] In a possible implementation, the preset light source score value may be 100.

[0208] In a possible implementation, the parameter score may include a maintenance score, and the processing of the equipment health parameter to obtain the parameter score includes:

[0209] Obtaining the number of maintenance times of the equipment to be detected in the maintenance record information within a preset time;

[0210] Performing a product processing on the maintenance times and the maintenance basic score to obtain a maintenance product result;

[0211] A difference process is performed on a preset maintenance score value and the maintenance integral result to obtain the maintenance score.

[0212] In a possible implementation, the preset time may be 180 days, the preset maintenance score value may be 100, and the maintenance basic score may be 10 points.

[0213] Through the above method, the present disclosure can count the number of times the equipment to be detected is repaired within a preset time, and evaluate the working status of the equipment to be detected according to the number of repairs or the repair score.

[0214] In a possible implementation, the parameter score may include a first maintenance score and a second maintenance score, and the processing of the equipment health parameter to obtain the parameter score includes:

[0215] Determine whether the equipment to be tested has been maintained within a preset time according to the maintenance record information;

[0216] When the maintenance record information shows that the equipment to be detected has not been maintained, performing a difference processing on a preset maintenance score value and a maintenance basic score to obtain the first maintenance score;

[0217] When the maintenance record information shows that the equipment to be detected has been maintained, a preset maintenance score is used as the second maintenance score.

[0218] In a possible implementation, the preset time may be, for example, 180 days, the preset maintenance score may be 100, and the basic maintenance score may be 10.

[0219] In a possible implementation, after maintaining the equipment to be detected, the user may record the maintenance record information in a storage device or a server.

[0220] Through the above method, the present disclosure can count the maintenance times of the equipment to be detected within a preset time, and calculate according to the maintenance times to obtain the first maintenance score or the second maintenance score of the equipment to be detected, so as to monitor and evaluate the working status of the equipment to be detected.

[0221] In a possible implementation, processing the plurality of parameter scores and the weights corresponding to the plurality of parameter scores to obtain a device health score of the device to be detected may include:

[0222] Multiplying each parameter score by the corresponding weight value to obtain multiple intermediate results;

[0223] The multiple intermediate results are summed to obtain the device health score.

[0224] In a possible implementation, the sum of all weights may be 1, and the size of the weights may be dynamically set as needed.

[0225] In one possible implementation, after obtaining the device health score of the device to be tested, the size of the device health score can be judged, and different operations can be performed when the device health score meets different conditions. For example, when the device health score is less than a preset value, the user is notified to perform repairs or maintenance.

[0226] In a possible implementation, after obtaining the device health scores of multiple devices to be detected, the multiple device health scores can be processed, for example, the average value, variance, etc. of the multiple device health scores are obtained, and the status of the multiple devices to be detected is analyzed by obtaining the results.

[0227] In a possible implementation, the equipment health score of the equipment to be tested can be counted on a one-day basis. For example, the statistical period can be from 12:00 of the previous day to 12:00 of today. The statistical period from 12:00:00 on November 15, 2018 to 12:00:00 on November 16, 2018 can be used to represent the health status of the instrument on November 15, 2018.

[0228] In a possible implementation, a device health score graph (eg, a curve graph, a bar graph, etc.) may be generated based on device health scores obtained in multiple cycles to intuitively display the health status of the device to be detected.

[0229] In a possible implementation, the equipment to be detected includes a protein detection analyzer and other types of detection analyzers (automatic biochemical analyzer, immunoassay analyzer, etc.).

[0230] It should be noted that the above description of equipment health parameters (such as various scores) is exemplary and cannot be considered as a limitation of the present disclosure.

[0231] Through the above method, the present invention can simply and comprehensively detect the health status of the equipment to be detected. After obtaining the equipment health score of the equipment to be detected, the equipment health score can be used to monitor and evaluate the health status of the equipment to be detected. Users can choose to repair, maintain or otherwise process the equipment to be detected based on the equipment health score to ensure the normal operation of the equipment to be detected.

[0232] See also Figure 2, Figure 2 A block diagram of a data processing device according to an embodiment of the present disclosure is shown.

[0233] like Figure 2 As shown, the device comprises:

[0234] A first acquisition module 10 is used to acquire multiple device health parameters of the device to be detected;

[0235] A processing module 20, connected to the first acquisition module 10, for processing the plurality of device health parameters respectively to obtain parameter scores corresponding to the device health parameters;

[0236] A second acquisition module 30, connected to the processing module 20, for respectively acquiring weights corresponding to the plurality of device health parameters;

[0237] The calculation module 40 is connected to the second acquisition module 30, and is used to calculate the device health score of the device to be detected based on the parameter scores and weights of the multiple device health parameters.

[0238] Through the above device, the present disclosure can process different device health parameters separately after obtaining multiple device health parameters of the device to be detected, obtain parameter scores corresponding to each device health parameter, and use the obtained multiple parameter scores and corresponding weights to obtain the device health score of the device to be detected. The present disclosure uses multiple device health parameters to obtain device health scores, and these device health parameters belong to different categories. According to the device health score, it can be used to comprehensively detect and evaluate the health status of the device to be detected. Users can manage the device to be detected according to the device health score, thereby realizing the monitoring of the device to be detected.

[0239] In a possible implementation, the equipment health parameters include one or more of equipment abnormality information, cargo usage, quality control inspection data, calibration data, equipment warning information, cross-cargo information, positioning information, light source information, maintenance record information, and maintenance record information, wherein the equipment abnormality information includes the number of times the equipment to be inspected has abnormalities, the cargo usage includes the usage data of the cargo stored in the equipment to be inspected, the quality control inspection data includes the quality control measurement values ​​of the equipment to be inspected on the object to be inspected, the calibration data includes the corresponding values ​​of the object to be inspected at multiple calibration points, the cross-cargo information includes the first identification information and the second identification information of the cargo in the equipment to be inspected, the light source information includes the usage time information of the light-emitting device of the equipment to be inspected, the maintenance record information includes the number of maintenance times and maintenance time information of the equipment to be inspected, and the maintenance record information includes the number of maintenance times and maintenance time information of the equipment to be inspected.

[0240] In a possible implementation manner, the parameter score includes a detection score, and obtaining the parameter score corresponding to the device health parameter includes:

[0241] Obtaining the number of times the device to be detected has abnormalities from the device abnormality information;

[0242] Performing a quadrature process on the number of abnormalities and the preset detection base value to obtain a quadrature result;

[0243] The preset detection score value and the product result are subjected to difference processing to obtain the detection score.

[0244] In a possible implementation manner, the parameter score includes a first usage score and a second usage score, and the processing of the device health parameter to obtain the parameter score includes:

[0245] When the usage data of the goods on the current day in the goods usage is less than the usage data of the goods on the previous day, a preset usage score value is subtracted from a preset usage base value to obtain the first usage score;

[0246] When the usage data of the goods on the current day in the goods usage is greater than or equal to the usage data of the goods on the previous day, a preset usage score value is used as the second usage score.

[0247] In a possible implementation manner, the parameter score includes a quality control test score, and the processing of the device health parameter to obtain the parameter score includes:

[0248] Obtaining a first number of quality control measurement values ​​outside 2 times the standard deviation of the quality control mean value, a second number outside 3 times the standard deviation, and a third number that exceeds 2 times the standard deviation twice in a row in the quality control test data;

[0249] Obtaining a first product of the first data and the first basic score, a second product of the second number and the second basic score, and a third product of the third number and the third basic score;

[0250] performing a summation process on the first product, the second product and the third product to obtain a summation result;

[0251] The preset quality control test score value is subtracted from the sum result to obtain the quality control test score.

[0252] In a possible implementation, the parameter score includes a calibration detection score, and the processing of the device health parameter to obtain the parameter score includes:

[0253] When the calibration data does not satisfy the calibration mode, a difference is calculated between the preset calibration detection score value and the calibration basic score to obtain the calibration detection score.

[0254] In a possible implementation manner, the parameter score includes an alarm score, and the processing of the device health parameter to obtain the parameter score includes:

[0255] Obtain the number of serious warnings according to the device warning information, wherein the serious warnings include two consecutive ordinary warnings and two ordinary warnings in a preset number of detections;

[0256] Performing a product processing on the number of serious warnings and the serious warning basic score to obtain a warning product result;

[0257] A difference process is performed between a preset alarm score value and the warning product result to obtain the alarm score.

[0258] In a possible implementation manner, the parameter score includes a cross-selling score, and the processing of the device health parameter to obtain the parameter score includes:

[0259] Obtaining the number of goods in the mixed goods information whose first identification information and second identification information do not correspond to each other;

[0260] Performing product processing on the number of goods in confluence and the basic score of goods in confluence to obtain a result of product in confluence;

[0261] The preset cross-selling score value and the cross-selling product result are subtracted to obtain the cross-selling score.

[0262] In a possible implementation, the parameter score includes a positioning score, and the processing of the device health parameter to obtain the parameter score includes:

[0263] Obtaining the distance between the location address of the device to be detected and the preset address in the location information;

[0264] When the distance is greater than a preset distance value, a difference processing is performed on the preset positioning score value and the positioning basic score to obtain the positioning score.

[0265] In a possible implementation, the parameter score includes a light source score, and the processing of the device health parameter to obtain the parameter score includes:

[0266] Obtain the accumulated usage time of the light emitting device of the device to be detected in the light source information;

[0267] When the accumulated usage time meets a preset condition, a difference processing is performed on the preset light source scoring value and the light source basic score to obtain the light source score.

[0268] In a possible implementation manner, the parameter score includes a maintenance score, and the processing of the equipment health parameter to obtain the parameter score includes:

[0269] Obtaining the number of maintenance times of the equipment to be detected in the maintenance record information within a preset time;

[0270] Performing a product processing on the maintenance times and the maintenance basic score to obtain a maintenance product result;

[0271] A difference process is performed on a preset maintenance score value and the maintenance integral result to obtain the maintenance score.

[0272] In a possible implementation manner, the parameter score includes a first maintenance score and a second maintenance score, and the processing of the equipment health parameter to obtain the parameter score includes:

[0273] Determine whether the equipment to be tested has been maintained within a preset time according to the maintenance record information;

[0274] When the maintenance record information shows that the equipment to be detected has not been maintained, performing a difference processing on a preset maintenance score value and a maintenance basic score to obtain the first maintenance score;

[0275] When the maintenance record information shows that the equipment to be detected has been maintained, a preset maintenance score is used as the second maintenance score.

[0276] In a possible implementation, processing the plurality of parameter scores and the weights corresponding to the plurality of parameter scores to obtain a device health score of the device to be detected includes:

[0277] Multiplying each parameter score by the corresponding weight value to obtain multiple intermediate results;

[0278] The multiple intermediate results are summed to obtain the device health score.

[0279] In a possible embodiment, the device to be detected includes a protein detection analyzer.

[0280] See also Figure 3 , Figure 3 800 is a block diagram showing a data processing device according to an embodiment of the present disclosure. For example, the device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0281] Reference Figure 3, the device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0282] The processing component 802 generally controls the overall operation of the device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0283] The memory 804 is configured to store various types of data to support operations on the device 800. Examples of such data include instructions for any application or method operating on the device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0284] The power supply component 806 provides power to the various components of the device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 800.

[0285] The multimedia component 808 includes a screen that provides an output interface between the device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0286] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the device 800 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0287] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0288] The sensor assembly 814 includes one or more sensors for providing various aspects of the status assessment of the device 800. For example, the sensor assembly 814 can detect the open / closed state of the device 800, the relative positioning of components, such as the display and keypad of the device 800, and the sensor assembly 814 can also detect the position change of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, the orientation or acceleration / deceleration of the device 800, and the temperature change of the device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0289] The communication component 816 is configured to facilitate wired or wireless communication between the device 800 and other devices. The device 800 can access a wireless network based on a communication standard, such as Wi-Fi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0290] In an exemplary embodiment, the apparatus 800 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0291] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions, which can be executed by the processor 820 of the device 800 to perform the above method.

[0292] See also Figure 4 , Figure 4 1 is a block diagram showing a device for data processing according to an embodiment of the present disclosure. For example, the device 1900 may be provided as a server. Figure 4 , the apparatus 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions, such as an application, that can be executed by the processing component 1922. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0293] The device 1900 may also include a power supply component 1926 configured to perform power management of the device 1900, a wired or wireless network interface 1950 configured to connect the device 1900 to a network, and an input / output (I / O) interface 1958. The device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, MacOS X™, Unix™, Linux™, FreeBSD™, or the like.

[0294] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the device 1900 to perform the above method.

[0295] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0296] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0297] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0298] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0299] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0300] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0301] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0302] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0303] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A data processing method, characterized in that: The method comprises: Obtain multiple device health parameters of the device to be tested; Processing the multiple device health parameters respectively to obtain parameter scores corresponding to the device health parameters; Obtaining weights corresponding to the plurality of device health parameters respectively; Based on the parameter scores and weights of the multiple device health parameters, the device health score of the device to be detected is calculated.

2. The method according to claim 1, characterized in that The equipment health parameters include one or more of equipment abnormality information, cargo usage, quality control inspection data, calibration data, equipment warning information, cross-cargo information, positioning information, light source information, maintenance record information, and maintenance record information, wherein the equipment abnormality information includes the number of times the equipment to be inspected has abnormalities, the cargo usage includes the usage data of the cargo stored in the equipment to be inspected, the quality control inspection data includes the quality control measurement values ​​of the equipment to be inspected on the object to be inspected, the calibration data includes the corresponding values ​​of the object to be inspected at multiple calibration points, the cross-cargo information includes the first identification information and the second identification information of the cargo in the equipment to be inspected, the light source information includes the usage time information of the light-emitting device of the equipment to be inspected, the maintenance record information includes the number of maintenance times and maintenance time information of the equipment to be inspected, and the maintenance record information includes the number of maintenance times and maintenance time information of the equipment to be inspected.

3. The method according to claim 2, characterized in that The parameter score includes a detection score, and the obtaining of the parameter score corresponding to the device health parameter includes: Obtaining the number of times the device to be detected has abnormalities from the device abnormality information; Performing a quadrature process on the number of abnormalities and the preset detection base value to obtain a quadrature result; The preset detection score value and the product result are subjected to difference processing to obtain the detection score.

4. The method according to claim 2, characterized in that: The parameter score includes a first usage score and a second usage score, and the processing of the device health parameter to obtain the parameter score includes: When the usage data of the goods on the current day in the goods usage is less than the usage data of the goods on the previous day, a preset usage score value is subtracted from a preset usage base value to obtain the first usage score; When the usage data of the goods on the current day in the goods usage is greater than or equal to the usage data of the goods on the previous day, a preset usage score value is used as the second usage score.

5. The method according to claim 2, characterized in that: The parameter score includes a quality control test score, and the processing of the equipment health parameter to obtain the parameter score includes: Obtaining a first number of quality control measurement values ​​outside 2 times the standard deviation of the quality control mean value, a second number outside 3 times the standard deviation, and a third number that exceeds 2 times the standard deviation twice in a row in the quality control test data; Obtaining a first product of the first data and the first basic score, a second product of the second number and the second basic score, and a third product of the third number and the third basic score; performing a summation process on the first product, the second product and the third product to obtain a summation result; The preset quality control test score value is subtracted from the sum result to obtain the quality control test score.

6. The method according to claim 2, characterized in that The parameter score includes a calibration test score, and the processing of the device health parameter to obtain the parameter score includes: When the calibration data does not satisfy the calibration mode, a difference is calculated between the preset calibration detection score value and the calibration basic score to obtain the calibration detection score.

7. The method according to claim 2, characterized in that The parameter score includes an alarm score, and the processing of the device health parameter to obtain the parameter score includes: Obtain the number of serious warnings according to the device warning information, wherein the serious warnings include two consecutive ordinary warnings and two ordinary warnings in a preset number of detections; Performing a product processing on the number of serious warnings and the serious warning basic score to obtain a warning product result; A difference process is performed between a preset alarm score value and the warning product result to obtain the alarm score.

8. The method according to claim 2, characterized in that: The parameter score includes a cross-selling score, and the processing of the equipment health parameter to obtain the parameter score includes: Obtaining the number of goods in the mixed goods information whose first identification information and second identification information do not correspond to each other; Performing product processing on the number of goods in confluence and the basic score of goods in confluence to obtain a result of product in confluence; The preset cross-selling score value and the cross-selling product result are subtracted to obtain the cross-selling score.

9. The method according to claim 2, characterized in that: The parameter score includes a positioning score, and the processing of the device health parameter to obtain the parameter score includes: Obtaining the distance between the location address of the device to be detected and the preset address in the location information; When the distance is greater than a preset distance value, a difference processing is performed on the preset positioning score value and the positioning basic score to obtain the positioning score.

10. The method according to claim 2, characterized in that The parameter score includes a light source score, and the processing of the device health parameter to obtain the parameter score includes: Obtain the accumulated usage time of the light emitting device of the device to be detected in the light source information; When the accumulated usage time meets a preset condition, a difference processing is performed on the preset light source score value and the light source basic score to obtain the light source score.

11. The method according to claim 2, characterized in that The parameter score includes a maintenance score, and the processing of the equipment health parameter to obtain the parameter score includes: Obtaining the number of maintenance times of the equipment to be detected in the maintenance record information within a preset time; Performing a product processing on the maintenance times and the maintenance basic score to obtain a maintenance product result; A difference process is performed on a preset maintenance score value and the maintenance integral result to obtain the maintenance score.

12. The method according to claim 2, characterized in that: The parameter score includes a first maintenance score and a second maintenance score, and the processing of the equipment health parameter to obtain the parameter score includes: Determine whether the equipment to be tested has been maintained within a preset time according to the maintenance record information; When the maintenance record information shows that the equipment to be detected has not been maintained, performing a difference processing on a preset maintenance score value and a maintenance basic score to obtain the first maintenance score; When the maintenance record information shows that the equipment to be detected has been maintained, a preset maintenance score is used as the second maintenance score.

13. The method according to claim 1, characterized in that Processing the plurality of parameter scores and the weights corresponding to the plurality of parameter scores to obtain a device health score of the device to be detected includes: Multiplying each parameter score by the corresponding weight value to obtain multiple intermediate results; The multiple intermediate results are summed to obtain the device health score.

14. The method according to any one of claims 1 to 13, characterized in that The equipment to be detected includes a protein detection analyzer.

15. A data processing device, characterized in that: The device comprises: A first acquisition module is used to acquire multiple device health parameters of the device to be detected; a processing module, connected to the first acquisition module, configured to process the plurality of device health parameters respectively to obtain parameter scores corresponding to the device health parameters; A second acquisition module, connected to the processing module, for respectively acquiring weights corresponding to the plurality of device health parameters; A calculation module is connected to the second acquisition module and is used to calculate the device health score of the device to be detected based on the parameter scores and weights of the multiple device health parameters.

16. The device according to claim 15, characterized in that The equipment health parameters include one or more of equipment abnormality information, cargo usage, quality control inspection data, calibration data, equipment warning information, cross-cargo information, positioning information, light source information, maintenance record information, and maintenance record information, wherein the equipment abnormality information includes the number of times the equipment to be inspected has abnormalities, the cargo usage includes the usage data of the cargo stored in the equipment to be inspected, the quality control inspection data includes the quality control measurement values ​​of the equipment to be inspected on the object to be inspected, the calibration data includes the corresponding values ​​of the object to be inspected at multiple calibration points, the cross-cargo information includes the first identification information and the second identification information of the cargo in the equipment to be inspected, the light source information includes the usage time information of the light-emitting device of the equipment to be inspected, the maintenance record information includes the number of maintenance times and maintenance time information of the equipment to be inspected, and the maintenance record information includes the number of maintenance times and maintenance time information of the equipment to be inspected.

17. The device according to claim 16, characterized in that The parameter score includes a detection score, and the obtaining of the parameter score corresponding to the device health parameter includes: Obtaining the number of times the device to be detected has abnormalities from the device abnormality information; Performing a quadrature process on the number of abnormalities and the preset detection base value to obtain a quadrature result; The preset detection score value and the product result are subjected to difference processing to obtain the detection score.

18. The device according to claim 16, characterized in that The parameter score includes a first usage score and a second usage score, and the processing of the device health parameter to obtain the parameter score includes: When the usage data of the goods on the current day in the goods usage is less than the usage data of the goods on the previous day, a preset usage score value is subtracted from a preset usage base value to obtain the first usage score; When the usage data of the goods on the current day in the goods usage is greater than or equal to the usage data of the goods on the previous day, a preset usage score value is used as the second usage score.

19. The device according to claim 16, characterized in that The parameter score includes a quality control test score, and the processing of the equipment health parameter to obtain the parameter score includes: Obtaining a first number of quality control measurement values ​​outside 2 times the standard deviation of the quality control mean value, a second number outside 3 times the standard deviation, and a third number that exceeds 2 times the standard deviation twice in a row in the quality control test data; Obtaining a first product of the first data and the first basic score, a second product of the second number and the second basic score, and a third product of the third number and the third basic score; performing a summation process on the first product, the second product and the third product to obtain a summation result; The preset quality control test score value is subtracted from the sum result to obtain the quality control test score.

20. The device according to claim 16, characterized in that The parameter score includes a calibration test score, and the processing of the device health parameter to obtain the parameter score includes: When the calibration data does not satisfy the calibration mode, a difference is calculated between the preset calibration detection score value and the calibration basic score to obtain the calibration detection score.

21. The device according to claim 16, characterized in that The parameter score includes an alarm score, and the processing of the device health parameter to obtain the parameter score includes: Obtain the number of serious warnings according to the device warning information, wherein the serious warnings include two consecutive ordinary warnings and two ordinary warnings in a preset number of detections; Performing a product processing on the number of serious warnings and the serious warning basic score to obtain a warning product result; A difference process is performed between a preset alarm score value and the warning product result to obtain the alarm score.

22. The device according to claim 16, characterized in that The parameter score includes a cross-selling score, and the processing of the equipment health parameter to obtain the parameter score includes: Obtaining the number of goods in the mixed goods information whose first identification information and second identification information do not correspond to each other; Performing product processing on the number of goods in confluence and the basic score of goods in confluence to obtain a result of product in confluence; The preset cross-selling score value and the cross-selling product result are subtracted to obtain the cross-selling score.

23. The device according to claim 16, characterized in that The parameter score includes a positioning score, and the processing of the device health parameter to obtain the parameter score includes: Obtaining the distance between the location address of the device to be detected and the preset address in the location information; When the distance is greater than a preset distance value, a difference processing is performed on the preset positioning score value and the positioning basic score to obtain the positioning score.

24. The device according to claim 16, characterized in that The parameter score includes a light source score, and the processing of the device health parameter to obtain the parameter score includes: Obtain the accumulated usage time of the light emitting device of the device to be detected in the light source information; When the accumulated usage time meets a preset condition, a difference processing is performed on the preset light source score value and the light source basic score to obtain the light source score.

25. The device according to claim 16, characterized in that The parameter score includes a maintenance score, and the processing of the equipment health parameter to obtain the parameter score includes: Obtaining the number of maintenance times of the equipment to be detected in the maintenance record information within a preset time; Performing a product processing on the maintenance times and the maintenance basic score to obtain a maintenance product result; A difference process is performed on a preset maintenance score value and the maintenance integral result to obtain the maintenance score.

26. The device according to claim 16, characterized in that The parameter score includes a first maintenance score and a second maintenance score, and the processing of the equipment health parameter to obtain the parameter score includes: Determine whether the equipment to be tested has been maintained within a preset time according to the maintenance record information; When the maintenance record information shows that the equipment to be detected has not been maintained, performing a difference processing on a preset maintenance score value and a maintenance basic score to obtain the first maintenance score; When the maintenance record information shows that the equipment to be detected has been maintained, a preset maintenance score is used as the second maintenance score.

27. The device according to claim 15, characterized in that Processing the plurality of parameter scores and the weights corresponding to the plurality of parameter scores to obtain a device health score of the device to be detected includes: Multiplying each parameter score by the corresponding weight value to obtain multiple intermediate results; The multiple intermediate results are summed to obtain the device health score.

28. The device according to any one of claims 15 to 27, characterized in that The equipment to be detected includes a protein detection analyzer.

29. A data processing device, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to: Execute the method according to any one of claims 1 to 14.

30. A non-volatile computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 14 is implemented.

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